Bias Region Research Articles

TMIC-34. INFILTRATIVE BEHAVIOR OF PATIENT-DERIVED GLIOBLASTOMA CELL LINES IN REGION-SPECIFIC HUMAN BRAIN ORGANOIDS

Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults. GBMs are known for their highly invasive and proliferative properties and are associated with a dismal prognosis (~12-18 months post-diagnosis). Neuroanatomical disparities in the occurrence of GBMs suggest that in addition to microenvironmental impacts, tumor cells are impacted by the cellular and molecular diversity across varying regions of this nervous system. This idea has been difficult to study due to the lack of human-specific models that effectively recapitulate the biological behaviors of GBMs in region-specific microenvironments. To address these challenges, we engrafted GFP-tagged patient-derived glioma cell lines into human induced pluripotent stem cell (hiPSC)-derived organoids patterned to mimic the most forebrain (dorsal cortex) and hindbrain (spinal cord) regions of the nervous system. Immunostaining assays at 2, 7, 14, and 21 days post-engraftment compared the extent of infiltration, as measured by the distance traveled by glioma cells, in both regions. We found that glioma cells infiltrated a significantly greater distance in the dorsal cortical organoids compared to the spinal cord organoids. This suggests a more conducive environment for glioma infiltration in the forebrain vs. hindbrain at baseline. We are currently determining if these regional biases in infiltration capacity are modified with either optogenetic stimulation of the host brain organoids or in the presence of interregional interactions in an assembloid system. Thus, using this fully human-specific platform, we aim to gain insights into the infiltrative behaviors of GBM cells, which will advance our understanding of this devastating disease.

Chemical and structural features of spin-coated magnesium oxide (MgO) and its impact on the barrier parameters and current conduction process of Au/undoped-InP Schottky contact as an interfacial layer

This work examines the structural and chemical characteristics of spin-coated magnesium oxide (MgO) on undoped InP (un-InP) and its effects on the barrier parameters and current transport phenomena in the Au/un-InP Schottky contact (SC). Using XRD and XPS, the structural and chemical features of MgO are assessed, confirming that the MgO was deposited on un-InP. The current-voltage (log(I)-V) features were measured for the SC and Au/MgO/un-InP metal/insulator/semiconductor (MIS)-type Schottky contact. The MIS contact revealed an excellent rectification behavior as compared to the SC. The calculated barrier height (Φb) was higher for the MIS contact (0.61 eV) than the SC (0.52 eV), which implies that the MgO interlayer influences the Φb of the SC. The Φb was also estimated using the Cheung’s, Mikhelashvili and Norde procedures, the values are similar, indicating their stability and reliability. The acquired interface state density (NSS) of the MIS contact was less than the SC, proving that the MgO interlayer reduced the NSS. The ohmic behavior was observed in the lower bias region for the SC and MIS contacts, while the trap-free space-charge-limited current (SCLC) was noted in the moderate and upper bias regions of the MIS contact under forward bias. Poole-Frenkel emission (PFE) governs at a lower bias, while Schottky emission (SE) dominates at the upper bias of SC and MIS contacts under reverse bias. These findings demonstrate the potential application of MgO interlayer in advancing electronic devices.

Regional gender bias and year predict gender representation on civil trial teams.

There are documented gender disparities in the legal field. We examined whether gender representation on civil trial teams varied on the basis of (a) the degree of regional gender bias "in the air" and (b) time. We hypothesized that women were underrepresented both on trial teams and in leadership roles within those teams. We predicted that these gender disparities were exacerbated in regions with stronger regional gender bias and that these gender disparities attenuated over time. We coded attorney gender and case outcomes in real civil trials (N = 655). We created regional implicit and explicit gender bias scores based on the year and region of the case using Project Implicit data. Finally, we used order-constrained inference and Bayesian modeling to identify the best-performing models. Overall, women represented only 17% of attorneys at trial and 13% in leadership roles-indicating vast gender disparities. Gender disparities on teams and in leadership roles were more extreme in regions with high (vs. low) regional gender bias (teams: Bayes factor [BF] = 9,182; leadership: BF = 91,667) and improved over time (teams: BF = 6,420; leadership: BF = 3,495). Gender alone best predicted the likelihood of serving in a leadership role (BF = 1,197,397). Female attorneys were grossly underrepresented on civil trial teams. Gender representation on teams, but not leadership roles, has improved slightly over time. Culture may also contribute; women were less represented on trial teams in regions with greater gender bias in the air-particularly in leadership roles. Despite these slight improvements in representation on trial teams over time and in low-bias regions, gender disparities in leadership roles persist over time and levels of regional bias. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Seasonal biases in fluorescence-estimated chlorophyll-a derived from biogeochemical profiling floats

Marine phytoplankton biomass and chlorophyll-a concentration are often estimated from pigment fluorescence measurements, which have become routine despite known variability in the fluorescent response for a given amount of chlorophyll-a. Here, we present a near-global, monthly climatology of chlorophyll-a fluorescence measurements from profiling floats combined with ocean color satellite estimates of chlorophyll-a concentration to illuminate seasonal biases in the fluorescent response and expand upon previously observed regional patterns in this bias. Global biases span over an order of magnitude, and can vary seasonally by a factor of 10. An independent estimate of chlorophyll-a from light attenuation shows similar global patterns in the chlorophyll-fluorescence bias when compared to biases derived from satellite estimates. Without accounting for these biases, studies or models using fluorescence-estimated chlorophyll-a will inherit the seasonal and regional biases described here.

Subjective Perceptions of Difference in Multi-level States: Regional Values, Embeddedness, and Bias in Canadian Provinces

Subjective Perceptions of Difference in Multi-level States: Regional Values, Embeddedness, and Bias in Canadian Provinces

On the Unequal Burden of Obesity: Obesity’s Adverse Consequences Are Contingent on Regional Obesity Prevalence

Obesity has adverse consequences for those affected. We tested whether the association between obesity and its adverse consequences is reduced in regions in which obesity is prevalent and whether lower weight bias in high-obese regions can account for this reduction. Studies 1 and 2 used data from the United States (N = 2,846,132 adults across 2,546 counties) and United Kingdom (N = 180,615 adults across 380 districts) that assessed obesity’s adverse consequences in diverse domains: close relationships, economic outcomes, and health. Both studies revealed that the association between obesity and its adverse consequences is reduced (or absent) in high-obese regions. Study 3 used another large-scale data set (N = 409,837 across 2,928 U.S. counties) and revealed that lower weight bias in high-obese regions seems to account for (i.e., mediate) the reduction in obesity’s adverse consequences. Overall, our findings suggest that obesity’s adverse consequences are partly social and, thus, not inevitable.

Comparison of 2-m surface temperature data between reanalysis and observations over the Arabian Peninsula

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 °C) and maximum in summer (>35 °C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 °C) and hot (>30 °C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

Transferability and Explainability of Deep Learning Emulators for Regional Climate Model Projections: Perspectives for Future Applications

Abstract Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering multiple scenarios and driving Global climate models (GCMs) across regions. RCM emulators based on deep learning models have recently been introduced as a cost-effective and promising alternative that requires only short RCM simulations to train the models. Therefore, evaluating their transferability to different periods, scenarios, and GCMs becomes a pivotal and complex task in which the inherent biases of both GCMs and RCMs play a significant role. Here, we focus on this problem by considering the two different emulation approaches introduced in the literature as perfect and imperfect, that we here refer to as perfect prognosis (PP) and model output statistics (MOS), respectively, following the well-established downscaling terminology. In addition to standard evaluation techniques, we expand the analysis with methods from the field of explainable artificial intelligence (XAI), to assess the physical consistency of the empirical links learnt by the models. We find that both approaches are able to emulate certain climatological properties of RCMs for different periods and scenarios (soft transferability), but the consistency of the emulation functions differs between approaches. Whereas PP learns robust and physically meaningful patterns, MOS results are GCM dependent and lack physical consistency in some cases. Both approaches face problems when transferring the emulation function to other GCMs (hard transferability), due to the existence of GCM-dependent biases. This limits their applicability to build RCM ensembles. We conclude by giving prospects for future applications. Significance Statement Regional climate model (RCM) emulators are a cost-effective emerging approach for generating comprehensive ensembles of regional climate projections. Promising results have been recently obtained using deep learning models. However, their potential to capture the regional climate dynamics and to emulate other periods, emission scenarios, or driving global climate models (GCMs) remains an open issue that affects their practical use. This study explores the potential of current emulation approaches incorporating new explainable artificial intelligence (XAI) evaluation techniques to assess the reliability and transferability of the emulators. Our findings show that the different global and regional model biases involved in the different approaches play a key role in transferability. Based on the results obtained, we provide some prospects for potential applications of these models in challenging problems.

Continuous adoption of online healthcare platforms: an extension to the expectation confirmation model and network externalities

BackgroundOnline healthcare platforms (OHPs) promote the accessibility of health services while integrating online and offline service delivery. Continuous adoption of these platforms can reduce the information asymmetry between patients and healthcare providers. However, existing study has rarely focused on how the network externalities of these platforms influence patients’ continuous adoption. Expectation Confirmation Model (ECM) explains the continuous behavior after initial adoption and reveal the factors influencing satisfaction and continuous adoption intention. Few studies have integrated network externalities with the ECM to understand patients’ continuous adoption of OHPs.ObjectiveThis study aims to explore what factors affect the continuous adoption of OHPs based on an extension to the ECM and network externalities. We propose a comprehensive model for the continuous adoption of OHPs by patients with chronic diseases. We extend the conceptual framework by incorporating constructs related to perceived value, habits, and switching costs.MethodsThis study utilized a web-based survey to collect data from participants in China. We selected a sample of individuals who had experience with OHPs using a random sampling method. This method ensured that participants was randomly chosen from a pool of patients without any regional bias. A total of 568 questionnaires were collected and 518 valid questionnaires were obtained. Structural equation modeling was applied to assess the relationships among the constructs. Hierarchical regression analysis was utilized to examine the moderating effects, while the mediating effects were investigated using the bootstrapping approach.ResultsDirect network externality had significant positive impacts on the confirmation (β = 0.232, p < 0.001) and perceived value (β = 0.167, p < 0.010). Cross network externality had significant positive impacts on confirmation (β = 0.307, p < 0.001) and perceived value (β = 0.120, p < 0.05). Indirect network externality had significant positive impacts on confirmation (β = 0.169, p < 0.010) and perceived value (β = 0.270, p < 0.001). Confirmation had a significant positive impact on perceived value (β = 0.205, p < 0.001) and satisfaction (β = 0.508, p < 0.001). In addition, satisfaction had a significant positive impact on continuous adoption intention (β = 0.579, p < 0.001). Continuous adoption intention had a significant positive impact on continuous adoption behavior (β = 0.547, p < 0.001). Confirmation and perceived value significantly mediate the relationship between network externalities and satisfaction. Moreover, both confirmation and perceived value enhance continuous adoption intention by positively influencing satisfaction, which serves as a mediator. Additionally, confirmation directly influences satisfaction through the mediating role of perceived value. Habits and switching costs positively moderate the relationship between continuous adoption intention and behavior.ConclusionsThis study contributes by expanding the incorporation of network externalities into the ECM. Results enrich the existing literature on the continuous adoption of professional online platforms.

Application of deep learning in summer climate prediction over northwestern China based on CWRF model

This study analyzes the performance of the Climate–Weather Research and Forecasting (CWRF) model in predicting the summer temperature and precipitation in northwestern China (NWC) for the 1991–2021 period. It also examines the improvements in prediction resulting from the implementation of convolutional neural network (CNN) and long short-term memory (LSTM) downscaling methods. The results indicate that the CWRF model demonstrates reasonable ability in capturing the characteristics of climatological temperature and precipitation in NWC. Both the climatological temperature and precipitation predictions consistently demonstrate a systematic underestimation, revealing evident biases in regions characterized by complex terrain. In terms of interannual variation, the temperature prediction outperforms the precipitation prediction, whereas there is no significant difference in the temperature predictions for lead Months 1–3. However, uncertainties increase as the lead time is extended in precipitation prediction. Therefore, the combination of dynamical and statistical downscaling is employed to the summer temperature and precipitation prediction over NWC. It is shown both the CNN and LSTM downscaling methods can improve the prediction ability of the CWRF model for summer climatological temperature and precipitation. The LSTM method significantly reduces the root mean square error (RMSE) of precipitation and temperature predictions, indicating an improvement in predicting the spatial structure. At the interannual scale, the CNN method is less dependent on the lead time of prediction than the LSTM method is, and the interannual correlation coefficient of precipitation and temperature is greater than 0.1 compared with that of the raw CWRF model. These results provide valuable insights into understanding the prediction capabilities of the CWRF model in NWC and highlight the necessity of applying downscaling methods to the CWRF model to increase its prediction ability.

CWRF Downscaling with Improved Land Surface Initialization Enhances Spring–Summer Seasonal Climate Prediction Skill in China

Abstract This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate–Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during 7 of 10 years with relatively high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, the substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia–northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities. Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian high alongside a strengthened East Asian jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where the accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions.

「부론강」에 나타난 장소성 구축과 로컬 정체성 연구

Labor writer Lee In-hwi’s ‘Buron River’ clearly reveals ‘Buron-ness’ through the journey of the space called ‘Buron’ becoming a place from a minority anti-capitalist, other-oriented perspective. In the Anthropocene, where locality is highlighted and reflection on human-centered and human exceptional thinking is required, this novel moves away from people-centered and focuses on communication and response with nature such as trees, rivers, and land, and the meaning of historical figures and relics/remains between humans and humans. It presents an inhumane turn while providing equal and fair consideration and placement. Buron-myeon, Wonju-si, where the writer from Seoul happened to settle, is a place where self-care and wound healing are realized, and it contains topographical and historical significance with the characteristics of motherhood and inclusiveness of the Namhan River, Seom River, Chiaksan Mountain, Baekunsan Mountain, and Mireuksan Mountain. ‘Dream Seekers’, a bar and active community created by people from outside, is a place where self-sufficiency through communal farming, samulnori, caring for others, and local culture take place.Those who want to reflect on the use of land, which was a means of human profit, and escape from capitalism, overcome their wounds and open their hearts in Buron. The setting of a community made up of outsiders and strangers rather than natives or locals allows us to escape from the limitations of regional bias and a sense of home that is overly romanticized and portrayed only as beautiful and positive. In the Buron area, love of place and love of life are expressed through physical harmony with nature, such as thousand-year-old zelkova trees, a river that embraces and allows infinitely, and an abandoned temple site with solitude and mystery. In the literary representation of the Buron region, humans, Nature and history are arranged fairly and balancedly. “Buron River,” in which space is specifically transformed into a place, is evaluated as a very unique and original regional ecology (life) novel that highlights the identity of Buron, Wonju City, clearly and clearly enough to become a model.

Envisaging quantum capacitance in modified germanene: a first principle investigation

Two-dimensional (2D) nanomaterials with enhanced quantum capacitance have been in high demand over the past few years due to super capacitors application. A number of 2D nanosheet, including MoS2, arsenene, antimonene, and germanene, have also been explored for the same reason. The present investigation aims to explore modified germanene such as monovacant germanene/divacant germanene(MVG/DVG) and their derivatives, such as Ns substituted MVG/DVG and transition metal incorporated MVG. It is observed that 1N-MVG/3N-MVG shows the characteristics of a p-type semiconductor, while 3N-MVG/4N-DVG is considered as semiconductor. A robust binding of under-coordinated Ge to transition metals (TMs) at MVG surfaces suggests such functionalization can be accomplished. Except Ti and Co other studied TMs-MVG show metallic nature. Furthermore, it is revealed that there is an asymmetric CQ dispersion in 1N-MVG, 2N-MVG, 3N-MVG, and 4N-DVG, as opposed to the pristine germanene/MVG/DVG. Additionally, it is predicted that TMs such as Ti, V, Cr, Mn, Fe and Co incorporated MVG can provide high quantum capacitance (CQ). Enormous amount of CQ is noticed for 3N-MVG with maximum of 726 μF cm−2 in the positive biased region. Among TM-MVG, V-MVG and Mn-MVG are well suited to serve as anodes for asymmetric super capacitors due to their CQ peak of 978 μF cm−2 and 1180 μF cm−2, respectively, in negative bias region.

Contrasting Discrepancy in the Sea Level Budget Between the North and South Atlantic Ocean Since 2016

AbstractThe discrepancy in the observed global mean sea level budget increased significantly since 2016, but the budget discrepancy over basin‐scales is unclear. In this contribution, we investigate the sea level budget discrepancies in major basins with observations from satellite altimetry, satellite gravimetry, and Argo floats. During 2016–2020, we find substantial discrepancy of 5.72 ± 0.98 mm/yr over the North Atlantic Ocean, and the basin scale discrepancies are smaller elsewhere. Our analysis suggests that three factors, including the wet tropospheric correction (WTC) effect, deep ocean warming signal, and the contemporary ocean bottom deformation (OBD), together reduce the discrepancy by only 1 mm/yr for the North Atlantic Ocean. We decompose sea level observations into the spherical harmonic domain and observe increased discrepancy in low‐degree variations of C10 and C21 since 2016. These two coefficients result in a contrasting signal between the North and South Atlantic Ocean and contribute to the large discrepancy over the North Atlantic Ocean. We further demonstrate that the C10 and C21 discrepancies are independent of the three factors. However, we find regional salinity biases in the Argo data that reduce the discrepancy for the North Atlantic Ocean. Our findings add to the debate about recent sea level budget and imply that further analysis of the Argo North Atlantic data set may be useful.

Drivers of the mean biases of the tropical atmospheric circulation in a moist static energy framework

In this study, we apply the moist static energy for first baroclinic mode (MSEB) model to examine the drivers of the mean tropical atmospheric circulation biases over oceanic regions. The model diagnoses the vertical motion in an air column of the tropical regions based on net energy heat flux and advection of moisture or heat into the air column in relation to the stability of the air column due to the gradients in moist static energy. Analysis of Coupled Model Intercomparison Project (CMIP) and Atmospheric Model Intercomparison Project (AMIP) simulations helped to identified errors intrinsic to the atmospheric models or errors due to atmosphere–ocean coupling process. Despite some limitations of the MSEB model, our multi-model mean analysis over the entire tropical ocean reveals that the primary drivers of the tropical circulation biases mostly result from intrinsic atmospheric model errors in top of the atmosphere longwave radiation and surface latent heats fluxes, suggesting a link to biases in the hydrological cycle. Oceanic coupling significantly enhanced some of the biases. Biases in the advection of moist static energy also play an important role, while biases in the gross moist stability profiles play only a minor role. Further, we examine the inter-model variations in four main regional large-scale biases (double-ITCZ, Pacific cold tongue, southward shift of ITCZ over the Atlantic, and dipole bias over the Indian Ocean). The analysis suggests that regional bias patterns across general circulation models are primarily driven by coupling errors, except for the bias in the Indian Ocean, which is intrinsic to the atmospheric model but amplified by coupling. Notably, longwave radiation biases at the top of the atmosphere are prevalent among the four bias patterns, as well as biases in moisture advection over the Atlantic. Our results underscore the significant role of net longwave radiation at the top of the atmosphere, an aspect not sufficiently emphasized in previous studies.

Evaluation method of tunnel cracking disease under biased pressure based on enhanced image fractal features

This paper focuses on the tunnel cracking disease under biased pressure. Through model tests on three lining conditions, the study investigates the expansion process of surface cracks on the lining under biased pressure. Proposing an improved method for calculating fractal characteristics of lining crack disease based on box counting dimension. Quantifying crack disease through digital image processing to obtain two parameters: total width and fractal dimension. Discussed the relationship between quantification parameters of surface crack disease in linings and the service performance of the lining. 29 groups of crack disease quantization parameters obtained by model test were selected to establish the disease sample space, and the spatial data were automatically learned and grouped based on clustering method. Finally, a new diagnostic Index of Tunnel Lining Defect Index-Crack & Dimension (TLDI-C&D) and its disease classification criteria for the whole lining ring are proposed. The results indicate that under the biased pressure, regardless of the presence of pre-existing defects, the biased pressure region is the most severely diseased area in terms of cracking. Compared to the intact lining, the initial cracking loads for arch crown defects and arch waist defects are advanced by 33.1 % and 73.0 %, respectively. After the apparent cracks in the lining are fully formed, pre-existing defects no longer affect the speed and distribution of crack propagation.The larger the fractal dimension, the more complex the morphology of the corresponding crack disease. Under the same image area, the ratio of crack quantity to width has a greater impact on the fractal dimension. There is a corresponding relationship between quantification parameters of crack disease and the bearing performance of the lining structure, making them indicators for measuring the load-bearing capacity of the lining. The most reasonable grading level for lining crack disease under the biased pressure is four levels. The diagnostic index for tunnel lining cracks, TLDI-C&D is 1.0～2.2 (level I), 2.2～3.2 (level II), 3.2～4.3 (level III), and 4.3～5.5 (level IV). On-site measurements have proven the accuracy of this crack disease diagnostic evaluation method.

Regional bias in monolingual English language models

In Natural Language Processing (NLP), pre-trained language models (LLMs) are widely employed and refined for various tasks. These models have shown considerable social and geographic biases creating skewed or even unfair representations of certain groups. Research focuses on biases toward L2 (English as a second language) regions but neglects bias within L1 (first language) regions. In this work, we ask if there is regional bias within L1 regions already inherent in pre-trained LLMs and, if so, what the consequences are in terms of downstream model performance. We contribute an investigation framework specifically tailored for low-resource regions, offering a method to identify bias without imposing strict requirements for labeled datasets. Our research reveals subtle geographic variations in the word embeddings of BERT, even in cultures traditionally perceived as similar. These nuanced features, once captured, have the potential to significantly impact downstream tasks. Generally, models exhibit comparable performance on datasets that share similarities, and conversely, performance may diverge when datasets differ in their nuanced features embedded within the language. It is crucial to note that estimating model performance solely based on standard benchmark datasets may not necessarily apply to the datasets with distinct features from the benchmark datasets. Our proposed framework plays a pivotal role in identifying and addressing biases detected in word embeddings, particularly evident in low-resource regions such as New Zealand.

Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0

Abstract. Estimating global river solids, nitrogen (N), and phosphorus (P), in both quantity and composition, is necessary for understanding the development and persistence of many harmful algal blooms, hypoxic events, and other water quality issues in inland and coastal waters. This requires a comprehensive freshwater model that can resolve intertwined algae, solid, and nutrient dynamics, yet previous global watershed models have limited mechanistic resolution of instream biogeochemical processes. Here we develop the global, spatially explicit, and process-based Freshwater Algae, Nutrient, and Solid cycling and Yields (FANSY) model and incorporate it within the Land Model (LM3). The resulting model, LM3-FANSY v1.0, is intended as a baseline for eventual linking of global terrestrial and ocean biogeochemistry in next-generation Earth system models to project global changes that may challenge empirical approaches. LM3-FANSY explicitly resolves interactions between algae, N, P, and solid dynamics in rivers and lakes at 1° spatial and 30 min temporal resolution. Simulated suspended solids (SS), N, and P in multiple forms (particulate or dissolved, organic or inorganic) agree well with measurement-based yield (kg km−2 yr−1), load (kt yr−1), and concentration (mg L−1) estimates across a globally distributed set of large rivers, with an accuracy comparable to other global nutrient and SS models. Furthermore, simulated global river loads of SS, N, and P in different forms to the coastal ocean are consistent with published ranges, though regional biases are apparent. River N loads are estimated to contain approximately equal contributions by dissolved inorganic N (41 %) and dissolved organic N (39 %), with a lesser contribution by particulate organic N (20 %). For river P load estimates, particulate P, which includes both organic and sorbed inorganic forms, is the most abundant form (64 %), followed by dissolved inorganic and organic P (25 % and 11 %). Time series analysis of river solid and nutrient loads in large US rivers for the period ∼ 1963–2000 demonstrates that simulated SS and N loads in different N forms covary with variations of measurement-based loads. LM3-FANSY, however, has less capability to capture interannual variability of P loads, likely due to the lack of terrestrial P dynamics in LM3. Analyses of the model results and sensitivity to components, parameters, and inputs suggest that fluxes from terrestrial litter and soils, wastewater, and weathering are the most critical inputs to the fidelity of simulated river nutrient loads for observation-based estimates. Sensitivity analyses further demonstrate a critical role of algal dynamics in controlling the ratios of inorganic and organic nutrient forms in freshwaters. While the simulations are able to capture significant cross-watershed contrasts at a global scale, disagreement for individual rivers can be substantial. This limitation is shared by other global river models and could be ameliorated through further refinements in nutrient sources, freshwater model dynamics, and observations. Current targets for future LM3-FANSY development include the additions of terrestrial P dynamics, freshwater carbon, alkalinity, enhanced sediment dynamics, and anthropogenic hydraulic controls.

P-463 Transforming teen fertility awareness: the impact of a specialized health education initiative delivered by senior human reproduction nurses in high schools

Abstract Study question Can we enhance fertility awareness among young individuals through targeted health education? Summary answer The initiative, led by senior human reproduction nurses, demonstrated a remarkable increase of 23.78% in fertility knowledge among adolescents. What is known already Understanding factors impacting fertility is key to making informed reproductive decisions. However, a pervasive gap exists in evidence-based fertility knowledge within Western societies, underscoring the need for specialized health education. Young adults often overlook fertility considerations until they encounter challenges in conceiving. Interestingly, recent findings indicate that although fertility awareness is valued amongst young people, it can also lead to stress and anxiety. Therefore to foster early and impactful learning, introducing fertility education to high school curriculums is recommended. Study design, size, duration The reproductive health and fertility awareness initiative was conducted across five public high schools in Catalonia, Spain, during the 2022-2023 academic year. The training consisted of 45-minute presentations delivered by 2 senior human reproduction nurses and focused on modifiable factors influencing fertility. To assess the success of the initiative, we compared teenagers’ knowledge before and after the educational sessions. Participants/materials, setting, methods During the academic year, presentations reached 157 high school students. Assessment was carried out through the virtual platform “Kahoot”, employing 10 multiple-choice questions with 4 response options. The responses before and after the intervention were recorded and analyzed using a paired Fisher’s exact test. P-values &amp;lt;0.05 were considered significant. Main results and the role of chance Prior to the initiative, the correct response rates to the fertility questions varied, with averages ranging from 47.6% and 73.8%. Following the fertility awareness presentation, when students were asked the same questions, there was a marked improvement in student performance. Average scores improved to 89.0%, 90.2%, 88.1%, 84.5%, and 89.5% across the respective schools. These improved scores were significant across all five high schools (p-values: 0.0074, 0.0229, 0.0221, 0.0006, 0.0163), emphasizing the effectiveness of the initiative in varying educational environments. The overall average of correct answers initially stood at 64.5%, and significantly increased to to 88.3% (p &amp;lt; 0,0001) post-intervention, reflecting a relative improvement of 23.8%. The consistent increase in knowledge across diverse student demographics suggests that the program’s content was well-received and comprehensible, contributing to its success. Limitations, reasons for caution The initiative only included participants from Catalonia, which may introduce regional biases, limiting broader applicability. Translating increased knowledge into sustained behavior change remains a challenge. A longer-term follow-up period would provide insights into the retention and application of fertility knowledge over time Wider implications of the findings The positive reception and rapid knowledge assimilation among students underscores the potential of targeted health education, extending empowerment beyond classrooms to influence their future reproductive health decisions. This initiative also lays groundwork for addressing potential disparities in fertility knowledge, which has implications for promoting equitable access to reproductive health information Trial registration number not applicable

Sensitive Response of Atmospheric Oxidative Capacity to the Uncertainty in the Emissions of Nitric Oxide (NO) From Soils in Amazonia

AbstractSoils are a major source of nitrogen oxides, which in the atmosphere help govern its oxidative capacity. Thus the response of soil nitric oxide (NO) emissions to forcings such as warming or forest loss has a meaningful impact on global atmospheric chemistry. We find that the soil emission rate of NO in Amazonia from a common inventory is biased low by at least an order of magnitude in comparison to tower‐based observations. Accounting for this regional bias decreases the modeled global methane lifetime by 1.4%–2.6%. In comparison, a fully deforested Amazonia, representing a 37% decrease in global emissions of isoprene, decreases methane lifetime by at most 4.6%, highlighting the sensitive response of oxidation rates to changes in emissions of NO compared to those of terpenes. Our results demonstrate that improving our understanding of soil NO emissions will yield a more accurate representation of atmospheric oxidative capacity.