bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring public engagement with nature using Google Trends
Authors: Phillips Benjamin B. , Burgess Katherine , Willis Cheryl , Gaston Kevin J. 
Published: July 2022

Abstract

How humans interact with nature affects the health of both people and ecosystems. Yet, long‐term data on nature engagement are scarce because traditional survey methods are expensive, time consuming and require commitment over multiple years. Digital data sources (e.g. aggregated data from online searches) have major potential as a supplementary source of information and, in the absence of available data, as a proxy for more direct measurement of nature engagement.

Using Google Trends, we created a list of refined and relevant search terms relating to diverse outdoor spaces and activities. We then compared trends in Google search volumes in England across both a 1‐year and 10‐year period to those from Google Community Mobility Reports, and from nationally representative survey data (Natural England's People and Nature Survey and the Monitor of Engagement with the Natural Environment).

Search, survey and mobility data all support a general increase in public engagement with nature since 2009, and a more substantial increase during, or following, the initial national ‘lockdown’ period of the COVID‐19 pandemic in England. Search volumes increased for many urban and rural outdoor green spaces (e.g. woodlands), blue spaces (e.g. reservoirs), exercise activities (e.g. walking, running and hiking) and explicitly nature‐based activities (e.g. fishing, wild swimming and encouraging wildlife).

Overall, volumes of Google searches were more closely related with longer‐term (10‐year) trends from survey data, than shorter‐term changes during the COVID‐19 pandemic. There were statistically significant relationships between search volumes, survey data (self‐reported past behaviour) and mobility data (movement trends) for around half of comparisons. Of these, an average of 13–44% of variation in the data was explained.

The findings show that Google Trends provides valuable information about public engagement with nature, which can help to supplement existing survey data by providing new insights about behavioural trends. The paper also provides a proof of concept for using Google Trends to understand changes in public engagement with nature, which could be applied to the many countries that lack long‐term survey monitoring.

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Development of artificial intelligence‐based neural network prediction model for responses of additive manufactured polylactic acid parts
Authors: Singh Jatinder , Goyal Kapil Kumar , Kumar Rakesh , Gupta Vishal 
Published: July 2022

AbstractFused deposition modeling (FDM) is one of the most economical and popular technology amongst numerous additive manufacturing techniques. The quality of FDM fabricated parts is highly sensitive to the production parameters. Thus, in the present work, an investigation on the FDM printed polylactic acid parts has been performed considering six printing process parameters, that is, nozzle diameter, build orientation, raster pattern, layer height and print speed to develop the feedforward backpropagation (FFBP) artificial neural network prediction model for the prediction of responses, namely, tensile strength, material consumption, build time and surface quality. Tensile specimens as per L27 orthogonal array are printed considering the various combination of parameters. The printed samples have been subjected to tensile strength testing, surface roughness measurement, build time recording, and material consumption evaluation. The highest tensile strength of 57.633 MPa, lowest surface roughness of 1.71 μm, lowest build time of 0.35 h and lowest material consumption of 7.8 g are observed. The experimental results have been used to develop the artificial intelligence‐based prediction model through FFBP algorithm and sigmoid transfer function to predict the responses. The best performance of the developed neural network with R2 for testing (0.99343), training (0.99366), and validation (0.99372) of data is recorded for prediction of responses with minimum percentage error. The study concluded that developed model is capable of predicting the responses of FDM process according to the input process parameters.
Mechanical and thermal properties of basalt fiber reinforced epoxy composites modified with CaCO3 nanoparticles
Authors: Tripathy Priyanka , Biswas Sandhyarani 
Published: July 2022

AbstractIn this study, calcium carbonate (CaCO3) nanoparticles were added to basalt fiber reinforced epoxy (BFRP) composites in varied weight fractions to examine their effects on the materials' mechanical, thermal, and physical properties. Through magnetic stirring and sonication, the epoxy matrix was altered with various weight fractions of CaCO3 nanoparticles (0%, 0.5%, 0.7%, 1%, and 1.5%). It has been found that adding 1 wt% of nanoparticles to the matrix produced the greatest results in terms of tensile, flexural, impact, and inter‐laminar shear strength (ILSS). With 1.5 wt% of nano CaCO3 in the composites, the best results for the micro‐hardness and glass transition temperature were obtained. The diffraction peaks of the nanocomposites have been identified using XRD investigation. Scanning electron microscopy (SEM) analyses has been done on the fracture surfaces of the nanocomposites to analyze the influence of the nanoparticles on the interfacial adhesion of fiber and the matrix.
Combination of LC‐Q‐TOF‐MS/MS, network pharmacology, and nanoemulsion approaches identifies active compounds of two Artemisia species responsible for tackling early diabetes‐related metabolic complications in the liver
Authors: Moulahoum Hichem , Ghorbanizamani Faezeh , Khiari Zineb , Toumi Mohamed , Benazzoug Yasmina , Timur Suna , Zihnioglu Figen 
Published: July 2022

AbstractIntroductionThe chronicity of advanced glycation end‐products (AGEs) imparts various damages resulting in metabolic dysfunction and diseases involving inflammation and oxidative stress. The use of plant extracts is of high interest in complementary medicine. Yet, extracts are multicomponent mixtures, and difficult to pinpoint their exact mechanism.ObjectivesWe hypothesise that network pharmacology and bioinformatics can help experimental findings depict the exact active components and mechanism of action by which they induce their effects. Additionally, the toxicity and variability can be lowered and standardised with proper encapsulation methods.MethodologyHere, we propose the formulation of phytoniosomes encapsulating two Artemisia species (Artemisia dracunculus and Artemisia absinthium) to mitigate AGEs and their induced cell redox dysregulation in the liver. Extracts from different solvents were identified via liquid chromatography quadrupole time‐of‐flight mass spectrometry (LC‐Q‐TOF‐MS/MS). Phytoniosomes were explored for their anti‐glycating effect and modulation of AGE‐induced damages in THLE‐2 liver cells. Network pharmacology tools were used to identify possible targets and signalling pathways implicated.ResultsData demonstrated that A. absinthium phytoniosomes had a significant anti‐AGE effect comparable to reference molecules and higher than A. dracunculus. They were able to restore cell dysfunction through the restoration of tumour necrosis alpha (TNF‐α), interleukin 6 (IL‐6), nitric oxide, and total antioxidant capacity. Phytoniosomes were able to protect cells from apoptosis by decreasing caspase 3 activity. Network pharmacology and bioinformatic analysis confirmed the induction of the effect via Akt‐PI3K‐MAPK and AGE‐RAGE signalling pathways through quercetin and luteolin actions.ConclusionThe current report highlights the potential of Artemisia phytoniosomes as strong contenders in AGE‐related disease therapy.
Identification and quantification of eight alkaloids in Aconitum heterophyllum using UHPLC‐DAD‐QTOF‐IMS: A valuable tool for quality control
Authors: Punia Ashwani , Joshi Robin , Kumar Rajiv 
Published: July 2022

AbstractIntroductionAconitum spp. are prime medicinal plants rich in alkaloids and have been used as the main constituents of traditional medicine in India and China. The whole plant can be toxic and creates pathophysiological conditions inside the human body. Therefore, simultaneous quantification of alkaloids within plant parts and herbal medicines associated with this genus is essential for quality control.ObjectiveWe aimed to develop and validate methods using ultra‐high‐performance liquid chromatography–diode array detector–quadrupole time‐of‐flight ion mobility mass spectrometry (UHPLC‐DAD‐QTOF‐IMS) and to develop an analytical strategy for the identification and quantification of alkaloid compounds (aconitine, hypaconitine, mesaconitine, aconine, benzoylmesaconitine, benzoylaconine, bulleyaconitine A, and deoxyaconitine) from Aconitum heterophyllum.MethodologyWe developed a simultaneous identification and quantification method for eight alkaloids using UHPLC‐DAD‐QTOF‐IMS. The method was validated as per International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines and also in IMS mode.ResultsThe developed method has good linearity (r2 = 0.997–0.999), LOD (0.63–8.31 μg/mL), LOQ (0.63–2.80 μg/mL), recovery (86.01–104.33%), reproducibility, intra‐ and inter‐day variability (<3.25%), and stability. Significant qualitative and quantitative variations were found among different plant parts (flower, leaf, stem, root, and tuber) and five market products of A. heterophyllum. Furthermore, a total of 21 metabolites were also profiled based on the fragmentation pattern of MS2 using the validated method.ConclusionAn appropriate mobile phase using acetonitrile and water in a gradient elution gave a satisfactory chromatographic separation of eight Aconitum alkaloids with their adjacent peaks. Therefore, this method could provide a scientific and technical platform for quality control assurance.
Associations between the number of antipsychotics prescribed and metabolic parameters in Japanese patients with schizophrenia
Authors: Watanabe Yuichiro , Ono Shin , Sugai Takuro , Suzuki Yutaro , Yamazaki Manabu , Sugawara Norio , Yasui‐Furukori Norio , Shimoda Kazutaka , Mori Takao , Ozeki Yuji , Matsuda Hiroshi , Okamoto Kurefu , Sagae Toyoaki , Someya Toshiyuki 
Published: July 2022

AbstractAimThere is little evidence on the effects of antipsychotic polypharmacy on metabolic parameters in patients with schizophrenia. Thus, this cross‐sectional study explored the associations between the number of antipsychotics prescribed and metabolic parameters in Japanese patients with schizophrenia.MethodsWe obtained metabolic parameter data from 19,675 patients with schizophrenia. Of these, 1380 (7.0%), 8422 (42.8%), 6326 (32.2%), and 3547 (18.0%) were treated with none, one, two, and three or more antipsychotics, respectively. We compared eight metabolic parameters among the four groups using univariate analyses. We then performed multiple regression analysis to assess the effect of the number of antipsychotics prescribed on metabolic parameters after controlling for the effects of age, sex, type of care (outpatient/inpatient), chlorpromazine‐equivalent dose, and antipsychotic type (aripiprazole, olanzapine, and risperidone).ResultsThere were significant differences in body mass index (BMI), systolic and diastolic blood pressure (dBP), total cholesterol, low‐density lipoprotein cholesterol, and triglycerides among the four groups. The multiple regression analysis showed that the number of antipsychotics prescribed was significantly correlated with BMI and dBP (standardized regression coefficient = 0.031 and 0.026, respectively).ConclusionOur results suggested that the number of antipsychotics prescribed adversely affects BMI and dBP. Clinicians should avoid inappropriate antipsychotic polypharmacy, especially polypharmacy involving three or more antipsychotics.
Improvement in school‐aged children with asthma during the Covid‐19 pandemic
Authors: Akelma Zülfikar , Başkaya Nevzat , Çetin Sema , Bostancı İlknur , Özmen Serap 
Published: July 2022

AbstractIntroductionThe coronavirus 2019 disease (Covid‐19) pandemic led to a number of measures being introduced in many countries worldwide. Lockdowns were imposed on individuals aged <18 years, education was delivered online, and mask‐wearing was made compulsory in public places, resulting in an unprecedented period for children. Real‐life data showing how children with asthma are affected by major changes are limited. This study investigated how asthmatic children are affected by pandemic conditions based on real‐life data.MethodsPatients with asthma aged 6–18 years followed up from March to May 2019—before the Covid‐19 pandemic—were included in the study. Data from March to May 2020 and 2019 were then compared to reveal the effects of pandemic‐related lifestyle changes on symptoms, frequency of exacerbations, and drug use in asthmatic children.ResultsEighty‐six children with asthma aged 6–18 years were included in this study. Time spent inside the home was significantly higher in 2020 than in 2019. Rescue medication requirements and emergency department visits were significantly lower in 2020 compared to 2019 (p < 0.001). The number of well‐controlled patients with asthma was higher in 2020 than in 2019 (p < 0.0001). The number of patients using prophylactic drugs within the previous 3 months was lower in 2020 compared to 2019 (p = 0.007).ConclusionThe present study yielded valuable insights, based on real‐life data, into the condition of children over the age of 6 years during the Covid‐19 pandemic. Numbers of asthmatic exacerbations, rescue drug use, and asthma control were positively affected in school‐aged children with asthma during the pandemic.
The mechanism of exogenous gibberellin A3 protecting sorghum shoots from S‐metolachlor Phytotoxicity
Authors: Zhang Yuxin , Liu Qinghao , Su Wangcang , Sun Lanlan , Xu Hongle , Xue Fei , Lu Chuantao , Wu Renhai 
Published: July 2022

AbstractBACKGROUNDS‐metolachlor (MET) was used to prevent weed infestation in sorghum fields, but inappropriate application could result in phytotoxicity on sorghum. Exogenous gibberellin A3 (GA3) has been applied for alleviating the phytotoxicity of MET. However, its detoxification mechanism is still not well known.RESULTSLeaf deformity of sorghum caused by 200 mg/L MET was alleviated by treating sorghum shoots with 800 mg/L GA3, and the injury recovery rate of growth index was over 73%. More importantly, GA3 could not accelerate the metabolic rate of MET in sorghum. The result of phytohormone metabolomics showed that endogenous GA3 content in sorghum decreased by 78.10% with MET treatment, while abscisic acid (ABA) content increased by 120.2%, resulting in 10.3‐fold increase of ABA/GA3 ratio. Content of ABA and GA3 increased by 11.9‐ and 21.1‐fold with MET and GA3 treatment, respectively, leading to ABA/GA3 ratio restoration. Moreover, MET inhibited the expression of genes encoding key enzymes related to GA synthesis including CPS1, KO2, KAO, GA20ox1D and ABA8ox gene related to ABA metabolism. The transcription levels of GA metabolism‐related genes CYP714D1 and GA2ox were up‐regulated by 11.2‐ and 7.2‐fold, while ABA synthesis‐related genes NCED and ZEP were up‐regulated by 8.0‐ and 3.0‐fold, respectively, with MET and GA3 treatment.ConclusionIn this study, exogenous GA3 protecting sorghum shoots from MET phytotoxicity was due to supplement the MET‐induced GA3 deficiency by absorbing exogenous GA3, and restore homeostasis of ABA and GA3 by promoting ABA synthesis, which provides novel insights for mechanism of GA3 alleviating MET phytotoxicity. © 2022 Society of Chemical Industry.
Targeting autophagy and neuroinflammation pathways with plant‐derived natural compounds as potential antidepressant agents
Authors: Mokhtari Tahmineh 
Published: July 2022

AbstractMajor depressive disorder (MDD) is a life‐threatening disease that presents several characteristics. The pathogenesis of depression still remains poorly understood. Moreover, the mechanistic interactions of natural components in treating depression to target autophagy and neuroinflammation are yet to be evaluated. This study overviewed the effects of plant‐derived natural components in regulating critical pathways, particularly neuroinflammation and autophagy, associated with depression. A list of natural components, including luteolin, apigenin, hyperforin, resveratrol, salvianolic acid b, isoliquiritin, nobiletin, andrographolide, and oridonin, have been investigated. All peer‐reviewed journal articles were searched by Scopus, MEDLINE, PubMed, Web of Science, and Google Scholar using the appropriated keywords, including depression, neuroinflammation, autophagy, plant, natural components, etc. The neuroinflammation and autophagy dysfunction are critically associated with the pathophysiology of depression. Natural components with higher efficiency and lower complications can be used for targeting neuroinflammation and autophagy. These components with different doses showed the beneficial antidepressant properties in rodents. These can modulate autophagy markers, mainly AMPK, LC3II/LC3I ratio, Beclin‐1. Moreover, they can regulate the NLRP3 inflammasome, resulting in the suppression of proinflammatory cytokines (e.g., IL‐1β and IL‐18). Future in vitro and in vivo studies are required to develop novel therapeutic approaches based on plant‐derived active components to treat MDD.
A transformed gamma process for bounded degradation phenomena
Authors: Fouladirad Mitra , Giorgio Massimiliano , Pulcini Gianpaolo 
Published: July 2022

AbstractMost of the stochastic models adopted to describe the evolution over time of degradation phenomena of technological units assume that their degradation level can increase indeterminately. However, these degradation phenomena are typically subjected to obvious bounds, if only because technological units have finite size. In fact, very often, this inconsistency does not significantly affect the effectiveness of unbounded degradation models, since degrading units are usually assumed to fail when their degradation level exceeds a failure threshold that is much smaller than the obvious bounds. Nevertheless, in some cases, due to the very nature of the underlying degradation mechanism, less obvious bounds could exist, which are not necessarily far from the failure thresholds. The question that arises is whether the use of a bounded degradation model, in this latter type of experimental situations, could be beneficial. For this purpose, since a bounded degradation process should necessarily have dependent increments, in this paper we investigate the potential of a new bounded transformed gamma (TG) process to adequately describe bounded degradation phenomena and predict their future evolution. Differently from other existing gamma process based bounded degradation models, here the upper bound is treated as an unknown parameter that has to be estimated from the available degradation data. A numerical example is presented where the parameters of the proposed model are estimated from simulated data. Then the model is applied to a set of wear measures of cylinder liners that equip a diesel engine for marine propulsion, which have also stimulated this study. Model parameters are estimated by using the maximum likelihood (ML) method. The fitting ability of the proposed new bounded process is compared to that of an unbounded gamma process, which was previously adopted to analyze the same liner wear data. Obtained results are critically discussed in the paper.
The adaptive weight learning function for reliability analysis and its application to multiple active learning methods
Authors: Li Jingkui , Liu Wenqi , Zhou Yan , Li Zhandong 
Published: July 2022

AbstractIn engineering, evaluating the failure probability of structures with complex performance functions is a challenging task. Some active learning methods based on the Kriging model gain considerable attention for structural reliability analysis. The existing learning functions offer new learning criterions to select sample points, but the probability density function (PDF) of sample points is ignored in some learning functions. To decrease selecting sample points in low PDF regions, this study presents a new adaptive weight learning function (WLF). The learning function WLF constituted by the adaptive orient function and the joint PDF can be applicable to multiple learning functions. Depending on the importance degree of candidate sample points, the sample points can be assigned different weight by learning function WLF. When the leaning function WLF is applicated to the existing active learning methods, the sample point with a higher PDF in neighborhood of the limit state function (LSF) can be given a larger weight to preferentially select into design of experiments (DoE). Therefore, the learning function WLF can help the multiple learning functions to select informative sample points with high PDF, which can further improve the efficiency of these learning functions. Four examples are used to illustrate the accuracy and efficiency as well as the suitability of the learning function WLF.
Increased risk of obstetric complications in patients with adenomyosis: A narrative literature review
Authors: Harada Takashi , Taniguchi Fuminori , Harada Tasuku 
Published: July 2022

AbstractBackgroundReproductive‐age women occasionally face the pathological condition of adenomyosis, which is often concurrent with endometriosis. It is believed that endometriosis and adenomyosis increases the risk of obstetric complications. Although new insights into the mechanism of obstetric complications due to endometriosis are emerging, there is little information on the etiology of adverse pregnancy outcomes in pregnant women with adenomyosis.MethodsWe performed a literature review focusing on the pathophysiological pathways of obstetric complications in women with adenomyosis using currently available basic and clinical studies. We used the internet search engines PubMed and Google Scholar to search for studies published between January 2000 and June 2021. We carefully read pertinent sections within each document to ensure relevancy.Main findingsThe prevalence of adverse pregnancy outcomes in women with adenomyosis is increased after adjusting for conceiving by assisted reproductive technology. Thus, adenomyosis emerges as a relevant factor associated with several obstetric complications such as preterm birth, preterm premature rupture of membranes, placental abruption, small for gestational age/fetal growth restriction, and preeclampsia.ConclusionIt is plausible that the impact of adenomyosis on pregnancy outcomes is not always the same; rather it is dependent on the degree of uterine involvement and subtypes.