bluuebunny/crossref_metadata_2025_split huggingface dataset:
The PA profession in Israel
Authors: Ozer Roi 
Published: October 2022

ABSTRACT
Implementation of physician assistants (PAs) in EDs throughout Israel began in May 2016. The profession is now showing some progress but at a relatively slow rate. This article provides an overview of Israel and its healthcare system, and describes the implementation, deployment, education, training, and scope of practice of PAs in Israel, plus recent developments and suggestions for advancing the profession.
Assessing and benchmarking equity, diversity, and inclusion in healthcare professions
Authors: Kayingo Gerald , Bradley-Guidry Carolyn , Burwell Nicole , Suzuki Sumihiro , Dorough Ramona , Bester Vanessa 
Published: October 2022

ABSTRACT
Healthcare professions are among the fastest growing careers in the United States, but the issues of equity, diversity, and inclusion remain a challenge. The percentage of underrepresented minority groups is disproportionately small despite many efforts to increase workforce diversity over the past several decades. As the demographics in our nation are rapidly changing, increasing racial, ethnic, and cognitive diversity is crucial to achieving a workforce with the capacity to provide accessible and equitable healthcare. Given the growing evidence that a diverse workforce improves healthcare outcomes, fosters unique perspectives (cognitive diversity), and helps to reduce health disparities, there is an urgent need to address perennial barriers such as structural racism, biases, and stereotypes. Educational institutions should be held accountable to integrate equity, diversity, and inclusion in all aspects of healthcare professions education and practice. This article discusses potential areas for innovation and opportunities that can be leveraged in assessing and benchmarking equity, diversity, and inclusion in healthcare professions. The ability to thoroughly assess the PA profession's culture in relationship to diversity, equity, and inclusion provides an opportunity to determine how well the profession is performing, and to strategically plan for what else need to be done.
What is rilonacept's role in treating recurrent pericarditis?
Authors: Schwier Nicholas C. 
Published: October 2022

ABSTRACT
The interleukin (IL)-1 antagonist rilonacept is approved by the FDA to treat recurrent pericarditis. In adults and adolescents with multiple recurrences of pericarditis, compared with placebo, rilonacept is associated with a lower risk of recurrent pericarditis, more persistent clinical response, normalized C-reactive protein within 7 days, and a higher number of days with no or minimal pericarditis symptoms. Patients receiving rilonacept could be weaned off of standard therapies for recurrent pericarditis and eventually be transitioned to rilonacept monotherapy. The most common adverse reactions to rilonacept include injection-site reactions, upper respiratory tract infections, and increased lipids. A loading dose of rilonacept is administered subcutaneously (SQ), along with weekly SQ maintenance dosing, which can be self-administered. Rilonacept is indicated for the treatment of recurrent pericarditis and reduction in risk of recurrence in patients age 12 years and older who have an elevated C-reactive protein level and significant disease burden.
The effect of sarcopenia and serum myokines on prognosis and survival in cirrhotic patients: a multicenter cross-sectional study
Authors: Boga Salih , Yildirim Abdullah Emre , Ucbilek Enver , Koksal Ali Riza , Sisman Sevil Tokdemir , Durak Ibrahim , Sen Ilker , Dogu Beril , Serin Erdinc , Ucbilek Ayse Bolat , Yildirim Makbule Ozge , Erturk Sukru Mehmet , Alkim Huseyin , Alkim Canan 
Published: October 2022

Objective
Sarcopenia is one of the most significant contributors to morbidity in patients with chronic liver disease. Serum myokines are potential biomarkers for detecting early sarcopenia. We aimed to investigate the relationship between serum myokines and cirrhosis-related mortality in the early stages of the disease.

Methods
In total, 262 patients and 50 healthy controls were enrolled in this study, which was designed as a multicenter cross-sectional study. At the beginning of the study, sarcopenia was defined by computed tomography scans using the third lumbar vertebra skeletal muscle index. Serum myostatin, irisin, and follistatin levels, nutritional status of the patients, and muscle strength as measured by the handgrip test were recorded. Cirrhosis-related mortality and overall survival were evaluated in the fourth year of the study as the second checkpoint of cross-sectional analysis.

Results
A total of 145 (55.3%) patients were diagnosed with sarcopenia. Multivariate analysis revealed that low BMI, high levels of myostatin, and decreased irisin levels were independent predictors of sarcopenia. While serum irisin level was the most predictive parameter in terms of 4th-year cirrhosis-related mortality in the CHILD A group, serum myostatin levels were found more indicative in the CHILD BC group regardless of sarcopenia status (P < 0.001).

Conclusion
Serum myostatin levels predict sarcopenia in all stages of cirrhosis. Serum irisin levels can also be used as a potential biomarker to predict both treatable sarcopenia and cirrhosis-related mortality in CHILD A patients.
Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors
Authors: Singh Rohit , Wu Alexander P. , Mudide Anish , Berger Bonnie 
Published: October 2022

AbstractSingle-cell expression dynamics from differentiation trajectories or RNA velocity have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either neglect these expression dynamics or require cells to be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph (DAG) of cells constructed from pseudotime or RNA velocity measurements. In contrast to previous approaches, Velorama is able to work directly with RNA velocity-based cell-to-cell transition probabilities and enables estimates of TF interaction speeds with their target genes. On a set of synthetic datasets, Velorama substantially outperforms existing approaches, improving area under the precision-recall curve (AUPRC) by 3.7–4.8x over the next best method. Applying Velorama to four RNA velocity datasets, we uncover evidence that the speed of a TF’s interactions is tied to its regulatory function. For human corticogenesis, we find slow TFs to be linked to gliomas and co-regulate preferentially with fast TFs, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to be a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.Software availabilityhttps://cb.csail.mit.edu/cb/velorama
Insitutype: likelihood-based cell typing for single cell spatial transcriptomics
Authors: Danaher Patrick , Zhao Edward , Yang Zhi , Ross David , Gregory Mark , Reitz Zach , Kim Tae K. , Baxter Sarah , Jackson Shaun , He Shanshan , Henderson Dave , Beechem Joseph M. 
Published: October 2022

AbstractAccurate cell typing is fundamental to analysis of spatial single-cell transcriptomics, but legacy scRNA-seq algorithms can underperform in this new type of data. We have developed a cell typing algorithm, Insitutype, designed for statistical and computational efficiency in spatial transcriptomics data.Insitutype is based on a likelihood model that weighs the evidence from every expression value, extracting all the information available in each cell’s expression profile. This likelihood model underlies a Bayes classifier for supervised cell typing, and an Expectation-Maximization algorithm for unsupervised and semi-supervised clustering. Insitutype also leverages alternative data types collected in spatial studies, such as cell images and spatial context, by using them to inform prior probabilities of cell type calls. We demonstrate rapid clustering of millions of cells and accurate fine-grained cell typing of kidney and non-small cell lung cancer samples.
Identification of RNA-based cell-type markers for the stem cell manufacture system with a statistical scoring function
Authors: Shi Yu , Yang Weilong , Lin Haishuang , Han Li , Cai Alyssa J. , Saraf Ravi , Lei Yuguo , Zhang Chi 
Published: October 2022

AbstractCell-type biomarkers are useful in stem cell manufacture to monitor cell purification, cell quantity, and quality. However, the study on cell-type markers, specifically for stem cell manufacture, is limited. The emerging questions are which RNA transcripts can serve as biomarkers during stem cell culture, and what method can efficiently and accurately discover these biomarkers. We developed a scoring function system to identify RNA biomarkers with RNA-seq data. We applied the method to two data sets, one for extracellular RNAs (ex-RNAs) and the other for intracellular microRNAs (miRNAs). The data set have RNA-seq data of ex-RNAs from cell culture media for six different types of cells, including human embryonic stem cells. To get the RNA-seq data from intracellular miRNAs, we cultured three types of cells: human embryonic stem cells (H9), neural stem cells (NSC), hESC-derived endothelial cells (EC) and conducted small RNA-seq to their intracellular miRNAs. Using these data, we identified a set of ex-RNAs/smRNAs as candidates of biomarkers for different types of cells for cell manufacture. We also used deep-learning based prediction methods and simulated data to validate these discovered biomarkers.
QClus: A droplet-filtering algorithm for enhanced snRNA-seq data quality in challenging samples
Authors: Schmauch Eloi , Ojanen Johannes , Galani Kyriakitsa , Jalkanen Juho , Harju Kristiina , Hollmén Maija , Kokki Hannu , Gunn Jarmo , Halonen Jari , Hartikainen Juha , Kiviniemi Tuomas , Tavi Pasi , Kaikkonen Minna U. , Kellis Manolis , Linna-Kuosmanen Suvi 
Published: October 2022

AbstractSingle nuclei RNA sequencing (snRNA-seq) remains a challenge for many human tissues, as incomplete removal of background signal masks cell-type-specific signals and interferes with downstream analyses. Here, we present QClus, a droplet-filtering algorithm targeted toward challenging samples, using cardiac tissue as an example. QClus uses specific metrics such as cell-type-specific marker gene expression to cluster nuclei and filter empty and highly contaminated droplets, providing reliable cleaning of samples with varying number of nuclei and contamination levels. In a benchmarking analysis against seven alternative methods across six datasets consisting of 252 samples and over 1.9 million nuclei, QClus achieved the highest quality in the greatest number of samples over all evaluated quality metrics and recorded no processing failures, while robustly retaining numbers of nuclei within the expected range. QClus combines high quality, automation, and robustness with flexibility and user-adjustability, catering to diverse experimental needs and datasets.
scCobra: Contrastive cell embedding learning with domain-adaptation for single-cell data integration and harmonization
Authors: Zhao Bowen , Wei Dong-Qing , Xiong Yi , Ding Jun 
Published: October 2022

AbstractThe rapid development of single-cell technologies has underscored the need for more effective methods in the integration and harmonization of single-cell sequencing data. The prevalent challenge of batch effects, resulting from technical and biological variations across studies, demands accurate and reliable solutions for data integration. Traditional tools often have limitations, both due to reliance on gene expression distribution assumptions and the common issue of over-correction, particularly in methods based on anchor alignments. Here we introduce scCobra, a deep neural network tool designed specifically to address these challenges. By leveraging a deep generative model that combines a contrastive neural network with domain adaptation, scCobra effectively mitigates batch effects and minimizes over-correction without depending on gene expression distribution assumptions. Additionally, scCobra enables online label transfer across datasets with batch effects, facilitating the continuous integration of new data without retraining, and offers features for batch effect simulation and advanced multi-omic batch integration. These capabilities make scCobra a versatile data integration and harmonization tool for achieving accurate and insightful biological interpretations from complex datasets.
The impact of lean production on operational performance: a case study
Authors: Memari Ashkan , Panjehfouladgaran Hamid Reza , Abdul Rahim Abd. Rahman , Ahmad Robiah 
Published: October 2022

PurposeThis paper aims to investigate the impact of adopting lean manufacturing principles on operational efficiency by eliminating seven major lean wastes (or Muda) in a Malaysian stationery manufacturer. Much of the research on lean considers its application to larger organisations with stable demand patterns. This research examines a small- and medium-sized enterprise (SME) with a volatile demand pattern.Design/methodology/approachA process activity mapping (PAM) methodology was utilized to identify the potential for waste elimination. PAM is a visual tool that considers every step in a production process. Value-added and non-value-added activities are therefore examined to understand hidden wastes and their sources.FindingsThe results revealed that the adopted lean principles significantly reduce the waiting times. This time reduction resulted in savings (reduction of cycle time) and to a certain extent, can be a crucial driver in continuous improvement sustainability in the production process.Research limitations/implicationsThe study focuses on a single case study and provides a springboard for further research. Future studies examining the results across a broader sample of organisations would develop the findings further.Practical implicationsThe extant literature cites mixed success for lean implementation programmes. The results demonstrate that lean is still recognised as a powerful approach to improving operations in SMEs.Originality/valueThis paper reflects on the application of lean in a real case study showing the impact of lean on operational performance of an SME.
Rahat-Sahayog(relief support): examining disaster emergency response in the aftermath of the Nepal Earthquake 2015
Authors: Karki Jeevan , Matthewman Steve , Grayman Jesse Hession 
Published: October 2022

PurposeThis paper aims to critically examine the post-disaster emergency response amongst marginalised and disadvantaged social groups following the 2015 Nepal Earthquake (7.8 Mw).Design/methodology/approachA qualitative research method was employed by conducting interviews with disaster survivors from marginalised and ethnic social groups, humanitarian aid workers and government officials in the four districts worst hit by the 2015 Nepal Earthquake.FindingsThis research found that community members demonstrated remarkable cooperation in the aftermath of the disaster; however, caste-based discrimination still manifested in post-earthquake emergency environments. Further, this research showed that the engagement of government and local and international humanitarian organisations was noteworthy in the earthquake emergency response in Nepal as it localised relief packages and adapted the assistance corresponding to the fast-changing post-disaster environments. However, some relief materials were culturally inappropriate and climatically unsuitable. This paper also shows that the poor dissemination of relief distribution plans, resource duplication and ineffective targeting disproportionately impacted the oppressed and marginalised households in receiving humanitarian assistance.Originality/valueStudies have been undertaken on the emergency response to the 2015 Nepal Earthquake, however, few have focussed on the lived experience of marginalised and disadvantaged social groups. Further, this research builds on, and contributes to, thehumanitarian mobile sovereigntydiscourse.
A summary of grey forecasting models
Authors: Xie Naiming 
Published: October 2022

PurposeThe purpose of this paper is to summarize progress of grey forecasting modelling, explain mechanism of grey forecasting modelling and classify exist grey forecasting models.Design/methodology/approachGeneral modelling process and mechanism of grey forecasting modelling is summarized and classification of grey forecasting models is done according to their differential equation structure. Grey forecasting models with linear structure are divided into continuous single variable grey forecasting models, discrete single variable grey forecasting models, continuous multiple variable grey forecasting models and discrete multiple variable grey forecasting models. The mechanism and traceability of these models are discussed. In addition, grey forecasting models with nonlinear structure, grey forecasting models with grey number sequences and grey forecasting models with multi-input and multi-output variables are further discussed.FindingsIt is clearly to explain differences between grey forecasting models with other forecasting models. Accumulation generation operation is the main difference between grey forecasting models and other models, and it is helpful to mining system developing law with limited data. A great majority of grey forecasting models are linear structure while grey forecasting models with nonlinear structure should be further studied.Practical implicationsMechanism and classification of grey forecasting models are very helpful to combine with suitable real applications.Originality/valueThe main contributions of this paper are to classify models according to models' structure are linear or nonlinear, to analyse relationships and differences of models in same class and to deconstruct mechanism of grey forecasting models.
Environmental cognition and environmental performance in restaurants: roles of green supplier selection and joint actions
Authors: Shin Soomin , Cho Meehee 
Published: October 2022

Purpose
Although much empirical literature confirmed the foodservice industry’s significant impact on the environment, little scholarly attention has been paid to how restaurants can improve environmental performance. This study aimed to verify the impact of restaurant environmental cognition (EC) on environmental performance through green supplier selection (GSS) and joint actions (JAs).

Design/methodology/approach
Using an online survey company, the questionnaire was distributed to restaurant owners/or managers who were qualified to provide accurate information regarding their operational practices. A total of 285 responses were included in the analysis. Confirmatory factor analysis was conducted to verify the validity and reliability of the measures. The SPSS PROCESS macro was used to test the hypothesized relationships.

Findings
The study showed that EC has positive and direct effects on restaurant environmental performance (REP). Each mediator of GSS and JAs played a positive mediating role between EC and REP. GSS and JAs also played multiple positive mediating roles between EC and REP. This study revealed that a path mediating between EC and REP through GSS can be used to derive the most optimal results.

Originality/value
This study focused on the importance of restaurant EC, which is a fundamental driver for restaurant environmental practices, but is overlooked in the relevant literature. The findings provide novel information that can be applied to operate green restaurants specifically through GSS and JAs. In particular, the present approach expands the existing knowledge on sustainable restaurant management by investigating EC from an organization perspective.
Institutional quality, employment, FDI and environmental degradation in developing countries: evidence from the balanced panel GMM estimator
Authors: Xaisongkham Sorphasith , Liu Xia 
Published: October 2022

PurposeThe main purpose of this research is to examine the impact of institutional quality and sectoral employment on environmental degradation in developing countries. This paper also re-examined the validity of the Environmental Kuznets Curve (EKC) hypothesis and estimated the long run impact of explanatory variables on CO2 emissions.Design/methodology/approachIn this paper, the balanced panel data for the period 2002–2016 was used based on data availability and applied two-step SYS-GMM estimators.FindingsThe results showed that institutional quality such as government effectiveness (GE) and the rule of law (RL) reduce CO2 emissions and promote environmental quality in developing countries. Interestingly, the authors found new evidence that employment in agriculture and industry has a positive impact on pollution, while employment in the service sector was negatively associated with CO2 emissions, and the validity of the EKC hypothesis was confirmed. In addition, the research suggests that strong institutional frameworks and their effective implementation are the most important panacea and should be treated as a top priority to counteract environmental degradation and achieve the UN Sustainable Development Goals.Originality/valueThis is the first study to examine the short run and long run effects of institutional quality and sectoral employment on environmental degradation using the balanced panel data for a large sample of developing countries. This paper also used a special technique of Driscoll and Kraay standard error approach to confirm the robustness results and showed the different roles of sectoral employment on environmental quality.
The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms
Authors: Leoni Luna , Ardolino Marco , El Baz Jamal , Gueli Ginetta , Bacchetti Andrea 
Published: October 2022

PurposeThis paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).Design/methodology/approachIn the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.FindingsUsing a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.Practical implicationsThis study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.Originality/valueThis study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.