bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and comparison of a hybrid to a traditional in-person point-of-care ultrasound course
Authors: Janjigian Michael , Dembitzer Anne , Srisarajivakul-Klein Caroline , Mednick Aron , Hardower Khemraj , Cooke Deborah , Zabar Sondra , Sauthoff Harald 
Published: March 2022

Abstract
Background
Traditional introductory point-of-care ultrasound (POCUS) courses are resource intensive, typically requiring 2–3 days at a remote site, consisting of lectures and hands-on components. Social distancing requirements resulting from the COVID-19 pandemic led us to create a novel hybrid course curriculum consisting of virtual and in-person components.

Methods
Faculty, chief residents, fellows and advanced practice providers (APPs) in the Department of Medicine were invited to participate in the hybrid curriculum. The course structure included 4 modules of recorded lectures, quizzes, online image interpretation sessions, online case discussions, and hands-on sessions at the bedside of course participant’s patients. The components of the course were delivered over approximately 8 months. Those participants who completed a minimum of 3 modules over the year were invited for final assessments. Results from the hybrid curriculum cohort were compared to the year-end data from a prior traditional in-person cohort.

Results
Participant knowledge scores were not different between traditional (n = 19) and hybrid (n = 24) groups (81% and 84%, respectively, P = 0.9). There was no change in POCUS skills as measured by the hands-on test from both groups at end-of-course (76% and 76%, respectively, P = 0.93). Confidence ratings were similar across groups from 2.73 traditional to 3.0 hybrid (out of possible 4, P = 0.46). Participants rated the course highly, with an average overall rating of 4.6 out 5.

Conclusions
A hybrid virtual and in-person POCUS course was highly rated and as successful as a traditional course in improving learner knowledge, hands-on skill and confidence at 8 months after course initiation. These results support expanding virtual elements of POCUS educational curricula.
Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models
Authors: Klibaite Ugne , Kislin Mikhail , Verpeut Jessica L. , Bergeler Silke , Sun Xiaoting , Shaevitz Joshua W. , Wang Samuel S.-H. 
Published: March 2022

Abstract
Background
Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD.

Methods
We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait.

Results
Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy.

Limitations
Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability.

Conclusions
Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
Therapeutic role of uterine-derived stem cells in acute kidney injury
Authors: Mamillapalli Ramanaiah , Cho SiHyun , Mutlu Levent , Taylor Hugh S. 
Published: March 2022

Abstract
Background
Acute kidney injury (AKI) causes abrupt deterioration in kidney function that disrupts metabolic, electrolyte and fluid homeostasis. Although the prevalence of AKI is steadily increasing, no definitive treatment options are available, leading to severe morbidity and mortality. We evaluated the role of uterine-derived multipotent stem cells in kidney regeneration after ischemic AKI.

Methods
Female C57BL/6J mice were hysterectomized and subsequently subject to AKI by either unilateral or bilateral renal ischemia–reperfusion injury. Uterine-derived cells (UDCs), containing a population of uterine stem cells, were isolated from the uteri of female transgenic DsRed mice and injected intravenously to AKI mice. Engraftment of DsRed cells was analyzed by flow cytometry while serum creatinine levels were determined colorimetrically. Expression of UDC markers and cytokine markers were analyzed by immunohistochemical and qRT-PCR methods, respectively. The Kaplan–Meier method was used to analyze survival time while unpaired t test with Welch’s correction used for data analysis between two groups.

Results
Mice with an intact uterus, and hence an endogenous source of UDCs, had a higher survival rate after bilateral ischemic AKI compared to hysterectomized mice. Mice treated with infusion of exogenous UDCs after hysterectomy/AKI had lower serum creatinine levels and higher survival rates compared to controls that did not receive UDCs. Engraftment of labeled UDCs was significantly higher in kidneys of bilateral ischemic AKI mice compared to those that underwent a sham surgery. When unilateral ischemic AKI was induced, higher numbers of UDCs were found in the injured than non-injured kidney. Immunofluorescence staining demonstrated double-positive DsRed/Lotus tetragonolobus agglutinin (LTA) positive cells and DsRed/CD31 positive cells indicating contribution of UDCs in renal tubular and vascular regeneration. Expression of Cxcl12, Bmp2, Bmp4, and Ctnf in renal tissue was significantly higher in the UDCs injection group than the control group.

Conclusions
UDCs engrafted injured kidneys, contributed to proximal tubule and vascular regeneration, improved kidney function and increased survival in AKI mice. UDC administration is a promising new therapy for AKI. Endogenous uterine stem cells likely also preserve kidney function, suggesting a novel interaction between the uterus and kidney. We suggest that hysterectomy may have a detrimental effect on response to renal injury.
Integrative analysis reveals a lineage-specific circular RNA landscape for adipo-osteogenesis of human mesenchymal stem cells
Authors: Huang Hai-Bo , Luo Hai-Tao , Wei Na-Na , Liu Miao-Ling , He Fei , Yang Wei , Dong Jun , Yang Xiao-Fei , Li Fu-Rong 
Published: March 2022

Abstract
Background
The balance between osteogenesis and adipogenesis of mesenchymal stem cells (MSCs) is critical to skeletal development and diseases. As a research hotspot, circular RNAs (circRNAs) have expanded our understanding of a hidden layer of the transcriptome. Yet, their roles during adipo-osteogenesis remain poorly described.

Methods
The identity of human MSCs derived from bone marrow and adipose were first determined by flow cytometry, cellular staining, and quantitative polymerase chain reaction (qPCR). Multi-strategic RNA-sequencing was performed using Poly A, RiboMinus and RiboMinus/RNase R methods. Integrative analysis was performed to identify lineage-specific expressed circRNAs. The structural and expressional characteristics were identified by Sanger sequencing and qPCR, respectively. The regulatory effects of adipogenesis-specific circ-CRLF1 were confirmed using siRNA transcfection and qPCR.

Results
We generated a whole transcriptome map during adipo-osteogenesis based on 10 Poly A, 20 RiboMinus and 20 RiboMinus/ RNase R datasets. A total of 31,326 circRNAs were identified and quantified from ~ 3.4 billion paired-end reads. Furthermore, the integrative analysis revealed that 1166 circRNA genes exhibited strong lineage-specific expression patterns. Their host genes were enriched in distinct biological functions, such as cell adhesion, cytokine signaling, and cell division. We randomly selected and validated the back-spliced junction sites and expression patterns of 12 lineage-specific circRNAs. Functional analysis indicated that circ-CRLF1 negatively regulated adipogenesis.

Conclusions
Our integrative analysis reveals an accurate and generally applicable lineage-specific circRNA landscape for adipo-osteogenesis of MSCs and provides a potential therapeutic target, circ-CRLF1, for the treatment of skeleton-related disease.
Application of deep metric learning to molecular graph similarity
Authors: Coupry Damien E. , Pogány Peter 
Published: March 2022

AbstractGraph based methods are increasingly important in chemistry and drug discovery, with applications ranging from QSAR to molecular generation. Combining graph neural networks and deep metric learning concepts, we expose a framework for quantifying molecular graph similarity based on distance between learned embeddings separate from any endpoint. Using a minimal definition of similarity, and data from the ZINC database of public compounds, this work demonstrate the properties of the embedding and its suitability for a range of applications, among them a novel reconstruction loss method for training deep molecular auto-encoders. Finally, we compare the applications of the embedding to standard practices, with a focus on known failure points and edge cases; concluding that our approach can be used in conjunction to existing methods.
Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles
Authors: Liu Chengyou , Hogan Andrew M. , Sturm Hunter , Khan Mohd Wasif , Islam Md. Mohaiminul , Rahman A. S. M. Zisanur , Davis Rebecca , Cardona Silvia T. , Hu Pingzhao 
Published: March 2022

Abstract
Motivation
Chemical–genetic interaction profiling is a genetic approach that quantifies the susceptibility of a set of mutants depleted in specific gene product(s) to a set of chemical compounds. With the recent advances in artificial intelligence, chemical–genetic interaction profiles (CGIPs) can be leveraged to predict mechanism of action of compounds. This can be achieved by using machine learning, where the data from a CGIP is fed into the machine learning platform along with the chemical descriptors to develop a chemogenetically trained model. As small molecules can be considered non-structural data, graph convolutional neural networks, which can learn from the chemical structures directly, can be used to successfully predict molecular properties. Clustering analysis, on the other hand, is a critical approach to get insights into the underlying biological relationships between the gene products in the high-dimensional chemical-genetic data.

Methods and results
In this study, we proposed a comprehensive framework based on the large-scale chemical-genetics dataset built in Mycobacterium tuberculosis for predicting CGIPs using graph-based deep learning models. Our approach is structured into three parts. First, by matching M. tuberculosis genes with homologous genes in Escherichia coli (E. coli) according to their gene products, we grouped the genes into clusters with distinct biological functions. Second, we employed a directed message passing neural network to predict growth inhibition against M. tuberculosis gene clusters using a collection of 50,000 chemicals with the profile. We compared the performance of different baseline models and implemented multi-label tasks in binary classification frameworks. Lastly, we applied the trained model to an externally curated drug set that had experimental results against M. tuberculosis genes to examine the effectiveness of our method. Overall, we demonstrate that our approach effectively created M. tuberculosis gene clusters, and the trained classifier is able to predict activity against essential M. tuberculosis targets with high accuracy.

Conclusion
This work provides an analytical framework for modeling large-scale chemical-genetic datasets for predicting CGIPs and generating hypothesis about mechanism of action of novel drugs. In addition, this work highlights the importance of graph-based deep neural networks in drug discovery.
Second malignant neoplasms in lymphomas, secondary lymphomas and lymphomas in metabolic disorders/diseases
Authors: Yu Youxi , Shi Xiaoju , Wang Xingtong , Zhang Ping , Bai Ou , Li Yan 
Published: March 2022

AbstractWith inconsistent findings, evidence has been obtained in recent years that metabolic disorders are closely associated with the development of lymphomas. Studies and multiple analyses have been published also indicating that some solid tumor survivors develop a secondary lymphoma, whereas some lymphoma survivors subsequently develop a second malignant neoplasm (SMN), particularly solid tumors. An interaction between the multiple etiologic factors such as genetic factors and late effects of cancer therapy may play an important role contributing to the carcinogenesis in patients with metabolic diseases or with a primary cancer. In this review, we summarize the current knowledge of the multiple etiologic factors for lymphomagenesis, focusing on the SMN in lymphoma, secondary lymphomas in primary cancers, and the lymphomas associated to metabolic disorders/diseases, which have been received less attention previously. Further, we also review the data of coexistence of lymphomas and hepatocellular carcinoma (HCC) in patients with infection of hepatitis C virus and hepatitis B virus.Graphical Abstract
Flight maneuver intelligent recognition based on deep variational autoencoder network
Authors: Tian Wei , Zhang Hong , Li Hui , Xiong Yuan 
Published: March 2022

AbstractThe selection and training of aircraft pilots has high standards, long training cycles, high resource consumption, high risk, and high elimination rate. It is the particularly urgent and important requirement for the current talent training strategy of national and military to increase efficiency and speed up all aspects of pilot training, reduce the training cycle and reduce the elimination rate. To this end, this paper uses deep variational auto-encoder network and adaptive dynamic time warping algorithms as support to explore the establishment of an integrated evaluation system for flight maneuver recognition and quality evaluation, solve the industry difficulty faced by current flight training data mining applications, and achieve accurate recognition and reliable quality evaluation of flight regimes under the background of high mobility. It will fully explore the benefits of existing airborne flight data for military trainee pilots, support the personalized and accurate training of flight talents, and reduce the rate of talent elimination.
Prevalence of periodontal disease among Indigenous and non-Indigenous populations: protocol for systematic review and meta-analysis
Authors: Nath Sonia , Poirier Brianna , Ju Xiangqun , Kapellas Kostas , Haag Dandara , Jamieson Lisa 
Published: March 2022

Abstract
Background
Indigenous populations globally experience worse oral health than their non-Indigenous counterpart. Globally, the occurrence of periodontal diseases such as gingivitis and chronic periodontitis is high among Indigenous people. This systematic review aims to quantify, at a global level, the prevalence of periodontal disease among Indigenous populations compared to non-Indigenous populations.

Methods
This review will only consider studies that have reported the prevalence (%) of periodontal disease among Indigenous and compared against non-Indigenous populations. Studies that have no comparative population or data only on one particular population or lack of data on periodontal clinical assessment will be excluded. An electronic search will be conducted using keywords and appropriate MeSH terms across several databases capturing both published and unpublished articles. The search will be conducted from the time of database inception to February 2021. After the initial search, duplicates will be removed, and the remaining titles and abstracts will be assessed for eligibility. The full text of eligible studies will be assessed by two independent reviewers who will also complete the critical appraisals and data extraction. Outcomes measures would be the mean prevalence (%) and standard deviation of periodontal disease among Indigenous and non-Indigenous populations. From the selected studies, we will conduct a random-effects meta-analysis using standardized mean difference as the effect measure. Forest plots will be used for the visualization of differences in the prevalence of periodontitis. A subgroup analysis will be conducted based on the definition of periodontitis, age, publication type, and geographical location. Heterogeneity among studies will be assessed by I2 and chi-square test. Egger’s test and funnel plots will be used to assess publication bias.

Discussion
Our systematic review and meta-analysis will facilitate an increased understanding of the magnitude of periodontal disease inequalities that exist globally for Indigenous populations through pooled prevalence estimates. The findings will be helpful to design selective targeted preventive and interventional strategies for periodontal disease for reducing oral health inequalities at a global level.

Systematic review registration
PROSPERO CRD42020188531
A second-order low-regularity integrator for the nonlinear Schrödinger equation
Authors: Ostermann Alexander , Wu Yifei , Yao Fangyan 
Published: March 2022

AbstractIn this paper, we analyze a new exponential-type integrator for the nonlinear cubic Schrödinger equation on the d dimensional torus $\mathbb{T}^{d}$

T
d

. The scheme has also been derived recently in a wider context of decorated trees (Bruned et al. in Forum Math. Pi 10:1–76, 2022). It is explicit and efficient to implement. Here, we present an alternative derivation and give a rigorous error analysis. In particular, we prove the second-order convergence in $H^{\gamma }(\mathbb{T}^{d})$

H
γ

(

T
d

)
for initial data in $H^{\gamma +2}(\mathbb{T}^{d})$

H

γ
+
2

(

T
d

)
for any $\gamma > d/2$
γ
>
d
/
2
. This improves the previous work (Knöller et al. in SIAM J. Numer. Anal. 57:1967–1986, 2019).The design of the scheme is based on a new method to approximate the nonlinear frequency interaction. This allows us to deal with the complex resonance structure in arbitrary dimensions. Numerical experiments that are in line with the theoretical result complement this work.
Some parameterized Simpson’s type inequalities for differentiable convex functions involving generalized fractional integrals
Authors: You Xuexiao , Ali Muhammad Aamir , Budak Hüseyin , Kara Hasan , Zhao Dafang 
Published: March 2022

AbstractIn this paper, we establish some new inequalities of Simpson’s type for differentiable convex functions involving some parameters and generalized fractional integrals. The results given in this study are a generalization of results proved in (Du, Li and Yang in Appl. Math. Comput. 293:358–369, 2017).
Improved firefly algorithm with courtship learning for unrelated parallel machine scheduling problem with sequence-dependent setup times
Authors: Huang Xingwang , Chen Lingqing , Zhang Yuxin , Su Shubin , Lin Yangbin , Cao Xuhui 
Published: March 2022

AbstractThe Unrelated Parallel Machines Scheduling Problem (UPMSP) with sequence-dependent setup times has been widely applied to cloud computing, edge computing and so on. When the setup times are ignored, UPMSP will be a NP problem. Moreover, when considering the sequence related setup times, UPMSP is difficult to solve, and this situation will be more serious in the case of high-dimensional. This work firstly select the maximum completion time as the optimization objective, which establishes a mathematical model of UPMSP with sequence-dependent setup times. In addition, an improved firefly algorithm with courtship learning is proposed. Finally, in order to provide an approximate solution in an acceptable time, the proposed algorithm is applied to solve the UPMSP with sequence-dependent setup times. The experimental results show that the proposed algorithm has competitive performance when dealing with UPMSP with sequence-dependent setup times.
What evidence exists for the impact of climate change on the physiology and behaviour of important aquaculture marine crustacean species in Asia? A systematic map protocol
Authors: Azra Mohamad Nor , Noor Mohd Iqbal Mohd , Eales Jacqualyn , Sung Yeong Yik , Ghaffar Mazlan Abd 
Published: March 2022

Abstract
Background
Climate is one of the most important driving factors of future changes in terrestrial, coastal, and marine ecosystems. Any changes in these environments can significantly influence physiological and behavioural responses in aquatic animals, such as crustacea. Crustacea play an integral role as subsistence predators, prey, or debris feeders in complex food chains, and are often referred to as good indicators of polluted or stressed conditions. They also frequently have high production, consumption, and commercial significance. However, crustacean’s responses to climate change are likely to vary by species, life-history stage, reproduction status and geographical distribution. This map is undertaken as part of the Long-Term Research Grant project which aims to identify any interactive effect on physiological compensation and behavioural strategy of how marine organisms, especially crustaceans, deal with stress from environmental change. Our proposed map will aim to outline the evidence currently existing for the impacts of climate change on the physiology and behaviour of important aquaculture crustacean species within Asia.

Methods
We will document peer-reviewed articles in English using published journal articles and grey literature. Two bibliographic databases (Scopus and Web of Science) and multiple organizational websites with Google scholars will be searched. The systematic map protocol will follow in accordance with the Collaboration for Environmental Evidence Guidelines and Standards. Literature will be screened at the title, abstract, and full-text level using pre-defined inclusion criteria. The map will highlight marine crustacea physiological compensation and behavioural strategies to cope with climate change. It will also improve our knowledge of the available evidence and current gaps for future research recommendations.