Initial Map Research Articles

Clorf Encodes Carotenoid Isomerase and Regulates Orange Flesh Color in Watermelon (Citrullus lanatus L.).

Flesh color is a significant characteristic of watermelon. Although various flesh-color genes have been identified, the inheritance and molecular basis of the orange flesh trait remain relatively unexplored. In the present study, the genetic analysis of six generations derived from W1-1 (red flesh) and W1-61 (orange flesh) revealed that the orange flesh color trait was regulated by a single recessive gene, Clorf (orange flesh). Bulk segregant analysis (BSA) locked the range to ∼4.66 Mb, and initial mapping situated the Clorf locus within a 688.35-kb region of watermelon chromosome 10. Another 1,026 F2 plants narrowed the Clorf locus to a 304.62-kb region containing 32 candidate genes. Subsequently, genome sequence variations in this 304.62-kb region were extracted for in silico BSA strategy among 11 resequenced lines (one orange flesh and ten nonorange flesh) and finally narrowed the Clorf locus into an 82.51-kb region containing nine candidate genes. Sequence variation analysis of coding regions and gene expression levels supports Cla97C10G200950 as the most possible candidate for Clorf, which encodes carotenoid isomerase (Crtiso). This study provides a genetic resource for investigating the orange flesh color of watermelon, with Clorf malfunction resulting in low lycopene accumulation and, thus, orange flesh.

Toward Shared Decision-Making in Degenerative Cervical Myelopathy: Protocol for a Mixed Methods Study.

Health care decisions are a critical determinant in the evolution of chronic illness. In shared decision-making (SDM), patients and clinicians work collaboratively to reach evidence-based health decisions that align with individual circumstances, values, and preferences. This personalized approach to clinical care likely has substantial benefits in the oversight of degenerative cervical myelopathy (DCM), a type of nontraumatic spinal cord injury. Its chronicity, heterogeneous clinical presentation, complex management, and variable disease course engenders an imperative for a patient-centric approach that accounts for each patient's unique needs and priorities. Inadequate patient knowledge about the condition and an incomplete understanding of the critical decision points that arise during the course of care currently hinder the fruitful participation of health care providers and patients in SDM. This study protocol presents the rationale for deploying SDM for DCM and delineates the groundwork required to achieve this. The study's primary outcome is the development of a comprehensive checklist to be implemented upon diagnosis that provides patients with essential information necessary to support their informed decision-making. This is known as a core information set (CIS). The secondary outcome is the creation of a detailed process map that provides a diagrammatic representation of the global care workflows and cognitive processes involved in DCM care. Characterizing the critical decision points along a patient's journey will allow for an effective exploration of SDM tools for routine clinical practice to enhance patient-centered care and improve clinical outcomes. Both CISs and process maps are coproduced iteratively through a collaborative process involving the input and consensus of key stakeholders. This will be facilitated by Myelopathy.org, a global DCM charity, through its Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy community. To develop the CIS, a 3-round, web-based Delphi process will be used, starting with a baseline list of information items derived from a recent scoping review of educational materials in DCM, patient interviews, and a qualitative survey of professionals. A priori criteria for achieving consensus are specified. The process map will be developed iteratively using semistructured interviews with patients and professionals and validated by key stakeholders. Recruitment for the Delphi consensus study began in April 2023. The pilot-testing of process map interview participants started simultaneously, with the formulation of an initial baseline map underway. This protocol marks the first attempt to provide a starting point for investigating SDM in DCM. The primary work centers on developing an educational tool for use in diagnosis to enable enhanced onward decision-making. The wider objective is to aid stakeholders in developing SDM tools by identifying critical decision junctures in DCM care. Through these approaches, we aim to provide an exhaustive launchpad for formulating SDM tools in the wider DCM community. DERR1-10.2196/46809.

Noise insensitive volumetric fusion method for enhanced photoacoustic microscopy.

Photoacoustic imaging is an emerging imaging modality that combines the high contrast of optical imaging and the high penetration of acoustic imaging. However, the strong focusing of the laser beam in optical-resolution photoacoustic microscopy (OR-PAM) leads to a limited depth-of-field (DoF). Here, a volumetric photoacoustic information fusion method was proposed to achieve large volumetric photoacoustic imaging at low cost. First, the initial decision map was built through the focus detection based on the proposed three-dimensional Laplacian operator. Majority filter-based consistency verification and Gaussian filter-based map smoothing were then utilized to generate the final decision map for the construction of photoacoustic imaging with extended DoF. The performance of the proposed method was tested to show that our method can expand the limited DoF by a factor of 1.7 without the sacrifice of lateral resolution. Four sets of multi-focus vessel data at different noise levels were fused to verify the effectiveness and robustness of the proposed method. The proposed method can efficiently extend the DoF of OR-PAM under different noise levels.

Developing an Online Interactive Volumetric Surface Viewer for Visualizing Spatial Distribution of Intracranial Lesions.

The exact location of intracranial neoplasms and metastatic lesions within the cortex may provide key insights into predicting treatment effectiveness and toxicities. Modern neuroimaging approaches have enabled precise, rapid diffeomorphic alignment from individual brains to an atlas, allowing the registration of contours and dose distributions onto a common template. However, there is a paucity of intuitive tools to perform such functions. Here, we developed an online interactive volumetric surface viewer that displays distribution of brain lesions of specific clinical features within the context of prior radiation treatment in populations of patients on a common atlas. Data from 647 patients treated at our institution with radiotherapy for intracranial lesions were analyzed with FreeSurfer to perform intensity normalization, skull-stripping, and affine registration to Talairach coordinates. A custom-built VoxelMorph model was trained to register T1 MRIs to a common atlas. Radiation dose maps and contours on underlying CT simulation scans were rigidly aligned to the T1 MRIs. The diffeomorphic warps were then applied to the radiation dose maps and contours, including the gross tumor volumes. Surface rendering of the template atlas was performed using FreeSurfer and visualized using PyCortex. Clinical information including primary sites for brain metastases, lesion size, survival, recurrence, use of anti-epileptic medications, radiation necrosis, and neuro-cognitive toxicity was embedded into the PyCortex viewer. The custom-trained VoxelMorph model enabled rapid normalization of the T1MRIs to the atlas brain. PyCortex allows for interactive visualization of the spatial distribution of lesions on the cortical surface across a rich array of clinical parameters. Initial maps have qualitatively revealed a predilection for metastases in the right hemisphere. This WebGL viewer allows intuitive clinical interrogation on a visual surface enabling population inferences. For example, one can visualize the distribution of brain metastases of a large patient cohort based on their primary tumor sites, propensity for seizure and risk for radionecrosis. We successfully built an online interactive viewer to visualize the spatial distribution of intracranial lesions on a common reference. Our approach extends prior work allowing visual interaction of lesions and real-time generation of inference maps by combining a front-end viewer and back-end database of clinical data. This viewer may represent a critical step towards uncovering the location-dependent breakdown of the blood-brain-barrier, differentiating the propensity of primary lesions metastasizing to brain regions, and allowing the discovery of associations of previously understudied spatial neighborhoods. We plan to release a publicly available web-based viewer to further explore, interrogate, and inform future radiation treatments.

Critical Zone Mapping Based on Land Use and Slope Terrain Parameters, A Case Study of the Keureuto Sub-catchment

Land use change, which contributes to a critical area, upstream of the Keureuto Sub-catchment is obvious based on previous studies. The critical area mapping is important for the watershed and land management considering the Keureto dam will be operated in the year 2024 at the downstream. The map can be categorized based on land cover, terrain slope, erosion risk classes, land productivity, and land management. The sub-catchment is prone to changes in all or any of these parameters. In this case, the initial critical zone map is developed for the 238 km2 Keureuto sub-catchment based on two parameters which are land use and terrain slope. Land use is produced from Landsat Satellite images from the years 2015 and 2021. Digital Elevation Model (DEM) data is used to produce terrain slope and sub-catchment boundary which the outlet is the Keureto dam. The analysis is based on overlaying the land cover and slope layer. Agricultural land covers part of the upstream area – steeper slope and middle – moderate slope of the sub-catchment increasing by about 8% within seven years. The critical zone mapping based on these two parameters is produced for a further study of the slope stability, the erosion risk, the watershed, and land management for the functionality of the Keureuto dam at the downstream site.

Fast Radio Map Construction with Domain Disentangled Learning for Wireless Localization

The accuracy of wireless fingerprint-based indoor localization largely depends on the precision and density of radio maps. Although many research efforts have been devoted to incremental updating of radio maps, few consider the laborious initial construction of a new site. In this work, we propose an accurate and generalizable framework for efficient radio map construction, which takes advantage of readily-available fine-grained radio maps and constructs fine-grained radio maps of a new site with a small proportion of measurements in it. Specifically, we regard radio maps as domains and propose a Radio Map construction approach based on Domain Adaptation (RMDA). We first employ the domain disentanglement feature extractor to learn domain-invariant features for aligning the source domains (available radio maps) with the target domain (initial radio map) in the domain-invariant latent space. Furthermore, we propose a dynamic weighting strategy, which learns the relevancy of the source and target domain in the domain adaptation. Then, we extract the domain-specific features based on the site's floorplan and use them to constrain the super-resolution of the domain-invariant features. Experimental results demonstrate that RMDA constructs a fine-grained initial radio map of a target site efficiently with a limited number of measurements. Meanwhile, the localization accuracy of the refined radio map with RMDA significantly improved by about 41.35% after construction and is comparable with the dense surveyed radio map (the reduction is less than 8%).

On the field with social science and humanities participatory research

Citizen science is a well-established domain in many scientific disciplines. Far less is known about the practices of citizen science in the social sciences and humanities. The COESO project is fostering and highlighting citizen science practices in the social sciences and humanities. Ten European-based case studies – ”pilots” – reporting on specific challenges have fed the COESO project: five of them began at the beginning of the project in January 2021, while the other five were chosen through an Open Call process in early 2022. Each pilot presents a distinct type and model of collaboration between different kinds of stakeholders, addressing specific societal issues. This diversity has contributed to generating knowledge on challenges and innovative knowledge sharing practices found within participatory practices in the social sciences and humanities research fields. The project promotes developing synergies between practices found within various social sciences and humanities disciplines, including sociology, anthropology, political science, and the arts, with practices employed by non-academic professionals working on societal issues. The 10 pilots provide a platform for collaboration between researchers, practitioners, and citizens, allowing for the co-creation of new knowledge and innovative solutions to pressing societal challenges. The aim of this poster is to show the different engagement strategies co-developed with researchers and community members to involve active citizen participation in the research, as well as the actions and creative outputs carried out to raise awareness of the projects and maintain the interest of participants. From initial stakeholder mapping, information management, data collection and storage to communication and dissemination of results, each pilot has deployed a unique engagement strategy, making COESO an excellent repository of information on the different ways of fostering community engagement with citizen science through social science and humanities initiatives. By showcasing the diversity and potential of these pilots, this poster invites further exploration and collaboration in the exciting and evolving domain of citizen science in the SSH.

Slovenian folk-pop music as a place and nation making strategy between heritage and popular culture

ABSTRACT Cultural heritage tourism partly depends on authorized heritage definitions, and partly on complex bottom-up processes of heritage identification, interpretation, and communication. This paper addresses the ways in which music, when understood as intangible heritage, may be used for place making and nation branding purposes, and the dynamic between these two processes, as seen from the perspective of cultural and heritage tourism workers. To analyze this dynamic, we focus on the genre of Slovenian folk-pop music. Invented in the 1950s, it has since then become the prevalent (popular) musical element of the Slovenian cultural landscape, while its variants have also, and in parallel to ‘national’ characterization of the genre, been appropriated in various local contexts. We trace how Slovenian folk pop simultaneously partakes in the construction of the country’s national brand and in local place making strategies of heritage promotion, deployed by national, regional, and local stakeholders. We draw on an extensive literature review, document analysis, and interviews with folk-pop festival organizers. Based on this initial mapping of the major stakeholders, we propose a classification of folk-pop music festivals that accounts for the different ways in which folk pop is used as an instrument of heritage tourism, place making, nation branding, and entertainment industries.

A real-time contouring feedback tool for consensus-based contour training.

Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8mm ([-16.7, 34.5], 5.1) and 0.2mm ([-3.9, 1.6],0.7) for the LV. A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

Efficient application mapping approach based on grey wolf optimization for network on chip

In modern chip designs with multiple processors, network-on-chip (NoC) has emerged as a critical solution, offering scalability, flexibility, modularity, and efficiency. However, a significant challenge in application mapping is minimizing communication costs as they directly affect performance and energy consumption. Although several mapping algorithms have been developed, they often suffer from complexity, suboptimal performance, and slow convergence. To address these challenges, this paper proposes a novel approach for mapping using the grey wolf optimization (GWO) technique inspired by nature. The proposed GWO-based mapping approach for NoC (GNoC) is augmented with an effective clustering-based initial mapping strategy, further enhancing its capabilities. Furthermore, a second heuristic algorithm is introduced, incorporating a modified GWO method with polynomial regression (GNoC-PR). This modification optimizes the runtime efficiency and enhances the overall mapping quality. The proposed method was evaluated through comprehensive simulations and compared with state-of-the-art solutions. The results demonstrate that the proposed algorithm outperforms existing methods in several key metrics, including a power consumption reduction of 19.7%, energy efficiency improvement of 17%, and a significant 40% decrease in computational overhead on average. To validate the effectiveness of our mapping technique, we evaluated both embedded and synthetic applications, considering various metrics such as communication cost, average network latency, and power consumption.

Rendering Post-Anthropocentric Visions

This paper formulates a strategic activation of speculative-computational practices of worlding by situating them as networked epistemologies of resistance. Through the integration of Deleuze and Guattari's concept of a ‘minor literature’ with the distributed software ontologies of algorithmic worlds, a tentative politics for thinking-with worlds is mapped, anchored in the potential of worlding to counter the dominant narratives of our techno-capitalist cultural imaginary. With particular attention to the ways in which the affordances of software can become operative and offer alternative scales of engagement with modes of being-otherwise, an initial theoretical mapping of how worlding operates as a multi-faceted and critical storytelling practice is formulated.

A patch filling method for thematic map refinement: a case study on forest cover mapping in the Greater Mekong Subregion and Malaysia

ABSTRACT Accurate forest cover mapping is essential for monitoring the status of forest extent in Southeast Asia. However, tropical areas frequently experience cloud cover, resulting in invalid or missing data in thematic maps. The initial 2005 and 2010 forest cover maps produced by the collaboration of the Greater Mekong Subregion and Malaysia (GMS+) economies contain unclassified pixels in the areas affected by cloud or cloud shadow. To enhance the usability and effectiveness of the 2005 and 2010 GMS+ forest cover maps for further analysis and applications, we present a novel method for accurately mapping forest cover in the presence of cloud cover. We employed a pixel-based algorithm to create clear view composites and automatically generated land cover training labels from the existing forest cover maps. We then reclassified the invalid areas and produced updated maps. The land cover types for all previously missing pixels have been successfully reclassified. The accuracy of this method was assessed at both the pixel and region level, with an overall accuracy of 94.2% at the forest/non-forest level and 86.6% at the finer classification level by pixel level assessment across all reclassified patches, and 93.2% at the forest/non-forest level and 89.9% at the finer level by region level for the selected site. There are 2.6% of forest and 0.7% of non-forest areas in the 2005 map, as well as 2.7% of forest and 0.6% of non-forest in the 2010 map have been reclassified from invalid pixels. This approach provides a framework for filling invalid areas in the existing thematic map toward improving its spatial continuity. The updated outputs provide more accurate and reliable information than the initial maps on the status of forest extent in the GMS+, which is critical for effective forest management and sustainable use in the region.

Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park

We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction–diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia Project (LIFE12 BIO/IT/000213), a project aimed at the eradication of A. altissima in the Alta Murgia National Park.

Penyuluhan Pencegahan Pernikahan Dini pada Usia Remaja di Sekolah Menengah Pertama

This service aims to provide education as an effort to prevent early marriage at a young age for students at SMP Negeri 4 Selong, East Lombok. This service is carried out using the Participatory Action Research (PAR) method with the following stages: (1) initial mapping, (2) building communication with teachers and students, (3) determining service topics and mapping problems, (5) compiling technical activities, ( 6) carry out community service activities, (7) form discussion groups, (8) reflect. The results of the participant satisfaction survey for this community service activity showed very positive results. One of them is shown by the number of questions asked by the participants, related to the material presented. In addition, the participants also shared their personal experiences with those closest to them who were under direct pressure from their parents to marry early. The satisfaction of the participants was also inseparable from the speakers presented who were competent and focused on social issues, humble in nature so that they could place themselves well in the midst of teenage students.

Extended ray-mapping method based on differentiable ray-tracing for non-paraxial and off-axis freeform illumination lens design.

The ray-mapping method has been widely used for designing freeform illumination lenses. However, in non-paraxial or off-axis situations, it remains challenging to obtain an integrable ray-mapping, often requiring a complex iterative correction process for the initial mapping. To address this challenge, we propose an extended ray-mapping method that incorporates differentiable ray-tracing into the design pipeline of the ray-mapping method. This enables accurate surface construction according to ray-mapping and efficient shape correction based on irradiance distribution. The proposed method involves two optimization stages. In the first stage, the freeform surface is preliminarily optimized to closely match the optimal transport mapping. The obtained freeform surface is then further optimized in the second stage to minimize the divergence between the target and simulated irradiance distributions. Additionally, the mean curvature of the freeform surface is also constrained in the second stage to facilitate the fabrication of the final freeform surface. Non-paraxial illumination lenses and off-axis illumination lenses have been designed using the proposed method within ten minutes, and simulations demonstrate that the approach is effective and robust.

Advancing Toward a Common Data Model in Ophthalmology: Gap Analysis of General Eye Examination Concepts to Standard Observational Medical Outcomes Partnership Concepts

Evaluate the degree of concept coverage of the general eye examination in one widely used electronic health record (EHR) system using the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Study of data elements. Not applicable. Data elements (field names and predefined entry values) from the general eye examination in the Epic foundation system were mapped to OMOP concepts and analyzed. Each mapping was given a Health Level 7 equivalence designation-equal when the OMOP concept had the same meaning as the source EHR concept, wider when it was missing information, narrower when it was overly specific, and unmatched when there was no match. Initial mappings were reviewed by 2 graders. Intergrader agreement for equivalence designation was calculated using Cohen's kappa. Agreement on the mapped OMOP concept was calculated as a percentage of total mappable concepts. Discrepancies were discussed and a final consensus created. Quantitative analysis was performed on wider and unmatched concepts. Gaps in OMOP concept coverage of EHR elements and intergrader agreement of mapped OMOP concepts. A total of 698 data elements (210 fields, 488 values) from the EHR were analyzed. The intergrader kappa on the equivalence designation was 0.88 (standard error 0.03, P<0.001). There was a 96% agreement on the mapped OMOP concept. In the final consensus mapping, 25% (1% fields, 31% values) of the EHR to OMOP concept mappings were considered equal, 50% (27% fields, 60% values) wider, 4% (8% fields, 2% values) narrower, and 21% (52% fields, 8% values) unmatched. Of the wider mapped elements, 46% were missing the laterality specification, 24% had other missing attributes, and 30% had both issues. Wider and unmatched EHR elements could be found in all areas of the general eye examination. Most data elements in the general eye examination could not be represented precisely using the OMOP CDM. Our work suggests multiple ways to improve the incorporation of important ophthalmology concepts in OMOP, including adding laterality to existing concepts. There exists a strong need to improve the coverage of ophthalmic concepts in source vocabularies so that the OMOP CDM can better accommodate vision research. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Fundamental Theory and R-linear Convergence of Stretch Energy Minimization for Spherical Equiareal Parameterization

Abstract Here, we extend the finite distortion problem from bounded domains in ℝ2 to closed genus-zero surfaces in ℝ3 by a stereographic projection. Then, we derive a theoretical foundation for spherical equiareal parameterization between a simply connected closed surface M and a unit sphere 𝕊2 by minimizing the total area distortion energy on ̅ℂ. After the minimizer of the total area distortion energy is determined, it is combined with an initial conformal map to determine the equiareal map between the extended planes. From the inverse stereographic projection, we derive the equiareal map between M and 𝕊2. The total area distortion energy is rewritten into the sum of Dirichlet energies associated with the southern and northern hemispheres and is decreased by alternatingly solving the corresponding Laplacian equations. Based on this foundational theory, we develop a modified stretch energy minimization function for the computation of spherical equiareal parameterization between M and 𝕊2. In addition, under relatively mild conditions, we verify that our proposed algorithm has asymptotic R-linear convergence or forms a quasi-periodic solution. Numerical experiments on various benchmarks validate that the assumptions for convergence always hold and indicate the efficiency, reliability, and robustness of the developed modified stretch energy minimization function.

Omernik's Ecoregion Framework: a Legacy for Understanding Regional Patterns in Attainable Resource Quality.

An initial and comprehensive map of ecological regions across the conterminous United States was provided by Omernik in 1987. Because that paper was the most-cited published by the Annals of the American Association of Geographers, we sought to assess and quantify its contribution to science. To do so, we conducted a scientometric analysis to address the following main questions: 1) What are the temporal and spatial citation trends? We expected that Omernik's paper would still be employed 36 years after its publication, and mostly in the United States of America. 2) For what types of environments and organisms has it been applied? Based on its generality, we expected that it had been applied to both terrestrial and aquatic ecosystems. 3) What are the main applications of Omernik's article? We predicted that it would mostly be used for describing and delineating study sites and management areas, as well as for selecting regional reference sites. The number of citations presented a positive temporal increase, indicating its continued applicability. Most papers dealt with aquatic environments, mainly in streams carried out predominantly in the United States of America, as was one of its earliest applications. The usefulness of ecoregions for assessing and managing biotic and abiotic patterns and distributions were the main topics addressed by scientists. Ecoregions have offered a general framework for developing regional expectations and rational regional management policies across large areas, as was their original intent. In addition, ecoregion maps were used for communicating patterns-or the lack of them-to interested scientists, citizens, and decision-makers. That comprehensiveness of Omernik's ecoregion approach has led to its widespread applicability and continued usefulness to a diverse set of scientific and management disciplines.

An attention-based and deep sparse priori cascade multi-view stereo network for 3D reconstruction

Recently 3D reconstruction has received extensive attention and deep research due to the continuous development of the field of computer vision. Nonetheless, the learning-based methods for 3D reconstruction are not robust enough, resulting in the reconstruction model having many occlusions and outliers. In this study, we aim to improve feature matching correlation, aggregate global contextual information, and enhance the robustness of depth estimation to improve the quality of the reconstruction. We propose an attention-based deep sparse priori cascade multi-view stereo network, ADS-MVSNet. Firstly, we propose a feature extraction module based on the attention mechanism to obtain the regions of interest in the input scene. Secondly, we propose a depth sparse prior strategy module to estimate the depth map of the input scene more accurately. It is followed by refinement of the initial depth map using a coarse-to-fine method to improve the accuracy of point cloud reconstruction. These two modules are lightweight and effective, improving the robustness of depth map estimation. We conduct experiments on three common datasets (DTU, ETH3D, Tanks & Temples) and a dataset of a real scene created by us. The experimental results show that ADS-MVSNet performs better in reconstruction quality compared to classical methods.

Map-based B0 shimming for single voxel brain spectroscopy at 7T.

While B0 shimming is an important requirement for in vivo brain spectroscopy, for single voxel spectroscopy (SVS), the role for advanced shim methods has been questioned. Specifically, with the small spatial dimensions of the voxel, the extent to which inhomogeneities higher than second order exist and the ability of higher order shims to correct them is controversial. To assess this, we acquired SVS from two loci of neurophysiological interest, the rostral prefrontal cortex (rPFC; 8 cc) and hippocampus (Hc; 9 cc). The rPFC voxel was placed using SUsceptibility Managed Optimization (SUMO) and an initial B0 map that covers the entire cerebrum to cerebellum. In each location, we compared map-based shimming (Bolero) with projection-based shimming (FAST(EST)MAP). We also compared vendor-provided spherical harmonic first- and second-order shims with additional third- and fourth-order shim hardware. The 7T SVS acquisition used stimulated echo acquisition mode (STEAM) TR/TM/TE of 6 s/20 ms/8 ms, a tissue water acquisition for concentration reference, and LCModel for spectral analysis. In the rPFC (n = 7 subjects), Bolero shimming with first- and second-order shims reduced the residual inhomogeneity from 9.8 ± 4.5 Hz with FAST(EST)MAP to 6.5 ± 2.0 Hz. The addition of third- and fourth-order shims further reduced to 4.0 ± 0.8 Hz. In the Hc (n = 7 subjects), FAST(EST)MAP, Bolero with first- and second-order shims, and Bolero with first- to fourth-order shims achieved values of 8.6 ± 1.9, 5.6 ± 1.0, and 4.6 ± 0.9 Hz, respectively. The spectral linewidth, , was estimated with a Voigt lineshape using and T2 = 130 ms. significantly correlated with the Cramer-Rao lower bounds and concentrations of several metabolites, including glutamate and glutamine in the rPFC. In both loci, if the B0 distribution is well described by a Gaussian model, the variance of the metabolite concentrations is reduced, consistent with the LCModel fit based on a unimodal lineshape. Overall, the use of the high order and map-based B0 shim methods improved the accuracy and consistency of spectroscopic data.