bluuebunny/crossref_metadata_2025_split huggingface dataset:
Angiotensin Receptor Blocker is Associated with A Lower Fracture Risk: An Updated Systematic Review and Meta-Analysis
Authors: Wu Jing , Du Xin-Yi , Guo Man , Wang Mei , Tan Xiao-Zhen , Teng Fang-Yuan , Xu Yong 
Published: September 2021

Abstract
Background

The angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) are prevalently used in the treatment of hypertension. Hypertension often presents with osteoporosis. However, over the past decade, it has been unclear whether long-term use of ACEI/ARB reduces fracture risk. This meta-analysis aims to investigate the potential clinical effects of ACEI/ARB on osteoporosis fracture (OF).
Methods

This meta-analysis was identified through PubMed, EMBASE, Cochrane Library, and Web of Science. Related studies about ACEI/ARB with the risk of fracture were published from inception to September, 2021.
Results

Nine qualified prospective designed studies, involving 3,649,785 subjects, were included in this analysis. Overall, the RR of ACEI compared with the non-users were 0.98 (95% CI: 0.88, 1.10; P < 0.001) for composite fractures and 0.96 (95% CI: 0.87, 1.05; P = 0.048) for hip fractures; the RR of ARB compared to the non-users were 0.82 (95% CI: 0.73, 0.91; P < 0.001) for composite fractures and 0.85 (95% CI: 0.74, 0.97; P = 0.028) for hip fractures. Furthermore, in the subgroup analysis, male may benefit from ARB (RR = 0.65, 95% CI 0.49, 0.89, P = 0.028), and the European may also benefit from ARB (RR = 0.86, 95% CI 0.80, 0.93, P = 0.015).
Conclusions

ACEI usage will not decrease the risk of osteoporosis fracture. On the contrary, ARB usage can decrease the risk of total fracture and hip fracture, especially for the male and European. Compared with ACEI, for patients at higher risk of fracture in cardiovascular diseases such as hypertension, the protective effect of ARB should be considered.
Multiband and Low-Profile Frequency Reconfigurable Microstrip Patch Antenna Design Using Single PIN diode for WiFi/GPS applications
Authors: Patel Shobhit K. , khalifa sana ben , chebaane saleh , Lavadiya Sunil , Parmar Yagnesh , Parmar Juveriya 
Published: September 2021

Abstract
The novel and simple approach for achieving frequency tunability is presented. The frequency tunability is achieved using a single PIN diode. PIN diode located on the upper side of the structure to reduces the biasing complexity. The superstrate structure along with the corner truncation helps for enhancing the performance parameters, as well the location of feed and slot of the patch is varied for locating best performing points. This design provides the multiband frequency tunability behavior with the maximum frequency tunability of 100 MHz. The design provides the minimum reflectance coefficient of -22.56 dB, the directivity of 6.98 dB, the total gain of 3.683 dB, normalized directivity of 88 0 using the low profile (FR-4) substrate which helps for the cost reduction and mass production. The simulation is carried out using the HFSS tool. The fabrication of the proposed design is also presented. This design suitable for Wifi, GPS, and many more wireless communication applications.
Strain-mediated Ferromagnetism and Low-field Magnetic Reversal in Co Doped Monolayer W S2
Authors: Jena Anjan Kumar , Mallik Sameer Kumar , Sahu Mousam Charan , Sahoo Sandhyarani , Sahoo Ajit Kumar , Sharma Neha Kapila , Mohanty J. , Gupta Sanjeev K. , Ahuja Rajeev , Sahoo Satyaprakash 
Published: September 2021

Abstract
Strain-mediated magnetism in 2D materials and dilute magnetic semiconductors hold multi-functional applications for future nano-electronics. Herein, First principles calculations are employed to study the influence of biaxial strain on the magnetic properties of Co-doped monolayer W S2. The non-magnetic W S2 shows ferromagnetic signature upon Co doping due to spin polarization, which is further improved at low compressive (-2 %) and tensile (+2 %) strains. From the PDOS and spin density analysis, the opposite magnetic ordering is found to be favourable under the application of compressive and tensile strains. The double exchange interaction and p-d hybridization mechanisms make Co-doped W S2 a potential host for magnetism. More importantly, the competition between exchange and crystal field splittings, i.e. (∆ ex > ∆ c f s), of the Co-atom play pivotal roles in deciding the values of the magnetic moments under applied strain. Micromagnetic simulation reveals, the ferromagnetic behavior calculated from DFT exhibits low-field magnetic reversal (190 Oe). Moreover, the spins of Co-doped W S2 are slightly tilted from the easy axis orientations showing slanted ferromagnetic hysteresis loop. The ferromagnetic nature of Co-doped W S2 suppresses beyond ±2 % strain, which is reflected in terms of decrease in the coercivity in the micromagnetic simulation. The understanding of low-field magnetic reversal and spin orientations in Co-doped W S2 may pave the way for next-generation spintronics and straintronics applications.
Synthesis and Characterization of Functionalized Hafnium Oxide Nanoparticles for Supercapacitor Applications
Authors: M Jayavel , J Venkatachalam , N RAMALAKSHMI , A SIVASANKARAN 
Published: September 2021

Abstract
Functionalized metal oxide electrode material plays an important role in the energy application of the supercapacitor. In this work, the comparative study of super-capacitance performance of hafnium oxide (HfO2) and sulfonated hafnium oxide (S-HfO2) nanomaterials is reported. The HfO2 nanoparticles were synthesized by the precipitation method. Subsequently, the prepared HfO2 nanoparticles were functionalized using sulfuric acid (H2SO4). Further, the synthesized nanoparticles were characterized and confirmed by X-Ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, High-resolution transmission electron microscope (HR-TEM), Field-Emission scanning electron microscope (FE-SEM) and Energy Dispersive X-Ray spectroscopy (EDS) techniques. The electrochemical properties and ion transfer characteristics of the supercapacitor were investigated by the cyclic voltammeter (CV) and galvanostatic charge-discharge (GCD) experiments. Moreover, the internal resistances of the material (HfO2 and S-HfO2) were analysed using Electrochemical Impedance Spectroscopy
Flood Susceptibility Prediction Using Hybrid-Based Approaches of Support Vector Regression Model and Meta-Heuristic Algorithms
Authors: Sorajar Hossein Hamedi , Alesheikh Ali Asghar , Panahi Mahdi , Lee Saro 
Published: September 2021

Abstract
Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a critical role in identifying prone areas and reducing the damage caused by landslides in each area. In the present study, deep learning algorithms including convolutional neural network (CNN) and long short-term memory (LSTM) were used to identify landslide prone areas in Ardabil province, Iran. Equql to 312 landslide locations were identified and randomly divided into train and test datasets at 70–30% ratios. Then, according to previous studies and environmental conditions in the study area, twelve factors affecting the occurrence of landslides were selected, namely altitude, slope angle, slope aspect, topographic wetness index (TWI), profile curvature, plan curvature, land-use, lithology, distance to faults, distance to rivers, distance to roads, and rainfall. The ratio of the importance of each influential factor in landslide occurrence was obtained through information gain ranking filter (IGRF) method and it was found that land-use and profile curvature had the highest and lowest impacts, respectively. Afterwards, LSMs were generated using CNN and LSTM algorithms. In the next step, the performance of the models was evaluated based on the area under curve (AUC) value of receiver operating characteristics curve and the root mean square error (RMSE) method. The AUC values for CNN and LSTM models were 0.821 and 0.832, respectively. Furthermore, the RMSE values in the CNN model for each of the training and testing dataset were 0.121 and 0.132, respectively. The RMSE values in the LSTM model for each of the training and testing dataset were 0.185 and 0.188, respectively. Therefore, it can be concluded that CNN performance is slightly better than LSTM; but in general, both models have close performance and the accuracy of both models is acceptable.
A Pilot Proficiency Testing Study for Assessing Cancer Gene Panel using Patient Samples and Next-generation Sequencing in Japan
Authors: Maekawa Masato , Taniguchi Terumi , Nishio Kazuto , Sakai Kazuko , Matsushita Kazuyuki , Ishige Takayuki , Nakatani Kaname , Ikejiri Makoto , Nishihara Hiroshi , Sunami Kuniko , Yatabe Yasushi , Hatanaka Kanako , Hatanaka Yutaka , Yamamoto Yoshihiro , Fukuyama Keita , Oda Shinya , Saito Kayoko , Yokomura Mamoru , Kubo Yuji , Sato Hiroko , Tanaka Yoshinori , Fuchioka Misa , Yamasaki Tadashi , Matsuda Koichiro , Kurachi Kiyotaka , Funai Kazuhiro , Baba Satoshi , Iwaizumi Moriya 
Published: September 2021

Abstract
To implement precision oncology, analytical validity as well as clinical validity and utility are important. However, proficiency testing (PT) to assess validity has not yet been systematically performed in Japan. To investigate the quality of next-generation sequencing (NGS) platforms and cancer genome testing prevalent in laboratories, we performed pilot PT using patient samples. We prepared 5 samples from patients with lung or colorectal cancer, extracted genomic DNA from the cancer tissue and peripheral blood, and distributed these to 15 laboratories. Most participating laboratories successfully identified the pathogenic variants, except for two closely located KRAS variants, and 25-bp delins in the EGFR exon 19, not identified by the in vitro diagnostics testing. Conversely, the EGFR L858R variant was successfully identified, and the allele frequency was similar for all the laboratories. A high DNA integrity number led to excellent depth and reliable NGS results. All NGS platforms and bioinformatics pipelines have advantages and disadvantages. We propose the use of a PT program using patient samples to ascertain the quality status of cancer gene testing in laboratories and to ensure that laboratories have sufficient information to develop advancements in precision medicine for cancer.
Sugary beverage intake and genetic risk of dementia in relation to incident dementia and brain structure: evidence from the UK Biobank
Authors: Chen Hui , Chen Jie , Cao Yaying , Sun Yuhao , Huang Liyan , Ji John S. , Shen Jie , Zheng Yan , Zong Geng , Yuan Changzheng 
Published: September 2021

Abstract
Importance: Sugary beverage intake was associated with higher risk of dementia, but the specific amounts and types related to it and its interactions with genetic predisposition to dementia remained poorly understood.Objective: To investigate the relation of sugary beverage intake and genetic preposition to the long-term risk of dementia and brain structure.Design, Setting, and Participants: We leveraged data of 187,994 UK Biobank participants without dementia at baseline and followed them until March 2021.Exposures: Intake of sugary beverages (SBs, one unit=250 ml), including sugar-sweetened beverages (SSB), artificially-sweetened beverages (ASB), and natural sweet juices (NSJ), was assessed using repeated web-based 24-h dietary recall from 2009 to 2012. A polygenic risk score (PRS) was calculated to capture each participant’s load of common genetic variants related to the risk of dementia.Main Outcomes and Measures: Incident dementia was identified through hospital admissions and death registries. Brain magnetic resonance imaging was conducted in a subgroup of 12,566 participants in 2014.Results: During a total of 1,790,996 person-years of follow up, 1,351 incident dementia cases were identified. Higher intake of SSB and ASB (>2 units/d v. none) was independently associated with a substantially increased risk of dementia (p-trend=0.013 for SSB, and <0.001 for ASB). The corresponding multivariable-adjusted hazard ratio [HR] and 95% confidence intervals [CI] were 1.47 (1.13~1.92), and 1.41 (1.00~1.99), respectively. The significant association of ASB was observed among ASB consumptions regardless of the intake level. In contrast, moderate intake of NSJ (0~1 unit/d v. none) was related to a decreased risk of dementia (HR=0.80, 0.71~0.90) and a larger volume of brain grey matter (beta=0.03, 0.01~0.06) and a lower volume of white matter hyperintensities (beta=-0.08, -0.13~-0.02). Moreover, the genetic risks were significantly magnified by higher intake of SSB and ASB, and was instead attenuated by moderate intake of NSJ (P-interaction<0.002).Conclusions and Relevance: Higher intake of SSB and ASB was associated with higher risk of dementia, especially among individuals at high genetic risk for dementia. Inversely, moderate NSJ intake was associated with a reduced risk of dementia, possibly through the beneficial role maintaining brain grey matter volume and reducing white matter hyperintensities.
Estimation of groundwater recharge using multiple climate models in Bayesian frameworks
Authors: Achieng Kevin O. , Zhu Jianting 
Published: September 2021

AbstractGroundwater recharge plays a vital role in replenishing aquifers, sustaining demand, and reducing adverse effects (e.g. land subsidence). In order to manage climate change-induced effects on groundwater dynamics, climate models are increasingly being used to predict current and future recharges. Even though there has been a number of hydrological studies that have averaged climate models’ predictions in a Bayesian framework, few studies have been related to the groundwater recharge. In this study, groundwater recharge estimates from 10 regional climate models (RCMs) are averaged in 12 different Bayesian frameworks with variations of priors. A recession-curve-displacement method was used to compute recharge from measured streamflow data. Two basins of different sizes located in the same water resource region in the USA, the Cedar River Basin and the Rainy River Basin, are selected to illustrate the approach and conduct quantitative analysis. It has been shown that groundwater recharge prediction is affected by the Bayesian priors. The non-Empirical Bayes g-Local-based Bayesian priors result in posterior inclusion probability values that are consistent with the performance of the climate models outside the Bayesian framework. With the proper choice of priors, the Bayesian frameworks can produce good results of groundwater recharge with R2, percent bias error, and Willmott's index of agreement of &gt;0.97, &lt;2%, and &gt;0.97, respectively, in the two basins. The Bayesian framework with an appropriate prior provides opportunity to estimate recharge from multiple climate models.
Neuroprotective Effects of Neuropeptide Y against Neurodegenerative
Disease
Authors: Pain Stéphanie , Brot Sébastien , Gaillard Afsaneh 
Published: September 2021

:
Neuropeptide Y (NPY), a 36 amino acid peptide, is widely expressed in the mammalian
brain. Changes in NPY levels in different brain regions and plasma have been described in several neurodegenerative
diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic
Lateral Sclerosis, and Machado-Joseph disease. The changes in NPY levels may reflect the
attempt to set up an endogenous neuroprotective mechanism to counteract the degenerative process.
Accumulating evidence indicates that NPY can function as an anti-apoptotic, anti-inflammatory, and
pro-phagocytic agent, which may be used effectively to halt or to slow down the progression of the
disease. In this review, we will focus on the neuroprotective roles of NPY in several neuropathological
conditions, with a particular focus on the anti-inflammatory properties of NPY.
In Silico Identification of New Anti-SARS-CoV-2 Agents from Bioactive Phytocompounds Targeting the Viral Spike Glycoprotein and Human TLR4
Authors: Das Nabarun Chandra , Labala Rajendra Kumar , Patra Ritwik , Chattoraj Asamanja , Mukherjee Suprabhat 
Published: September 2021

Background:
The recent outbreak of novel coronavirus disease (COVID-19) pandemic caused
by SARS-CoV-2 has posed a tremendous threat to mankind. The unavailability of a specific drug or vaccine
has been the major concern to date. Spike (S) glycoprotein of SARS-CoV-2 plays the most crucial
role in viral infection and immunopathogenesis, and hence this protein appears to be an efficacious target
for drug discovery.

Objective:
The objective of this study was to identify potent bioactive phytocompound that can target
viral spike (S) glycoprotein and human TLR4 to reduce immunopathological manifestations of COVID-
19.

Method:
A series of thirty (30) bioactive phytocompounds, previously documented for antiviral activity,
were theoretically screened for their binding efficacy against key proteins related to the pathogenesis of
SARS-CoV-2, namely viral spike (S) glycoprotein, and human TLR4. MD simulation was employed to
verify the postulations of molecular docking study, and further ADME analysis was performed to predict
the most effective one.

Results:
Studies hypothesized that two new phytochemicals, viz. cajaninstilbene acid (-8.83 kcal/mol)
and papaverine (-5.81 kcal/mol), might be the potent inhibitors of spike glycoprotein with stout binding
affinity and favourable ADME attributes. MD simulation further ratified the stability of the docked complexes
between the phytochemicals and S protein through strong hydrogen bonding. Our in silico data
also indicated that cajaninstilbene acid and papaverine might block human TLR4, which could be useful
in mitigating SARS-CoV-2-induced lethal proinflammatory responses.

Conclusion:
Experimental data collectively predict cajaninstilbene acid as the potential blocker of S protein
which may be used as an anti-viral against COVID-19 in the future. However, further experimental
validations alongside toxicological detailing are needed for claiming the candidature of these molecules
as future anti-corona therapeutics.
Clinical and Imaging Features of Tumors in the Scapula
Authors: Zhong Zhi-Wei , Wu Wen-Juan , Song Yan , Liu Jie , Cao Lei , Yu Bao-Hai , Sun Tao , Shi Liang , Shi Yun-Heng , Gao Bu-Lang 
Published: September 2021

Background:
The scapula is a small irregular-shaped flat bone, which may suffer from a
variety of tumors or tumor-like lesions. As the imaging manifestations are complex and changeable,
correct imaging diagnosis is difficult.

Introduction:
At present, there are few related radiology literatures, and it is necessary to fully analyze
the imaging signs of different types of benign and malignant tumors in scapula to guide clinical
treatment. This study was to investigate clinical and imaging presentations of tumors and tumor-
like lesions in the scapula so as to increase the diagnostic accuracy of diseases in the scapula.

Methods:
Patients with scapular tumors confirmed by pathology were enrolled. The imaging and
clinical data were analyzed.

Result:
Among 108 patients, benign tumors were in 53 (49.1%) cases, intermediate in seven
(6.5%), and malignant in 48 (44.4%) involving 16 diseases. Osteochondroma was the first benign
tumors in 45 cases accounting for 84.9% of all benign scapular tumors, followed by chondroma in
four cases (7.5%). The intermediate tumors were mainly eosinophilic granuloma in four cases. Metastatic
tumors were the commonest malignant tumor (27 cases or 56.2% of all malignant tumors),
followed by chondrosarcoma (in 13 cases). Except for the one case of chondroblastoma in which
the lesion involved the glenoid cavity, all the other cartilaginous tumors were located in the scapular
body and processes. The type of lesions in the bony processes is the same as in the scapular
body, the common lesions in the central area of the body were malignant tumors, and the commonest
lesions in the glenoid area were metastasis. Common imaging features of malignant scapular tumors
were ill-defined margins, cortical destruction and soft tissue involvement. The imaging features
of chondrosarcoma lack specificity except for calcification. Benign lesions usually had a clear
boundary and marginal sclerosis.

Conclusion:
A wide variety of benign and malignant tumors may occur in the scapula with mostly
cartilaginous and metastatic tumors, and the location and distribution of lesions are similar in the
scapula to those in the long bones.
Integration of Multi-Omics Data Using Probabilistic Graph Models and
External Knowledge
Authors: Tripp Bridget A. , Otu Hasan H. 
Published: September 2021

Background:
High-throughput sequencing technologies have revolutionized the ability to
perform systems-level biology and elucidate molecular mechanisms of disease through the comprehensive
characterization of different layers of biological information. Integration of these heterogeneous
layers can provide insight into the underlying biology but is challenged by modeling complex interactions.

Objective:
We introduce OBaNK: omics integration using Bayesian networks and external knowledge,
an algorithm to model interactions between heterogeneous high-dimensional biological data to elucidate
complex functional clusters and emergent relationships associated with an observed phenotype.

Method:
Using Bayesian network learning, we modeled the statistical dependencies and interactions
between lipidomics, proteomics, and metabolomics data. The strength of a learned interaction between
molecules was altered based on external knowledge.

Results :
Networks learned from synthetic datasets based on real pathways achieved an average area under
the curve score of ~0.85, an improvement of ~0.23 from baseline methods. When applied to real
multi-omics data collected during pregnancy, five distinct functional networks of heterogeneous biological
data were identified, and the results were compared to other multi-omics integration approaches.

Conclusion:
OBaNK successfully improved the accuracy of learning interaction networks from data integrating
external knowledge, identified heterogeneous functional networks from real data, and suggested
potential novel interactions associated with the phenotype. These findings can guide future hypothesis
generation. OBaNK source code is available at: https://github.com/bridgettripp/OBaNK.git, and a
graphical user interface is available at: http://otulab.unl.edu/OBaNK.