Early Phases Of Development Research Articles

Biofouling evolution driven by autoinducer-2 quorum sensing in reverse osmosis (RO) for water reclamation

Membrane biofouling remains a key challenge for reverse osmosis (RO) technology in water reclamation. This study investigated the biofouling development with time on RO membrane surface. During the biofouling evolution, the biofilm was examined through confocal laser scanning microscopy (CLSM) observation and foulant composition analysis. Results showed that organic/biofouling was the key contributor, and autoinducer-2 (AI-2), quorum sensing (QS) signaling molecules, were found to play an important role in coordinating community-level behaviors associated with RO membrane fouling. In the initial stage of fouling layer development, the attached cell and biopolymer contents increased with rising AI-2 activity. As development continued, the microbial community changed remarkably, exhibiting peak AI-2 activity as the membrane surface became fully covered with cells and the EPS matrix. However, in later stages, despite a decrease in AI-2 content and biological activity, the rate of increase in fouling resistance accelerated significantly due to continually accumulating foulants. As a result, the AI-2 QS was crucial during early phases of biofilm development in RO systems. This study helps to look deeply into the biological fundamentals of AI-2 mediated QS in RO fouling, enhancing the understanding of the mechanisms behind RO membrane fouling in water reclamation, and may facilitate the development of anti-fouling strategies.

Entanglement classification – a comparative study of 𝑺𝑼(𝟐) and 𝑺𝑳(𝟐) developed operator model

Entanglement classification is a core aspect of quantum information theory. It ensures successful quantum information processing. This article presents a comparative study of entanglement classification using developed operator models for the special unitary group and special linear group. This study was built upon prior work in entanglement classification in a pure three-qubit quantum system environment, where the operator models for each mathematical group were independently developed. Through extensive analysis, both synthesized models are functionally effective and yield the desired results. However, the comparative analysis reveals that the operator model exhibits certain limitations, particularly in its early phase of development compared to. This study provides significant enlightenments into the practical abilities of the developed operator models in entanglement classification and underlines the theoretical distinction between and paving the path for future research in quantum information theory, specifically entanglement classification.

Evaluation of a goalkeeper-specific motor coordination assessment in youth football

ABSTRACT Talent identification and development (TID) in football can be enhanced through objective assessments of talent predictors. Yet, available instruments rarely consider the unique demands of goalkeepers (GKs). During early phases of talent development, considering a GK’s giftedness relating to, for example, different abilities (e.g. motor coordination), can complement views on highly specialized GK-specific attributes (e.g. technical skills). Therefore, this study aimed to evaluate a GK-specific motor coordination assessment to support TID in football at early developmental phases. Six tests were designed to assess ball control relevant to GKs. Their content and face validity were confirmed by independent experts (N = 8). The assessment was evaluated with GKs selected for the German Football Association’s TID program (U12-U15; N = 120). This study examined the assessment’s test–retest reliability and agreement, the structural validity, and the concurrent validity. The overall test score demonstrated good test–retest reliability, although some individual tests showed lower coefficients. Additionally, limited agreement between repeated measurements due to considerable measurement error as well as issues with the structural validity of the test battery were identified. Nevertheless, the findings support the assessment’s concurrent validity as higher-rated (i.e. more talented) GKs outperformed lower-rated individuals. Yet, the test battery’s diagnostic accuracy is not high enough to justify individual decisions for talent selection. Overall, these findings support the consideration of motor coordination as a talent predictor in youth GKs and emphasize the assessment’s potential to enhance coaches’ evaluations. The identified psychometric weaknesses in some individual tests provide impetus to further optimize the test battery.

The Occupation Content of Trade

Abstract This paper develops new data to track the occupation content of trade. These data are based on measuring the value-added exports of workers cross-classified by their industry of work as well as their occupational class, in short by “task.” Countries initially specialize along the extensive margin (shifting exports of value added across industries) and later on along the intensive margin (shifting export income across occupations within industries). Path dependency is strong in the early phase of development, as new task specializations are closely related to the initial task export basket, but this dependence weakens afterwards. Leveraging the new data holds great promise in delivering deeper insights into patterns of trade, structural change, and economic development.

Melanoma Diagnosis in the Mihm Era-And Beyond.

During the illustrious career of Martin C. Mihm Jr., MD, the diagnosis of melanoma underwent significant changes, to which he made many contributions. In early descriptions, melanomas were fungating tumor masses that were obviously malignant, and highly lethal. In seminal work by Dr. Mihm and his mentor, Wallace H. Clark, Jr., MD, the early phases of development of these neoplasms were recognized and distinguished from the more advanced disease. It was generally believed that the early stage of melanoma, termed radial growth phase (RGP) and characterized by absence of vertical growth phase (VGP) and by favorable microstaging attributes could be recognized, excised, and cured, thus preventing the development of advanced disease. However, strenuous efforts in this direction over several decades have resulted in little or no change in mortality, leading to the recognition that many of these neoplasms, at least, may not be true biological malignancies, and to the conclusion that overdiagnosis commonly occurs in this disease, which is defined as representing diagnosis as melanoma of a neoplasm that would not have had the capacity to cause death or symptoms in the lifetime of the host. Although there may be other subsets of neoplasms in this category, an important category of overdiagnosis in melanomas is concentrated in T1a melanomas that lack VGP. If these neoplasms can be recognized with sensitive and specific criteria, which may already be available, changes in terminology may be appropriate, recognizing that some of them may have low malignant potential, whereas others may have no capacity at all for metastasis and may not warrant the use of the term "melanoma."

Ensuring the Correctness and Reliability of CBPS System Using Event‐B

ABSTRACTDuring the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

Effects of diuron and two of its metabolites in biochemical markers and behavior of zebrafish larvae.

Diuron (3-(3,4-dichlorophenyl)-1,1-dimethylurea) is an herbicide used in many crops, including sugar cane cultivation. It is commonly found in aquatic ecosystem and is of high concern due to its ability to persist in the environment. Diuron metabolites include DCA (3,4-dichloroaniline) and DCPMU (3-(3,4-dichlorophenyl-1-methylurea). The objective of this study was to evaluate the effects of diuron and two of its metabolites in zebrafish (Danio rerio) developing embryos, from biochemical to individual level. Activities of the enzymes acetylcholinesterase (AChE), catalase (CAT), glutathione-S-transferase (GST), andlactate dehydrogenase (LDH) and the levels of lipid peroxidation (LPO), swimming activity, and body length were investigated after an exposure of 120h, and the heart rate was determined after 48h of exposure. The range of concentrations tested was 0.003-3.000mg/L diuron, 0.020-1.500mg/L DCA, and 0.020-2.100mg/L DCPMU. Results showed that AChE activity was inhibited by diuron (3.000mg/L) and DCPMU (0.326, 0.828mg/L). However, the swimming activity of fish larvae exposed to diuron or its metabolites was not affected. The CAT was induced by DCPMU, and GST was induced by diuron. This suggests that CAT is acting to cope with oxidative stress induced by DCPMU and GST might have a role in the detoxification of diuron. In addition, larvae exposed to DCA (0.633 and 1.500mg/L) had a reduction in their length, and larvae exposed to diuron (0.754 and 3.000mg/L) and DCA (0.267, 0.633, and 1.500mg/L) presented bradycardia, suggesting cardiotoxicity. Overall, results indicate that diuron, DCA, or DCPMU induces adverse effects during the early phases of zebrafish development, such as the impairment of neurotransmission and cardiovascular function and alterations in antioxidant enzymes and growth. Diuron appeared as more toxic than its metabolites since the lowest LOEC (0.012mg/L) and higher integrated biomarker response (IBR) values were obtained with exposure to this herbicide. Furthermore, as it is fast degraded into DCA and DCPMU, which also affected the zebrafish developing embryos at environmentally relevant concentrations, its use might be of concern in ecosystems that receive agriculture runoff due to their potential adverse effects to aquatic biota.

Language choice and code-mixing in a longitudinal study of an Estonian-English bilingual child

The input that bilingual children receive influences their language proportions, language development, and code-mixing. Most studies on these topics have included early bilinguals whose input proportions undergo large changes in early childhood and whose parents use the one-parent-one-language family language policy. This paper examines the input-output proportions of an Estonian-English bilingual child over a period of 2.5 years (2;3-5;01) using recorded spontaneous speech from a situation where the input language proportions did not change and where the family language policy was different from the one-parent-one-language policy that is presented in most studies: the family rotated the language they all spoke by the day of the week. Additionally, the child’s code-mixing rate and her MLU scores are investigated to provide an overview of these factors in an unstudied input situation. Lastly, it is analyzed whether code-mixing by older siblings influences the code-mixing rate of the younger sibling. The results indicate that in the early phases of language development the child uses all the linguistic resources available to her, and as her language develops, she responds more in the language of the conversation and code-mixes less. However, there is also a period where the child unexpectedly almost stops speaking in Estonian regardless of the unchanged input. The data shows that code-mixed utterances are the longest, hence supporting previous research findings and indicating that code-mixing is a tool that helps the child communicate better. Code-mixing by siblings does not show any signs of affecting the younger sibling’s code-mixing rate, though a more thorough analysis is necessary. Hence, the results indicate the importance of input and shed light on input effects in bilingual language acquisition in an understudied input situation.

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings

BackgroundIntegrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system.MethodsWe used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs’ feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances.ResultsThe backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term “high risk” of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs’ workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability.ConclusionsThe final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

Even if you build it, they may not come: challenges in the uptake of workplace mental health toolkits

BackgroundStrategies to promote workplace mental health can target system, organization, team, and individual levels exclusively or in concert with each other. Creating toolkits that include these different levels is an emerging innovative strategy to support employees working in various sectors. Our paper describes the development, implementation, and refinement of two different online toolkits: the Healthy Professional Worker Toolkit for Education Workers and the Health Worker Burnout Toolkit.MethodsThe Knowledge to Action Framework guided the team during the development and early interventions phases of toolkit development. Stakeholder engagement regarding the intended use of the toolkit of promising practices for workplace interventions was integrated throughout with different forms of feedback in a research capacity between 2022 and 2024.ResultsReflecting on the different phases of the KTA Framework, we describe first the engagement involved in building the toolkits and then on their utilization. Our toolkits were built to include different resources aimed at empowering workers, teams, and employers offering innovative ideas to address the mental health-leaves of absence and return to work cycle in one case and the different forms and consequences of burnout in the other. Criteria for inclusion were informed by ongoing research with a range of stakeholders and other intended toolkit users including managers, supervisors, executives, human resource specialists, staff, and others in healthcare and educational organizations and settings. In the implementation phase, the volume of resources available in each toolkit considered a strength by some was overwhelming for some partners and individual workers to navigate. Capacity, engagement, time, and readiness for change, are themes that heavily influenced if and when organizations interacted with each toolkit, and how much time they spent exploring the resources provided.ConclusionIt is critical to ground toolkits in the experiential evidence of workplace mental health as is linking these to evidence-informed interventions that correspond to workplace concerns. Organizational readiness to adopt and adapt resources and implement changes is a key consideration. Ultimately, user engagement is what brought these toolkits to life.

Clinical characteristics of gastrointestinal stromal tumors with hypoglycemia.

The development of tyrosine-kinase inhibitors has improved survival rates for patients with gastrointestinal stromal tumors (GISTs). Despite the progress, not all the patients can universally receive the benefit from treatment due to the individual underlying conditions in a real-world setting. The present study focused on the well-known but understudied condition of GIST with hypoglycemia. Hypoglycemia in GIST is characterized by hypoglycemic symptoms such as dizziness, sweating and confusion. It is caused by several factors such as multiple liver metastases, drug adverse effects, postoperative complications and paraneoplastic syndrome [non-islet cell tumor hypoglycemia (NICTH)]. Comprehensive analysis of this condition has been hindered due to its rarity, and has been mostly limited to case reports. In the present study, a single-institution retrospective analysis of GIST with hypoglycemia was conducted to investigate its prevalence and prognosis, and the cause of this condition. The present study identified that the prevalence of hypoglycemic episodes of GIST was 4.1% in all patients with GIST, and recurrent hypoglycemic cases had a poor prognosis. The present study identified 1 case with recurrent hypoglycemia due to NICTH. Since NICTH is a rare hypoglycemic cause and requires further evaluation, an autopsy and genetic sequencing were performed using the available clinical materials. Through this histological and genetic investigation, the histological diversity of NICTH-GIST was revealed and insulin-like growth factor II (IGF-II) amplification was identified. Furthermore, a chronological analysis was performed using multiple resected archived samples from the same case, and revealed that diffuse IGF-II expression may have occurred in the early phase of tumor development. The present study catalogued the characteristics of GIST with hypoglycemia with a focus on NICTH-GIST.

New antibiotics in clinical pipeline for treating infections caused by metallo-β-lactamases producing Gram-negative bacteria.

To discuss novel antibiotics under clinical development, focusing on agents showing in-vitro activity against metallo-β-lactamases (MBL)-producing carbapenem-resistant Gram-negative bacteria (CR-GNB). Currently, only a few approved agents show activity, alone or in synergistic combinations, against MBL-producing CR-GNB. If approved by regulatory agencies in case of favorable results from ongoing (and, for some agents, already completed) phase-3 studies, some novel β-lactam/β-lactamase inhibitor (BL/BLI) combinations could become available in the next few years as additional important options for treating MBL-producing CR-GNB infections. Additional interesting agents that belong both to BL/BLI combinations and to antibiotic classes other than BL and BL/BLI combinations have also shown activity against MBL-producing CR-GNB, with most of them being in early phases of clinical development. Improving the use of these novel agents through virtuous antimicrobial stewardship frameworks able to guarantee both the efficacious treatment of infections requiring their use and the avoidance of their use whenever not necessary remains a challenge of utmost importance that should not be overlooked.

Commonly used genomic arrays may lose information due to imperfect coverage of discovered variants for autism spectrum disorder

BackgroundCommon genetic variation has been shown to account for a large proportion of ASD heritability. Polygenic scores generated for autism spectrum disorder (ASD-PGS) using the most recent discovery data, however, explain less variance than expected, despite reporting significant associations with ASD and other ASD-related traits. Here, we investigate the extent to which information loss on the target study genome-wide microarray weakens the predictive power of the ASD-PGS.MethodsWe studied genotype data from three cohorts of individuals with high familial liability for ASD: The Early Autism Risk Longitudinal Investigation (EARLI), Markers of Autism Risk in Babies-Learning Early Signs (MARBLES), and the Infant Brain Imaging Study (IBIS), and one population-based sample, Study to Explore Early Development Phase I (SEED I). Individuals were genotyped on different microarrays ranging from 1 to 5 million sites. Coverage of the top 88 genome-wide suggestive variants implicated in the discovery was evaluated in all four studies before quality control (QC), after QC, and after imputation. We then created a novel method to assess coverage on the resulting ASD-PGS by correlating a PGS informed by a comprehensive list of variants to a PGS informed with only the available variants.ResultsPrior to imputations, None of the four cohorts directly or indirectly covered all 88 variants among the measured genotype data. After imputation, the two cohorts genotyped on 5-million arrays reached full coverage. Analysis of our novel metric showed generally high genome-wide coverage across all four studies, but a greater number of SNPs informing the ASD-PGS did not result in improved coverage according to our metric.Limitations.The studies we analyzed contained modest sample sizes. Our analyses included microarrays with more than 1-million sites, so smaller arrays such as Global Diversity and the PsychArray were not included. Our PGS metric for ASD is only generalizable to samples of European ancestries, though the coverage metric can be computed for traits that have sufficiently large-sized discovery findings in other ancestries.ConclusionsWe show that commonly used genotyping microarrays have incomplete coverage for common ASD variants, and imputation cannot always recover lost information. Our novel metric provides an intuitive approach to reporting information loss in PGS and an alternative to reporting the total number of SNPs included in the PGS. While applied only to ASD here, this metric can easily be used with other traits.

Direct glycosylation analysis of intact monoclonal antibodies combining ESI MS of glycoforms and MALDI-in source decay MS of glycan fragments

Monoclonal antibody (mAb) glycoengineering has the potential to improve the efficacy of biopharmaceuticals by fine-tuning specific biological properties. Glycosylation analysis is key to monitoring the glycoengineering process. Various mass spectrometry (MS)-based methods are available to characterize mAb glycosylation at different structural levels, but comprehensive analysis is typically time-consuming and costly. Here, we present an approach that combines conventional intact mass measurement of glycoforms by direct infusion ESI-MS with an advanced MALDI-in-source decay FT-ICR MS method for direct analysis of glycans in intact mAbs, without the need for enzymatic release and separation. Using a sodium-doped MALDI matrix, glycans were directly released as ISD fragment ions from the intact mAbs during the ionization process. Measurement of 0,2A fragment signals yielded reproducible glycan profiles that were consistent with conventional methods, yet was achieved with unprecedented speed, providing complementary information to that obtained through intact mass measurement. The methods were applied to standard and glycoengineered trastuzumab and rituximab, allowing rapid glycosylation profiling and structural analysis of glycans by tandem MS of selected ISD fragment ions. This fast approach can facilitate the early-phase development of glycoengineering processes by constraining further in-depth analyses. We envision a broader applicability in studies focused on glycosylation changes in mAbs.

Transfer learning for crash design

When designing the structure of a new vehicle, car manufacturers need to ensure the compliance with strict safety requirements. Aiming to support the engineers in the early phase of this process, we propose a transfer learning framework for crashworthiness. This work explores the possibility to infer knowledge on future situations by exploiting data coming from past development processes. During the early phases of automotive development, assessing the crash safety implies dealing with the challenge of low data availability. Here, the engineers have no hardware test to rely on and can access only few finite element simulations. Under these circumstances, an attractive concept to investigate is the development of a machine learning approach able to learn from the past designs and to transfer the acquired knowledge to the new ones. Transfer learning can serve to this aim. With it, one learns the basic knowledge from a source domain A, and transfers it to a target domain B, characterized by low data availability. Here, we propose a transfer learning framework and apply it to an explicatory industrial crash example. The components produced in the past constitute the source domain; the new component design is the target domain. The proposed methodology can serve as an innovative solution to support car manufacturers in the early phase of vehicle development and thus improve the performance in crashworthiness scenarios.

Artificial Intelligence Supporting Early Automotive Engineering Processes

Abstract Due to progressive increase of complexity, the automotive industry is subject to constantly changing trends varying from the introduction of greener products and components to the deployment of technological advances in development and engineering processes. In relation to both, sustainable automotive products as well as the deployment of technological advances, the integration of AI (Artificial Intelligence) approaches in combination with virtual products in automotive development and engineering processes is of great importance. In combination with knowledge-based CAx (Computer-Aided engineering), the integration of AI approaches delivers an enormous potential to enhance automotive development processes. In addition to process optimizations, the integration of various AI approaches considers sustainability (e.g., optimization of component geometries and materials, reduction of emissions over the entire life cycle, CO2 reduction through improved development) and economical aspects (e.g., resources savings throughout the entire development process, time and cost savings through earlier error detection, avoidance of unnecessary process steps). The present approach deals with the integration of AI and knowledge-based engineering methods in the early phases of automotive development and engineering processes. Furthermore, the paper points to the time, cost and resources reduction potential, leading to earlier market entries and a greener industry. Finally, the paper demonstrates the integration of AI technologies into industrial development and engineering processes based on selected application scenarios.

The maternal microbiome in normal pregnancy and at delivery by cesarean section and the early developmental phase of the neonatal microbiome—presentation of a longitudinal pilot study

Abstract Aim In this study by Foessleitner et al., both the maternal microbiome in the third trimester of pregnancy and the factors that influence the development of the child’s microbiome after cesarean delivery were investigated. Methods Maternal vaginal and rectal swabs were collected at inclusion in the last trimester of pregnancy and on the day of the cesarean section. In addition, placental and intrauterine swabs as well as infant dermal, buccal, and meconium swabs were taken during the cesarean section immediately after birth and subsequently on the second/third day of life. All samples were analyzed for microbial composition using 16s rRNA amplicon sequencing. Results A total of 30 mothers and their newborns were included in the study, with microbiome samples available for all maternal, intrauterine cavity, and placenta samples, as well as for 18 out of the 30 newborns. The vaginal and rectal microbiome was stable over the course of the third trimester and showed no significant changes (permutational multivariate analysis of variance [PERMANOVA]; p > 0.05). Both the intraoperative samples (placental, intrauterine) and the neonatal swabs at the time of birth were consistently sterile. However, rapid infant microbial colonization subsequently occurred, with neonatal buccal mucosa and stool samples showing significantly different microbial colonization from their mothers as early as the second/third day of life (PERMANOVA; p < 0.01). Conclusion The conclusion of the presented study was therefore that the vaginal and rectal microbiome of healthy pregnant women does not change in the last trimester, the infant and the placenta are not microbially colonized at the time of birth, and the development of the newborn’s microbiome after birth appears to be influenced mainly by environmental exposure.

Why do Mandarin speakers code-switch? A case study of conversational code-switching in China

Multilingualism is becoming increasingly common worldwide, with multilingual studies becoming more popular and accelerating interpersonal language contacts; however, it remains controversial and ambiguous in China. The current study aims to explore code-switching (CS) patterns and factors among Mandarin-dominated mainland Chinese speakers and investigate multilingual development in China through CS, as it is a multilingual practice. Conversational analysis was conducted to analyse daily interactions in short videos posted online, categorise language varieties and frequencies, identify CS patterns, and examine the factors influencing CS patterns. The results revealed that foreign languages and dialects other than Mandarin were spoken by Mandarin-dominated speakers. Three of four CS patterns were also pinpointed regarding language practices in China. Insertion was the most predominant pattern, followed by backflagging and alternation. The CS patterns employed by Mandarin-dominated mainland Chinese speakers were influenced primarily by participant-related factors, followed by linguistic-related factors, sociopsychological factors, convenience, and situational factors. The prevalence of the insertional CS pattern and the influence of personal factors suggest that China’s Mandarin-speaking community is in an early phase of multilingual development.

Macrophage Polarization during MRONJ Development in Mice

Macrophages are important regulators of bone remodeling, and M1 polarization is observed in the setting of medication-related osteonecrosis of the jaws (MRONJ). Here, we characterize the phenotype of macrophages during early stages of MRONJ development in zoledronate (ZA)–treated mice with periodontal disease and explore the role of rosiglitazone, a drug that has been reported to lower the M1/M2 macrophage ratio, in MRONJ burden. Mice received ZA, and experimental periodontal disease (EPD) was induced around their second left maxillary molar. The mice were euthanized 1, 2, or 4 wk later. Micro–computed tomography and histologic and immunohistochemical analyses were carried out. In a separate experiment, mice were treated with ZA in the absence or presence of rosiglitazone, EPD was induced for 5 wk, and the MRONJ burden was assessed. An M1 predilection was noted in ZA versus vehicle (Veh) mice at 1, 2, or 4 wk after ligature placement. M1 cells were found to be positive for MMP-13, and their presence coincided with disruption of the surrounding collagen network in ZA mice. Rosiglitazone caused a reversal in the M1/M2 polarization in Veh and ZA mice. Rosiglitazone did not cause significant radiographic changes 5 wk after EPD in Veh or ZA animals. Importantly, percentage osteonecrosis and bone exposure were decreased in the rosiglitazone-treated versus nontreated ZA sites 5 wk after EPD. Our data point to an important role of M1 macrophage polarization with an overexpression of MMP-13 in the early phases of MRONJ development and provide insight into the use of interventional approaches promoting an M2 phenotype as a preventative means to alleviate MRONJ burden.

State-led carbon data value chain development: a case study of the Republic of Korea

ABSTRACT Introduction: This study investigates the state-led development and management of the value chain for corporate carbon performance data. By tracing the progression path of the carbon data value chain in the Republic of Korea, we aim to assess the functionality of a state-led system. Discussion:The state-driven value chain development provides structured support, particularly in the early development phases. However, it also presents limitations and inefficiencies, including a mismatch between the expansive role of the public sector and the constrained market activities in the downstream segments of the value chain. These dynamics are significantly influenced by country-specific elements such as economic and industrial policies, market receptivity to the net-zero transition, and the decarbonization strategies of both the state and corporations. Conclusion: To advance the carbon data value chain towards greater maturity, we recommend enhancing capacity building, fostering private sector engagement, and improving data transparency, accessibility, and usability.