Multiple Maps Research Articles

Perfluorooctanesulfonic acid (PFOS) removal from aqueous solution through N-doped porous copper-carbon composite derived from recycled copper obtained from fly ash incinerator: Water decontamination via municipal waste remnants.

Invincible growth in waste production is the consequence of overpopulation, which should be addressed to reduce the occupied landfill surface needed for their disposal and to alleviate the leachate of extremely hazardous material into the soil and water bodies. In this study, copper (Cu) was extracted from fly ash of a municipal solid waste incinerator by an electro-chemical method, which was optimized to recover the highest amount of Cu, and then it was chelated with 4-aminobenzoic acid (AM) and terephthalic acid (TM) in an aqueous phase. The obtained composites were then heated to form a porous calcinated copper-carbon composite and utilized to adsorb the forever contaminant of PFOS from aqueous solutions. As the calcinated composite of Cu/AM with a ratio of 1:1 removed a greater amount of PFOS from the aqueous solution than Cu/TA, it was utilized as the ultimate adsorbent. The platform adsorbent was subjected to multiple characterizations, including XRD, FESEM, elemental mapping, TEM, BET, EDS, ICP-OES, FTIR, DLS, and point of zero charges, as well as optimization of several operational parameters involving pH, adsorbent dosage, initial PFOS concentration, and contact time. At the neutral pH, under the optimal conditions (adsorbent dosage of 1 g L-1 and 5 h), 97.23% of PFOS was eliminated from the solution spiked with 5 mg L-1 of PFOS. The equilibrium data were best fitted with Frundlich isotherm, and the maximum adsorption capacity of 402 mg g-1 was achieved. The optimal conditions were also applied to PFOA, demonstrating high adsorption of different types of PFAS. The recovery tests of the adsorbent conducted 5 times on the solution spiked with 10 mg L-1 of PFOS showed a slight decrease in PFOS removal at least for 5 regeneration cycles, demonstrating the high adsorption capacity and its reusability, thereby validating its feasibility for large-scale applications.

When multiple route map designs are used by the same bus company

Abstract This paper reports three separate studies. Study 1 consisted of route map observations; study 2 looked at map creator’s perspective, and study 3 considered user research and route map usage. It was observed that the route maps issued by one bus service provider in Fukuoka, Japan, have as many as 24 types of design variations. According to the route map creators, limited resources was the main reason for this practice. Route map usability testing, keyword sorting and interviews were conducted as part of a user-focused research. Map-reading tests revealed several practices that have a negative impact on user’s ability to read route maps, and the user research revealed that users prefer a unified design.

Refining dual RNA-seq mapping: sequential and combined approaches in host-parasitic plant dynamics.

Transcriptional profiling in host plant-parasitic plant interactions is challenging due to the tight interface between host and parasitic plants and the percentage of homologous sequences shared. Dual RNA-seq offers a solution by enabling in silico separation of mixed transcripts from the interface region. However, it has to deal with issues related to multiple mapping and cross-mapping of reads in host and parasite genomes, particularly as evolutionary divergence decreases. In this paper, we evaluated the feasibility of this technique by simulating interactions between parasitic and host plants and refining the mapping process. More specifically, we merged host plant with parasitic plant transcriptomes and compared two alignment approaches: sequential mapping of reads to the two separate reference genomes and combined mapping of reads to a single concatenated genome. We considered Cuscuta campestris as parasitic plant and two host plants of interest such as Arabidopsis thaliana and Solanum lycopersicum. Both tested approaches achieved a mapping rate of ~90%, with only about 1% of cross-mapping reads. This suggests the effectiveness of the method in accurately separating mixed transcripts in silico. The combined approach proved slightly more accurate and less time consuming than the sequential approach. The evolutionary distance between parasitic and host plants did not significantly impact the accuracy of read assignment to their respective genomes since enough polymorphisms were present to ensure reliable differentiation. This study demonstrates the reliability of dual RNA-seq for studying host-parasite interactions within the same taxonomic kingdom, paving the way for further research into the key genes involved in plant parasitism.

Quantitative Trait Loci Mapping for Powdery Mildew Resistance in Wheat Genetic Population.

Powdery mildew is a prevalent wheat disease that affects yield and quality. The characterization and fine mapping of genes associated with powdery mildew resistance can benefit marker-assisted breeding. In this study, quantitative trait loci (QTL) associated with powdery mildew were mapped using a high-density 35K DArT genetic linkage map developed from a population of double haploid lines (DHs) created by crossing "Jinmai 33 (a highly resistance line) with Yannong 19 (a highly susceptible line)". Three stable QTLs for powdery mildew were identified on chromosomes 1B, 2B, and 6A combined with the composite interval graphing method and multiple interval mapping, explaining phenotypic variations (PVE) that range from 4.98% to 13.25%. Notably, Qpm.sxn-1B and Qpm.sxn-2B were identified across three environments, with the PVE ranging from 9.37% to 13.25% and from 4.98% to 5.23%, respectively. The synergistic effects of these QTLs were contributed by the parental line "Jinmai 33". Qpm.sxn-1B was the major stable QTL, and Qpm.sxn-2B was close to Pm51. Furthermore, Qpm.sxn-6A was identified in two environments, accounting for PVE values of 7.13% and 7.65%, respectively, with the resistance effects originating from the male parent. Remarkably, this locus has not been reported previously, indicating that Qpm.sxn-6A represents a newly dis-covered QTL governing powdery mildew genes. Conclusions Five molecular markers available for mark-er-assisted selection were selected for tracking Qpm.sxn-1B and Qpm.sxn-2B in the program. The identification of this novel newly discovered QTL and markers reported in this study will be useful for marker-assisted selection of powdery mildew resistance.

Blockchain With Hierarchical Auto‐Associative Polynomial Convolutional Neural Network Fostered Cryptography for Securing Image

ABSTRACTNowadays, the image security is one of the most challenging issues to address the technological age. Security is the primary issue in data management and transmission because of the original data form that is read, abused and destroyed. The cloud companies struggle to secure the file. The cloud security is the major concern in cloud computing context. Numerous researches have been presented so far to protect the cloud environment. But, none of them provides the sufficient security. Therefore, this paper proposes a Blockchain‐based technique for Image Security that combines Hierarchical Auto‐Associative Polynomial Convolutional Neural Network Fostered Cryptography (BC‐SIE‐HAPCNN‐FODCE). The Flickr30k dataset is used to collect the input images. At that point, cryptographic pixel values of picture are kept on blockchain to defend security of picture information. It uses Delegated Proof of Stake Consensus (DT‐DPoS) approach appointed confirmation of stake agreement approach. The performance parameters, like processing time, reaction time, runtime, correlation coefficient analysis, entropy analysis, mean square error, and availability are used to determine the efficacy of the proposed BC‐SIE‐HAPCNN‐FODCE approach. The performance of the proposed technique attains 18.81%, 32.05%, and 22.28% higher correlation coefficient and 25.38%, 20.81%, and 26.04% higher entropy compared with existing methods, such as Multiple Rossler lightweight Logistic sine mapping dependent Federated convolutional method with cyber blockchain in medical image encryption (BC‐SIE‐FCAL‐MRLLSM), color image encryption under Hénon‐zigzag map with chaotic restricted Boltzmann machine over Blockchain (BC‐SIE‐CRBM‐HZM) and blockchain‐assisted safe picture transmission along detection method on Internet of Medical Things Environment (BC‐SIE‐ECC‐DBN), respectively.

Multiple Upward Residual Gravity and Magnetic Maps to Estimate the Deep Crustal Layers and Moho Discontinuity in Eastern Iraq

The upward continuation technique was applied to the gravity and magnetic data to obtain the residual anomalies maps in eastern Iraq. A new processing method is introduced to calculate the crustal layers depending on the relation between the total range of the residual anomalies of gravity, magnetic data and the upward continuation, with multi-elevations. The significant relationship shows slope variations, which may reflect the maximum contrast in the physical properties (density and/or susceptibility) and the intersection point representing the depth of layer contacts. This method was applied to the eastern central part of Iraq, adjacent to the Iranian borders. It is found that the average depth range to the Jurassic formations, top basement rocks, transition layer (Curie point), and Moho discontinuity are 3.1- 4.8 km, 8-12.9 km, 20.9-22.4 km and 43.6 km, respectively. The Sedimentary cover in the study area ranges from 8 to 13 km. The maximum depth to the Moho discontinuity obtained by gravity data in the study area is 43.6 km. The radially average power spectrum technique was applied in the study area to calculate the gravity depth and magnetic sources' depth. Three depth levels of sources from gravity and magnetic data were obtained. These depths are shallow sources, average basement rock and deep sources (Moho) at 3km, 13.2-12.1 km, and 32.2-34km, respectively. The obtained results concerning the main layers in the crust were compared between the two methods, and the new method shows more reliable results. The detected depth levels refer to top Jurassic, basement rocks, and Moho discontinuity. The new method is believed to be used to detect the crustal layers depth with good results.

High density mapping accuracy and efficiency in atrial fibrillation: real-world outcomes from the SECURE registry

Abstract Background/Introduction A high-density (HD) mapping catheter is valuable for validating pulmonary vein isolation (PVI) and analysing arrhythmic substrates of the left atrium (LA) during atrial fibrillation (AF) ablation. Multiple HD mapping catheters are available for use in clinical practice. A novel octa-spline mapping catheter with a close internal unipolar reference electrode is now available and may improve mapping efficiencies and image resolutions (Figure). Purpose This study aims to report real-world experiences with different commercially available HD mapping catheters in AF ablation and compare mapping performance. Methods SECURE is an ongoing observational, prospective, post-marketing study collecting real-world data on ablation procedures performed per standard-of-care. At the time of data extraction (Nov. 2023), 16 sites across 8 countries had enrolled 2,366 patients (pts), with 12-month follow-up data available for 828 pts. The current analysis, which included consecutive pts undergoing AF ablation, evaluated mapping efficiency and procedural outcomes with 3 HD mapping catheters: circular catheter (CC), penta-spline catheter (PSC), and octa-spline catheter (OSC). Mapping data were analysed with a cloud-based data management and artificial intelligence-powered algorithm. Results Overall, 859 pts with AF (paroxysmal AF [PAF], n=465; persistent AF [PsAF], n=240; long-standing PsAF [LS PsAF], n=154) were included in this analysis. The CC, PSC, and OSC were used in 428, 210, and 221 pts, respectively. Procedures using the OSC had a significantly higher mean mapping area (31,989.9 mm2) than those performed with the CC (23,152.2 mm2; P<0.0001) and PSC (29,804.6 mm2; P=0.0027), as well as a >3-fold higher mean mapping rate (138.4 vs 20.8 and 43.4 points/min, respectively; both P<0.0001). The higher number of mapping points translated to a higher mean mapping density (OSC, 0.34 vs CC, 0.05 and PSC, 0.07 points/mm2; both P<0.0001), with more uniform point distribution and less interpolation. With the CC, PSC, and OSC, respectively, total mean procedure times (incl PVI + other targets) were 118.0, 118.7, and 165.1 min, (P<0.0001 for OSC vs. both CC and PSC) and mean pre-ablation mapping times were 25.2, 25.0, and 21.5 min (P<0.0004 for OSC vs. both CC and PSC). The improved mapping quality and efficiency with the OSC allowed to include specific ablation targets beyond PVI. Fewer PVI-only procedures were performed with the OSC (29.4%) versus the CC (62.1%) and PSC (54.8%), while ablation of the PVI+ other targets was more common with the OSC versus the CC and PSC, including the cavotricuspid isthmus (37.1% vs 17.3% and 16.7%), LA roof (42.5% vs 13.1% and 21.4%), and LA posterior wall (21.7% vs 10.5% and 16.2%). Results were overall consistent per AF type (Table). Conclusions By providing more accurate mapping, the OSC allows for improved analysis of the arrhythmic substrate, which may improve ablation outcomes for pts with PAF and PsAF.Figure 1

Broad-line Region of the Quasar PG 2130+099. II. Doubling the Size Over Four Years?

Over the past three decades, multiple reverberation mapping (RM) campaigns conducted for the quasar PG 2130+099 have exhibited inconsistent findings with time delays ranging from ∼10 to ∼200 days. To achieve a comprehensive understanding of the geometry and dynamics of the broad-line region (BLR) in PG 2130+099, we continued an ongoing high-cadence RM monitoring campaign using the Calar Alto Observatory 2.2 m optical telescope for an extra four years from 2019 to 2022. We measured the time lags of several broad emission lines (including He ii, He i, Hβ, and Fe ii) with respect to the 5100 Å continuum, and their time lags continuously vary through the years. Especially, the Hβ time lags exhibited approximately a factor of 2 increase in the last two years. Additionally, the velocity-resolved time delays of the broad Hβ emission line reveal a back-and-forth change between signs of virial motion and inflow in the BLR. The combination of negligible (∼10%) continuum change and substantial time-lag variation (over 2 times) results in a significant scatter in the intrinsic R Hβ –L 5100 relationship for PG 2130+099. Taking the consistent changes in the continuum variability time scale and the size of the BLR into account, we tentatively propose that the changes in the measurement of the BLR size may be affected by “geometric dilution”

Enhanced Chaotic Pseudorandom Number Generation Using Multiple Bernoulli Maps with Field Programmable Gate Array Optimizations

Certain methods for implementing chaotic maps can lead to dynamic degradation of the generated number sequences. To solve such a problem, we develop a method for generating pseudorandom number sequences based on multiple one-dimensional chaotic maps. In particular, we introduce a Bernoulli chaotic map that utilizes function transformations and constraints on its control parameter, covering complementary regions of the phase space. This approach allows the generation of chaotic number sequences with a wide coverage of phase space, thereby increasing the uncertainty in the number sequence generation process. Moreover, by incorporating a scaling factor and a sine function, we develop a robust chaotic map, called the Sine-Multiple Modified Bernoulli Chaotic Map (SM-MBCM), which ensures a high degree of randomness, validated through statistical mechanics analysis tools. Using the SM-MBCM, we propose a chaotic PRNG (CPRNG) and evaluate its quality through correlation coefficient analysis, key sensitivity tests, statistical and entropy analysis, key space evaluation, linear complexity analysis, and performance tests. Furthermore, we present an FPGA-based implementation scheme that leverages equivalent MBCM variants to optimize the electronic implementation process. Finally, we compare the proposed system with existing designs in terms of throughput and key space.

Segmentation of multi-view video speed extraction method that can cross lane dividing lines

Aiming at the problem that the existing video traffic parameter extraction methods are overly dependent on manual annotation and a single perspective cannot effectively correct the dynamic driving deviation of vehicles on site, a multi-perspective video traffic parameter extraction method that can segment and cross lane dividing lines is proposed. The method consists of an automatic annotation point generation module and a multi-perspective correction module. The automatic annotation point generation module realizes the process of automatically generating annotation points by constructing a reference block based on equal-length lane dividing lines. The multi-perspective correction module proposes multiple mapping methods between vehicles and lane dividing lines and a correction speed measurement method based on the average speed probability density function to correct the two types of deviations generated by vehicles during dynamic driving. Experimental results on public data sets and measured data sets show that the vehicle speed extraction accuracy of the proposed method is better than other speed measurement methods, and it has a certain degree of universality.

MapGPT: an autonomous framework for mapping by integrating large language model and cartographic tools

ABSTRACT The mapping process generally involves intricate operations, such as symbol design, layout design, and text annotation, demanding a high level of professional expertise. The high requirement for map producers hinders the promotion and widespread adoption of mapping. Consequently, researchers are concentrating on techniques to automate and enhance the intelligence of the mapping process. For example, some studies attempt to train deep learning models for mapping, including methods like map style transfer. However, these approaches typically treat the entire map as a global input and generate a new map as output, lacking the flexibility to consider and control detailed elements within a map. Therefore, in this paper, we propose a large language model-based intelligent mapping framework, termed MapGPT, which can be used for mapping by considering the map as an integration of various map elements. Specifically, multiple professional mapping tools are designed in MapGPT, and each tool is designed to control a corresponding map element. With these tools, a large language model is used to first understand the demand of users based on mere natural language descriptions, and subsequently automatically invoke appropriate tools in sequence to generate a map. Furthermore, by utilizing a memory component to store interaction information, users can interact with MapGPT through conversation to adjust map elements such as color and position. In conclusion, MapGPT offers user-friendly mapping experience, showing potential to be a mapping assistant for professional map producers. A comprehensive demonstration of this framework is provided in a visual case study video, accessible at https://github.com/AGI-GIS/MapGPT.

Discovery of a major QTL for resistance to the guava root-knot nematode (Meloidogyne enterolobii) in ‘Tanzania’, an African landrace sweetpotato (Ipomoea batatas)

Sweetpotato, Ipomoea batatas (L.) Lam. (2n = 6x = 90), is among the world’s most important food crops and is North Carolina’s most important vegetable crop. The recent introduction of Meloidogyne enterolobii poses a significant economic threat to North Carolina’s sweetpotato industry and breeding resistance into new varieties has become a high priority for the US sweetpotato industry. Previous studies have shown that ‘Tanzania’, a released African landrace, is resistant to M. enterolobii. We screened the biparental sweetpotato mapping population, ‘Tanzania’ x ‘Beauregard’, for resistance to M. enterolobii by inoculating 246 full-sibs with 10,000 eggs each under greenhouse conditions. ‘Tanzania’, the female parent, was highly resistant, while ‘Beauregard’ was highly susceptible. Our bioassays exhibited strong skewing toward resistance for three measures of resistance: reproductive factor, eggs per gram of root tissue, and root gall severity ratings. A 1:1 segregation for resistance suggested a major gene conferred M. enterolobii resistance. Using a random-effect multiple interval mapping model, we identified a single major QTL, herein designated as qIbMe-4.1, on linkage group 4 that explained 70% of variation in resistance to M. enterolobii. This study provides a new understanding of the genetic basis of M. enterolobii resistance in sweetpotato and represents a major step towards the identification of selectable markers for nematode resistance breeding.

SMedIR: secure medical image retrieval framework with ConvNeXt-based indexing and searchable encryption in the cloud

The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data.

Static Scheduling of Weight Programming for DNN Acceleration with Resource Constrained PIM

Most existing architectural studies on ReRAM-based processing-in-memory (PIM) DNN accelerators assume that all weights of the DNN can be mapped to the crossbar at once. However, these studies are over-idealized. ReRAM crossbar resources for calculation are limited because of technological limitations, so multiple weight mapping procedures are required during the inference process. In this article, we propose a static scheduling framework which generates the mapping between DNN weights and ReRAM cells with minimum runtime weight programming cost. We first build a ReRAM crossbar programming latency model by simultaneously considering the DNN weight patterns, ReRAM programming operations, and PIM architecture characteristics. Then, the model is used in the searching process to obtain an optimized weight-to-OU mapping table with minimum online programming latency. Finally, an OU scheduler is used to coordinate the activation sequences of OUs in the crossbars to perform the inference computation correctly. Evaluation results show the proposed framework significantly reduces the weight programming overhead and the overall inference latency for various DNN models with different input datasets.

Semantic Environment Atlas for Object-Goal Navigation

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat Savva et al. (2019)scenarios show that our method not only achieves a success rate of 39.0%—an improvement of 12.4% over the current state-of-the-art—but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low.

SiamMAF: A multipath and feature-enhanced thermal infrared tracker

Thermal infrared (TIR) images are visually blurred and low in information content. Some TIR trackers focus on enhancing the semantic information of TIR features, neglecting the equally important detailed information for TIR tracking. After target localization, detailed information can assist the tracker in generating accurate prediction boxes. In addition, simple element-wise addition is not a way to fully utilize and fuse multiple response maps. To address these issues, this study proposes a multipath and feature-enhanced Siamese tracker (SiamMAF) for TIR tracking. We design a feature-enhanced module (FEM) based on complementarity, which can highlight the key semantic information of the target and preserve the detailed information of objects. Furthermore, we introduce a response fusion module (RFM) that can adaptively fuse multiple response maps. Extensive experimental results on two challenging benchmarks show that SiamMAF outperforms many existing state-of-the-art TIR trackers and runs at a steady 31FPS.

Broad feature extraction and multi-directional imbalanced weighted broad learning system for the unsupervised stereo matching method

The practical application of supervised stereo matching methods is often limited by the requirement of obtaining ground-truth disparity maps in advance. To address this issue, we introduce a novel unsupervised stereo matching method based on the broad feature extraction and multi-directional imbalanced weighted broad learning system. In this study, an end-to-end unsupervised stereo matching method based on broad feature extraction was developed to generate an initial disparity map. Subsequently, a multi-directional imbalanced weighted broad learning system that considers the uneven distribution of disparity values was designed to effectively remove and redefine outlier disparities and enhance the accuracy of generating multiple candidate disparity maps. Multi-direction consistency verification was integrated into the system to further eliminate anomalous disparity values. This verification relies on uniqueness, which guarantees the consistency of the disparity truth values at the same location in various directions. Finally, the local gravity weight method based on a broad feature space was introduced to select a suitable disparity value from neighboring pixels to replace invalid pixel positions. Experiments were conducted under both indoor and outdoor scenarios to demonstrate the effectiveness and versatility of our approach, confirming that it outperforms state-of-the-art techniques, highlighting its superiority.

Implications of data topology for deep generative models

Many deep generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), learn an immersion mapping from a standard normal distribution in a low-dimensional latent space into a higher-dimensional data space. As such, these mappings are only capable of producing simple data topologies, i.e., those equivalent to an immersion of Euclidean space. In this work, we demonstrate the limitations of such latent space generative models when trained on data distributions with non-trivial topologies. We do this by training these models on synthetic image datasets with known topologies (spheres, torii, etc.). We then show how this results in failures of both data generation as well as data interpolation. Next, we compare this behavior to two classes of deep generative models that in principle allow for more complex data topologies. First, we look at chart autoencoders (CAEs), which construct a smooth data manifold from multiple latent space chart mappings. Second, we explore score-based models, e.g., denoising diffusion probabilistic models, which estimate gradients of the data distribution without resorting to an explicit mapping to a latent space. Our results show that these models do demonstrate improved ability over latent space models in modeling data distributions with complex topologies, however, challenges still remain.

Feasibility of 3D MRI fingerprinting for rapid knee cartilage T1, T2, and T1ρ mapping at 0.55T: Comparison with 3T.

Low-field strength scanners present an opportunity for more inclusive imaging exams and bring several challenges including lower signal-to-noise ratio (SNR) and longer scan times. Magnetic resonance fingerprinting (MRF) is a rapid quantitative multiparametric method that can enable multiple quantitative maps simultaneously. To demonstrate the feasibility of an MRF sequence for knee cartilage evaluation in a 0.55T system we performed repeatability and accuracy experiments with agar-gel phantoms. Additionally, five healthy volunteers (age 32 ±4years old, 2 females) were scanned at 3T and 0.55T. The MRI acquisition protocols include a stack-of-stars T1ρ-enabled MRF sequence, a VIBE sequence with variable flip angles (VFA) for T1 mapping, and fat-suppressed turbo flash (TFL) sequences for T2 and T1ρ mappings. Double-Echo steady-state (DESS) sequence was also used for cartilage segmentation. Acquisitions were performed at two different field strengths, 0.55T and 3T, with the same sequences but protocols were slightly different to accommodate differences in signal-to-noise ratio and relaxation times. Cartilage segmentation was done using five compartments. T1, T2, and T1ρ values were measured in the knee cartilage using both MRF and conventional relaxometry sequences. The MRF sequence demonstrated excellent repeatability in a test-retest experiment with model agar-gel phantoms, as demonstrated with correlation and Bland-Altman plots. Underestimation of T1 values was observed on both field strengths, with the average global difference between reference values and the MRF being 151 ms at 0.55T and 337 ms at 3T. At 0.55T, MRF measurements presented significant biases but strong correlations with the reference measurements. Although a larger error was present in T1 measurements, MRF measurements trended similarly to the conventional measurements for human subjects and model agar-gel phantoms.

Retinotopy drives the variation in scene responses across visual field map divisions of the occipital place area.

The occipital place area (OPA) is a scene-selective region on the lateral surface of human occipitotemporal cortex that spatially overlaps multiple visual field maps, as well as portions of cortex that are not currently defined as retinotopic. Here we combined population receptive field modeling and responses to scenes in a representational similarity analysis (RSA) framework to test the prediction that the OPA's visual field map divisions contribute uniquely to the overall pattern of scene selectivity within the OPA. Consistent with this prediction, the patterns of response to a set of complex scenes were heterogeneous between maps. To explain this heterogeneity, we tested the explanatory power of seven candidate models using RSA. These models spanned different scene dimensions (Content, Expanse, Distance), low- and high-level visual features, and navigational affordances. None of the tested models could account for the variation in scene response observed between the OPA's visual field maps. However, the heterogeneity in scene response was correlated with the differences in retinotopic profiles across maps. These data highlight the need to carefully examine the relationship between regions defined as category-selective and the underlying retinotopy, and they suggest that, in the case of the OPA, it may not be appropriate to conceptualize it as a single scene-selective region.