Large Periods Of Time Research Articles

Deep roots of admixture-related cognitive differences in the USA

This study attempted to determine if the association between genetic admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. First, we used the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the association between cognitive ability and parental-reported race, genetically-predicted color, and genetic ancestry among Puerto Ricans, non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to trace ancestry-associated cognitive differences back to the early 19th and early 20th century among African American, American Indian, and Puerto Rican groups by using age-heaping-based numeracy as a proxy for cognitive ability. In the ABCD sample, we found that European ancestry is positively associated with cognitive ability within race/ethnic groups. In the census data, among African Americans and American Indians but not Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy, and this holds when we subset by age, sex, and literacy status. Therefore, we established that admixture is associated with cognitive ability in the 21st century as it was in the 19th and early 20th century. The implications of these findings are discussed.

Windmapper: An Efficient Wind Downscaling Method for Hydrological Models

AbstractEstimates of near‐surface wind speed and direction are key meteorological components for predicting many surface hydrometeorological processes that influence critical aspects of hydrological and biological systems. However, observations of near‐surface wind are typically spatially sparse. The use of these sparse wind fields to force distributed models, such as hydrological models, is greatly complicated in complex terrain, such as mountain headwaters basins. In these regions, wind flows are heavily impacted by overlapping influences of terrain at different scales. This can have a great impact on calculations of evapotranspiration, snowmelt, and blowing snow transport and sublimation. The use of high‐resolution atmospheric models allows for numerical weather prediction (NWP) model outputs to be dynamically downscaled. However, the computation burden for large spatial extents and long periods of time often precludes their use. Here, a wind‐library approach is presented to aid in downscaling NWP outputs and terrain‐correcting spatially interpolated observations. This approach preserves important spatial characteristics of the flow field at a fraction of the computational costs of even the simplest high‐resolution atmospheric models. This approach improves on previous implementations by: scaling to large spatial extents O(1M km2); approximating lee‐side effects; and fully automating the creation of the wind library. Overall, this approach was shown to have a third quartile RMSE of 1.8 and a third quartile RMSE of 58.2° versus a standalone diagnostic windflow model. The wind velocity estimates versus observations were better than existing empirical terrain‐based estimates and computational savings were approximately 100‐fold versus the diagnostic model.

Bison influences on composition and diversity of riparian plant communities in Yellowstone National Park

AbstractRiparian zones are among the most biologically diverse ecosystems in the Intermountain West, USA, and provide valuable ecosystem services, including high rates of biotic productivity, nutrient processing, and carbon storage. Thus, their sustainability is a high priority for land managers. Large ungulates affect composition and structure of riparian/stream ecosystems through herbivory and physical effects, via trailing and trampling. Bison (Bison bison) in Yellowstone National Park (YNP) have been characterized as “ecosystem engineers” because of their demonstrated effects on phenology, aboveground productivity of grasses, and woody vegetation structure. Bison have greatly increased in numbers during the last two decades and spend large periods of time in the broad open floodplains of the Northern Range of the Park, where they are hypothesized to have multiple effects on plant species composition and diversity. We sampled indicators of bison use as well as riparian vegetation composition, diversity, and structure along eight headwater streams within YNP's Northern Range. Total fecal density ranged from 333 to 1833 fecal chips and/or piles/ha, stubble heights ranged from 7 to 49 cm, and streambank disturbance ranged from 9% to 62%. High levels of bison use were positively correlated with exotic species dominance and negatively correlated with species richness, native species diversity, willow (Salix spp.) cover, and wetland species dominance. At three sites, the intensity of bison use exceeded recommended utilization thresholds to avoid degradation of streams and riparian zones on public lands. The influences of large herbivores, principally bison, on vegetation composition and structure suggest the cumulative effects of the current densities on the Northern Range are contributing to biotic impoverishment, representing the loss of ecosystem services that are provided by native riparian plant communities. In addition, contemporary levels of bison use may be exacerbating climate change effects as observed through ungulate‐related shifts in composition toward plant assemblages adapted to warmer and drier conditions. However, the resilience of native riparian vegetation suggests that sites currently heavily utilized by bison would have the potential for recovery with a reduction in pressure by this herbivore.

Monitoring invasive pines using remote sensing: a case study from Sri Lanka

Production plantation forestry has many economic benefits but can also have negative environmental impacts such as the spreading of invasive pines to native forest habitats. Monitoring forest for the presence of invasive pines helps with the management of this issue. However, detection of vegetation change over a large time period is difficult due to changes in image quality and sensor types, and by the spectral similarity of evergreen species and frequent cloud cover in the study area. The costs of high-resolution images are also prohibitive for routine monitoring in resource-constrained countries. This research investigated the use of remote sensing to identify the spread of Pinus caribaea over a 21-year period (2000 to 2021) in Belihuloya, Sri Lanka, using Landsat images. It applied a range of techniques to produce cloud free images, extract vegetation features, and improve vegetation classification accuracy, followed by the use of Geographical Information System to spatially analyze the spread of invasive pines. The results showed most invading pines were found within 100 m of the pine plantations’ borders where broadleaved forests and grasslands are vulnerable to invasion. However, the extent of invasive pine had an overall decline of 4 ha over the 21 years. The study confirmed that remote sensing combined with spatial analysis are effective tools for monitoring invasive pines in countries with limited resources. This study also provides information to conservationists and forest managers to conduct strategic planning for sustainable forest management and conservation in Sri Lanka.

Геоинформационное картографирование размещения населения для анализа его пространственной динамики

The change in the population is the most important indicator, the reflection of which on the map allows you to obtain a visual picture of many interconnected processes occurring in the studied territory. The study solved the task of creating a series of cards that reflect a change in the population in the settlements of the Tyumen Region based on these censuses of the population of 1926 and 2010. This work provides for the development of modern geoinformation and cartographic support of socio-economic studies related to the analysis of the location of the population based on spatial databases. The choice of a large period of time and the associated significant changes in the names and the general structure of the placement of settlements leads to the need for a thorough and comprehensive study of information, determining its relevance to solve the tasks. Further, a number of problems were solved: methodological (development of the structure and content of maps) and methodical (organization of the geodatabase (BGD), the creation and design of thematic cards). As part of the study, the design of the structure of the database of spatial data was performed, and then its filling based on the censuses of the population and official statistical indicators about the population of the Tyumen Region (including the Khanty-Mansi Autonomous Okrug—Yugra and the Yamalo-Nenetskij Autonomous Okrug). For all presented cards, a geographical basis is configured with unified installations of cartographic generalization and a coordinated system of conventional signs. The developed series of maps allows you to identify and visually assess the spatial patterns of changes in the population and the network of settlements. The collected structured material organized in the BGD format provides modern cartographic support for socio-economic research related to the analysis of the population. The work was performed as part of the preparation of the demographic section of the Atlas of the Tyumen Region.

Amniotic band syndrome and limb body wall complex in Europe 1980-2019.

Amniotic band syndrome (ABS) and limb body wall complex (LBWC) have an overlapping phenotype of multiple congenital anomalies and their etiology is unknown. We aimed to determine the prevalence of ABS and LBWC in Europe from 1980 to 2019 and to describe the spectrum of congenital anomalies. In addition, we investigated maternal age and multiple birth as possible risk factors for the occurrence of ABS and LBWC. We used data from the European surveillance of congenital anomalies (EUROCAT) network including data from 30 registries over 1980-2019. We included all pregnancy outcomes, including live births, stillbirths, and terminations of pregnancy for fetal anomalies. ABS and LBWC cases were extracted from the central EUROCAT database using coding information responses from the registries. In total, 866 ABS cases and 451 LBWC cases were included in this study. The mean prevalence was 0.53/10,000 births for ABS and 0.34/10,000 births for LBWC during the 40 years. Prevalence of both ABS and LBWC was lower in the 1980s and higher in the United Kingdom. Limb anomalies and neural tube defects were commonly seen in ABS, whereas in LBWC abdominal and thoracic wall defects and limb anomalies were most prevalent. Twinning was confirmed as a risk factor for both ABS and LBWC. This study includes the largest cohort of ABS and LBWC cases ever reported over a large time period using standardized EUROCAT data. Prevalence, clinical characteristics, and the phenotypic spectrum are described, and twinning is confirmed as a risk factor.

Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.

The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children's ADHD and acquired children's skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%.

Bacto_tracker: a method for single-cell tracking of M. xanthus in dense and multispecies colonies

Cell motility and predation are important for the dynamics of many multi-cellular ecosystems, such as the gut or the soil. Approaches to image cell dynamics in such complex systems are scant, and high-throughput analysis methods to segment and track single-cell behaviors are currently lacking. Here, we addressed these limitations by implementing a fast fluorescence microscopy technique enabling the high-resolution acquisition of cell movement over large areas and long time periods. Next, we applied deep learning to semantically segment two different bacteria species within complex micro-environments . We implemented a method to build single cell traces by combining the cell masks from different time points to follow the dynamics of single cells with high spatial and temporal resolutions and over long periods of time. We applied and validated these methods by characterizing the dynamics of Escherichia coli predation by Myxococcus xanthus.

Inferring volumetric changes at a shallow lake from subpixel satellite-derived shorelines

• Satellite-Derived Shorelines at Gallocanta Lake from mid-resolution satellite imagery. • SDS with subpixel accuracy leads to an accurate definition of the lake's perimeter. • A large package of SDS to define the three-dimensionality of the shore (1984–2020). • Volume is estimated by modeling the relationship between water surface and elevation. • Efficient RS approach for monitoring water bodies through large periods of time.

The need to tailor the omission of axillary lymph node dissection to patients with good prognosis and sentinel node micro-metastases.

Results of IBCSG-23-01-trial which included breast cancer patients with involved sentinel nodes (SN) by isolated-tumor-cells or micro-metastases supported the non-inferiority of completion axillary-lymph-node-dissection (cALND) omission. However, current data are considered insufficient to avoid cALND for all patients with SN-micro-metastases. To investigate the impact of cALND omission on disease-free-survival (DFS) and overall survival (OS), we analyzed a cohort of 1421 patients <75 years old with SN-micro-metastases who underwent breast conservative surgery (BCS). We used inverse probability of treatment weighting (IPTW) to obtain adjusted Kaplan-Meier estimators representing the experience in the analysis cohort, based on whether all or none had been subject to cALND omission. Weighted log-rank tests comparing adjusted Kaplan-Meier survival curves showed significant differences in OS (p-value=0.002) and borderline significant differences in DFS (p-value=0.090) between cALND omission versus cALND. Cox's regression using stabilized IPTW evidenced an average increase in the risk of death associated with cALND omission (HR=2.77, CI95%=1.36-5.66). Subgroup analyses suggest that the rates of recurrence and death associated with cALND omission increase substantially after a large period of time in the half sample of women less likely to miss cALND. Using IPTW to estimate the causal treatment effect of cALND in a large retrospective cohort, we concluded cALND omission is associated with an increased risk of recurrence and death in women of <75 years old treated by BCS in the absence of a large consensus in favor of omitting cALND. These results are particularly contributive for patients treated by BCS where cALND omission rates increase over time.

Modulating Gene Expression within a Microbiome Based on Computational Models.

Simple SummaryDevelopment of computational biology methodologies has provided comprehensive understanding of the complexity of microbiomes, and the extensive ways in which they influence their environment. This has awakened a new research goal, aiming to not only understand the mechanisms in which microbiomes function, but to actively modulate and engineer them for various purposes. However, current microbiome engineering techniques are usually manually tailored for a specific system and neglect the different interactions between the new genetic information and the bacterial population, turning a blind eye to processes such as horizontal gene transfer, mutations, and other genetic alterations. In this work, we developed a generic computational method to automatically tune the expression of heterologous genes within a microbiome according to given preferences, to allow the functionality of the engineering process to propagate in longer periods of time. This goal was achieved by treating each part of the gene individually and considering long term fitness effects on the environment, providing computational and experimental evidence for this approach.Recent research in the field of bioinformatics and molecular biology has revealed the immense complexity and uniqueness of microbiomes, while also showcasing the impact of the symbiosis between a microbiome and its host or environment. A core property influencing this process is horizontal gene transfer between members of the bacterial community used to maintain genetic variation. The essential effect of this mechanism is the exposure of genetic information to a wide array of members of the community, creating an additional “layer” of information in the microbiome named the “plasmidome”. From an engineering perspective, introduction of genetic information to an environment must be facilitated into chosen species which will be able to carry out the desired effect instead of competing and inhibiting it. Moreover, this process of information transfer imposes concerns for the biosafety of genetic engineering of microbiomes as exposure of genetic information into unwanted hosts can have unprecedented ecological impacts. Current technologies are usually experimentally developed for a specific host/environment, and only deal with the transformation process itself at best, ignoring the impact of horizontal gene transfer and gene-microbiome interactions that occur over larger periods of time in uncontrolled environments. The goal of this research was to design new microbiome-specific versions of engineered genetic information, providing an additional layer of compatibility to existing engineering techniques. The engineering framework is entirely computational and is agnostic to the selected microbiome or gene by reducing the problem into the following set up: microbiome species can be defined as wanted or unwanted hosts of the modification. Then, every element related to gene expression (e.g., promoters, coding regions, etc.) and regulation is individually examined and engineered by novel algorithms to provide the defined expression preferences. Additionally, the synergistic effect of the combination of engineered gene blocks facilitates robustness to random mutations that might occur over time. This method has been validated using both computational and experimental tools, stemming from the research done in the iGEM 2021 competition, by the TAU group.

Social Groups, Economic Turmoil and Support for the European Union.

In recent decades, the literature on public support for the European Union (EU) has examined utilitarian and affective theories. Nonetheless, a comprehensive study of the variations in support across social groups, particularly in times of economic turmoil and across large periods of time, has been neglected. Drawing on previous studies, we investigate the support for EU membership across social groups, and the potential distinct impact that economic crises have on their attitudes towards the EU. In order to test these propositions, we integrate several databases from the Eurobarometer across a thirty-year period (1990-2020), with economic data from FEDEA and attitudinal data from the Integrated Values Survey. Even when controlling for alternative explanations of EU support, our results reveal that social groups with more mobility, education/skills and less dependence on the national welfare system, tend to support EU membership in higher numbers than the population average. Likewise, our results indicate that economic crises at the regional level impact the public appraisal of the EU across all social groups, but students and skilled manual workers hold their ground in higher numbers than the rest of occupations. Finally, we also find differential attitudes across Spanish regions that should inspire further research regarding EU funds, transnational connections to other EU countries and regions, and the role of nationalism and party cues in driving support to the EU. We reinforce the robustness of our findings with two specifications of logistic regressions, and robust standard errors.

Standardized NEON organismal data for biodiversity research

AbstractUnderstanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.

A model of interacting quantum neurons with a dynamic synapse

Motivated by recent advances in neuroscience, in this work, we explore the emergent behaviour of quantum systems with a dynamical biologically-inspired qubits interaction. We use a minimal model of two interacting qubits with an activity-dependent dynamic interplay as in classical dynamic synapses that induces the so-called synaptic depression, that is, synapses that present synaptic fatigue after heavy presynaptic stimulation. Our study shows that in absence of synaptic depression the two-qubits quantum system shows typical Rabi oscillations whose frequency decreases when synaptic depression is introduced, so one can trap excitations for a large period of time. This creates a population imbalance between the qubits even though the Hamiltonian is Hermitian. This imbalance can be sustained in time by introducing a small energy shift between the qubits. In addition, we report that long time entanglement between the two qubits raises naturally in the presence of synaptic depression. Moreover, we propose and analyse a plausible experimental setup of our two-qubits system which demonstrates that these results are robust and can be experimentally obtained in a laboratory.

Spatiotemporal Analysis of MODIS Aerosol Optical Depth Data in the Philippines from 2010 to 2020

Satellite remote sensing for air quality assessment provides information over a large spatial coverage and time period that shows the trends and effects of anthropogenic activities. Using data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite from the years 2010 to 2020, the spatiotemporal variations to aerosol optical depth (AOD) in Koronadal City and Quezon City were studied. Validation showed a strong relationship between the MODIS AOD values and the Aerosol Robotic Network (AERONET) AOD values (R2 = 0.83) and a low root mean square error (RMSE) of 0.26. Annual variation in the AOD of the two study areas showed a peak AOD value in 2015 due to an immense biomass burning in Indonesia and a low AOD value in 2020 due to the COVID-19 lockdown. Koronadal City experienced a high AOD value during the fall season due to aerosols from biomass burning in Indonesia that were carried by the southwest monsoon. Quezon City experienced a high AOD value during spring from increased local sources, as well as long-range transport pollutants from East Asia that were carried by northeasterly winds. Overall, this study provides an understanding of the spatiotemporal variations in aerosols in the Philippines, which could be used in environmental management, air quality regulations, and health assessment studies. This shows the urgency of monitoring and mitigating poor air quality in the Philippines.

AUTOMATIC ALGORITHM FOR GEOREFERENCING HISTORICAL-TO-NOWADAYS AERIAL IMAGES ACQUIRED IN NATURAL ENVIRONMENTS

Abstract. Automatic georeferencing for historical-to-nowadays aerial images represents the main ingredient for supplying territory evolution analysis and environmental monitoring. Existing georeferencing methods based on feature extraction and matching reported successful results for multi-epoch aerial images acquired in structured and man-made environments. While improving the state-of- the-art of the multi-epoch georeferencing problem, such frameworks present several limitations when applied to unstructured scenes, such as natural feature-less environments, characterized by homogenous or texture-less areas. This is mainly due to the lack of structured areas which often results in sparse and ambiguous feature matches, introducing inconsistencies during the pose estimation process. This paper addresses the automatic georeferencing problem for historical aerial images acquired in unstructured natural environments. The research work presented in this paper introduces a feature-less algorithm designed to perform historical-to- nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span).

Mental health in smart cities: The role of technology during COVID-19 pandemic

COVID-19 has changed the daily lives of citizens around the world and exposed the vulnerability of life and the functioning of cities. The pandemic has forced citizens to adapt to new models of remote work during quarantine and access to smart technology-based services. The article aims to assess citizens' perceptions of the technological solutions provided by smart cities and, in particular, the municipality of Paphos before and after the Covid-19 pandemic, as well as to evaluate their impact on citizens' mental health. The results of the study contribute to the understanding of the mental health benefits for citizens living in smart cities compared to people living in low-tech areas, revealing the potential positive effects of smart technologies, as well as the change in people's digital decision-making before and after the pandemic

The influence of thinning and prescribed burning on future forest fires in fire-prone regions of Europe

Climate change is expected to increase fire risk in many forested regions, posing a potential threat to forest functioning (i.e. carbon pools and fluxes). At the same time, expansion of the wildland-urban interface threatens to bring more and more people, property, and infrastructure into contact with wildfire events. It is critical that fire be managed in a way that minimizes risk to human health and well-being and maintains forest climate change mitigation potential without affecting the important ecological role fire plays in many ecosystems. Dynamic global vegetation models (DGVMs) simulate processes over large geographic regions and long time periods and could provide information that supports fire and fuel management programs by assessing performance of such measures under different climate change scenarios in different regions. However, thus far DGVMs have not been put to this use. In this work, we introduce a novel prescribed burning (PB) module to the LPJ-GUESS DGVM. Focusing on two regions (Eastern Europe and the Iberian Peninsula), we compare the effectiveness of PB and mechanical thinning on various aspects of the fire regime under two climate change scenarios through the end of the 21st century. We find that PB and thinning, by reducing fuel load, reduce fireline intensity; this suggests that what wildfires do occur could be more easily controlled. While this would reduce risks to human health and well-being, PB comes with the tradeoff of increased fire emissions, which could contribute to respiratory problems. Mechanical thinning reduces fireline intensity by as much or more while also reducing emissions. While net primary production remained unaffected by fire management, cumulative net biome production until the end of the 21st century declined especially under the influence of thinning. While these results are based on stylized management treatments, this work shows the potential of DGVMs in exploring fire management options.

Safety of Minimizing Intensity of Follow-up on Active Surveillance for Clinical Stage I Testicular Germ Cell Tumors

BackgroundWe have recommended active surveillance as the preferred management option for clinical stage I (CSI) testicular germ cell tumors (GCTs) since 1980. Over time, the recommended intensity of surveillance has decreased; however, the impact on relapse detection has not been investigated. ObjectiveTo examine relapse rate, time to relapse, extent of disease, and burden of treatment at relapse across decreasing surveillance intensity over time. Design, setting, and participantsCSI GCT patients under active surveillance from 1981 to 2021 were included in this study. Outcome measurements and statistical analysisThrough four major iterations in both nonseminomatous (NSGCT) and seminoma surveillance schedules, visit frequency, blood testing, and imaging have been decreased successively. Low-dose, noncontrast computed tomography (CT) scans were adopted in 2011. Categorical variables and time to relapse were compared using chi-square and Fisher’s exact or Kruskal-Wallis test, respectively. Results and limitationsA total of 1583 consecutive patients (942 with seminoma and 641 with NSGCT) were included. In seminoma, chest x-rays were reduced from 13 to one and CT scans were reduced from 20 to ten. Relapse rate, time to relapse, N or M category, and International Germ Cell Cancer Collaborative Group (IGCCCG) classification did not change. In NSGCT, chest x-rays were reduced from 27 to zero and CT scans were reduced from 11 to five. Relapse rate (from 46.2% to 21.2%, p = 0.002) and the median time to relapse (from 6.54 to 4.47 mo, p = 0.025) decreased. No difference in relapsed disease burden was identified by N, M, and S category or IGCCCG classification. Treatment burden at relapse and GCT cancer deaths remained similar for seminoma and NSGCT. Limitations include the retrospective design and large time period covered. ConclusionsDespite considerable reductions in surveillance intensity, we did not observe an increase in disease extent, treatment burden, or GCT cancer deaths upon relapse. These results support that our current lower-intensity active surveillance schedules are safe for managing CSI GCT. Patient summaryOur current reduced-intensity surveillance schedules for clinical stage I germ cell tumors appear to be safe.

Late stage coarsening kinetics induced by a phase-dependent mobility: A phase field study of FeCr in the spinodal regime

An efficient algorithm is presented in this work to compute the late stage coarsening kinetics within the phase-field framework. Microstructures resulting from off-critical quenches over large time periods have been simulated in the FeCr system, which exhibits substantial variations in atomic mobilities between the Fe-matrix and Cr-rich precipitates. To understand the impact of such a phase-dependent mobility on the microstructure, 2D simulations have been performed in the spinodal regime using a constant, symmetric, and phase-dependent mobility. Detailed analyses of these simulations highlight the fact that the more realistic situation where atomic mobility is non-uniform induces a competition between coalescence and coarsening of precipitates. We use these simulations to shed light upon previously published numerical and experimental results.