Database Approach Research Articles

Examining the Moderating Role of Affective Commitment on the Influence of Job Satisfaction on Intent to Stay: Evidence from Healthcare Technical Staff.

Healthcare technical workers are responsible for service delivery and are indispensable to the organisation. However, being in a client facing role, they perform under client pressures thereby experiencing stress. The enormity of the healthcare industry ensures the existence of multiple competing organisations. Therefore, attrition is a cause of concern for these industries. Not only do organisations suffer as a consequence of such actions, the end users of such services may also be affected by attrition. The purpose of this study is to examine whether the intent to stay of healthcare workers is influenced by their job satisfaction and affective commitment. It also attempts to understand if affective commitment moderates the influence of job satisfaction on the intent to stay. The study has adopted a quantitative survey-based and primary data-based approach. The sample consists of technical healthcare staff such as medical imaging technicians, pathology laboratory technicians and medical equipment technicians. Two stage sampling has been adopted, with convenience sampling being used to approach private sector hospitals, and random sampling being used to select the respondents. Standard and established scales have been used in the study. The results indicate a significant correlation among job satisfaction, affective commitment and intent to stay. Further, the findings of analysis indicate that job satisfaction significantly influences the intent to stay. Affective commitment also significantly impacts the intent to stay. Moreover, affective commitment moderates the influence of job satisfaction on the intent to stay. Healthcare organisations need to focus not only on enhancing the job satisfaction of healthcare staff but also pay attention effective commitment. They therefore need to focus not only on enhancing job skills and competencies but also provide a congenial organisation climate conducive to growth and development.

Entity-Driven New Paradigm of Mine Data: Model Construction and Application

In the construction of a three-dimensional real scene (3D real scene) of a mine, we encounter challenges in organizing and correlating multi-source data. To surmount these challenges, we innovatively adopted the entity organization method. We proposed a definition of a “mining entity” and conducted an in-depth analysis of its main characteristics. Based on these characteristics, we classified and coded the main mining entities and further constructed an entity expression pattern layer that could fully reflect the characteristics of the mine. Based on multi-source heterogeneous mine data, we constructed a mining entity data layer. Furthermore, by adopting a graph database approach, we were able to create a graphical representation and correlation application of the complex relationships among mining entities. This work shows that compared to traditional data management methods, a mine data organization approach centered on entities can more effectively integrate and correlate multi-source mine data, providing strong support for the digital management of mines and the construction of 3D real scenes.

Exploring the Intersection of Brands and Linguistics: A Comprehensive Bibliometric Study

Branding has emerged as a multidiscipline with complication, conveyed by integrated digital technologies, AI, and rich linguistic approaches. This paper depicts the research landscape at the juncture of branding and linguistics, supported by VOSviewer, Biblioshiny, and CiteSpace in pinpointing key trends, themes, and collaboration networks. Drawing on data collected from Scopus, this study investigates the shifts in traditional branding strategies caused by technologies of machine learning, sentiment analysis, and social media platforms. It highlights the contributions made by leading authors, major publication sources, and patterns of international collaboration in influential works. Finally, thematic and co-occurrence analysis shows significant movement into data-based and computational approaches. This also points out some research gaps in the field of digital branding’s long- term effects, ethical considerations of AI-photo strategies, and multilingual branding approaches that provide a useful basis for future research and practical application innovation in the field of branding.

Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study.

Numerous studies have been conducted to predict depressive symptoms using passive smartphone data, mostly integrating the GPS signal as a measure of mobility. Environmental factors have been identified as correlated with depressive symptoms in specialized studies both before and during the pandemic. This study combined a data-based approach using passive smartphone data to predict self-reported depressive symptoms with a wide range of GPS-based environmental factors as predictors. The CORONA HEALTH app was developed for the purpose of data collection, and this app enabled the collection of both survey and passive data via smartphone. After obtaining informed consent, we gathered GPS signals at the time of study participation and evaluated depressive symptoms in 249 Android users with the Patient Health Questionnaire-9. The only GPS-based data collected were the participants' location at the time of the questionnaire, which was used to assign participants to the nearest district for linking regional sociodemographic data. Data collection took place from July 2020 to February 2021, coinciding with the COVID-19 pandemic. Using GPS data, each dataset was linked to a wide variety of data on regional sociodemographic, geographic, and economic characteristics describing the respondent's environment, which were derived from a publicly accessible database from official German statistical offices. Moreover, pandemic-specific predictors such as the current pandemic phase or the number of new regional infections were matched via GPS. For the prediction of individual depressive symptoms, we compared 3 models (ie, ridge, lasso, and elastic net regression) and evaluated the models using 10-fold cross-validation. The final elastic net regression model showed the highest explained variance (R2=0.06) and reduced the dataset from 121 to 9 variables, the 3 main predictors being current COVID-19 infections in the respective district, the number of places in nursing homes, and the proportion of fathers receiving parental benefits. The number of places in nursing homes refers to the availability of care facilities for the elderly, which may indicate regional population characteristics that influence mental health. The proportion of fathers receiving parental benefits reflects family structure and work-life balance, which could impact stress and mental well-being during the pandemic. Passive data describing the environment contributed to the prediction of individual depressive symptoms and revealed regional risk and protective factors that may be of interest without their inclusion in routine assessments being costly.

The Environmental Neuroactive Chemicals List of Prioritized Substances for Human Biomonitoring and Neurotoxicity Testing: A Database and High-Throughput Toxicokinetics Approach

There is a diversity of chemicals to which humans are potentially exposed. Few of these chemicals have linked human biomonitoring data, and most have very limited neurotoxicity testing. Of particular concern are environmental exposures impacting children, who constitute a population of heightened susceptibility due to rapid neural growth and plasticity, yet lack biomonitoring data compared to other age/population subgroups. This study set out to develop a prioritized list of neuroactive substances, titled the Environmental NeuRoactIve CHemicals (ENRICH) list, to be used as a defined screening library in the evaluation of human biological samples, with emphasis on early childhood-relevant environmental exposures. In silico database mining approaches were used to prioritize chemicals based upon likelihood of neuroactivity, human exposure, and feasible detection in biological samples. Evidence of neuroactivity was compiled across in vitro high-throughput screening, animal testing, and/or human epidemiological findings. Chemicals were considered for their likelihood of human exposure and detection presence in biological samples (including metabolites), with additional evidence indicating presence within child-relevant products. The resulting list of 1827 chemicals were ranked using a Chemical Prioritization Index. Manual inclusion/exclusion criteria were employed for the top-ranking chemical candidates to ensure that chemicals were within the study's scope (i.e., environmentally relevant) and, for the purposes of biomonitoring, had properties amenable to mass spectrometry methods. These elements were combined to produce the ENRICH list of 250 top-ranking chemicals, spanning pesticides and those used in home maintenance, personal care, cleaning products, vehicles, arts and crafts, and consumer electronics, among other sources. Chemicals were additionally evaluated for high-throughput toxicokinetics to predict how much of a chemical and/or its metabolite(s) may reach urine, as an example biological matrix for practical use in biomonitoring efforts. This novel study couples databases and in silico-based predictions to prioritize chemicals in the environment with potential neurological impacts for future study.

Data-driven control of echo state-based recurrent neural networks with robust stability guarantees

In this work we propose a new data-based approach for robust controller design for a rather general class of recurrent neural networks affected by bounded measurement noise. We first identify the model set compatible with available data in a selected model class via set membership (SM). Then, incremental input-to-state stability and desired performances for the closed loop system are enforced robustly to all models in the identified model set via a linear matrix inequality (LMI) optimization problem. Numerical results show the effectiveness of the comprehensive method.

Human-CoMo: A Combination of Cognitive Knowledge Engineering and Data Modelling for Human-Cyber-Physical Systems in Intelligent Manufacturing

Many cyber-physical systems face the challenge of appropriately integrating domain-specific human expert knowledge into the cyber part to create a shared sphere of knowledge and intelligent interactions between humans and the semi-autonomous technical system. Cognitive engineering contributes methods and insights into higher-order cognition that help to embed human knowledge in an appropriate way. The original research introduces a novel transdisciplinary framework called Human-CoMo, which demonstrates a systematic modelling process, different human perspectives, and the integration of expert knowledge at multiple hierarchical levels. Fundamental principles inspired by human cognition, such as conceptual chunking and knowledge precision, are characterised. Furthermore, it is shown how knowledge hierarchies can be methodically reflected in appropriate data analysis and modelling levels for small and big data applications including artificial intelligence approaches. Combined knowledge- and data-based modelling approaches offer more flexibility to integrate the strengths of humans and technology in a complementary way. The cognitive foundations and their computational reflections are outlined for the technical example process electroplating from the field of materials and surface engineering. The possibilities and limitations of integrating human knowledge through formalisation and implications for future forms of human-machine interaction are discussed.

Approaches to Disaster Recovery in Cloud Databases: A Comparative Analysis of Current Mechanisms

Disaster recovery (DR) is a critical aspect of maintaining the availability, integrity, and continuity of cloud databases, which store and manage vast amounts of mission-critical data. With the increasing reliance on cloud platforms for business operations, organizations must implement effective disaster recovery mechanisms to safeguard against data loss, downtime, and system failures. This paper presents a comparative analysis of current disaster recovery approaches in cloud databases, examining the strengths and weaknesses of various strategies, including backup and restore, data replication, and automated failover systems. We evaluate how these mechanisms are applied in different cloud models (public, private, and hybrid), considering factors such as recovery time objectives (RTO), recovery point objectives (RPO), cost, and scalability. The paper also discusses emerging technologies and trends in cloud DR, such as real-time data synchronization, machine learning-based predictive recovery, and multi-cloud disaster recovery solutions. Additionally, the role of cloud service providers’ Service Level Agreements (SLAs) in defining DR expectations is analyzed. By comparing the advantages and limitations of these mechanisms, this paper provides insights into how organizations can select the most appropriate DR strategy based on their specific business needs and risk tolerance. The study concludes by offering recommendations for optimizing disaster recovery plans in cloud environments to ensure business continuity and data protection in the face of unexpected disruptions.

Quantitative reconstruction of sedimentary processes, provenance, and binary sources in Horqin Sandy Land, NE China: An integrated approach

As the largest semi-fixed dune field in China, the Horqin Sandy Land (HSL) provides a valuable opportunity to study the surface processes and paleoenvironmental evolution of drylands in the monsoon region. However, limited data and a single qualitative approach hinder our understanding of the sediment erosion and transport process of the HSL. The ability to trace and quantify sediment provenance of the HSL is increasingly needed to properly understand the source-to-sink processes, and to accurately interpret aeolian–fluvial interactions in terms of a comprehensive database and multidisciplinary approach perspective. This study integrates previously existing grain-size, heavy mineral, element geochemistry, Sr-Nd-Hf isotopic, and zircon-geochronological databases with new petrographic, TIMA automation heavy mineral data and statistical analysis. Together, the high degree of recycling of HSL severely limits the provenance identification of element fingerprints, although these elements are largely inherited from the parent rocks and are not affected by chemical weathering. The heavy minerals, Sr-Nd-Hf isotopes, and zircon U-Pb age composition together reveal that the HSL sediments are a binary mixture of the Palaeozoic-Mesozoic igneous rocks in the Great Xing’an Range (GXR) and the Precambrian metamorphic rocks in the Yanshan Mountains. The isotopic end-element mixing models, multidimensional scaling (MDS) diagrams and inverse Monte Carlo models of detrital zircon ages indicate that GXR and Yanshan Mountains account for ∼ 60 % and ∼ 40 % of the source contributions, respectively. Under the strong influence of the northwest to west winds controlled by the Mongolia-Siberian High, detrital material from the GXR is consistently transported to the interior of the dune field and intensively reworked. The Laoha River and the Yangxumu River originating from the Yanshan Mountains provide stable detrital sources for the HSL. In addition, the transverse flow of the Xilamulun River and the West Liao River has provided sufficient space and sediment for the dunes, which is conducive to the full mixing of materials inside the dune field. The sediment in the southeast of HSL shows an obvious Yanshan Mountains source dominance and a grain-size dependence. The coarse fractions (>63 μm) mainly modified from the river channel sand and alluvial plain of the Yangxumu River (∼75.8 %), while the fine fractions (<63 μm) have a strong affinity with the loess deposit of Yanshan Mountains (∼52.4 %). The fluvial-lacustrine deposits during the last glacial maximum served as a temporary transit station, providing a direct dust source for the modern dunes in the HSL region. Aeolian–fluvial interaction controls the geomorphology and landscape evolution of the HSL in the monsoon region; however, it is challenging to derive insights into the landscape evolution from studying surface samples.

A Big Data-based Approach to Web User Behavior Analysis

In China, the Internet has developed to a relatively mature scale, and Internet applications have gradually transitioned from being singular to diversified. The Internet is changing people's ways of learning, working, and living, and even influencing the progress of the entire society. Against the backdrop of rapid Internet development, we have gradually entered the "big data era." Faced with such a vast amount of data, single-machine statistics have become inadequate. This paper first introduces the background and significance of the research, providing a preliminary description of network traffic from the perspective of network services. It then elaborates on the concepts and classifications of network user behavior, along with key data, mainly introducing analysis methods. The primary method used in this paper is cluster analysis, including distance and similarity coefficients in cluster analysis, with a focus on the main steps and algorithm process of k-means clustering. Finally, the paper conducts user behavior analysis based on the clustering results.

BIOM-49. PATIENT-CENTRIC INTEGRATED GRAPH DATABASE REVEALS CRITICAL BIOMARKERS IN THE RECURRENCE OF IDH WILD-TYPE GLIOMA

Abstract BACKGROUND IDH wild-type glioblastoma (GBM) represents a formidable therapeutic challenge due to its consistent recurrence and progression despite standard treatments. Understanding the molecular and cellular mechanisms driving GBM recurrence through data integration is crucial. This study employs a novel graph database strategy to uncover insights into these tumors and identify functional biomarkers through graph algorithms-based pathway enrichment analysis. METHOD We developed a stand-alone graph database using Neo4j for primary-recurrent paired GBMs, integrating clinical and gene expression data from the Glioma Longitudinal AnalySiS (GLASS) Consortium and internal MD Anderson cohort. Based on log2 fold-change, we used hierarchical trees for differentially expressed genes to decompose the expression profile. Nodes in the database represent patients with considerable absolute log2 values determined by hierarchical trees, while shared patients define the edges. We applied graph algorithms such as degree centrality and community detection to identify potential biomarkers and supplemented these analyses with pathway identification to refine the biomarker list. Further, we divided the data set into 80% training and 20% testing sets. We evaluated the predictive performance of these biomarkers using machine learning models, including logistic regression, random forest, SVM, and neural networks. RESULTS DEG analysis revealed 33 down-regulated and 418 up-regulated genes in recurrent tumors. The top enriched pathways included neuroactive ligand-receptor interaction and GPCR ligand binding. Our database approach implicated 35 out of 451 genes as significant drivers of recurrence in IDH wild-type GBM. The prediction performance of these biomarkers was comparable to features selected through methods such as mRMR and logistic lasso and target identification methods like WGCNA. CONCLUSION Our innovative systems biology approach reveals that ligand-receptor interactions are crucial in driving recurrence in IDH wild-type GBM. This method also yielded promising mechanistic targets for recurrence, prompting focused and therapeutically relevant functional analyses. Moreover, this methodology is not limited to our study but can be applied in other multi-omics settings, offering a robust tool for biomarker discovery and validation.

Despite Contradictions, Artificial Intelligence and Block Chain Technology are Composing the Future Together

Now times, Block Chain technology and enhancements in Artificial Intelligence paradigm are facing more attention in the field of research with new advancements in many security techniques in all the sectors of IT and business. One cannot deny from the fact that the adoption of Artificial Intelligence paradigms and block-chain technology is shaping the new face of market. Beyond their contradictions both are proceeding at lightning speed. Both of them are aimed at providing something new to the world but the degree of complexity of each is quite different. With a secure, decentralized, and trustworthy system, block-chain technology did automate the bit-coin payments and provide the users an access to a shared ledger of records, transactions and data. With the useof smart contracts, a block-chain can also regulate the user interactions without the need for any central authority. In the contradiction, AI provides agents with the ability of reasoning, decision making and human-level intellect. At bottom one can say, block chain is concerned with keeping correct records, authentication and implementation while AI deals in conducting assessments, examining, and coming to a decision analyzing certain patterns and datasets, eventually giving rise to a self- directed interaction. Artificial Intelligence along with block chain allocates several descriptions which will make a seamless communication prospect certainly. AI and Block-Chain need sharing of data. The decentralized approach of database focuses on the significance of data sharing among various clients on a meticulous network. In the same way, AI depends very much on Big-Data, exclusively, for data distribution. The first section in the proposed article offers the Introduction part. The second section focuses on the majorly emphasized topics and domains about block chain technology indulging Artificial Intelligence into it, presents a detailed literature-review of many of the contributions by eminent research analysts. The third section describes the challenges in the collaborating AI with block-chain. The last section expresses a brief conclusion of the research work. Keywords- Block Chain Technology, Artificial Intelligence, AI, Machine Learning

Narrative Literature Review: Digital-based Post-Operative Pain Management Innovation

Effective postoperative pain management is a key element in patient recovery after surgery, but often standard protocols do not take advantage of the potential of real-time data and digital technology for more responsive therapy adjustments. The aim of this study was to identify the latest innovations in digital-based postoperative pain management. A literature review examined the use of digital technology in postoperative pain management, focusing on smartphone apps, wearable devices, and real-time data analytics for monitoring patient pain with the keywords "Pain Management AND Digital Postoperative" OR "Digital Health AND Pain Management" AND "Technology" OR "Information Systems". The year of the source of the literature taken is 2019-2024. Database searches use Scopus, Pub Med, Sage Journal, and Google Scholar. The findings of this review suggest that the integration of digital technology in postoperative pain management can improve the personalization of care by allowing for more accurate and responsive therapy adjustments to changes in patients' pain conditions. Data-Based Approaches in the Digital Era are Virtual Reality (VR), PASHA Application (pain assessment, stimulating and healing application), A wireless intelligent PCA (Wi-PCA) AI (Artificial Intelligence), Cell Phone Application, Wearable Devices, Mhealth, NeuroCuple, LINE, Digital Intervention Group, The Panda App and A Digital Application. Wearable apps and devices effectively monitor pain and enable real-time therapy adjustments. Digital-based innovation can be used as an alternative nursing intervention for post-operative pain management.

Blue Accounting Literacy Perspective on the Tourism Sector in Indonesia

This research aims to analyze the challenges, role of government, and understanding of stakeholders in managing marine and coastal resources in Indonesia through the Blue Accounting approach. This research uses qualitative methodology with literature analysis of twenty-five relevant articles. The data was analyzed to identify the main challenges, government role, and stakeholder understanding in implementing Blue Accounting. Management of marine and coastal resources in Indonesia faces various challenges, including inadequate infrastructure and low public awareness. The government's role is critical in developing regulations, coordinating conservation efforts, and involving stakeholders. Stakeholder understanding of Blue Accounting and multi-stakeholder collaboration have proven effective in overcoming these challenges and improving resource welfare. This research provides insight into the importance of strong regulation, public education, and collaboration between governments, communities, and the private sector in the management of marine and coastal resources. The practical implications are the need to develop a blue accounting model that is more integrated with information and communication technology to improve the effectiveness of environmental monitoring and management. The study emphasizes the importance of data-based Blue Accounting approaches and multi-stakeholder collaboration in marine and coastal resource management, which has not been much applied in previous research.

Implementasi Program Peningkatan Edukasi di bidang Lingkungan Hidup

The implementation of the Education Improvement Program in the field of Environment is a strategic effort to increase public awareness and knowledge about environmental issues. The program is designed to meet increasingly complex environmental challenges by providing an effective and sustainable education. This study evaluates the effectiveness of various environmental education methods, including seminars, workshops, and media campaigns. Through a data-based approach, the analysis was carried out to measure the impact of the program on changes in participants' attitudes and behaviors towards environmental issues. The results show that interactive and participatory methods are more successful in increasing community understanding and engagement. This finding is expected to be a reference for the development of better environmental education programs in the future.

On political interests and self-serving diversity: How state policymakers envision public service media

ABSTRACT Since 2012, diversity has been a declared pillar of Public Service Media (PSM) organizations worldwide – yet the term lacks conceptual clarity. This article offers an empirical analysis of how diversity is discussed and conceptualized in the process of legislating a new PSM law. Through reflexive thematic analysis of 160 transcripts of Israel’s parliament discussions during 2012–2018, we find that policymakers approach diversity as a crucial feature in PSM organizations, yet seem unable to produce an operationalizable definition of it. Our findings suggest diversity is easily manipulated for political interests – a phenomenon we coin as “self-serving diversity.” The Israeli case herein contributes a data-based approach to theorizing media diversity as a bendable ideology rather than a concrete embracive approach to society.

Perancangan Strategi Digital Marketing Pada Brand Sofia And Jenna Menggunakan Metode SOSTAC

The development of the Muslim Fashion Industry is increasingly rapid every year. This indicates that competition is getting tighter and the potential market for Muslim fashion in Indonesia continues to increase. Sofia and Jenna are one of the business actors operating in the Muslim fashion sector. Sofia and Jenna is a local brand that produces its own clothes (self-manufactured) which was founded in 2020. Even though it has been operating for several years, sales of Sofia and Jenna from January 2023 to October 2023 experienced fluctuations, in fact sales growth was still below Indonesian fashion industry. This is caused by several factors, namely low heart share and suboptimal use of digital marketing platforms, so this affects Sofia and Jenna's sales performance. For this reason, deeper observations were made regarding alternative solutions related to product, place, resource and promotion aspects. Of these four aspects, there is one aspect that shows the roots of the problem more than the other aspects, namely the promotion aspect. This research was conducted using the SOSTAC method which went through six stages, namely Situation Analysis, Objectives, Strategy, Tactics, Action, and Control. The aim of designing this digital marketing strategy is as a promotional medium and is expected to increase sales and marketing performance so that it can increase awareness of the Sofia and Jenna brand. Based on the digital marketing strategy design, this final project produced a design for the use of Social Media Optimization (SMO), Content Marketing, Partnership, Video Marketing, Linktree, User Generated Content (UGC), and Mobile Commerce Optimization tools. Then, the design is visualized using a Strategy Map. Through a structured and data-based approach, Sofia and Jenna are expected to be able to improve their position in the Muslim fashion market, as well as increase competitiveness and consumer loyalty towards the brand.

Identification of Pathogenic Copy Number Variants in Mexican Patients With Inherited Retinal Dystrophies Applying an Exome Sequencing Data-Based Read-Depth Approach.

Retinal dystrophies (RDs) are the most common cause of inherited blindness worldwide and are caused by genetic defects in about 300 different genes. While targeted next-generation sequencing (NGS) has been demonstrated to be a reliable and efficient method to identify RD disease-causing variants, it doesn't routinely identify pathogenic structural variant as copy number variations (CNVs). Targeted NGS-based CNV detection has become a crucial step for RDs molecular diagnosis, particularly in cases without identified causative single nucleotide or Indels variants. Herein, we report the exome sequencing (ES) data-based read-depth bioinformatic analysis in a group of 30 unrelated Mexican RD patients with a negative or inconclusive genetic result after ES. CNV detection was performed using ExomeDepth software, an R package designed to detect CNVs using exome data. Bioinformatic validation of identified CNVs was conducted through a commercially available CNV caller. All identified candidate pathogenic CNVs were orthogonally verified through quantitative PCR assays. Pathogenic or likely pathogenic CNVs were identified in 6 out of 30 cases (20%), and of them, a definitive molecular diagnosis was reached in 5 cases, for a final diagnostic rate of ~17%. CNV-carrying genes included CLN3 (2 cases), ABCA4 (novel deletion), EYS, and RPGRIP1. Our results indicate that bioinformatic analysis of ES data is a reliable method for pathogenic CNV detection and that it should be incorporated in cases with a negative or inconclusive molecular result after ES.

Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy

The study addresses the challenge of integrating complex landscape-hydrological interactions into predictive models for improved water resource management. The aim is to investigate the effectiveness of landscape metrics—quantitative indices measuring landscape composition and configuration—as predictors of WES in the Arno River Basin, Italy. Utilizing two hydrological models alongside a random forest algorithm, we assessed spatial and temporal variations in water yield, runoff, and groundwater recharge. The findings indicate that landscape metrics derived from high-resolution land use data significantly impact WES outcomes. Specifically, the models demonstrated average landscape metric importances of 16.8 % for spatial and 17.8 % for temporal predictions concerning runoff. For water yield, these averages were 32.9 % spatially and 43.5 % temporally, while groundwater modeling showed importances of 14.09 % spatially and 33.8 % temporally. Key landscape metrics identified include the core area index for broad-leaved forests and the perimeter-to-area ratio for non-irrigated agricultural areas as critical spatial and temporal predictors of water yield and groundwater recharge. Thresholds were observed, indicating landscape configurations that minimize hydrological variability. For instance, runoff variation is minimal when the landscape exhibits high forest fragmentation (over 1000 coniferous patches), low aggregation (aggregation index <75), and reduced connectivity (cohesion index under 80). Similarly, groundwater variation is minimized with decreased boundary length of vegetation patches (perimeter-to-area ratio <0.8), agricultural lands (perimeter-to-area ratio under 1), and the presence of low core agricultural areas (core area index above 8). The identified thresholds could inform land-use policies, such as targeted afforestation or crop diversification strategies, to optimize WES provision.

Transcriptomic signatures during normothermic liver machine perfusion correspond with graft quality and predict the early graft function

A better understanding of the molecular events during liver normothermic machine perfusion (NMP) is warranted to develop a data-based approach for the identification of biomarkers representative of graft quality and posttransplant outcome. We analysed the dynamic transcriptional changes during NMP and linked them to clinical and biochemical parameters. 50 livers subjected to NMP for up to 24h were enrolled. Bulk RNA sequencing was performed in serial biopsies collected pre and during NMP, and after reperfusion. Perfusate was sampled to monitor liver function. qPCR and immunohistochemistry were performed to validate findings. Molecular profiles were compared between transplanted and non-transplanted livers, and livers with and without early allograft dysfunction. Pathways related to immune and cell stress responses, cell trafficking and cell regulation were activated during NMP, while cellular metabolism was downregulated over time. Anti-inflammatory responses and genes involved in tissue remodelling were induced at later time-points, suggesting a counter-response to the immediate damage. NMP strongly induced a gene signature associated with ischemia-reperfusion injury. A 7-gene signature corresponds with the benchmarking criteria for transplantation or discard at 6h NMP (area under curve 0.99). CD274 gene expression (encoding programmed cell-death ligand-1) showed the highest predictive value. LEAP2 gene expression at 6h NMP correlated with impaired graft function. Assessment of gene expression markers could serve as a reliable tool to evaluate liver quality during NMP and predicts early graft function after transplantation. The research was supported by "In Memoriam Dr. Gabriel Salzner Stiftung", Tiroler Wissenschaftsfond, Jubiläumsfonds-Österreichische Nationalbank and MUI Start grant.