bluuebunny/crossref_metadata_2025 huggingface dataset:
Mega or Micro? Influencer Selection Using Follower Elasticity
Authors: Tian Zijun , Dew Ryan , Iyengar Raghuram
Published: October 2023

Influencer marketing, in which companies sponsor social media personalities to promote their brands, has exploded in popularity in recent years. One common criterion for selecting an influencer partner is popularity. While some firms collaborate with “mega” influencers with millions of followers, other firms partner with “micro” influencers with only several thousand followers, but who also cost less to sponsor. To quantify this trade-off between popularity and cost, the authors develop a framework for estimating the follower elasticity of impressions (FEI), which measures a video's percentage gain in impressions (i.e., views) corresponding to a percentage increase in the number of followers of its creator. Computing FEI involves estimating the causal effect of an influencer's popularity on the view counts of their videos, which is achieved through a combination of (1) a unique data set collected from TikTok, (2) a representation learning model for quantifying video content, and (3) a machine learning–based causal inference method. The authors find that FEI is always positive, averaging .10, but often nonlinearly related to follower size. They examine the factors that predict variation in these FEI curves and show how firms can use these results to better determine influencer partnerships.
Effects of New Public Management Reforms on Human Resource Practices: A Case Study in Jordan
Authors: Mahmoud Mo’men Hani , Othman Rosly
Published: October 2023

This paper aims to investigate the effects of new public management reforms on human resource practices of a public organization in Jordan, a Middle Eastern country in the developing world. The data was collected from fifteen purposeful semi-structured interviews in the Greater Amman Municipality (GAM), one of Jordan’s biggest public organizations. The data was analyzed using thematic analysis (i.e., a coding technique). The findings revealed the challenges and gaps in Human Resource (HR) practices from gaps in implementing New Public Management (NPM) reforms which weakened the performance of the public organization. The discussion and conclusion are presented at the end.
Is Female Genital Circumcision a Driver of Income Inequality in Sub-Saharan Africa? Evidence from Nigeria
Authors: Ajide Kazeem B. , Elu Juliet U. , Price Gregory N.
Published: October 2023

This study considers the effects of female genital circumcision (FGC) on household income inequality in Nigeria. To the extent that FGC has adverse health consequences, it can lower female labour productivity and earnings relative to males. Theoretically, we motivate our inquiry within a simple game-theoretic household norm bargaining framework in which there are several possible equilibria based on the decisions of representative males to require FGC, and representative females accepting, or not. We link regional data on Nigeria from the Afrobarometer survey to existing regional FGC surveillance data in Nigeria to estimate with Ordinal Logit specifications, the effect of regional FGC rates on a household’s position in the income distribution. Our parameter estimates reveal that as the ratio of daughter to mother genital circumcision increases in a region, the odds of a household being in the lower income decile increases. The FGC effects are consistent with a political economy of gender bargaining about FGC choices in which males have more bargaining power in governing/managing the household relative to females. Our results suggest that any policy intervention that eradicates FGC, including interventions that affect a political economy of bargaining that at least equalises bargaining power between males and females, could reduce household income inequality in Sub-Saharan Africa.

JEL: I14, I18, J16, O5, Z13
Perceptions of the European Union’s Policy Impact: Europeanisation of Public Attitudes in Hungary
Authors: Bíró-Nagy András , Szászi Áron József
Published: October 2023

This article investigates the perceptions of the European Union’s policy impact by revealing the Hungarian public’s awareness of the European Union’s domestic policy influence and its attitudes towards Europeanisation. The study is based on descriptive and regression analyses of cross-sectional survey data. With respect to general knowledge about the European Union, as well as knowledge about the competences of the European Union and the member states, we found that slightly more than a third of the respondents can be classified as well-informed. We observed indications of “wishful thinking” about Europeanisation, as there was a significant correlation between the perceived and desired levels of policy integration. Respondents were more likely to support the EU integration of those policies that are in fact Europeanised to a larger extent. Pro-government supporters and voters of right-wing identity have a lower probability of supporting EU integration of policies. Although the classic “referendum question” suggests that the public support of Hungary’s EU membership is stable, our results demonstrate that there are major limitations to the willingness of Hungarian society to support further integration, and a policy favouring national sovereignty has strong roots in society. The divisions in society we found with respect to the distribution of policy competences between the European Union and Hungary lead us to conclude that the debates about the future of the European Union will likely continue to polarise Hungarian public opinion in the future.
Factors Influencing Behavioral Intention Toward Using Travel Mobile Application: Examining the Mediating Role of Reviews and Ratings
Authors: Taneja Mayur , Nath Vikas , Saxena Noopur
Published: October 2023

The purpose of this study is to identify the major factors that affect tourist intention to use mobile application for the purchase of travel and tourism services. This study integrates the theory of M-S-QUAL with two additional factors, price consciousness and information quality, to develop a conceptual framework. Data was collected from 510 users of travel mobile applications from Delhi/NCR, and analyzed using structural equation modeling. The results indicated that efficiency, privacy, price consciousness, and information quality are the main determinants of m-satisfaction. Additionally, m-satisfaction significantly affects tourists’ behavioral intentions. Reviews and ratings was found to partially mediate the relationship between m-satisfaction and tourists’ behavioral intention. The framework is helpful for decision-makers to identify the factors that can satisfy tourists when using a travel mobile application and will also provide useful insights for the travel service providers dealing in m-applications to device their future strategies.
Performance in an Audiovisual Selective Attention Task Using Speech-Like Stimuli Depends on the Talker Identities, But Not Temporal Coherence
Authors: Cappelloni Madeline S. , Mateo Vincent S. , Maddox Ross K.
Published: October 2023

Audiovisual integration of speech can benefit the listener by not only improving comprehension of what a talker is saying but also helping a listener select a particular talker's voice from a mixture of sounds. Binding, an early integration of auditory and visual streams that helps an observer allocate attention to a combined audiovisual object, is likely involved in processing audiovisual speech. Although temporal coherence of stimulus features across sensory modalities has been implicated as an important cue for non-speech stimuli (Maddox et al., 2015), the specific cues that drive binding in speech are not fully understood due to the challenges of studying binding in natural stimuli. Here we used speech-like artificial stimuli that allowed us to isolate three potential contributors to binding: temporal coherence (are the face and the voice changing synchronously?), articulatory correspondence (do visual faces represent the correct phones?), and talker congruence (do the face and voice come from the same person?). In a trio of experiments, we examined the relative contributions of each of these cues. Normal hearing listeners performed a dual task in which they were instructed to respond to events in a target auditory stream while ignoring events in a distractor auditory stream (auditory discrimination) and detecting flashes in a visual stream (visual detection). We found that viewing the face of a talker who matched the attended voice (i.e., talker congruence) offered a performance benefit. We found no effect of temporal coherence on performance in this task, prompting an important recontextualization of previous findings.
Multi-centric study for development and validation of a CT head rule for mild traumatic brain injury in direct oral anticoagulants: the HERO-M nomogram
Authors: Park Naria , Barbieri Greta , Turcato Gianni , Cipriano Alessandro , Zaboli Arian , Giampaoli Sara , Bonora Antonio , Ricci Giorgio , Santini Massimo , Ghiadoni Lorenzo
Published: October 2023

Background

Nomograms are easy-to-handle clinical tools which can help in estimating the risk of adverse outcome in certain population. This multi-center study aims to create and validate a simple and usable clinical prediction nomogram for individual risk of post-traumatic Intracranial Hemorrhage (ICH) after Mild Traumatic Brain Injury (MTBI) in patients treated with Direct Oral Anticoagulants (DOACs).

Methods

From January 1, 2016 to December 31, 2019, all patients on DOACs evaluated for an MTBI in five Italian Emergency Departments were enrolled. A training set to develop the nomogram and a test set for validation were identified. The predictive ability of the nomogram was assessed using AUROC, calibration plot, and decision curve analysis.

Results

Of the 1425 patients in DOACs in the study cohort, 934 (65.5%) were included in the training set and 491 (34.5%) in the test set. Overall, the rate of post-traumatic ICH was 6.9% (7.0% training and 6.9% test set). In a multivariate analysis, major trauma dynamic (OR: 2.73, p = 0.016), post-traumatic loss of consciousness (OR: 3.78, p = 0.001), post-traumatic amnesia (OR: 4.15, p < 0.001), GCS < 15 (OR: 3.00, p < 0.001), visible trauma above the clavicles (OR: 3. 44, p < 0.001), a post-traumatic headache (OR: 2.71, p = 0.032), a previous history of neurosurgery (OR: 7.40, p < 0.001), and post-traumatic vomiting (OR: 3.94, p = 0.008) were independent risk factors for ICH. The nomogram demonstrated a good ability to predict the risk of ICH (AUROC: 0.803; CI95% 0.721–0.884), and its clinical application showed a net clinical benefit always superior to performing CT on all patients.

Conclusion

The Hemorrhage Estimate Risk in Oral anticoagulation for Mild head trauma (HERO-M) nomogram was able to predict post-traumatic ICH and can be easily applied in the Emergency Department (ED).
Semi-rational engineering an aldo–keto reductase for stereocomplementary reduction of α-keto amide compounds
Authors: Bai Ruixuan , Chen Baoling , Zheng Liangyu
Published: October 2023

Enantio-pure α-hydroxy amides are valuable intermediates for the synthesis of chiral pharmaceuticals. The asymmetric reduction of α-keto amides to generate chiral α-hydroxy amides is a difficult and challenging task in biocatalysis. In this study, iolS, an aldo–keto reductase from Bacillus subtilis 168 was exhibited as a potential biocatalyst, which could catalyze the reduction of diaryl α-keto amide such as 2-oxo-N, 2-diphenyl-acetamide (ONDPA) with moderate S-selectivity (76.1%, ee) and 60.5% conversion. Through semi-rational engineering, two stereocomplementary variants (I57F/F126L and N21A/F126A) were obtained with ee value of 97.6% (S) and 99.9% (R) toward ONDPA (1a), respectively, delivering chiral α-hydroxy amide with > 98% conversions. Moreover, the excellent S- and R-preference variants displayed improved stereoselectivities toward the other α-keto amide compounds. Molecular dynamic and docking analysis revealed that the two key residues at 21 and 126 were identified as the “switch”, which specifically controlled the stereopreference of iolS by regulating the shape of substrate binding pocket as well as the substrate orientation. Our results offer an effective strategy to obtain α-hydroxy amides with high optical purity and provide structural insights into altering the stereoselectivity of AKRs.

Graphical Abstract
A qualitative assessment of key considerations for drug checking service implementation
Authors: Grace Rose Chloe , Pickard A. Simon , Kulbokas Victoria , Hoferka Stacey , Friedman Kaitlyn , Epstein Jennifer , Lee Todd A.
Published: October 2023

Background

With many drug-related deaths driven by potent synthetic opioids tainting the illicit drug supply, drug checking services are becoming a key harm reduction strategy. Many drug checking technologies are available, ranging from fentanyl test strips to mass spectrometry. This study aimed to identify key considerations when implementing drug checking technologies and services to support harm reduction initiatives.

Methods

Key informant interviews were conducted with harm reduction stakeholders throughout Illinois. Participants included members of existing drug checking services and recovery centers. Interviews were recorded, transcribed, and coded by two researchers using the framework method. Findings were contextualized according to micro (client)-, meso (organization)-, and macro (policy)-level themes.

Results

Seven interviews were conducted with ten participants. Fourier transform infrared spectroscopy was consistently identified as a technology of choice given its accuracy, range of substance detection, portability, and usability. Recommendations included the use of confirmatory testing, which can help address the limitations of technologies and provide a mechanism to train technicians. Locations of drug checking services should maximize public health outreach and leverage existing harm reduction agencies and staff with lived experience, who are critical to developing trust and rapport with clients. Criminalization and loss of privacy were major concerns for clients using drug checking services. Additional issues included the need to raise awareness of the legitimacy of services through public support from governing bodies, and funding to ensure the sustainability of drug checking services.

Conclusions

This research facilitated the identification of issues and recommendations from stakeholders around key considerations for the adoption of drug checking technologies, which not only included the cost and technical specifications of instrumentation, but also broader issues such as accessibility, privacy, and well-trained personnel trusted by clients of the service. Successful implementation of drug checking services requires knowledge of local needs and capacity and an in-depth understanding of the target population.
Systematic in vivo candidate evaluation uncovers therapeutic targets for LMNA dilated cardiomyopathy and risk of Lamin A toxicity
Authors: Tan Chia Yee , Chan Pui Shi , Tan Hansen , Tan Sung Wei , Lee Chang Jie Mick , Wang Jiong-Wei , Ye Shu , Werner Hendrikje , Loh Ying Jie , Lee Yin Loon , Ackers-Johnson Matthew , Foo Roger S. Y. , Jiang Jianming
Published: October 2023

Background

Dilated cardiomyopathy (DCM) is a severe, non-ischemic heart disease which ultimately results in heart failure (HF). Decades of research on DCM have revealed diverse aetiologies. Among them, familial DCM is the major form of DCM, with pathogenic variants in LMNA being the second most common form of autosomal dominant DCM. LMNA DCM is a multifactorial and complex disease with no specific treatment thus far. Many studies have demonstrated that perturbing candidates related to various dysregulated pathways ameliorate LMNA DCM. However, it is unknown whether these candidates could serve as potential therapeutic targets especially in long term efficacy.

Methods

We evaluated 14 potential candidates including Lmna gene products (Lamin A and Lamin C), key signaling pathways (Tgfβ/Smad, mTor and Fgf/Mapk), calcium handling, proliferation regulators and modifiers of LINC complex function in a cardiac specific Lmna DCM model. Positive candidates for improved cardiac function were further assessed by survival analysis. Suppressive roles and mechanisms of these candidates in ameliorating Lmna DCM were dissected by comparing marker gene expression, Tgfβ signaling pathway activation, fibrosis, inflammation, proliferation and DNA damage. Furthermore, transcriptome profiling compared the differences between Lamin A and Lamin C treatment.

Results

Cardiac function was restored by several positive candidates (Smad3, Yy1, Bmp7, Ctgf, aYAP1, Sun1, Lamin A, and Lamin C), which significantly correlated with suppression of HF/fibrosis marker expression and cardiac fibrosis in Lmna DCM. Lamin C or Sun1 shRNA administration achieved consistent, prolonged survival which highly correlated with reduced heart inflammation and DNA damage. Importantly, Lamin A treatment improved but could not reproduce long term survival, and Lamin A administration to healthy hearts itself induced DCM. Mechanistically, we identified this lapse as caused by a dose-dependent toxicity of Lamin A, which was independent from its maturation.

Conclusions

In vivo candidate evaluation revealed that supplementation of Lamin C or knockdown of Sun1 significantly suppressed Lmna DCM and achieve prolonged survival. Conversely, Lamin A supplementation did not rescue long term survival and may impart detrimental cardiotoxicity risk. This study highlights a potential of advancing Lamin C and Sun1 as therapeutic targets for the treatment of LMNA DCM.

Graphical Abstract
Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy
Authors: Xie Xin , Song Yuchun , Ye Feng , Wang Shulian , Yan Hui , Zhao Xinming , Dai Jianrong
Published: October 2023

Background

Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model.

Methods

To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance.

Results

The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05).

Conclusions

The introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.
Use of levosimendan in hemodynamic management of heart failure in two neonates with intracranial arteriovenous shunts: a case series
Authors: Landolfo Francesca , Giliberti Paola , De Rose Domenico Umberto , Pugnaloni Flaminia , Santisi Alessandra , Columbo Claudia , Martini Ludovica , Ronchetti Maria Paola , Di Chiara Luca , Toscano Alessandra , Gandolfo Carlo , Dotta Andrea , Capolupo Irma
Published: October 2023

Background

The hemodynamic status of newborns with intracranial arteriovenous shunts (AVSs) may be extremely complex. Mini-invasive hemodynamic monitoring through innovative techniques such as Near-Infrared Spectroscopy (NIRS) and Pressure Recording Analytical Method (PRAM) may help in understanding hemodynamics in newborns with AVSs. Levosimendan is a calcium sensitizer and inodilator, and it is known to improve ventricular function, but its use in newborns is limited. In our cases, we evaluated the effect of levosimendan on hemodynamics through NIRS and PRAM.

Case presentation

Herein, we report the cases of two neonates with intracranial arteriovenous shunts, in whom we used levosimendan to manage cardiac failure refractory to conventional treatment. Levosimendan was used at a dosage of 0.1 mcg/kg/min for 72 h. Combined use of NIRS and PRAM helped in real-time monitoring of hemodynamic effects; in particular, levosimendan determined significant improvement in myocardium contractility as well as a reduction of heart rate.

Conclusion

In two neonatal cases of AVSs, levosimendan led to an overall hemodynamic stabilization, documented by the combination of NIRS and PRAM. Our results suggest introducing levosimendan as a second-line treatment in cases of severe cardiac dysfunction due to AVSs without improvement using standard treatment strategies. Future prospective and larger studies are highly warranted.
A CT-based radiomics approach to predict intra-tumoral tertiary lymphoid structures and recurrence of intrahepatic cholangiocarcinoma
Authors: Xu Ying , Li Zhuo , Yang Yi , Li Lu , Zhou Yanzhao , Ouyang Jingzhong , Huang Zhen , Wang Sicong , Xie Lizhi , Ye Feng , Zhou Jinxue , Ying Jianming , Zhao Hong , Zhao Xinming
Published: October 2023

Purpose

To predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative CT radiomics.

Patients and methods

A total of 116 ICC patients were included (training: 86; external validation: 30). The enhanced CT images were performed for the radiomics model. The logistic regression analysis was applied for the clinical model. The combined model was based on the clinical and radiomics models.

Results

A total of 107 radiomics features were extracted, and after being eliminated and selected, six features were combined to establish a radiomics model for TLSs prediction. Arterial phase diffuse hyperenhancement and AJCC 8th stage were combined to construct a clinical model. The combined (radiomics nomogram) model outperformed both the independent radiomics model and clinical model in the training cohort (AUC, 0.85 vs. 0.82 and 0.75, respectively) and was validated in the external validation cohort (AUC, 0.88 vs. 0.86 and 0.71, respectively). Patients in the rad-score no less than −0.76 (low-risk) group showed significantly better RFS than those in the less than −0.76 (high-risk) group (p < 0.001, C-index = 0.678). Patients in the nomogram score no less than −1.16 (low-risk) group showed significantly better RFS than those of the less than −1.16 (high-risk) group (p < 0.001, C-index = 0.723).

Conclusions

CT radiomics nomogram could serve as a preoperative biomarker of intra-tumoral TLSs status, better than independent radiomics or clinical models; preoperative CT radiomics nomogram achieved accurate stratification for RFS of ICC patients, better than the postoperative pathologic TLSs status.

Critical relevance statement

The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models and better prognosis stratification than postoperative pathologic TLSs status in ICC patients, which may facilitate identifying patients benefiting most from surgery and subsequent immunotherapy.

Key points

• The combined (radiomics nomogram) model consisted of the radiomics model and clinical model (arterial phase diffuse hyperenhancement and AJCC 8th stage).

• The radiomics nomogram showed better performance in predicting TLSs than independent radiomics or clinical models in ICC patients.

• Preoperative CT radiomics nomogram achieved more accurate stratification for RFS of ICC patients than the postoperative pathologic TLSs status.

Graphical Abstract
Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
Authors: Lei Xiyao , Cao Zhuo , Wu Yibo , Lin Jie , Zhang Zhenhua , Jin Juebin , Ai Yao , Zhang Ji , Du Dexi , Tian Zhifeng , Xie Congying , Yin Weiwei , Jin Xiance
Published: October 2023

Background

Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC.

Methods

Histologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) was applied to select optimal radiomics features from PET, CT, and fused PET-CT images, respectively. Logistic regression (LR) was applied to classify the T stage (T1,2 vs. T3,4), lymph node metastasis (LNM) (LNM(−) vs. LNM(+)), and pathological state (pstage) (I–II vs. III–IV) with features from CT (CT_LR_Score), PET (PET_LR_Score), fused PET/CT (Fused_LR_Score), and combined CT and PET features (CT + PET_LR_Score), respectively.

Results

Seven, 10, and 7 CT features; 7, 8, and 7 PET features; and 3, 6, and 3 fused PET/CT features were selected using mRMR for the prediction of T stage, LNM, and pstage, respectively. The area under curves (AUCs) for T stage, LNM, and pstage prediction in the validation cohorts were 0.846, 0.756, 0.665, and 0.815; 0.769, 0.760, 0.665, and 0.824; and 0.727, 0.785, 0.689, and 0.837 for models of CT_LR_Score, PET_ LR_Score, Fused_ LR_Score, and CT + PET_ LR_Score, respectively.

Conclusions

Accurate prediction ability was observed with combined PET and CT radiomics in the prediction of T stage, LNM, and pstage for EC patients.

Critical relevance statement

PET/CT radiomics is feasible and promising to stratify stages for esophageal cancer preoperatively.

Key points

• PET-CT radiomics achieved the best performance for Node and pathological stage prediction.

• CT radiomics achieved the best AUC for T stage prediction.

• PET-CT radiomics is feasible and promising to stratify stages for EC preoperatively.

Graphical Abstract
Cardiomyocyte precursors generated by direct reprogramming and molecular beacon selection attenuate ventricular remodeling after experimental myocardial infarction
Authors: Bachamanda Somesh Dipthi , Klose Kristin , Maring Janita A. , Kunkel Désirée , Jürchott Karsten , Protze Stephanie I. , Klein Oliver , Nebrich Grit , Becker Matthias , Krüger Ulrike , Nazari-Shafti Timo Z. , Falk Volkmar , Kurtz Andreas , Gossen Manfred , Stamm Christof
Published: October 2023

Background

Direct cardiac reprogramming is currently being investigated for the generation of cells with a true cardiomyocyte (CM) phenotype. Based on the original approach of cardiac transcription factor-induced reprogramming of fibroblasts into CM-like cells, various modifications of that strategy have been developed. However, they uniformly suffer from poor reprogramming efficacy and a lack of translational tools for target cell expansion and purification. Therefore, our group has developed a unique approach to generate proliferative cells with a pre-CM phenotype that can be expanded in vitro to yield substantial cell doses.

Methods

Cardiac fibroblasts were reprogrammed toward CM fate using lentiviral transduction of cardiac transcriptions factors (GATA4, MEF2C, TBX5, and MYOCD). The resulting cellular phenotype was analyzed by RNA sequencing and immunocytology. Live target cells were purified based on intracellular CM marker expression using molecular beacon technology and fluorescence-activated cell sorting. CM commitment was assessed using 5-azacytidine-based differentiation assays and the therapeutic effect was evaluated in a mouse model of acute myocardial infarction using echocardiography and histology. The cellular secretome was analyzed using mass spectrometry.

Results

We found that proliferative CM precursor-like cells were part of the phenotype spectrum arising during direct reprogramming of fibroblasts toward CMs. These induced CM precursors (iCMPs) expressed CPC- and CM-specific proteins and were selectable via hairpin-shaped oligonucleotide hybridization probes targeting Myh6/7-mRNA–expressing cells. After purification, iCMPs were capable of extensive expansion, with preserved phenotype when under ascorbic acid supplementation, and gave rise to CM-like cells with organized sarcomeres in differentiation assays. When transplanted into infarcted mouse hearts, iCMPs prevented CM loss, attenuated fibrotic scarring, and preserved ventricular function, which can in part be attributed to their substantial secretion of factors with documented beneficial effect on cardiac repair.

Conclusions

Fibroblast reprogramming combined with molecular beacon-based cell selection yields an iCMP-like cell population with cardioprotective potential. Further studies are needed to elucidate mechanism-of-action and translational potential.

Graphical Abstract
Examining students’ course trajectories using data mining and visualization approaches
Authors: Maqsood Rabia , Ceravolo Paolo , Ahmad Muhammad , Sarfraz Muhammad Shahzad
Published: October 2023

The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.
Impact of a digital and conventional prevention program on work ability, physical health, and mental health among employees with initial impairments
Authors: Schmidt Detlef , Fritsch Julian , Feil Katharina , Weyland Susanne , Jekauc Darko
Published: October 2023

Background

This quasi-experimental study aimed to compare the effectiveness of a digital prevention intervention on work ability, physical health, and mental health with a conventional prevention program for employees with initial impairments. The study recruited 245 participants, of whom 173 completed the study, 98 (65 female, 66.3%) in the intervention group and 75 (55 female, 73.3%) in the control group. Both groups received prevention programs, with the intervention group using the Caspar digital platform and the control group using the conventional BETSI/RV Fit program. There were three measurement points in the study: T0 before the intervention, T1 in the middle of the intervention, and T2 at the end of the intervention. Participants’ health was assessed using the SF-12 health status questionnaire, while their work ability was measured using the short version of the Work Ability Index.

Results

Repeated-measures analyses of variance indicated that both prevention programs were effective in improving work ability and mental health, while physical health did not show any significant improvement. Additionally, the results of the study suggest that younger individuals benefited more from the digital prevention intervention, while older individuals benefited more from the conventional prevention program.

Conclusion

The study emphasizes the need for further research and improvements in both research and practice. Future studies should include larger sample sizes, randomized controlled trials, and follow-up assessments to enhance understanding of the effectiveness and the durability of effects of prevention programs.
The Impact of Mental Fatigue on a Strength Endurance Task: Is there a Role for the Movement-Related Cortical Potential?
Authors: Proost Matthias , Habay Jelle , De Wachter Jonas , De Pauw Kevin , Marusic Uros , Meeusen Romain , De Bock Sander , Roelands Bart , Van Cutsem Jeroen
Published: October 2023

Purpose

Several mechanisms have been proposed to explain how mental fatigue degrades sport performance. In terms of endurance performance, a role for an increased perceived exertion has been demonstrated. Utilizing electroencephalography (EEG) and, more specifically, the movement-related cortical potential (MRCP), the present study explored the neural mechanisms that could underly the mental fatigue-associated increase in perceived exertion.

Methods

Fourteen participants (age: 23 ± 2 y, 5 females, 9 males) performed one familiarisation and two experimental trials in a randomised, blinded, cross-over study design. Participants had to complete a submaximal leg extension task after a mentally fatiguing task (EXP; individualized 60-min Stroop task) or control task (CON; documentary). The leg extension task consisted of performing 100 extensions at 35% of 1RM, during which multiple physiological (heart rate, EEG, ratings of perceived exertion) and psychological measures (self-reported feeling of mental fatigue, cognitive load, motivation) were assessed.

Results

Self-reported feeling of mental fatigue was higher in EXP (72 ± 18) compared to CON (37 ± 17; p < 0.001). A decrease in flanker accuracy was detected only in EXP (from 0.96 ± 0.03 to 0.94 ± 0.03; p < 0.05). No significant differences between conditions were found in MRCP characteristics and perceived exertion. Specifically in EXP, alpha wave power increased during the leg extension task (p < 0.01).

Conclusions

Mental fatigue did not impact the perceived exertion or MRCP characteristics during the leg extension task. This could be related to low perceived exertion and/or the absence of a performance outcome during the leg extension task. The increase in alpha power during the leg extension task in EXP, suggests participants may engage a focused internal attention mechanism to maintain performance and mitigate feelings of fatigue.
Unbiased Time-Average Estimators for Markov Chains
Authors: Kahalé Nabil
Published: October 2023

We consider a time-average estimator fk of a functional of a Markov chain. Under a coupling assumption, we show that the expectation of fk has a limit μ as the number of time steps goes to infinity. We describe a modification of fk that yields an unbiased estimator [Formula: see text] of μ. It is shown that [Formula: see text] is square integrable and has finite expected running time. Under certain conditions, [Formula: see text] can be built without any precomputations and is asymptotically at least as efficient as fk, up to a multiplicative constant arbitrarily close to one. Our approach also provides an unbiased estimator for the bias of fk. We study applications to volatility forecasting, queues, and the simulation of high-dimensional Gaussian vectors. Our numerical experiments are consistent with our theoretical findings.
Design of Gamified Crowdsourcing for Tourism Participatory of Urban Problem the Management of Smart City Initiatives
Authors: Chernbumroong Suepphong , Thongthip Phimphakan , Sirasakamol Orasa , Jansukpum Kanjana , Julrode Phichete , Puritat Kitti
Published: October 2023

Chiang Mai is a well-known province in Thailand, attracting a large number of tourists every year. Nonetheless, due to the large number of tourists visiting Chiang Mai, they may confront a variety of issues, including urban issues, which may create annoyance and create unpleasant experiences. In this study, we proposed the design and development of gamified mobile application which employed the concept of participatory crowdsourcing and the system architecture for management of tourism participatory of urban problem for smart city of government officer in order to motivate tourists to collaborate in providing their comments and feedback regarding urban problems encountered in Chiang Mai city. To evaluate the approach, we developed and launched our data-gathering application for tourists. The results reveal five significant types of urban problems encountered by 352 tourists in Chiang Mai. Moreover, we found that the most urban problems in Chiang Mai by tourists are communication (31.34%), vendors (23.88%) and transportation (15.67%). Furthermore, the study revealed that gamification can motivate tourists’ attention of the participatory sensing to providing feedback and complaints to the officer government to solve the problems.
Deep Learning Technique Based State-Of-The-Art in Skin Cancer Detection: A Review
Authors: Srilakshmi CH. , Lydia E. Laxmi , Ramakrishnaiah N.
Published: October 2023

Research of skin cancer images through visual survey and manual evaluation to investigate skin threatening development has always been abnormal. This manual evaluation of skin injuries to recognize melanoma is monotonous as well as somewhat long. With movement in advancement and fast development in computational resources, different AI techniques and significant learning methods have emerged for assessment of clinical pictures most especially the skin lesion images. In late years, AI arising as an innovation equipped for tackling issues connected with horticulture, medical services, business, and soon. To diminish the endanger to human existence we can embrace AI calculations in the medical care area and can foresee the deadliest skin illnesses like dangerous melanoma in beginning phases. The point of the research is to give bits of knowledge about various classifications of skin lesions and strategies executed to arrange and foresee skin diseases and the job of dermatologists while fostering the models, at last gives a general rundown of existing work.
Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease
Authors: Khade Anindita , Vidhate Amarsinh V. , Vidhate Deepali
Published: October 2023

Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD.
Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach.
Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%.
Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.
Enhancing Mobile Multimedia Trustworthiness through Federated AI-based Content Authentication: Enhancing Mobile Multimedia
Authors: Rajesh M. , Vengatesan K. , Sitharthan R. , Dhanabalan Shanmuga Sundar , Gawali Mahendra Bhatu
Published: October 2023

The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches.
Quantitative Experimental Evaluation of RFID Propagation Loss with Wooden and Metal Bookshelves
Authors: Supramongkonset Jatuporn , Promwong Sathaporn
Published: October 2023

Radio frequency identification (RFID) is wireless multimedia applications for bookshelves experimental system to replace the barcodes media technology. The RFID propagation channel characteristics and environment effect should be known. In this study to evaluate the RFID propagation loss with wooden and metal bookshelves based on data of measurement. Experimental study evaluation of RFID multimedia system with bookshelves is using vector network analyzer (VNA) and the microstrip patch antennas of transmitter (Tx) and receiver (Rx) antennas at a frequency range from 2.4 GHz to 2.5 GHz. The results of experiment are considering the path loss, received signal strength (RSS), and comparison the path loss differences with cumulative distribution function (CDF) to evaluated, respectively. In this research work are necessary for RFID antenna design and evaluate the RFID multimedia systems.
Real Time Asthma Disease Detection and Identification Technique from Speech Signals Using Hybrid Dense Convolutional Neural Network
Authors: Iqbal Md. Asim , Devarajan K. , Ahmed Syed Musthak
Published: October 2023

Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Multi-Feature Extraction, Selection with Hybrid Classifiers (MFESHC). Initially, speech signals are preprocessed by using Maximum likelihood estimation based spread spectrum analysis (MLE-SSA) method. Then, Improved prefix Beam Search (IPBS) based natural language processing (NLP) method is used to extract and select the best features from the preprocessed speech signals. Then, hybrid dense convolutional neural networks (HDCNN) are used to classify the type of asthma such as normal, stridor, wheezes and rattle classes. Further, Modified Crow Search (MCS) is used to optimize the losses generated in the HDCNN model. The simulation results shows that the proposed MFESHC method resulted in superior performance as compared to state of art approaches because the MCS effectively reduced the losses in the model.
HDQNN-Net: An Optimal Asthma Disease Detection Technique for Voice Signal Using Hybrid Deep Q-Neural Networks
Authors: Iqbal Md. Asim , Devarajan K. , Ahmed Syed Musthak
Published: October 2023

Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Hybrid Deep Q Neural Networks (HDQNN). Initially, the features from the speech signals are extracted by using Krill herd optimization (KHO) approach, which extracts the detailed disease specific features. Further, the optimal features are extracted by using chaotic opposition krill herd optimization (COKHO) algorithm. Then, HDQNN is used to classify the type of asthma such as normal, and stridor classes. Further, COKHO is also used to optimize the losses generated in the HDQNN model. The simulation results shows that the proposed HDQNN method resulted in superior performance as compared to state of art approaches.