Complex Landscape Research Articles

Off-Crop and Off-Season Monitoring, Key Elements to Be Integrated into an Effective Strategy for the Control of Drosophila suzukii (Diptera: Drosophilidae)

Drosophila suzukii is a pest affecting a wide range of host plants, causing severe damage to small fruits, berries, and grapes. This study analyzed environmental factors influencing its population dynamics in regions where temperature is not a limiting factor. Data were collected in the spring–summer seasons of 2018 and 2019 across three vineyards in northwestern Portugal, examining the relationship between captured D. suzukii females, climatic variables, vine phenological stages, and ecological infrastructures. A stepwise linear model and Pearson correlation matrix were used. In 2020, a winter study was conducted in nine vineyards, focusing on landscape composition and its effect on D. suzukii populations. An ecological infrastructure index was created and correlated with captures data. Results show that vine phenological stages and nearby ecological infrastructures significantly affect population dynamics in spring and summer. Vineyards surrounded by complex landscapes, especially with wild hosts, supported higher D. suzukii populations during winter. These findings highlight the importance of ecological infrastructures in managing D. suzukii populations year-round and suggest their consideration in pest control strategies.

Empirical and Machine Learning Models for Soil Erosion Risk Assessment: A Case Study of Tsageri Municipality, Georgia

Soil erosion caused by water is one of the most common causes of land degradation worldwide. Within framework of this research soil erosion risk in Tsageri municipality, Georgia was evaluated using Revised Universal Soil Loss Equation (RUSLE) and a machine learning-based Random Forest (RF) model. Open access digital datasets and field observations collected in 2023-2024, which included visually identified erosion areas and GPS-recorded data on the presence or absence of erosion, were utilized in modeling process. Data processing and modeling conducted using ArcGIS Pro 3.0 and RStudio software. According to RUSLE results, 39.7% of the study area falls under the very low erosion risk zone, and 20.7% is in the very high risk zone. The RF model results indicated that 16.5% of the territory is under very low risk of erosion and 13.9% - very high risk. It was observed that RUSLE model tends to overestimate erosion rates on steep, forested slopes, while the RF model, by incorporating additional variables, provided more accurate prediction. These findings suggest that combining RUSLE with machine learning improves soil erosion risk assessment, particularly in complex landscapes such as in Tsageri municipality. Future researche should focus on testing additional variables to refine the modeling process further and enhance predictions. The generated digital thematic maps offer valuable insights for understanding the spatial dynamics of soil erosion within the study area, analyzing the factors driving the process and developing effective mitigation strategies.

Characterization of novel ACE2 mRNA transcripts: The potential role of alternative splicing in SARS-CoV-2 infection.

The human angiotensin converting enzyme 2 (ACE2) gene encodes a type I transmembrane protein, which is homologous to angiotensin I-converting enzyme (ACE) and belongs to the angiotensin-converting enzyme family of dipeptidyl carboxypeptidases. As highlighted by the COVID-19 pandemic, ACE2 is not only crucial for the renin-angiotensin-aldosterone system (RAAS), but also displays great affinity with the SARS-CoV-2 spike protein, representing the major receptor of the virus. Given the significance of ACE2 in COVID-19, especially among cancer patients, the present study aims to explore the transcriptional landscape of ACE2 in human cancer and non-cancerous cell lines through the design and implementation of a custom targeted long-read sequencing approach. Bioinformatics analysis of the massive parallel sequencing data led to the identification of novel ACE2 mRNA splice variants (ACE2 sv.7-sv.12) that demonstrate previously uncharacterized exon-skipping events as well as 5' and/or 3' alternative splice sites. Demultiplexing of the sequencing data elucidated the differential expression profile of the identified splice variants in multiple human cell types, whereas in silico analysis suggests that some of the novel splice variants could produce truncated ACE2 isoforms with altered functionalities, potentially influencing their interaction with the SARS-CoV-2 spike protein. In summary, our study sheds light on the complex alternative splicing landscape of the ACE2 gene in cancer cell lines, revealing novel splice variants that could have significant implications for SARS-CoV-2 susceptibility in cancer patients. These findings contribute to the increased understanding of ACE2's role in COVID-19 and highlight the importance of considering alternative splicing as a key factor in viral pathogenesis. Undoubtably, further research is needed to explore the functional roles of these variants and their potential as therapeutic targets in the ongoing fight against COVID-19.

Abstract B008: Exploring the Dual Roles of RNAs as Oncogenic Drivers and Therapeutic Targets in Cancer: A Cross-Sectional Study

Abstract RNAs have emerged as pivotal molecules in the complex landscape of cancer biology, acting as both drivers of oncogenesis and therapeutic targets. This cross-sectional experimental design research explores the dual roles of RNAs, particularly focusing on the interplay between non-coding RNAs (ncRNAs), messenger RNAs (mRNAs), and their regulatory networks in various cancer types. The study systematically analyzes RNA expression profiles across a broad spectrum of cancer samples, employing high-throughput sequencing technologies combined with bioinformatics tools to identify key RNA species that contribute to tumorigenesis. The experimental design includes the collection of tumor and adjacent normal tissues from patients diagnosed with breast, lung, and colorectal cancers. RNA extraction and sequencing are performed to generate comprehensive expression profiles. Differential expression analysis is conducted to pinpoint RNAs that are significantly dysregulated in cancerous tissues compared to their normal counterparts. Subsequently, functional assays, including RNA interference (RNAi) and CRISPR/Cas9-mediated gene editing, are utilized to validate the oncogenic or tumor-suppressive roles of these RNAs in cancer cell lines. To further elucidate the mechanistic pathways, the study integrates data from RNA-binding protein immunoprecipitation (RIP) and crosslinking immunoprecipitation (CLIP) assays, providing insights into the RNA-protein interactions that influence cancer progression. Additionally, the research evaluates the therapeutic potential of targeting these RNAs, using small interfering RNAs (siRNAs) and antisense oligonucleotides (ASOs) in preclinical cancer models. Preliminary findings highlight the significance of long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs) in modulating key signaling pathways involved in cell proliferation, apoptosis, and metastasis. This study underscores the importance of RNA-based diagnostics and therapeutics, paving the way for innovative treatment strategies in cancer management. Citation Format: Fathelrahman M Gameel. Exploring the Dual Roles of RNAs as Oncogenic Drivers and Therapeutic Targets in Cancer: A Cross-Sectional Study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B008.

An investigation into the shifting landscape preferences of rural residents in Taiwan and their relationship with ecological indicators.

Rapid urbanization has significantly altered landscape environments in both urban and rural regions, and these landscapes have been demonstrated to play a pivotal role in human well-being. This study develops a coherent framework that integrates landscape change, landscape ecological indicators, and landscape preferences within the context of the evolving landscape environments of rural communities in Taiwan. Four distinct types of rural communities were selected, and a quantitative methodology was employed to investigate the variations and transformations in landscape preferences among rural residents in the context of landscape change. A qualitative methodology was employed to investigate the relationship between landscape ecological indicators and landscape preferences. The study's findings indicate significant temporal variations in residents' landscape preferences, with landscape beauty, stewardship, and coherence emerging as key determinants in the evaluation of these preferences. Landscape ecological indicators were found to be significantly correlated with variables such as landscape complexity, landscape beauty, openness, naturalness, and comprehensive landscape assessment. The findings of this study indicate that design planners, land managers, and public sector organizations can employ multidimensional thinking in the management of rural landscapes to align with the visual aesthetic preferences of rural residents.

Monkeys engage in visual simulation to solve complex problems.

Visual simulation-i.e., using internal reconstructions of the world to experience potential future versions of events that are not currently happening-is among the most sophisticated capacities of the human mind. But is this ability in fact uniquely human? To answer this question, we tested monkeys on a series of experiments involving the "Planko" game, which we have previously used to evoke visual simulation in human participants. We found that monkeys were able to successfully play the game using a simulation strategy, predicting the trajectory of a ball through a field of planks while demonstrating a level of accuracy and behavioral signatures comparable with those of humans. Computational analyses further revealed that the monkeys' strategy while playing Planko aligned with a recurrent neural network (RNN) that approached the task using a spontaneously learned simulation strategy. Finally, we carried out awake functional magnetic resonance imaging while monkeys played Planko. We found activity in motion-sensitive regions of the monkey brain during hypothesized simulation periods, even without any perceived visual motion cues. This neural result closely mirrors previous findings from human research, suggesting a shared mechanism of visual simulation across species. Taken together, these findings challenge traditional views of animal cognition, proposing that nonhuman primates possess a complex cognitive landscape, capable of invoking imaginative and predictive mental experiences to solve complex everyday problems.

Psychological explanations for analysts' herding behavior: A study on the impact of information cascades and reputational concerns

This study investigates the influence of information cascades and reputational concerns on analysts' herding behavior in financial markets. Using a mixed-methods approach combining archival data analysis and surveys of 5,237 financial analysts covering S&P 500 companies from 2014 to 2023, we examine the interplay between these psychological factors. Our findings reveal that both information cascades and reputational concerns significantly impact herding behavior, with a notable synergistic effect when combined. Structural equation modeling demonstrates that information cascade strength positively correlates with herding behavior (β = 0.43, p < .001), partially mediated by perceived market consensus. Reputational concerns also show a strong positive association with herding (β = 0.51, p < .001), moderated by career stage. Importantly, the interaction between information cascades and reputational concerns (β = 0.24, p < .001) suggests that their combined effect is greater than the sum of their individual impacts. This effect varies across analyst characteristics and market conditions, with stronger influences observed in small-cap stocks and high-volatility periods. Time-series analysis reveals dynamic fluctuations in these effects over time, responding to significant market events. These results contribute to the theoretical understanding of financial market behavior by integrating social influence and career concern perspectives, offering insights for both scholars and practitioners in an increasingly complex market landscape.

Synthesis of a hydrological, water chemistry, and contaminants research program in the Peace-Athabasca Delta (Canada) to inform long-term monitoring of shallow lakes

In a multistressor world, evidence-based stewardship of aquatic ecosystems requires long-term monitoring data to understand the timing and magnitude of environmental change and potential causes. At the Peace-Athabasca Delta (PAD; northeastern Alberta, Canada), concern for aquatic ecosystem degradation has triggered renewed and urgent calls by Indigenous, national, and international governance bodies for implementation of a long-term lake monitoring program capable of tracking changes in hydrological conditions and contaminant deposition attributable to major energy projects located upstream, climate change, and other unnatural and natural processes. Challenges imposed by the delta's size, hydrological complexity, inaccessibility of lakes, and other factors, however, have long impeded implementation of a delta-wide lake monitoring program. To address this pressing need, here we review and synthesize results obtained during 7 years (2015–2021) of intensive, multifaceted research at 60 shallow lakes spanning the delta's broad hydroecological gradients to inform an integrated hydrology, water chemistry, and contaminants monitoring program. The research involved systematic, repeated measurements of water isotope composition, water depth variation, water chemistry and turbidity, and metal(loid) concentrations in lake surface sediment and periphytic biofilm. Results reveal marked spatial and temporal variation of hydrological processes and their affects on lake water balance and depth, strong association between hydrological processes and lake water chemistry, and that concentrations of nickel and vanadium (key oil sands indicators) remain within the range of natural variation. Correspondence of generalized additive model trendlines for isotope-derived lake evaporation-to-inflow ratios and water chemistry with climate indices (Pacific Decadal Oscillation, Oceanic Niño Index) demonstrates the sensitivity, and predictability, of lake ecosystem processes in the delta to large-scale climatic patterns. We provide recommendations for field sampling, sample analysis, data display, and integration of information for ongoing monitoring at the PAD. These approaches are readily transferable to other complex landscapes with abundant shallow waterbodies threatened by multiple stressors that may alter hydrological regimes and contaminant delivery.

Dissident Blood: Neo-Santeria in Barcelona and the Refusal of Sacrifices

This article explores the emergence and development of Neo-santeria in Barcelona, a contemporary trend of Afro-Cuban religious practices characterized by the rejection of animal sacrifice, a central ritual in traditional Santeria. The study identifies and analyzes four key arguments employed by Neo-santeros to legitimize this rejection within the secular and modern European context: the scientistic, de-traditionalist, individualistic, and ecologist arguments. Drawing on over a decade of ethnographic research, the article demonstrates how Neo-santeros navigate the tension between distancing themselves from certain traditional spiritual roots—particularly the practice of animal sacrifice—and the intertwining with European and contemporary cultural logics, particularly those related to secularism. The article situates Neo-santeria within the broader landscape of European holistic spiritualities, highlighting its strategic positioning as a religion that aligns with and challenges secularist expectations in modern Europe. The findings contribute to a deeper understanding of how Afro-Cuban religions, particularly Neo-Santería, can be defined by the ongoing creativity of their practitioners. This distinctive feature not only defines the fluid nature of these traditions but also contributes to the diversification and increased complexity of the spiritual landscape in European contexts, where Afro-Cuban practices are being intertwined in local cultural and religious frameworks.

FunCoup 6: advancing functional association networks across species with directed links and improved user experience.

FunCoup 6 (https://funcoup.org) represents a significant advancement in global functional association networks, aiming to provide researchers with a comprehensive view of the functional coupling interactome. This update introduces novel methodologies and integrated tools for improved network inference and analysis. Major new developments in FunCoup 6 include vastly expanding the coverage of gene regulatory links, a new framework for bin-free Bayesian training and a new website. FunCoup 6 integrates a new tool for disease and drug target module identification using the TOPAS algorithm. To expand the utility of the resource for biomedical research, it incorporates pathway enrichment analysis using the ANUBIX and EASE algorithms. The unique comparative interactomics analysis in FunCoup provides insights of network conservation, now allowing users to align orthologs only or query each species network independently. Bin-free training was applied to 23 primary species, and in addition, networks were generated for all remaining 618 species in InParanoiDB 9. Accompanying these advancements, FunCoup 6 features a new redesigned website, together with updated API functionalities, and represents a pivotal step forward in functional genomics research, offering unique capabilities for exploring the complex landscape of protein interactions.

Attitudes, perceptions, and intentions toward sports betting legalization: a cluster analysis of California residents

ABSTRACT In the United States, the potential legalization of sports betting in non legal states presents a complex landscape of opportunities and challenges. This study examines California voters’ attitudes toward sports betting, the perceived impacts of its legalization, and the factors influencing their voting intentions. Utilizing the Theory of Reasoned Action (TRA), the research explores how attitudes and subjective norms shape voter behavior. A K-Means clustering analysis identified three distinct voter segments: supporters, neutrals, and opponents. The analysis revealed significant differences in voting intentions, attitudes, and perceived impacts across these clusters. These findings highlight the importance of robust policy and tailored marketing strategies that address the specific concerns and motivations of each voter segment. By providing insights into voter preferences and behaviors, this research offers valuable guidance for policymakers, operators, and stakeholders involved in the sports betting industry, supporting informed decision-making and responsible policy and messaging strategy.

The context‐dependent benefits of organic farming on pollinator biodiversity: A meta‐analysis

Abstract Agricultural intensification is driving declines in pollinator diversity and the degradation of pollination services worldwide. Organic management, which prohibits the use of synthetic inputs, has considerable potential for combatting these declines by enhancing biodiversity in agricultural landscapes. However, the efficacy of organic farming for promoting pollinator diversity has recently been questioned. We conducted a meta‐analysis to determine the effect of organic management on pollinator species richness and abundance. We compiled 42 studies across four continents and calculated a Hedges' g effect size for each of 76 species richness and 57 abundance observations. We then estimated average effect sizes overall and across five moderators. Species richness and abundance of pollinating insects tended to be higher in organic farms than conventional ones, with average effect sizes of 0.68 and 0.74, respectively. Landscape context, crop type, pollinator group, sampling location, and sampling method were factors impacting the response of pollinator diversity/abundance to organic farming. We found benefits for pollinator diversity from organic farming in all landscape types, with the strongest response in simple compared with more complex landscapes. Pollinator diversity benefited the most in habitats within organic cereal systems and the least in organic pastures. Among pollinator groups, bumblebee diversity benefitted the most, while moths and hoverflies showed positive but non‐significant responses. Higher pollinator diversity/abundance was detected in organic farms at various sampling locations. Transect sampling showed the greatest diversity gains among sampling methods. Synthesis and applications. Our results affirm that organic farming has positive effects for pollinator species richness and abundance, but benefits are variable and not guaranteed. We recommend that future studies adopt a standard sampling protocol and cover a larger geographic range to understand the global potential of organic farming to promote pollinator biodiversity. Efforts to increase pollinator abundance and diversity through organic farming should focus on organic systems involving cereal phases, especially within simple landscapes, for the most positive outcomes. However, due to high variability in pollinator responses alongside the challenges to achieve economic profitability from organic farming, crafting organic farming systems to specific farm‐scale opportunities and needs may be necessary.

Spatio-temporal trends in the frost regime reveal late frost exposure to white spruce (Picea glauca [Moench] Voss) persists in northeastern America

Abstract The characteristics of the frost regime (intensity, frequency, and timing) contribute to shaping tree species adaptations and distribution as well as ecosystem productivity and functions. However, climate change increases the variability in extreme events; therefore, the different characteristics of the frost regime may diverge under climate change. Using the BioSim 11 software, we simulated daily air temperature at 512 locations over northeastern North America between 1901–2021 to determine how the spatio-temporal trends in the frost regime varied over this complex landscape and if spatio-temporal trends in extreme climatic events such as frosts are stronger compared to changes in aggregated climate variables such as mean annual air temperature and growing degree-days. We also used an eco-physiological model to conduct a case study focussing on white spruce to determine if trees are currently more exposed to growing season frosts than they were in the past by modelling the timings of budburst using the thermal time model. Our results showed that, at 67% of locations (343 locations), the day of the year of the last frost in spring (minimum daily air temperature<0 °C) occurred, on average, earlier by seven days during 2001–2021 compared to 1901–1920, whereas it occurred, on average, later by four days at 33% of locations (169 locations). The average temporal trends in frost occurrence were similar in magnitude to the average trends in aggregated climate variables; however, their variances were larger compared to the aggregated climate metrics, showing that the frost regime does not change uniformly throughout our study area. Our case study also revealed that white spruce remains exposed to late frosts of low and intermediate intensities (minimum daily air temperature <0; <−2 °C) compared to the past but exposure to high-intensity frosts (minimum daily air temperature <−4 °C) is rare. Since extreme events such as late frosts diverge in their response to climate change compared to aggregated climate variables, the mean annual temperature is not sufficient to predict how climate change will impact ecosystems through frost regimes.

EPCO-05. GENOMIC AND CLINICAL INSIGHTS FROM A LARGE-SCALE STUDY OF 172 RETINOBLASTOMAS

Abstract Retinoblastoma, a pediatric ocular cancer, is the only central nervous system tumor visible without specialized tools, readily detectable with the naked eye. This rare childhood cancer is mainly driven by mutations in RB1 or MYCN amplification, posing significant challenges in pediatric oncology. We conducted whole exome sequencing on 172 retinoblastoma tumors along with matched blood DNA from 171 patients, which included 119 unilateral, 49 bilateral, and three trilateral cases. Our analysis identified pathogenic variants in RB1 in 76 patients and MYCN duplication in 79 cases, highlighting their crucial roles in the disease. Additionally, we found mutations in RPTOR and TSC2, components of the mTOR signaling pathway, in two patients. Notably, our copy number alteration analysis revealed recurrent arm-level changes, including gains on 1q (23.3%), 2p (20.9%), and especially 6p (41.3%), along with other changes such as 5p gain (8.1%), 17q gain (8.1%), and 16q loss (5.8%). Our findings link these genetic alterations to specific clinical features; for instance, 6p gains were associated with Rubeosis, subretinal seeds, and tumor LC beyond, while 17q gains were linked to Rubeosis. Furthermore, gene collapsing techniques identified MATR3 and CYP4F2 as additional novel retinoblastoma-related genes, and module identification analysis pinpointed retinoblastoma-associated modules including HLA-DRB1-CBL, LPAR2, RB1, and TRIM28. Our study, the largest cohort of its kind, underscores the importance of next-generation sequencing from understudied populations in uncovering the complex genetic landscape of retinoblastoma, emphasizing the potential of precision medicine to enhance therapeutic strategies and improve outcomes for retinoblastoma patients.

Recent progress in atmospheric modeling over the Andes – part I: review of atmospheric processes

The Andes is the longest mountain range in the world, stretching from tropical South America to austral Patagonia (12°N-55°S). Along with the climate differences associated with latitude, the Andean region also features contrasting slopes and elevations, reaching altitudes of more than 4,000 m. a.s.l., in a relatively narrow crosswise section, and hosts diverse ecosystems and human settlements. This complex landscape poses a great challenge to weather and climate simulations. The interaction of the topography with the large-scale atmospheric motions controls meteorological phenomena at scales of a few kilometers, often inadequately represented in global (grid spacing ∼200–50 km) and regional (∼50–25 km) climate simulations previously studied for the Andes. These simulations typically exhibit large biases in precipitation, wind and near-surface temperature over the Andes, and they are not suited to represent strong gradients associated with the regional processes. In recent years (∼2010–2024), a number of modeling studies, including convection permitting simulations, have contributed to our understanding of the characteristics and distribution of a variety of systems and processes along the Andes, including orographic precipitation, precipitation hotspots, mountain circulations, gravity waves, among others. This is Part I of a two-part review about atmospheric modeling over the Andes. In Part I we review the current strengths and limitations of numerical modeling in simulating key atmospheric-orographic processes for the weather and climate of the Andean region, including low-level jets, downslope winds, gravity waves, and orographic precipitation, among others. In Part II, we review how climate models simulate surface-atmosphere interactions and hydroclimate processes in the Andes Cordillera to offer information on projections for land-cover/land-use change or climate change. With a focus on the hydroclimate, we also address some of the main challenges in numerical modeling for the region.

Gene Therapy in Cardiovascular Disease: Recent Advances and Future Directions in Science: A Science Advisory From the American Heart Association.

Cardiovascular disease remains the foremost cause of morbidity and mortality globally, affecting millions of individuals. Recent discoveries illuminate the substantial role of genetics in cardiovascular disease pathogenesis, encompassing both monogenic and polygenic mechanisms and identifying tangible targets for gene therapies. Innovative strategies have emerged to rectify pathogenic variants that cause monogenic disorders such as hypertrophic, dilated, and arrhythmogenic cardiomyopathies and hypercholesterolemia. These include delivery of exogenous genes to supplement insufficient protein levels caused by pathogenic variants or genome editing to correct, delete, or modify mutant sequences to restore protein function. However, effective delivery of gene therapy to specified cells presents formidable challenges. Viral vectors, notably adeno-associated viruses and nonviral vectors such as lipid and engineered nanoparticles, offer distinct advantages and limitations. Additional risks and obstacles remain, including treatment durability, tissue-specific targeting, vector-associated adverse events, and off-target effects. Addressing these challenges is an ongoing imperative; several clinical gene therapy trials are underway, and many more first-in-human studies are anticipated. This science advisory reviews core concepts of gene therapy, key obstacles, patient risks, and ongoing research endeavors to enable clinicians to understand the complex landscape of this emerging therapy and its remarkable therapeutic potential to benefit cardiovascular disease.

Students’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct

AbstractThis novel study explores AI-giarism, an emergent form of academic dishonesty involving AI and plagiarism, within the higher education context. The objective of this study is to investigate students’ perception of adopting generative AI for research and study purposes, and their understanding of traditional plagiarism and their perception of AI-plagiarism. A survey, undertaken by 393 undergraduate and postgraduate students from a variety of disciplines, investigated their perceptions of diverse AI-giarism scenarios. The findings portray a complex landscape of understanding with clear disapproval for direct AI content generation and ambivalent attitudes towards subtler uses of AI. The study introduces a novel instrument to explore conceptualisation of AI-giarism, offering a significant tool for educators and policy-makers. This scale facilitates understanding and discussions around AI-related academic misconduct, contributing to pedagogical design and assessment in an era of AI integration. Moreover, it challenges traditional definitions of academic misconduct, emphasising the need to adapt in response to evolving AI technology. The study provides pivotal insights for academics and policy-makers concerning the integration of AI technology in education.

Ceftazidime-avibactam treatment dilemma of blaKPC−2-containing Klebsiella pneumoniae due to the development of co-existence of mixed strains carrying blaKPC−2 or blaKPC−33 in lung transplant recipients

BackgroundCarbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant threat to immunocompromised populations, including lung transplant recipients. This study investigates mixed CRKP strains carrying either blaKPC−2 or blaKPC−33 following ceftazidime-avibactam (CAZ/AVI) exposure, particularly in the context of lung transplantation. Mixed CRKP strains with shifting resistance phenotypes were frequently identified in patients exposed to CAZ/AVI. We aimed to elucidate the transitional state of blaKPC variants by selecting CAZ/AVI-sensitive and -resistant CRKP strains from a lung transplantation patient.MethodsThe blaKPC-variant-carrying CRKP strains were collected from lung transplant recipients exposed to CAZ/AVI in less than two years. Antibiotic susceptibility testing (AST) was conducted using microbroth dilution, and whole-genome sequencing (WGS) was used to identify genotypes and resistance mechanisms. Limiting dilution, drop-plate, and in vitro induction experiments determined blaKPC-variant changes during CAZ/AVI administration. qPCR primers/probes were designed to identify blaKPC−2 mutations.ResultsAmong 104 lung transplant recipients infected by blaKPC-harboring CRKP strains and receiving CAZ/AVI, 10 (9.6%) experienced changing resistance phenotypes. The limiting dilution method found that Patient 10’s CRKP strains carried either blaKPC−2 or blaKPC−33. The drop-plate experiment showed differing growth patterns on CAZ/AVI mediums. The in vitro induction experiment demonstrated shifting from blaKPC−2 to blaKPC−33.ConclusionsThe study identified a “transitional state” of the mixed CRKP strains carrying either blaKPC−2 or blaKPC−33 in CAZ/AVI-exposed patients. Molecular diagnostics are crucial for identifying mixed strains and the transitional state of blaKPC variants, guiding treatment decisions in this complex landscape.

Valley Path Planning on 3D Terrains Using NSGA-II Algorithm

Valley path planning on 3D terrains holds significant importance in navigating and understanding complex landscapes. This specialized form of path planning focuses on finding optimal routes that adhere to the natural contours of valleys within three-dimensional terrains. The significance of valley path planning lies in its ability to address specific challenges presented by valleys, such as varying depths, steep slopes, and potential obstacles. By following the natural flow of valleys, path planning can enhance the efficiency of navigation and minimize the risk of encountering difficult terrain or hazards. In recent years, an increasing number of researchers have focused on the study of valley path planning on 3D terrains. This study presents a valley path planning method utilizing the NSGA-II (Non-dominated Sorting Genetic Algorithm II) approach. To ensure that the paths generated by the algorithm closely follow the valley lines, the algorithm establishes an optimization function that includes three optimization criteria: mean altitude, flight route length, and mean offset. To test the performance of this algorithm, we conducted experiments based on workspaces based on three datasets full of 3D terrains and compared it with three baseline algorithms. The evaluation indicates that the suggested algorithm successfully designs routes that closely follow the valley contours.

Policy Implementation in Military Organizations: The Impact of the Key Characteristics of Military Administration

Western military organizations face an increasingly complex landscape of threats and challenges from immense changes in the international security situation. At the same time, they are experiencing still more political and societal demands. Designing and implementing policies to meet these numerous and multifaceted demands call for effective, efficient, and legitimate public administration. Public administration research in military organizations is scarce. This article explores how critical characteristics of military organizations impact policy design and implementation within these organizations. Based on a systematic review of contemporary literature on specific military characteristics, the study identifies critical characteristics of military organizations related to structure, culture, profession, leadership, and management. Furthermore, strengths and challenges related to policy design and implementation in a military context are analyzed and discussed. The article argues that military organizations must scrutinize and strengthen their policy design and implementation efforts. Lastly, the article points to future research agendas.