bluuebunny/crossref_metadata_2025_split huggingface dataset:
GVCs and Environmental Sustainability in MENA: Do Digitalization and Institutions make a difference?
Authors: ElMassah Suzanna , Hassanein Eslam A. 
Published: May 2023

Abstract

Currently, the advent of digitalization has profoundly altered the structure of Global Value Chains (GVCs), with implications for environmental sustainability (ENS). Notwithstanding its importance, the dynamics of participation in GVCs, ENS, and digitalization have not been thoroughly investigated in empirical literature. On the other hand, despite its rapid evolution and growing popularity, GVCs participation is rarely considered when analyzing factors influencing ENS in the Middle East and North Africa (MENA) region. Extending this research line, the analysis scrutinizes the impact of GVCs participation on ENS in 15 countries in MENA between 1996 and 2018. The study further investigates the moderating impacts of two major policy variables, namely Institutional Quality (IQ) and Digitalization (DIGI). The study employs SYS-GMM panel method and Random Effects. The empirical results indicate that GVCs participation is environmentally useful in the MENA region. The findings remain robust/similar when considering forward value participation linkages and oil-importing countries, whereas backward linkages deteriorate the region ENS. The findings further reveal that the GVCs environmental impact is modulated through IQ and DIGI. IQ and GVCs are particularly complementary in promoting ENS in MENA and across both oil importing and exporting groups. Nonetheless, the GVCs interaction with DIGI produces a negative net effect. This negative effect is mitigated beyond a particular threshold of 10.23%, necessitating complementary policies related to the link between GVCs participation and ENS below this threshold. Additionally, the findings affirm that FDI improves the environment, whereas natural resource rents degrade it, supporting the resource curse hypothesis for the MENA region. The study findings provide new insights into achieving improved GVCs integration while maintaining sustainable environment.
JEL classification: F18, Q56, C26
COVID-19 mRNA vaccine-mediated antibodies in human breast milk and their association with breast milk microbiota composition
Authors: Zhao Shilin , Lok Kris , Sin Zhen , Peng Ye , Fan Heidi , Nagesh Nitya , Choi Martha , Kwok Jojo , Choi Edmond , Zhang Xi , Wai Hogan , Tsang Leo , Cheng Samuel , Wong Matthew , Zhu Jie , Mok Chris , Ng Siew , Chan Francis , Peiris Malik , Poon Leo , Tun Hein 
Published: May 2023

Abstract
Newborns can acquire immunological protection to SARS-CoV-2 through vaccine-conferred antibodies in human breast milk. However, there are some concerns around lactating mothers with regards to potential short- and long-term adverse events and vaccine-induced changes to their breast milk microbiome composition, which helps shape the early-life microbiome. Here, we recruited 49 lactating mothers from Hong Kong who received two doses of BNT162b2 vaccine between June 2021 and August 2021. Breast milk samples were self-collected by participating mothers pre-vaccination, one week post-first dose, one week post-second dose, and one month post-second dose. The levels of SARS-CoV-2 spike-specific IgA and IgG in breast milk peaked at one week post-second dose. Subsequently, the levels of both antibodies rapidly waned in breast milk, with IgA levels returning to baseline levels one month post-second dose. The richness and composition of human breast milk microbiota changed dynamically throughout the vaccination regimen, but the abundances of beneficial microbes such as Bifidobacterium species did not significantly change after vaccination. In addition, we found that baseline breast milk bacterial composition can predict spike-specific IgA levels at one week post-second dose (Area Under Curve: 0.72, 95% confidence interval: 0.58–0.85). Taken together, our results suggest that infants may acquire immunological protection from breast milk from SARS-CoV-2-vaccinated mothers by both the vertical transmission of antibodies and beneficial microbiota.
Predictors of Time to Full Enteral Feeding in Low Birth Weight Neonates Admitted to Neonatal Intensive Care Unit: a Multi Center Prospective Follow Up Study
Authors: Terefe Abraraw , Demtse Asrat , Abebe Fikertemariam , Mislu Esuyawkal , Tachbele Erdaw 
Published: May 2023

Abstract
Background
Survival of LBW and preterm infants has increased in recent years because of novel perinatal interventions, but optimal postnatal nutrition is lacking. In Ethiopia the proportion of preterm and low birth weight is thought to be 10.48% and 17.3% respectively. The purpose of this study was to determine the time to full enteral feeding (FEF) and its predictors in LBW neonates admitted to neonatal intensive care unit in selected hospitals of Addis Ababa, Ethiopia.
Method
An institutional based prospective follow up study was conducted from March 15 to June 15, 2022 among 282 LBW neonates admitted to six randomly selected hospitals. Both primary and secondary data was used by direct interview mothers and prospective medical chart review of neonates. The Cox regression model was used variables having a p-value less than 0.05 with 95% CIs in a multivariable analysis were declared as statistically significant association with time to full enteral feeding.
Result
out of 282 neonates involved in this study, 211 (74.8%) of them reached at FEF. The overall median time to full enteral feeding was 5 days. Predictors significantly associated with time to full enteral feeding were educational level, birth weight, cesarean delivery, hospital acquired infection, being on antibiotics, age at initiation of trophic feeding, routine gastric residual evaluation and NICU location (hospital).
Conclusions
This study demonstrated that there is a delay in full enteral feeding achievement among preterm and low birth weight neonates and there is a great deal of heterogeneity of practice among health care providers regarding feeding of infants as it was evidenced by a variation in feeding practice among hospitals and preterm nutrition should be considered as part of the management in neonatal intensive care units.
AI-Big Data-Mobile System Development of Measuring Nursing Workloads using Wearable device and Real Time Location Information
Authors: Park Hyunggon , Kang Younhee 
Published: May 2023

Abstract
Purpose: The purpose of this work was to develop an AI-Big Data-Mobile system for measuring nursing workloads and nurses’ physiological responses to work using wearable device and beacon.
Methods: This was a study of technological development, the IoT-Big data-Mobile system measuring nursing workloads and physiological responses to work using the real-time location information.
Results: The overall system consists of four components, which are data generation devices, nurses’ devices, servers, and database. The location and health data generated from beacons and wearable devices are stored in nurses’ smart phones. Then, the data are transmitted from the user to the server and is stored in database of the server. The location data is collected from multiple beacons if smart devices of nurses are in the proximity of the beacons. The health data on iOS can be accessed via HealthKit, which is the framework that allows the access and share of nurses' health and fitness data on devices. We use the Apple Watch paired with an iPhone and the data is automatically synchronized with the associated iPhone. For data transmission, we adopt the socket communication to transmit data to the server, as it can support real-time, bidirectional, and event-based communication.
Conclusion: Throughout this system developed, objective quantification of nursing workloads by the time and nursing space might be realized. Furthermore, based on data of accurate nursing workloads by types of hospital, department, and work-shift, the prediction model of nursing workloads could be developed with big data and Artificial Intelligence (AI). The appropriate arrangement of nursing workforce is a key prerequisite for the high-quality nursing care. The system developed in this study would be able to estimate nursing workforce needed to provide the optimum nursing services based on the scientific and objective data and inferences on workforce.
“I’ve been waiting for 10 years to start living again” – Self-perceived problems of Afghan asylum seekers and refugees and their experiences with a short psychological intervention
Authors: Kantor Viktoria , Weindl Dina , Schiess-Jokanovic Jennifer , Verginer Lucia , Lueger-Schuster Brigitte , Knefel Matthias 
Published: May 2023

Abstract
Background: This study examined Afghan asylum seekers and refugees’ experiences treated with an adapted version of the brief psychological intervention Problem Management Plus (aPM+) and explored which problems trouble them most, and how these problems influence their daily functioning. It further examined how various standardized outcome measures correlate with these subjectively perceived problems.
Method: This study is part of a larger research project (PIAAS study) and uses a multi-method approach consisting of two parts. First, it investigated the participants’ self-identified problems and subjective functional impairment with quantitative and an qualitative assessments within the treatment and control group (n=88). Second, we conducted qualitative in-depth interviews to gain a deeper understanding of personal experiences with aPM+ as well as suggestions for improvement with a subsample of the treatment group (n=24). Spearman correlations were applied for quantitative data and for qualitative data deductive and inductive approaches of thematic analysis were used.
Results: We identified six main themes of self-perceived problems (post migration living difficulties, general mental problems, interpersonal stressors, mental problems specifically associated with stress, and somatic problems) and their consequences as well as subjective functional impairment. Standardized measures of general mental health, posttraumatic psychopathology, and quality of life did not correlate with the perceived intensity of the self-perceived problems. aPM+ was perceived mostly positively and few participants had recommendations for its improvement.
Conclusion: The study aimed at giving a voice to Afghan trauma survivors to inform service providers and policy makers about their needs. Further, it includes their expertise to tailored interventions for their actual needs and its practical use. APM+ shows to be a positively perceived intervention that reduces subjective symptom burden and facilitate daily functioning. Thus, culture-sensitive treatments should be facilitated within (mental) health services to increase service use and (mental) health in a long run.
Seismic performance of a new type of RC shear walls confined with high-strength rectangular spiral reinforcements
Authors: Zhao Huajing , Li Sen , Hu Yifan , Liu Jiangyuan , Yang Penghui 
Published: May 2023

Abstract
To improve the seismic performance of structures, a new type of reinforced concrete (RC) shear walls confined with high-strength rectangular spiral reinforcements (HRSRs) is proposed. Pseudo-static tests were conducted on four 1/2-scale HRSR shear walls possessing different parameters. The influence of HRSR on the bearing capacity, ductility, and failure modes of the shear walls was systematically studied. The test results clearly indicate that using HRSR as hoops and transverse reinforcements can effectively restrain the deformation and capacity-decline stage of the high-strength concrete shear walls, improving their bearing capacity and ductility. Particularly, the seismic performance of the RC shear walls with a high axial-compression ratio can be significantly improved by applying HRSR. A simplified model was proposed for the ultimate flexural capacity of the HRSR shear walls, which precisely agreed with the experimental results. These results provide a new strategy for seismic-performance design of RC shear walls in engineering structures.
Changes in DNA methylation associated with type of delivery
Authors: Krumpolec Patrik , Kodada Dominik , Hadžega Dominik , Petrovič Oliver , Babišová Klaudia , Dosedla Erik , Turcsányiová Zuzana , Minárik Gabriel 
Published: May 2023

Abstract
Background: A type of delivery represents an epigenetic factor with potential to affect further development by multiple mechanisms. One of them can be DNA methylation, which represents major epigenetic mechanism inolving direct chemical modification to the DNA.
This study aims to examine wheather type of delivery induces changes of DNA methylation comparing the umbilical cord blood and periphery blood of the newborns.
Methods: The blood samples from infants, born by vaginal delivery and cesarean section, were used to preparation of the Methylseq library according to NEBNext enzymatic Methyl-seq Methylation Library Preparation Kit with further generation of target-enriched DNA libraries by Twist Human Methylome Panel. DNA methylation status was measured using Illumina next-generation sequencing.
Results: We identified 168 differentially methylated regions in umbilical cord blood samples and 157 regions in peripheral blood samples. These were associated with 59 common biological, metabolic and signaling pathways for umbilical cord and peripheral blood samples.
Conclusions: Cesarean section probably represents important epigenetic factor with potential to induce changes in genome, which could play an important role in development of broad spectrum of disorders. Our results could contribute to elucidate how epigenetic factors, like type of delivery, could have an adverse impact on health later in the life.
An automatic pipeline for PET/MRI attenuation correction validation in the brain
Authors: Hamdi Mahdjoub , Ying Chunwei , An Hongyu , Laforest Richard 
Published: May 2023

Abstract
Purpose
PET/MRI quantitative accuracy for neurological applications is challenging due to accuracy of the PET attenuation correction. In this work, we proposed and evaluated an automatic pipeline for assessing the quantitative accuracy of four different MRI = based attenuation correction (PET MRAC) approaches.
Methods
The proposed pipeline consists of a synthetic lesion insertion tool and the FreeSurfer neuroimaging analysis framework. The synthetic lesion insertion tool is used to insert simulated spherical, and brain regions of interest (ROI) into the PET projection space and reconstructed with four different PET MRAC techniques, while FreeSurfer is used to generate brain ROIs from T1 weighted MRI image. Using a cohort of 11 patients' brain PET dataset, the quantitative accuracy of four MRAC(s), which are: DIXON AC, DIXONbone AC, UTE AC, and Deep learning trained with DIXON AC, named DL-DIXON AC, were compared to the PET-based CT attenuation correction (PET CTAC). MRAC to CTAC activity bias in spherical lesions and brain ROIs were reconstructed with and without background activity and compared to the original PET images.
Results
The proposed pipeline provides accurate and consistent results for inserted spherical lesions and brain ROIs inserted with and without considering the background activity and following the same MRAC to CTAC pattern as the original brain PET images. As expected, the DIXON AC showed the highest bias; the second was for the UTE, then the DIXONBone, and the DL-DIXON with the lowest bias. For simulated ROIs inserted in the background activity, DIXON showed a -4.65% MRAC to CTAC bias, 0.06% for the DIXONbone, -1.70% for the UTE, and − 0.23% for the DL-DIXON. For lesion ROIs inserted without background activity, DIXON showed a -5.21%, -1% for the DIXONbone, -2.55% for the UTE, and − 0.52 for the DL-DIXON. For MRAC to CTAC bias calculated using the same 16 FreeSurfer brain ROIs in the original brain PET reconstructed images, a 6.87% was observed for the DIXON, -1.83% for DIXON bone, -3.01% for the UTE, and − 0.17% for the DL-DIXON.
Conclusion
The proposed pipeline provides accurate and consistent results for synthetic spherical lesions and brain ROIs inserted with and without considering the background activity; hence a new attenuation correction approach can be evaluated without using measured PET emission data.
Is fatigue mechanism implicated in intraoral fracture of narrow dental implants? A retrieval analysis study
Authors: Jabbari Youssef S. Al , Fournelle Raymond , Sufyan Arif , Zinelis Sprios 
Published: May 2023

Abstract
Purpose
This study aimed to perform a thorough failure analysis of two fractured narrow dental implants (NDIs) after medium-term in vivo use.
Materials and Methods
The top parts of two fractured NDI fixtures were retrieved from two different locations at two different times from the same patient. The first NDI fixture (NDI-specimen-1) was 12 months in service while the second NDI fixture (NDI-specimen-2) was 17 months in service. In both cases, the top parts of the fractured NDI fixtures that were attached to prosthetic components were retrieved and subjected to thorough, non-destructive (light microscopy [LM] and scanning electron microscopy [SEM]), and destructive testing (metallography, energy dispersive X-ray spectroscopy [EDS] and hardness testing).
Results
LM and SEM revealed that both the retrieved fractured NDI fixtures failed because of fatigue, characterized by beach and ratchet marks. Macroscopic examination revealed that fatigue cracks initiated at the internal thread surfaces of the implants and propagated around them until final fracture. Both samples fractured near the end of the retaining screw and followed the root of the internal thread (crest of the retaining screw). Both optical and SEM analyses revealed a uniform distribution of irregularly shaped grains with diameters ranging from approximately 2 to 5 µm. EDS analysis showed that the NDI-specimen-1 was made using Ti-14%Zr with traces of Si and Al and its microhardness was 288 ± 5 HV.
Conclusion
In both cases, the fracture occurred by a fatigue failure mechanism; thus, an increase in fatigue resistance will be beneficial for the longevity of NDI.
An exploration of motivations to conserve energy in public area
Authors: Dupre Mickael , Meineri Sebastien 
Published: May 2023

Abstract
The study contributes insight to the understanding of underlying cognition to behave eco-responsibly. The purpose of this study is to gain insight into motivation influencing a pro-environmental behavior (i.e. turning off a light). The aim of the study is to contribute to the understanding of decision-making regarding consumer behavior of energy conservation. More precisely, this research explores the behavioral effect of normative and guilt-inducing messages, and a combination of both. This research is based on an experiment which displayed five messages inciting individuals to turn off the light when they leave a room. 960 users of public toilets in a shopping center were involved in the experiment. This research used a quasi-experimental method. Results show that injunctive and persuasive messages do not increase a user’s likeliness to turn a light off while normative and guilt-inducing messages do increase this behavior. A combination of normative and guilt-inducing messages also increase the frequency of this pro-environmental behavior. These comparisons allow us to conclude that decisions to turn off a light use the automatic system 1 described by Khaneman. In this case, behavior is not motivated by altruism but by social influence. Moreover, findings argue in favor of normative messages to influence behavior. The aim of the study is to propose practical solutions to improve the communication of organizations targeting energy conservation. The research concludes that communicating on normative information could change environmental behavior in public- access buildings.
Utilization of deep learning to evaluate the measurement results of forced oscillation technique
Authors: Funaita Chiune , Furuie Wakaba , Koike Fumika , Oyama Saki , Endo Junji , Otani Yoshio , Ichikawa Yuri , Ito Minako , Nakamura Yoichi , Komatuzaki Keiko , Hirata Akira , Miyazaki Yasunari , Sumi Yuki 
Published: May 2023

Abstract
No reference values have been established for MostGraph, which measures total respiratory resistance and reactance using the forced oscillation technique, making it difficult to interpret the results. This study aimed to establish a reference range for MostGraph measurements and examine its usefulness in discriminating participants with asthma from controls (participants without any respiratory diseases). The study also aimed to investigate the effectiveness of deep learning in discriminating between the two aforementioned groups. To establish reference ranges, the MostGraph measurements of healthy controls (n = 215) were power-transformed to distribute the data more normally. After inverse transformation, the mean ± standard deviation ⋅ 2 of the transformed values were used to establish the reference ranges. The number of measured items from the reference ranges was evaluated to discriminate patients with asthma (n = 941) from controls. Additionally, MostGraph measurements were evaluated using deep learning. Although reference ranges were established, patients with asthma could not be discriminated from controls. However, with deep learning, we could discriminate between the two groups with 78% accuracy. Therefore, deep learning, which considers multiple measurements as a whole, was found to be more effective in interpreting MostGraph measurement results compared to the reference range, which considers each result individually.
Development of Evolutionally Algorithm-Based Protein Redesign Method
Authors: Ozawa Hiroki , Unno Ibuki , Sekine Ryohei , Ito Sohei , Nakano Shogo 
Published: May 2023

Abstract
Enzymes have the potential to become next-generation biocatalysts for synthesizing fine chemicals, but tailoring highly functional enzymes depending on the applications remains a challenging task. In this study, we developed a novel enzyme redesign tool called GAOptimizer to overcome this hurdle. GAOptimizer generates artificial enzymes by accumulating positively-affected mutations through a genetic algorithm, using a template structure as the starting point for the evolution; mutations are selected from phylogenetically conserved residues in homologs of the template. Both stability and non-stability based scores can be used as fitness functions to determine whether mutations may have positive effects. The successful design of three distinct enzymes using GAOptimizer suggests that the tool can generate highly functional enzymes with enhanced properties, such as thermostability, solubility, and activity, by modifying fitness functions. GAOptimizer is freely available at the following URL: https://github.com/shognakano/GAOptimizer.