Acquisition Of Spatial Information Research Articles

Time division multiplexing based multi-spectral semantic camera for LiDAR applications

The recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.

Fine grained analysis method for unmanned aerial vehicle measurement based on laser-based light scattering particle sensing

As an effective particle measurement method, laser-based particle sensors combined with unmanned aerial vehicles (UAVs) can be used for measuring air quality in near ground space. The Sniffer4D Mini2 features portability and real-time acquisition of accurate spatial distribution information on air pollution. Additionally, a new fine-grained analysis method called Co-KNN-DNN has been proposed to assess air quality between flight trajectories, allowing for a more detailed presentation of the continuous distribution of air quality. Therefore, this article introduces an unmanned aerial vehicle measurement fine-grained analysis method based on laser light scattering particle sensors. Firstly, the overall scheme was designed, M30T UAV was selected to carry the portable air quality monitoring equipment, with laser-based laser particulate matter sensor and Mini2, to collect AQI and related attributes of the near-ground layer in the selected research area, to do the necessary processing of the collected data, to build a data set suitable for model input, etc., to train and optimize the model, and to carry out practical application of the model. This article is based on the Co-KNN-DNN model for fine-grained analysis of air quality in spatial dimensions. Three experiments were conducted at different altitudes in the study area to investigate the practical application of fine-grained analysis of near-surface air quality. The experimental results show that the average R-squared value can reach 0.99. Choose to conduct experiments using the M30T UAV equipped with Sniffer4D Mini2 and a laser-based particulate matter sensor. The application research validates the effectiveness and practicality of the Co-KNN-DNN model.

Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell states

Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end-to-end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

Abstract 4881: Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics

Abstract Background: Recently, the study of microbiota has emerged as a novel area in cancer research. While next-generation sequencing (NGS) technologies are becoming more prevalent in the area, the acquisition of spatial information in microbiota is still challenging. Despite the possibility of detecting microbial RNAs on Visium (10X genomic, USA), methods depending on predefined host and microbial references cannot reliably capture microbial RNA reads, since Visium inherently lacks the ability to capture RNAs that are missing poly-A tails. Therefore, a reference-independent microbial RNA detection method needs to be developed for more trustworthy research on cancer microbiota on Visium. Methods: Initially, we reconstructed Visium data from a public raw dataset (Project: PRJNA811533, European Nucleotide Archive) derived from colorectal cancer (CRC) and oral squamous cell carcinoma (OSCC). BAM files were obtained by running Space Ranger on a human reference, GRCh38. These BAM files were firstly separated according to spatial barcodes with samtools and xargs. Then, microbial scores for each barcode were obtained using PathSeq. On the other hand, barcode-wise unmapped reads were extracted from the original BAM files simply by remaining unmapped reads, sorting them by spatial barcodes, and counting the number of reads. Then, the unmapped reads originating from host RNA reads were eliminated by regression analysis, remaining the microbial RNA reads. Subsequently, we conducted a comparison of microbial RNA reads from two different techniques. Results: By filtering out the unmapped reads associated with the host, microbial reads could be isolated. While these remaining microbial RNA reads show a significant correlation with the PathSeq scores, the unmapped reads were significantly higher than the PathSeq scores, increasing the statistical power for the downstream analysis with unmapped reads. Additionally, tissue regions with low PathSeq scores and high volumes of non-human unmapped reads were identified. These regions may indicate the presence of microbial sequences or RNA species that were not detected in the PathSeq analysis. Conclusion: The use of unmapped reads enables us to capture a greater number of RNA transcripts compared to PathSeq to indirectly analyze cancer microbiota. This means that we can observe hard-to-identify RNA variants, such as unstable RNAs or new species originating from the interaction between microbial RNAs and host RNAs. Our methods can provide plausible explanations for the existence of microbial RNAs on Visium, and we suggest a reference-independent detection of cancer microbota reads by utilizing unmapped reads. Citation Format: Jeongbin Park, Seo Hye Park, Dongjoo Lee, Jae Eun Lee, Daeseung Lee, Kwon Joong Na, Hongyoon Choi, Hyung-Jun Im. Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4881.

Letrozole delays acquisition of water maze task in female BALB/c mice: Possible involvement of anxiety

Letrozole, an aromatase inhibitor preventing estrogen synthesis from testosterone, is used as an adjuvant therapy in estrogen receptor-positive breast cancer patients. However, like other aromatase inhibitors, it induces many side effects, including impaired cognition. Despite its negative effect in humans, results from animal models are inconsistent and suggest that letrozole can either impair or improve cognition. Here, we studied the effects of chronic letrozole treatment on cognitive behavior of adult female BALB/c mice, a relevant animal model for breast cancer studies, to develop an appropriate animal model aimed at testing therapies to mitigate side effects of letrozole. In Morris water maze, letrozole 0.1 mg/kg impaired reference learning and memory. Interestingly, most of the letrozole 0.1 mg/kg-treated mice were able to learn the new platform position in reversal training and performed similar to control mice in a reversal probe test. Results of the reversal test suggest that letrozole did not completely disrupt spatial navigation, but rather delayed acquisition of spatial information. The delay might be related to increased anxiety as suggested by increased thigmotactic behavior during the reference memory training. The learning impairment was water maze-specific since we did not observe impairment in other spatial tasks such as in Y-maze or object location test. In contrast, the dose of 0.3 mg/kg did not have effect on water maze learning and facilitated locomotor habituation and recognition in novel object recognition test. The current study shows that letrozole dose-dependently modulates behavioral response and that its effects are task-dependent.

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACT Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine.

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

3D muscle networks based on vibrational mechanomyography

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers. Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG. Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force. Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Exploring the construction and analysis method of landscape spatial structure based on complex networks

Complex network theory proposes a structural network model that abstracts the elements in a complex system into nodes and the relationship between elements into ties, which proved to be a scientific method to study the structural system of complexities. This article aims to apply the complex network theory to interpret the organization of landscape spatial structure and propose a spatial structure network construction and quantitative analysis method suitable for landscape space. The following two questions are addressed in this research: (1) How to extract the structural elements from the landscape space and realize the construction of the structural network model based on the 3D spatial information? (2) How to quantitatively analyze the organization characteristics of a spatial structure according to the particularity of landscape space? The research content of this article includes the following five aspects, which are 3D spatial information acquisition, structural element extraction, structural element description, structural network model diagramming, and quantitative index selection. Taking Nanjing Lovers Park as an example, the feasibility of this method is verified. The results show that the method proposed can guarantee the accuracy of the landscape spatial structure model, visualize the attributes of structural elements, and achieve a refined analysis of the structure organization.

Offshore Hydrocarbon Exploitation Target Extraction Based on Time-Series Night Light Remote Sensing Images and Machine Learning Models: A Comparison of Six Machine Learning Algorithms and Their Multi-Feature Importance

The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning models has become one of the most novel techniques for OHE target extraction in recent years. Its performance is mainly influenced by machine learning models, target features, and regional differences. However, there is still a lack of internal comparative studies on the different influencing factors in this framework. Therefore, based on this framework, we selected four different typical experimental regions within the hydrocarbon basins in the South China Sea to validate the extraction performance of six machine learning models (the classification and regression tree (CART), random forest (RF), artificial neural networks (ANN), support vector machine (SVM), Mahalanobis distance (MaD), and maximum likelihood classification (MLC)) using time-series VIIRS night light remote sensing images. On this basis, the influence of the regional differences and the importance of the multi-features were evaluated and analyzed. The results showed that (1) the RF model performed the best, with an average accuracy of 90.74%, which was much higher than the ANN, CART, SVM, MLC, and MaD. (2) The OHE targets with a lower light radiant intensity as well as a closer spatial location were the main subjects of the omission extraction, while the incorrect extractions were mostly caused by the intensive ship activities. (3) The coefficient of variation was the most important feature that affected the accuracy of the OHE target extraction, with a contribution rate of 26%. This was different from the commonly believed frequency feature in the existing research. In the context of global warming, this study can provide a valuable information reference for studies on OHE target extraction, carbon emission activity monitoring, and carbon emission dynamic assessment.

Network QoS Impact on Spatial Perception through Sensory Substitution in Navigation Systems for Blind and Visually Impaired People

A navigation system for individuals suffering from blindness or visual impairment provides information useful to reach a destination. Although there are different approaches, traditional designs are evolving into distributed systems with low-cost, front-end devices. These devices act as a medium between the user and the environment, encoding the information gathered on the surroundings according to theories on human perceptual and cognitive processes. Ultimately, they are rooted in sensorimotor coupling. The present work searches for temporal constraints due to such human-machine interfaces, which in turn constitute a key design factor for networked solutions. To that end, three tests were conveyed to a group of 25 participants under different delay conditions between motor actions and triggered stimuli. The results show a trade-off between spatial information acquisition and delay degradation, and a learning curve even under impaired sensorimotor coupling.

A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods

Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration).

Cross-cultural assessment of the effect of spatial information on firefighters’ wayfinding performance: A virtual reality-based study

When firefighters conduct search and rescue operations during building fire emergencies, effective wayfinding plays a critical role in protecting them from disorientation and improving the efficiency of their operations. However, whether and how different cultural backgrounds affect firefighters' acquisition and utilization of spatial knowledge during wayfinding tasks has not been comprehensively explored. To narrow this knowledge gap, we conducted a virtual reality-based search and rescue experiment with a 4 (spatial information type: control, landmark, route, and survey condition) × 2 (culture: the US and China) mixed factorial design. For each trial, firefighters were required to find three trapped victims in a virtual office maze within 3 min after a 3-min review of a certain type of spatial information. The wayfinding performance metrics (number of victims found, rescue score, distance to the goal path), direction decision-making strategy, and cognitive load of 62 firefighter participants were measured and analyzed. The results indicated that spatial information type, culture, and the interaction influence between them all significantly affected the firefighters' wayfinding performance. We found that the route and survey information contributed to firefighters' wayfinding performance with different degrees of influence on the US and Chinese firefighters; whereas the landmark information increased firefighters’ cognitive load and impaired their wayfinding performance. We also found that the acquisition and utilization process of spatial information for navigation varied among different cultures. The findings of this study are expected to help develop better training strategies for firefighters, as well as improve their safety and efficiency during search and rescue operations.

Multiscale Analysis of Cellular Composition and Morphology in Intact Cerebral Organoids.

Simple SummaryWe have established a pipeline to analyze the structures of intact millimeter-scale cerebral organoids. By using this pipeline, the morphological and spatial distribution of neurons and GFAP-positive cells in organoids, as well as the spatial distribution of cortical neuron subtypes, were obtained by using fMOST imaging. This study introduced a new approach to monitor cellular composition and morphology of cerebral organoids.Cerebral organoids recapitulate in vivo phenotypes and physiological functions of the brain and have great potential in studying brain development, modeling diseases, and conducting neural network research. It is essential to obtain whole-mount three-dimensional (3D) images of cerebral organoids at cellular levels to explore their characteristics and applications. Existing histological strategies sacrifice inherent spatial characteristics of organoids, and the strategy for volume imaging and 3D analysis of entire organoids is urgently needed. Here, we proposed a high-resolution imaging pipeline based on fluorescent labeling by viral transduction and 3D immunostaining with fluorescence micro-optical sectioning tomography (fMOST). We were able to image intact organoids using our pipeline, revealing cytoarchitecture information of organoids and the spatial localization of neurons and glial fibrillary acidic protein positive cells (GFAP+ cells). We performed single-cell reconstruction to analyze the morphology of neurons and GFAP+ cells. Localization and quantitative analysis of cortical layer markers revealed heterogeneity of organoids. This pipeline enabled acquisition of high-resolution spatial information of millimeter-scale organoids for analyzing their cell composition and morphology.

3D Sparse ISAR Imaging With Multiple Plane Spiral OAM Electromagnetic Waves

Vortex electromagnetic (EM) waves carrying orbital angular momentum (OAM) have a helical phase front and a field strength with a singularity along the axial center, which is expected to obtain resolutions beyond the Rayleigh limit for imaging systems, and provide new instruments for accurate radar targets imaging. However, since the vortex EM beams feature a doughnut-like intensity profile with a central dark zone and the inconsistent divergence angles of the main lobe, it is difficult to simultaneously illuminate the target for vortex EM beams with different OAM mode, which results in limited echo energy. In addition, the restricted elevation resolution hinders the acquisition of target spatial information, which limits the development of the OAM-based radar detection technique. This contribution offers a new set of ideas and solutions for achieving the 3D ISAR imaging using the superposition of different plane spiral OAM (PSOAM) waves. First, eigenmode beam steering techniques based on radio PSOAM waves is introduced. Subsequently, the PSOAM-based ISAR imaging model for maneuvering targets is derived and the echo characteristics are analyzed. Third, the effective imaging method, based on the amplitude phase compensation and fast Tensor-SL0 reconstruction algorithm, is proposed to solve the 3D sparse imaging problem in a tensor way. Finally, simulations are carried out to verify that the proposed method not only significantly reduces the computational complexity of the image reconstruction, but also generates the 3D image with sidelobes and artifacts removed remarkably from sparse measurements.

Role of geospatial technology for agree- business management: A case study of Chitrakoot Satna MP

India is an influencing and fast changing economy. In recent decades, India has progressed towards high industrialization and technological advancement. As a consequence, decision-makers will continue to face several challenges for effective governance such as rapid growth in population, environmental concerns, resource shortages and security issues. India will require an efficient and advanced information and knowledge regime to brace itself for the envisaged economic growth. Geospatial technologies, with their unique ability for acquisition, integration and analysis of geographically-referenced spatial information, have in recent times been recognized as an effective tool for planning, management and decision making locally and globally. Among various other technologies, geospatial technologies with proven capabilities for supporting decision-making would be fundamental for information management in future with applications of social and national significance.

The role of inhibitory circuits in hippocampal memory processing.

GABAergic inhibitory circuits play an essential role in coordinating various hippocampal functions. Several decades of work dedicated to a thorough characterization of hippocampal inhibitory populations have highlighted how specific types of interneuron can contribute to network activity. Recent studies have used genetically targeted recordings and peturbations of activity during memory-related behaviours to determine how interneurons that inhibit distinct subcellular domains of principal cells or specialize in principal cell disinhibition may sculpt hippocampal memory. These studies highlight unique contributions of distinct interneuron types to the temporal binding of hippocampal ensembles, synaptic plasticity and the acquisition of spatial and contextual information. Here, we review the current state of knowledge around hippocampal inhibition and memory by discussing the multifaceted roles of populations of inhibitory cells at different stages of hippocampal mnemonic processing.

0121 Sleep facilitates memory, but not navigation across the pubertal transition in the novel Minecraft Memory and Navigation Task

Abstract Introduction The hippocampus uniquely supports the acquisition and retention of spatial information, and sleep supports the consolidation of hippocampal-dependent memories. However, support for the role of sleep in spatial memory and navigation is mixed. We developed and tested a novel Minecraft Memory and Navigation (MMN) task in adults which showed that sleep uniquely supports the memory for spatial locations, but navigation improved regardless of delay condition (Simon et al., submitted). Here, we tested participants during the developmental transition from childhood to adolescence, a time known for structural and functional hippocampal maturation. Methods Thirty-one youth (Mage = 10.6, SD = 1.56, 9-13 years, grouped: child, early adolescent, adolescent) were administered the MNN task. Training included two free-explorations trials to learn 12 unique object-locations associations in an open-field Minecraft environment. At test, participants were cued to place objects from randomly teleported locations. They were tested twice, immediately and 12-hours later, after a period of sleep or wake. Conditions and environments were counterbalanced in a within-subject design. Each step-by-step movement was recorded for navigation analyses including path length, orientation angle, and search behaviors. Results We found a significant benefit of sleep, compared to wake, on the spatial location accuracy when accounting for age group (F(1,27) = 6.153, p = .02), such that similar to young adults, youth’s metric accuracy of spatial locations improved. We found no change in navigation performances from Test 1 to Test 2 in either condition. Between the start and target locations, youth initiated and maintained similar trajectory angles with similar total path lengths (p’s > .068). Spatial location memory did not correlate with any navigation metrics (p’s > .361). Navigation metrics correlated among themselves, which withstood correction (p’s < .005). Conclusion Youth demonstrated similar sleep-dependent benefits for the metric accuracy of the spatial locations. In contrast to adults, youth’s navigation performance did not improve over delay, regardless condition. Our findings are in line with sleep’s promotion of hippocampal-dependent information, with retention of the spatial locations. Our data are consistent with the hypothesis that sleep does not facilitate navigation, and points to potential developmental maturation required to support navigation behaviors. Support (If Any)

IMPROVED ARTIFICIAL IMMUNE NETWORK CROP RECOGNITION ALGORITHM BASED ON DISPERSED VEGETATION INDEX GENETIC CHAIN

Abstract. The rapid and accurate acquisition of crop planting spatial location information using remote sensing is one of the important guarantees to maintain the sustainable development of agriculture. However, the accuracy of crop identification by remote sensing is currently limited by many factors, such as the influence of other ground objects and the lack of time-series data. To overcome the above problems, this paper proposes an algorithm named improved artificial immune network crop recognition algorithm based on dispersed vegetation index genetic chain (IaiNet). This algorithm can be combined with multi-spectral data from Sentinel-2 series satellites for crop identification. As a test case, we identified and evaluated 3 different crop recognition scenarios in Henan, China. The results show that IaiNet can accurately identify the spatial distribution of crop planting. In all identification results, the accuracy is higher than 90%, and the kappa coefficient is greater than 0.9. In addition, the crop recognition results of IaiNet are significantly better than the random forest algorithm and support vector machine algorithm.