bluuebunny/crossref_metadata_2025_split huggingface dataset:
PINK1 deficiency impairs osteoblast differentiation through aberrant mitochondrial homeostasis
Authors: Lee So-Young , An Hyun-Ju , Kim Jin Man , Sung Min-Ji , Kim Do Kyung , Kim Hyung Kyung , Oh Jongbeom , Jeong Hye Yun , Lee Yu Ho , Yang Taeyoung , Kim Jun Han , Lim Ha Jeong , Lee Soonchul 
Published: November 2021

Abstract
Background
PTEN-induced kinase 1 (PINK1) is a serine/threonine-protein kinase in mitochondria that is critical for mitochondrial quality control. PINK1 triggers mitophagy, a selective autophagy of mitochondria, and is involved in mitochondrial regeneration. Although increments of mitochondrial biogenesis and activity are known to be crucial during differentiation, data regarding the specific role of PINK1 in osteogenic maturation and bone remodeling are limited.

Methods
We adopted an ovariectomy model in female wildtype and Pink1−/− mice. Ovariectomized mice were analyzed using micro-CT, H&E staining, Masson’s trichrome staining. RT-PCR, western blot, immunofluorescence, alkaline phosphatase, and alizarin red staining were performed to assess the expression of PINK1 and osteogenic markers in silencing of PINK1 MC3T3-E1 cells. Clinical relevance of PINK1 expression levels was determined via qRT-PCR analysis in normal and osteoporosis patients.

Results
A significant decrease in bone mass and collagen deposition was observed in the femurs of Pink1−/− mice after ovariectomy. Ex vivo, differentiation of osteoblasts was inhibited upon Pink1 downregulation, accompanied by impaired mitochondrial homeostasis, increased mitochondrial reactive oxygen species production, and defects in mitochondrial calcium handling. Furthermore, PINK1 expression was reduced in bones from patients with osteoporosis, which supports the practical role of PINK1 in human bone disease.

Conclusions
In this study, we demonstrated that activation of PINK1 is a requisite in osteoblasts during differentiation, which is related to mitochondrial quality control and low reactive oxygen species production. Enhancing PINK1 activity might be a possible treatment target in bone diseases as it can promote a healthy pool of functional mitochondria in osteoblasts.
The effect of noise on the predictive limit of QSAR models
Authors: Kolmar Scott S. , Grulke Christopher M. 
Published: November 2021

AbstractA key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology.
Graphical Abstract
FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example
Authors: Vogt Lars 
Published: November 2021

AbstractBackgroundThe size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontologies and through representing them as semantic graphs. Here, we discuss two different semantic graph approaches of representing empirical data and metadata in a knowledge graph, with phenotype descriptions as an example. Almost all phenotype descriptions are still being published as unstructured natural language texts, with far-reaching consequences for their FAIRness, substantially impeding their overall usability within the life sciences. However, with an increasing amount of anatomy ontologies becoming available and semantic applications emerging, a solution to this problem becomes available. Researchers are starting to document and communicate phenotype descriptions through the Web in the form of highly formalized and structured semantic graphs that use ontology terms and Uniform Resource Identifiers (URIs) to circumvent the problems connected with unstructured texts.ResultsUsing phenotype descriptions as an example, we compare and evaluate two basic representations of empirical data and their accompanying metadata in the form of semantic graphs: the class-based TBox semantic graph approach called Semantic Phenotype and the instance-based ABox semantic graph approach called Phenotype Knowledge Graph. Their main difference is that only the ABox approach allows for identifying every individual part and property mentioned in the description in a knowledge graph. This technical difference results in substantial practical consequences that significantly affect the overall usability of empirical data. The consequences affect findability, accessibility, and explorability of empirical data as well as their comparability, expandability, universal usability and reusability, and overall machine-actionability. Moreover, TBox semantic graphs often require querying under entailment regimes, which is computationally more complex.ConclusionsWe conclude that, from a conceptual point of view, the advantages of the instance-based ABox semantic graph approach outweigh its shortcomings and outweigh the advantages of the class-based TBox semantic graph approach. Therefore, we recommend the instance-based ABox approach as a FAIR approach for documenting and communicating empirical data and metadata in a knowledge graph.
Prefrontal fNIRS-based clinical data analysis of brain functions in individuals abusing different types of drugs
Authors: Gu Xuelin , Yang Banghua , Gao Shouwei , Yan Lin Feng , Xu Ding , Wang Wen 
Published: November 2021

Abstract
Background
The activation degree of the orbitofrontal cortex (OFC) functional area in drug abusers is directly related to the craving for drugs and the tolerance to punishment. Currently, among the clinical research on drug rehabilitation, there has been little analysis of the OFC activation in individuals abusing different types of drugs, including heroin, methamphetamine, and mixed drugs. Therefore, it becomes urgently necessary to clinically investigate the abuse of different drugs, so as to explore the effects of different types of drugs on the human brain.

Methods
Based on prefrontal high-density functional near-infrared spectroscopy (fNIRS), this research designs an experiment that includes resting and drug addiction induction. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed drugs) were collected using fNIRS and analyzed by combining algorithm and statistics.

Results
Linear discriminant analysis (LDA), Support vector machine (SVM) and Machine-learning algorithm was implemented to classify different drug abusers. Oxygenated hemoglobin (HbO2) activations in the OFC of different drug abusers were statistically analyzed, and the differences were confirmed. Innovative findings: in both the Right-OFC and Left-OFC areas, methamphetamine abusers had the highest degree of OFC activation, followed by those abusing mixed drugs, and heroin abusers had the lowest. The same result was obtained when OFC activation was investigated without distinguishing the left and right hemispheres.

Conclusions
The findings confirmed the significant differences among different drug abusers and the patterns of OFC activations, providing a theoretical basis for personalized clinical treatment of drug rehabilitation in the future.
Prevalence and risk factors of depression in Chinese patients with type 2 diabetes mellitus: a protocol of systematic review and meta-analysis
Authors: Liu Xiaobo , Dong Chao , Jiang Hong , Zhong Dongling , Li Yuxi , Zhang Huiling , Zhang Jun , Fan Jin , Li Juan , Guan Li , Jin Rongjiang 
Published: November 2021

Abstract
Background
The prevalence of type 2 diabetes mellitus (T2DM) is growing in China. Depression is a significant complication of T2DM, leading to poor management of T2DM. Thus, early detection and treatment of depression in patients with T2DM are essential and effective. Therefore, we plan to conduct a systematic review and meta-analysis to evaluate the prevalence of depression in Chinese patients with T2DM and explore potential risk factors of depression in T2DM.

Methods
We will search literatures recorded in MEDLINE, EMBASE, the Cochrane Library, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database (VIP), and WanFang Database from their inception onwards. We will manually search gray literatures, reference lists of identified studies, relevant websites, and consult experts in this field. We will include population-based, cross-sectional surveys that investigated the prevalence of depression in Chinese patients with T2DM or/and the possible risk factors of depression in T2DM. Two reviewers will screen studies, extract data, and evaluate risk of bias independently. Agency for Healthcare Research and Quality methodology checklist will be used to assess the risk of bias. If feasible, we will conduct random effects meta-analysis of observational data to summarize the pooled prevalence, and use odds ratio for categorical data to explore potential risk factors. Prevalence estimates will be stratified according to age, gender, and other factors. Statistical heterogeneity will be estimated using Cochran’s Q and I2 index. We will conduct meta-regression to investigate the potential sources of heterogeneity, sensitivity analyses to assess robustness of the synthesized results, and funnel plots and Egger’s test to assess publication bias.

Discussion
This systematic review and meta-analysis will provide comprehensive evidence of the prevalence and potential risk factors of depression in Chinese patients with T2DM. We expect to provide evidence for healthcare practitioners and policy makers to pay attention to the mental health of patients with T2DM. Our data will highlight the need and importance of early detection and intervention for depression in patients with T2DM.

Systematic review registration
PROSPERO CRD42020182979.
Cross-national comparisons of health indicators require standardized definitions and common data sources
Authors: Tolonen Hanna , Reinikainen Jaakko , Koponen Päivikki , Elonheimo Hanna , Palmieri Luigi , Tijhuis Mariken J. ,   for the Joint Action on Health Information (InfAct)
Published: November 2021

Abstract
Background
Health indicators are used to monitor the health status and determinants of health of the population and population sub-groups, identify existing or emerging health problems which would require prevention and health promotion activities, help to target health care resources in the most adequate way as well as for evaluation of the success of public health actions both at the national and international level. The quality and validity of the health indicator depends both on available data and used indicator definition. In this study we will evaluate existing knowledge about comparability of different data sources for definition of health indicators, compare how selected health indicators presented in different international databases possibly differ, and finally, present the results from a case study from Finland on comparability of health indicators derived from different data sources at national level.

Methods
For comparisons, four health indicators were selected that were commonly available in international databases and available for the Finnish case study. These were prevalence of obesity, hypertension, diabetes, and asthma in the adult populations. Our evaluation has three parts: 1) a scoping review of the latest literature, 2) comparison of the prevalences presented in different international databases, and 3) a case study using data from Finland.

Results
Literature shows that comparability of estimated outcomes for health indicators using different data sources such as self-reported questionnaire data from surveys, measured data from surveys or data from administrative health registers, varies between indicators. Also, the case study from Finland showed that diseases which require regular health care visits such as diabetes, comparability is high while for health outcomes which can remain asymptomatic for a long time such as hypertension, comparability is lower. In different international health related databases, country specific results differ due to variations in the used data sources but also due to differences in indicator definitions.

Conclusions
Reliable comparison of the health indicators over time and between regions within a country or across the countries requires common indicator definitions, similar data sources and standardized data collection methods.
Assessing the migratory histories, trophic positions, and conditions of lake sturgeon in the St. Croix and Mississippi Rivers using fin ray microchemistry, stable isotopes, and fatty acid profiles
Authors: Ziegeweid Jeffrey R. , Bartsch Michelle R. , Bartsch Lynn A. , Zigler Steven J. , Kennedy Robert J. , Love Seth A. 
Published: November 2021

Abstract
Background
Reproducing populations of invasive carps (Hypophthalmichthys spp.) could alter aquatic food webs and negatively affect native fishes in the Mississippi National River and Recreation Area (MISS) and the St. Croix National Scenic Riverway (SACN). However, proposed invasive carp barriers may also threaten populations of native migratory fishes by preventing movements of fish between rivers that are necessary for life history requirements. In this study, nonlethal chemical techniques were used to provide baseline data related to the condition, trophic position, and migratory histories of lake sturgeon (Acipenser fulvescens) captured in the Mississippi and St. Croix Rivers.

Results
Fish length and weight measurements and age estimates determined from pectoral fin rays demonstrated that lake sturgeon from the Mississippi River had greater lengths-at-age compared to sturgeon from the St. Croix River. However, length–weight relations were similar for sturgeon from the Mississippi and St. Croix Rivers. Lake sturgeon captured from different locations had distinguishable fatty acid signatures, and stable isotope analyses demonstrated that lake sturgeon from the Mississippi River generally feed at a higher trophic level than those in the St. Croix River. Strontium-to-calcium ratios (Sr:Ca) from fin ray cross sections indicated that sturgeon captured from the Mississippi River had higher Sr:Ca values than sturgeon captured from the St. Croix River, and natal origins and capture locations were not significantly different among sturgeon captured within individual rivers. Most sturgeon were captured in water with a similar Sr:Ca signature as their natal waters, indicating that there is some separation between populations of lake sturgeon in the St. Croix and Mississippi Rivers. However, Sr:Ca data indicated substantial variation in movement patterns among individual lake sturgeon, indicating that populations interact through migrations of individual fish between rivers.

Conclusions
Study results provide baseline condition and food web structure index data for assessing changes in lake sturgeon populations should invasive carps become established in these areas of the Mississippi and St. Croix Rivers. Controlled-exposure and telemetry studies would help verify and enhance the relations between Sr:Ca signatures in water and lake sturgeon pectoral fin rays to further assess mixing of sturgeons between rivers.
Serological responses to a soluble recombinant circumsporozoite protein-VK210 of Plasmodium vivax (rPvCSP-VK210) among Iranian malaria patients
Authors: Nateghpour Mehdi , Etemadi Soudabeh , Motevalli Haghi Afsaneh , Eslami Hamid , Mohebali Mehdi , Farivar Leila 
Published: November 2021

AbstractBackgroundCircumsporozoite protein (CSP) has a central immune domain that includes short regions of repeating amino acid sequences. This immunodynamic region is an epitope of B cells that can elicit an immune response in human and laboratory animals. The aim of the present study was to express the recombinant PvCSP-VK210 antigen and evaluate it for assaying antibodies obtained during humanP. vivaxinfection by Western blotting and indirect ELISA (enzyme-linked immunosorbent assay).MethodGenomic DNA ofP. vivaxwas isolated from a blood sample of an Iranian person with vivax malaria, and by PCR, the fragment of the PvCSP-VK210 gene was amplified. The gene fragment was cut after gel purification by BamHI and HindIII enzymes and then cloned into pET28a expression vector. Finally, the recombinant pET28a was transformed into theE.coliBL21 (DE3) as the expression host. In order to produce His-tagged protein, the expression host was cultured in LB medium. The protein was purified by Ni–NTA columns and immobilized metal affinity chromatography, and after confirmation by Western blotting technique, was used as the antigen in the indirect ELISA test.ResultsThe recombinant protein was expressed and purified as a 32-kDa protein. The sensitivity and specificity of the indirect ELISA test with the recombinant PvCSP-VK210 antigen were 61.42% and 97.14%, respectively, based on OD  =  0.313. Between the results of the microscopic test and the indirect ELISA test with the recombinant PvCSP-VK210 antigen there was a Kappa coefficient of 0.586. The positive and negative predictive value and validity of the ELISA test with the recombinant PvCSP-VK210 antigen were 95.55%, 71.57%, 79.28%, respectively.ConclusionThe sensitivity of the indirect ELISA method with the recombinant PvCSP-VK210 antigen was 61.42%, which is the first report from Iran.
Minimum threshold determination method based on dataset characteristics in association rule mining
Authors: Hikmawati Erna , Maulidevi Nur Ulfa , Surendro Kridanto 
Published: November 2021

AbstractAssociation rule mining is a technique that is widely used in data mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. Before the rule is formed, it must be determined in advance which items will be involved or called the frequent itemset. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, user determines the minimum support value randomly. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. It causes a lot of memory and time consumption. This is because the rule formation process is repeated until it finds the desired number of rules. The value of minimum support in the adaptive support model is determined based on the average and total number of items in each transaction, as well as their support values. Furthermore, the proposed method also uses certain criteria as thresholds, therefore, the resulting rules are in accordance with user needs. The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics. The trial of the proposed adaptive support method uses 2 basic algorithms in the association rule, namely Apriori and Fpgrowth. The test is carried out repeatedly to determine the highest and lowest minimum support values. The result showed that 6 out of 8 datasets produced minimum and maximum support values for the apriori and fpgrowth algorithms. This means that the value of the proposed adaptive support has the ability to generate a rule when viewed from the quality as adaptive support produces at a lift ratio value of > 1. The dataset characteristics obtained from the experimental results can be used as a factor to determine the minimum threshold value.
Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents
Authors: Amirfakhrian Majid , Parhizkar Mahboub 
Published: November 2021

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.
Determination of total protein content in biomedical products by the PDMS-assisted lab-in-a-syringe assay using 3D printed scaffolds removal
Authors: Drastíková Ester , Konderlová Klára , Šebestová Andrea , Baron Daniel , Švecová Petra , Táborská Petra , Vítková Kateřina , Pospíšilová Veronika , Forostyak Serhiy , Kořístek Zdeněk , Porubová Ludmila , Petr Jan 
Published: November 2021

AbstractThe aim of our work was to develop a low-cost, portable device for the fast and easy determination of total protein content by using PDMS-based lab-in-a-syringe technology with removal of 3D-printed channels. We proposed two designs with a one-step PDMS curing and a two-step PDMS-curing fabrication procedure. The one-step PDMS microdevices were found to be the best in the view of preparation, repeatability, and stability of the reagent. This design was then applied for the determination of total protein content in biomedical products using the Bradford assay.