bluuebunny/crossref_metadata_2025_split huggingface dataset:
Outstanding Piezoelectricity of (K, Na)(Nb, Sb)O3-SrZrO3-(Bi, Ag)ZrO3 Piezoceramics and Their Application to a Planar-Type Actuator
Authors: Go Su-Hwan , Eum Jae-min , Kim Dae-Su , Chae Seok-Jun , Kim Sun-Woo , Kim Eun-Ji , Chae Yeon-Gyeong , Woo Jong-Un , Nahm S. 
Published: August 2021

Abstract
Fe2O3-added 0.96(K0.5Na0.5)(Nb0.94Sb0.06)-0.01SrZrO3-0.03(BiAg)ZrO3 [KNNS-0.01SZ-0.03BAZ] piezoceramic was well densified at 1090 °C. It exhibited a large piezoelectric charge constant (d33) of 650 pC/N, which is similar to the largest d33 value reported in the literature. This sample has a tetragonal-orthorhombic-rhombohedral (T-O-R) structure, in which each structure has a similar proportion. Moreover, nanodomains (2 nm × 15 nm) with low domain boundary energy were found in this sample, which exhibited relaxor properties. Therefore, the presence of a T-O-R multi-structure and nanodomains is responsible for the large d33 value of this sample. A KNNS-0.01SZ-0.03BAZ thick film was synthesized, and it also had a T-O-R multi-structure with a large d33 (630 pC/N). A planar-type actuator was produced using this thick film, and this actuator exhibited a large acceleration (335 G at 120 V/mm) and displacement (231 μm at 120 V/mm). Hence, the KNNS-0.01SZ-0.03BAZ thick film is a good candidate for lead-free piezoelectric actuators.
A review of transfer learning for medical image classification
Authors: Kim Hee E. , Cosa-Linan Alejandro , Maros Mate E. , Santhanam Nandhini , Jannesari Mahboubeh , Ganslandt Thomas 
Published: August 2021

Abstract
This review paper provides an overview of the peer-reviewed articles using transfer learning for medical image analysis, while also providing guidelines for selecting a convolutional neural network model and its configurations for the image classification task. The data characteristics and the trend of models and transfer learning types in the medical domain are additionally analyzed. Publications were retrieved from the databases PubMed and Web of Science of peer-reviewed articles published in English until December 31, 2020. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. With respect to the model, the majority of studies (n = 57) empirically evaluated numerous models followed by deep (n = 33) and shallow (n = 24) models. With respect to the transfer learning approaches, the majority of studies (n = 46) empirically searched for the optimal transfer learning configuration followed by feature extractor (n = 38) and fine-tuning scratch (n = 27), feature extractor hybrid (n = 7) and fine-tuning (n = 3). The investigated studies showed that transfer learning demonstrates either a better or at least a similar performance compared to medical experts despite the limited data sets. We hence encourage data scientists and practitioners to use models such as ResNet or Inception with a feature extractor approach, which saves computational costs and time without degrading the predictive power.
Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides
Authors: Mazheika Aliaksei , Wang Yanggang , Valero Rosendo , Vines Francesc , Illas Francesc , Ghiringhelli Luca , Levchenko Sergey , Scheffler Matthias 
Published: August 2021

Abstract
Using subgroup discovery, an artificial intelligence (AI) approach that identifies statistically exceptional subgroups in a dataset, we develop a strategy for a rational design of catalytic materials. We identify “materials genes” (features of catalyst materials) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The approach is used to address the conversion of CO2 to fuels and other useful chemicals. The AI model is trained on high-throughput first-principles data for a broad family of oxides. We demonstrate that bending of the gas-phase linear molecule, previously proposed as the indicator of activation, is insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI approach identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. The same conclusion is obtained by using the bending indicator only when incombination with the Sabatier principle. Based on these findings, we propose a set of new promising oxide-based catalyst materials for CO2 conversion, and a recipe to find more. Our analysis also reveals advantages of local pattern discovery methods such as subgroup discovery over standard global regression approaches in discovering combinations of materials properties that result in a catalytic activation.
Identification and validation of immunotypes for clear cell RCC prognosis
Authors: Yang Zhaolin , Zhou Jiale , Xue Yizheng , Zhang Yu , Zhou Kaijun , Zhang Ying , Zhang Jin , Huang Jiwei , Huang Yiran , Xue Wei , Li Yan , Zhai Wei , Chen Yonghui 
Published: August 2021

Abstract
Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.
Assessing the impact of climate on annual and seasonal discharges at the Sremska Mitrovica station on the Sava River, Serbia
Authors: Leščešen Igor , Šraj Mojca , Pantelić Milana , Dolinaj Dragan 
Published: August 2021

Abstract
Flood frequency analysis was performed on annual maxima series for 90 years (1928–2017) of discharge data recorded at the Sremska Mitrovica gauging station on the Sava River. The three-parameter distributions (PearsonIII, Log-PearsonIII) are more suitable for modelling annual maxima than distribution functions with only two parameters (Normal, Log-normal, Gumbel). The Mann–Kendall test statistic indicated that there is no statistically significant trend identified in annual maximum discharges or average annual discharges. A positive increasing trend was observed in annual temperature, while annual precipitation shows a decreasing trend which is non-significant. The seasonality analysis found a statistically non-significant weak negative trend in discharge in spring, summer and autumn and a statistically non-significant weak positive trend in winter. During winter, spring, and summer a non-significant negative trend in precipitation was observed, while autumn has experienced a statistically significant increasing trend. Temperatures show a positive trend in all seasons, but only temperatures during the warm period show a statistically significant increase. The results demonstrate that decreasing discharges of the Sava River at the Sremska Mitrovica gauging station are mainly the consequence of decreasing precipitation and increasing temperature (increasing evaporation), which is consistent with the results of other studies of the region.
Quantitative detection of human- and canine-associated Bacteroides genetic markers from an urban coastal lagoon
Authors: Yasar Serhat A. , Mills Toby J. T. , Uluturk Zehra I. , Ruszczyk Jason M. S. , LeBard Rebecca J. , Neilan Brett A. 
Published: August 2021

Abstract
The contamination of water catchments by nonpoint source faecal pollution is a major issue affecting the microbial quality of receiving waters and is associated with the occurrence of a range of enteric illnesses in humans. The potential sources of faecal pollution in surface waters are diverse, including urban sewage leaks, surface runoff and wildlife contamination originating from a range of hosts. The major contributing hosts require identification to allow targeted management of this public health concern. In this study, two high-performing Microbial Source Tracking (MST) assays, HF183/Bac242 and BacCan-UCDmodif, were used for their ability to detect host-specific Bacteroides 16Sr RNA markers for faecal pollution in a 12-month study on an urban coastal lagoon in Sydney, Australia. The lagoon was found to contain year-round high numbers of human and canine faecal markers, as well as faecal indicator bacteria counts, suggesting considerable human and animal faecal pollution. The high sensitivity and specificity of the HF183/Bac242 and BacCan-UCDmodif assays, together with the manageable levels of PCR inhibition and high level DNA extraction efficiency obtained from lagoon water samples make these markers candidates for inclusion in an MST ‘toolbox’ for investigating host origins of faecal pollution in urban surface waters.
Identification of Key Molecules in Recurrent Miscarriage Based on
Bioinformatics Analysis
Authors: Luo Songping , Gao Jie , Wu Haiwang , Ning Yan , Yu Qingying 
Published: August 2021

Background:
Recurrent Miscarriage (RM) affects 1% to 5% of couples, and the
mechanisms still stay unclear. In this study, we explored the underlying molecular mechanism and
potential molecular biomarkers of RM as well as constructed a miRNA-mRNA regulation network.

Methods:
The microarray datasets GSE73025 and GSE22490, which represent mRNA and miRNA
profiles, respectively, were downloaded from the Gene Expression Omnibus (GEO) database.
Differentially Expressed Genes (DEGs) with p-value < 0.05 and fold-change > 2 were identified
while the miRNAs with p-value < 0.05 and fold-change > 1.3 were considered as significant
differentially expressed miRNAs (DEMs).

Results:
A total of 373 DEGs, including 218 up-regulated genes and 155 down-regulated genes,
were identified, while 138 up-regulated and 68 down-regulated DEMs were screened out. After
functional enrichment analysis, we found GO Biological Process (BP) terms significantly enriched
in the Fc-gamma receptor signaling pathway involved in phagocytosis. Moreover, signaling
pathway analyses indicated that the neurotrophin signaling pathway (hsa04722) was the top KEGG
enrichment. 6 hub genes (FPR1, C5AR1, CCR1, ADCY7, CXCR2, NPY) were screened out to
construct a complex regulation network in RM because they had the highest degree of affecting the
network. Besides, we constructed miRNA-mRNA network between DEMs target genes and DEGs
in RM, including hsa-miR-1297- KLHL24 and hsa-miR-548a-5p-KLHL24 pairs.

Conclusions:
In conclusion, the novel differentially expressed molecules in the present study could
provide a new sight to explore the pathogenesis of RM as well as potential biomarkers and
therapeutic targets for RM diagnosis and treatment.
Targeting Triglyceride Metabolism for Colorectal Cancer Prevention and
Therapy
Authors: Yarla Nagendra , Madka Venkateshwar , Rao Chinthalapally 
Published: August 2021

Background:
Triglycerides (TG) are one of the major constituents of body fat and energy
reservoir, which consist of an ester derived from glycerol and three free fatty acids. TG lipase,
monoacylglycerol lipase, fatty acid synthase, and HMG-CoA reductase are some of the key enzymes
related to TG metabolism, and their roles in colorectal cancer (CRC) initiation and progression
are under investigation.

Methods:
The literature search was performed based on various published papers, mostly on triglyceride
metabolism relevant to CRC in PubMed, Google Scholar and other search engines. The
gene expression profiling of some of the TG metabolic pathway mediators was performed by transcriptomic
and/or proteomic data from The Cancer Genome Atlas (TCGA) database using R program
and cBioportal software.

Results and Discussion:
Accumulating pieces of evidence suggest that TG profiling may be used
as a biomarker for the diagnosis and/or prognosis of CRC. Dysregulation of TG metabolism is associated
with the initiation and progression of CRC. Most of the TG anabolic pathway mediators are
overexpressed and/or overactivated during CRC tumorigenesis, while most TG catabolic pathway
mediators are downregulated and/or inactivated based on literature search and correlated with TCGA
data. Metabolic enzymes of TG and FAs metabolic pathways are involved in CRC tumor
growth survival and metastasis.

Conclusion:
Overall studies from the previous literature and our TCGA data analysis demonstrated
that the area of research on TG-associated lipid metabolic pathways holds great promise and
warranted detailed investigations in this area for the implementation of novel preventive and therapeutic
strategies against CRC.
Engineering Metal-Organic Framework-based Nanozymes for Enhanced
Biosensing
Authors: Xu Weiqing , Wu Yu , Jiao Lei , Gu Wenling , Du Dan , Lin Yuehe , Zhu Chengzhou 
Published: August 2021

Background:
Nanozymes are a kind of emerging nanomaterials that can mimic the catalytic
activity of natural enzymes with good stability.

Objective:
Benefited by the unique coordination structure and constitution, metal-organic frameworks
(MOFs) have been widely exploited as novel nanozymes. Importantly, various MOFs engineered
with fascinating functions provide great opportunities to enhance their enzyme-like activity
and improve their applied performance, achieving the goal of vividly mimicking natural enzymes.

Conclusion:
This review summarized recent advances in the fabrication of the MOFs-based
nanozymes and their applications in biosensing. First, MOFs-based nanomaterials containing pristine
MOFs, functionalized MOFs, MOFs-based composites and MOFs derivatives are introduced,
where the design strategy, enzyme-like activity and the catalytic mechanisms are highlighted systematically.
Then, their applications in various target assays are summarized. Finally, the challenges
and possible research directions for the development and application of MOFs-based
nanozymes are provided.
Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study
Authors: Fu Guanghui , Song Changwei , Li Jianqiang , Ma Yue , Chen Pan , Wang Ruiqian , Yang Bing Xiang , Huang Zhisheng 
Published: August 2021

Background
Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder.

Objective
We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk.

Methods
To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts).

Results
Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data.

Conclusions
In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.
Conducting Digital Health Care Research: Document Analysis of Challenges Experienced During Intervention Development and Feasibility Study Setup of an Internet-Administered Intervention for Parents of Children Treated for Cancer
Authors: Woodford Joanne , Karlsson Mathilda , Hagström Josefin , Hägg Sylvén Ylva , Norbäck Kajsa , Grönqvist Helena , von Essen Louise 
Published: August 2021

Background
The design and conduct of research to develop, test, and evaluate complex health care interventions is challenging. Although the existing literature describes key challenges associated with the design and conduct of definitive (evaluation) trials, there is a lack of information concerning specific challenges associated with the intervention development phase and setup of feasibility studies. In particular, the literature is scarce concerning the challenges associated with conducting digital health care research, such as research on internet-administered interventions and research using digital features to support the execution of study procedures (eg, recruitment, consent, retention, and data collection and management). This study is conducted in the context of the intervention development and feasibility study setup phases of an internet-administered, guided, low-intensity cognitive behavioral therapy–based intervention for parents of children previously treated for cancer.

Objective
The aim of this study is to explore the challenges experienced during the development phase of the internet-administered intervention and digital features to support the execution of the study procedures and a feasibility study setup.

Methods
To explore the key challenges experienced, we conducted a document analysis of written records from all study meetings held by the research team (meeting minutes) between June 7, 2018, and January 10, 2020, guided by a thematic analysis approach. Furthermore, discussion groups with members of the research team were held to develop a more detailed understanding of the key challenges experienced. Methods and results are reported in accordance with the relevant items from the Standards for Reporting Qualitative Research checklist.

Results
Six main themes were identified: decision-making and communication, expertise, external constraints, flexibility, planning and scheduling, and technical constraints.

Conclusions
Significant challenges were experienced during the intervention development and setup phases of the feasibility study. Implications are discussed to inform future design, conduct, and planning of internet-administered intervention development and feasibility studies, especially within the context of digital health care research.
Questioning the Yelp Effect: Mixed Methods Analysis of Web-Based Reviews of Urgent Cares
Authors: Hu Dian , Liu Cindy Meng-Hsin , Hamdy Rana , Cziner Michael , Fung Melody , Dobbs Samuel , Rogers Laura , Turner Monique Mitchell , Broniatowski David André 
Published: August 2021

Background
Providers of on-demand care, such as those in urgent care centers, may prescribe antibiotics unnecessarily because they fear receiving negative reviews on web-based platforms from unsatisfied patients—the so-called Yelp effect. This effect is hypothesized to be a significant driver of inappropriate antibiotic prescribing, which exacerbates antibiotic resistance.

Objective
In this study, we aimed to determine the frequency with which patients left negative reviews on web-based platforms after they expected to receive antibiotics in an urgent care setting but did not.

Methods
We obtained a list of 8662 urgent care facilities from the Yelp application programming interface. By using this list, we automatically collected 481,825 web-based reviews from Google Maps between January 21 and February 10, 2019. We used machine learning algorithms to summarize the contents of these reviews. Additionally, 200 randomly sampled reviews were analyzed by 4 annotators to verify the types of messages present and whether they were consistent with the Yelp effect.

Results
We collected 481,825 reviews, of which 1696 (95% CI 1240-2152) exhibited the Yelp effect. Negative reviews primarily identified operations issues regarding wait times, rude staff, billing, and communication.

Conclusions
Urgent care patients rarely express expectations for antibiotics in negative web-based reviews. Thus, our findings do not support an association between a lack of antibiotic prescriptions and negative web-based reviews. Rather, patients’ dissatisfaction with urgent care was most strongly linked to operations issues that were not related to the clinical management plan.
Effect of salicylic acid and progesterone on physiological characteristics of Kentucky bluegrass under salinity stress
Authors: Sabzmeydani Elham , Sedaghathoor Shahram , Hashemabadi Davood 
Published: August 2021

Salinity is one of the most important limiting factors in plant growth. It is also a predominant constraint that impairs grass growth and quality. Plant hormones play important roles in the capability of plants to adapt to environmental stresses. Hence, the impact of two plant growth regulators (PGRs) i.e. salicylic acid (SA) and progesterone (P4) was studied on biological characteristics of Poa pratensis in saline conditions in a greenhouse experimnt. The experimental treatments were composed of salinity at four levels (0, 2, 4, and 6 dS m-1) and six levels of PGRs (control, 1 mg L-1 P4,10 mg L-1 P4, 1 mM SA, 3 mM SA, and 1 mg L-1 P4 + 1 mM SA). The results showed that leaf firing percentage was increased with the excess in salinity, but the use of SA and P4 eased the effects of salinity stress and reduced leaf firing under salinity. 6 dS m-1 and 3 mM SA salinity caused to the maximum electrolyte leakage. The highest relative water content was observed in 4 dS m-1 salinity and 1 mM SA treatment. The highest glycine betaine was related to 6 dS m-1 NaCl and no hormone application. Salinity increased total protein and catalase, and the simultaneous use of P4 and SA exhibited the highest total protein and catalase content, whilst the control plants showed the lowest ones. The application of salinity stress reduced chlorophyll content, but SA and P4 increased it. The application of the two growth regulators improved carotenoid content under salinity stress. Overall, the results showed that the application of SA and P4 improved salinity tolerance and increased pigments and antioxidant enzyme activities.