Spatial Information Research Articles

Wastewater monitoring of SARS-CoV-2 gene for COVID-19 epidemiological surveillance in Tucumán, Argentina.

Wastewater-based epidemiology provides temporal and spatial information about the health status of a population. The objective of this study was to analyze and report the epidemiological dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the province of Tucumán, Argentina during the second and third waves of coronavirus disease 2019 (COVID-19) between April 2021 and March 2022. The study aimed to quantify SARS-CoV-2 RNA in wastewater, correlating it with clinically reported COVID-19 cases. Wastewater samples (n = 72) were collected from 16 sampling points located in three cities of Tucumán (San Miguel de Tucumán, Yerba Buena y Banda del Río Salí). Detection of viral nucleocapsid markers (N1 gene) was carried out using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Viral loads were determined for each positive sample using a standard curve. A positive correlation (p < 0.05) was observed between viral load (copies/mL) and the clinically confirmed COVID-19 cases reported at specific sampling points in San Miguel de Tucumán (SP4, SP7, and SP8) in both months, May and June. Indeed, the high viral load concurred with the peaks of COVID-19 cases. This method allowed us to follow the behavior of SARS-CoV-2 infection during epidemic outbreaks. Thus, wastewater monitoring is a valuable epidemiological indicator that enables the anticipation of increases in COVID-19 cases and tracking the progress of the pandemic. SARS-CoV-2 genome-based surveillance should be implemented as a routine practice to prepare for any future surge in infections.

In Vivo Mapping of Myocardial Injury Outside the Infarct Zone: Tissue at an Intermediate Pathological State.

The goal was to determine the feasibility of mapping the injured-but-not-infarcted myocardium using 99mTc-duramycin in the postischemic heart, with spatial information for its characterization as a pathophysiologically intermediate tissue, which is neither normal nor infarcted. Coronary occlusion was conducted in Sprague Dawley rats with preconditioning and 30-minute ligation. Invivo single-photon emission computed tomography was acquired after 3 hours (n=6) using 99mTc-duramycin, a phosphatidylethanolamine-specific radiopharmaceutical. The 99mTc-duramycin+ areas were compared with infarct and area-at-risk (n=8). Cardiomyocytes and endothelial cells were isolated for gene expression profiling. Cardiac function was measured with echocardiography (n=6) at 4 weeks. Invivo imaging with 99mTc-duramycin identified the infarct (3.9±2.4% of the left ventricle and an extensive area 23.7±2.2% of the left ventricle) with diffuse signal outside the infarct, which is pathologically between normal and infarcted (apoptosis 1.8±1.6, 8.9±4.2, 13.6±3.8%; VCAM-1 [vascular cell adhesion molecule 1] 3.2±0.8, 9.8±4.1, 15.9±4.2/mm2; tyrosine hydroxylase 14.9±2.8, 8.6±4.4, 5.6±2.2/mm2), with heterogeneous changes including scattered micronecrosis, wavy myofibrils, hydropic change, and glycogen accumulation. The 99mTc-duramycin+ tissue is quantitatively smaller than the area-at-risk (26.7% versus 34.4% of the left ventricle, P=0.008). Compared with infarct, gene expression in the 99mTc-duramycin+-noninfarct tissue indicated a greater prosurvival ratio (BCL2/BAX [B-cell lymphoma 2/BCL2-associated X] 7.8 versus 5.7 [cardiomyocytes], 3.7 versus 3.2 [endothelial]), and an upregulation of ion channels in electrophysiology. There was decreased contractility at 4 weeks (regional fractional shortening -8.6%, P<0.05; circumferential strain -52.9%, P<0.05). The injured-but-not-infarcted tissue, being an intermediate zone between normal and infarct, is mapped invivo using phosphatidylethanolamine-based imaging. The intermediate zone contributes significantly to cardiac dysfunction.

Image-based machine learning approach for structural damage detection through wavelet transforms

Structural integrity is essential for safety in infrastructure, as it can help prevent catastrophic failures and financial losses. The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering. Concurrently, the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms. Nevertheless, these require extensive data from structure-affixed sensors, raising computational requirements. In an effort to address this challenge, we propose a novel approach utilizing a pre-trained convolutional neural network (CNN) based on images to identify and assess structural damage. This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data, preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis. Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model, to train the CNN. The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs, namely Alexnet and Resnet. We evaluate our method using different damage scenarios, and both Alexnet and Resnet show prediction accuracies over 90%.

An attention mechanism module with spatial perception and channel information interaction

An attention mechanism module with spatial perception and channel information interaction

Navigating business transformation and strategic decision-making in multinational energy corporations with geodata

In the dynamic landscape of multinational energy corporations, navigating business transformation and strategic decision-making requires innovative approaches. Geodata, encompassing spatial and location-based information, emerges as a pivotal asset in this endeavor. This paper outlines the integration of geodata into the fabric of energy corporations, facilitating informed decisions and operational excellence. The introduction elucidates the significance of geodata amidst evolving business paradigms within the energy sector. It explores the transformative potential of geospatial intelligence in fostering agility and resilience, crucial attributes for multinational corporations. Understanding business transformation delves into the fundamental concepts and drivers reshaping the energy industry. Drawing from case studies, it illustrates how corporations leverage strategic shifts to adapt to market dynamics and emerging technologies. The subsequent section emphasizes the pivotal role of geodata in strategic decision-making processes. It elucidates the diverse applications of geospatial information in optimizing operations, mitigating risks, and capitalizing on market opportunities. Integrating geodata into business processes outlines the methodologies and technologies underpinning effective utilization of spatial data. It underscores the importance of robust data management and geospatial analysis techniques for deriving actionable insights. Leveraging geodata for operational excellence explores how energy corporations optimize asset management, streamline supply chains, and enhance risk assessment through geospatial analytics. Case studies and best practices offer real-world examples of successful geodata integration, highlighting key lessons and future trends. Challenges and considerations encompass the complexities of data quality, regulatory compliance, and organizational change management. In conclusion, the paper underscores the transformative potential of geodata in shaping the future trajectory of multinational energy corporations, advocating for a strategic embrace of geospatial intelligence in decision-making processes. Keywords: Business Transformation, Strategic Decision-Making, Multinational Energy Corporations,  Geodata.

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70years. In 2020, the mangrove forest cover was estimated to be 201.73ha, equivalent to only 61.99% of the 325.43ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51ha, averaging 6.46ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

An effective method for small objects detection based on MDFFAM and LKSPP

Object detection is one of the research hotspots in computer vision. However, most existing object detectors struggle with the identification of small targets. Therefore, the paper proposes two modules: the MDFFAM (Multi-Directional Feature Fusion Attention Mechanism) and the LKSPP (Large Kernel Spatial Pyramid Pooling), to enhance the detector's effectiveness in identifying subtle faults on the surface of mechanical equipment. LKSPP aims to expand the receptive field to capture high-level semantic features through large kernels. Meanwhile, the MDFFAM allows the network to efficiently utilize spatial location information and adaptively recognize detection priorities. In the detection task, MDFFAM effectively captures feature information in three spatial directions: width, height, and channel, with the location information fully utilized to establish stable long-range dependencies. Moreover, LKSPP boasts a larger receptive field and imposes less computational burden compared to the SPPCSPC by YOLOv7. Finally, experiments demonstrate that the proposed module effectively improves the detection accuracy for small targets, surpassing the state-of-the-art object detector, YOLOv7. Remarkably, MDFFAM incurs almost negligible computational overhead.

Automatic loading method for robot guided by 3D vision for scattered and stacked shoe soles

This paper primarily introduces a method for the automated feeding of scattered stacked shoe soles using a 3D visual-guided robot. Initially, addressing issues like slow speed in pose estimation and poor robustness during the robot sorting and feeding process, we introduce an enhanced pose estimation algorithm. This algorithm combines the improved Super 4-Point Congruent Sets (Super 4PCS) with the Truncated Least Squares Semidefinite Relaxation (TEASER) algorithm, significantly boosting the speed and robustness of pose estimation during sole sorting, and achieving precise target pose estimation. Building upon this foundation, we present a sorting strategy for disordered stacked shoe soles. This strategy integrates spatial positional information of each sole, employing multi-objective decision-making and recognition algorithms to determine optimal grasping targets. Finally, an experimental platform for automated sole feeding is designed to validate the proposed method. The experimental results indicate that the pose estimation method proposed in this paper achieves an average distance error of 2.04 mm and an average angular error of 2.72°, with the robot’s average success rate in grasping reaching 97.08%. Moreover, the average processing time of the vision algorithm is only 1.34 s, demonstrating good efficiency, precision, and robustness. This method effectively meets the automated feeding needs of scattered and stacked shoe soles in the actual production processes of shoemaking enterprises.

Abstract PO5-25-03: Immune cell infiltrate profiles in primary Triple Negative Breast Cancer and co-relation with treatment response and survival outcomes

Abstract Introduction Tumor microenvironment (TME) in breast cancer is a heterogeneous landscape with multiple cell types, each modulating the tumor progression and the disease prognosis. These cell types include lymphocytes, RBCs, stromal cells, macrophages, cancer-associated fibroblasts (CAFs), and cancer stem cells. Each cell type in the TME influences tumor progression and clinical outcomes for patients in a good or bad way, depending on the interaction with tumor cells. Tumor-infiltrating lymphocytes (TILs) are shown to be prognostic for therapy response in many cancers in the western cohorts. Our recent study showed that a higher proportion of Indian TNBC patients had intra-tumoral TILs compared to Western cohorts, and these are predictive of better response to NACT and longer disease-free survival in patients, making it a distinct finding from Western reports. In the follow-up study, we are characterizing infiltrating immune cell subtypes and their comparative association with treatment response in TNBC patients. Results from preliminary analysis and comparative analysis between responders and non-responders will be presented here. We speculate that the pro- and anti-tumorigenic immune cell population and its spatial information would help us understand TME and its precise role in response to therapy. Methods A retrospective cohort of breast cancer patients from a single one-surgeon breast cancer clinic with a uniform treatment strategy was evaluated for this study. FFPE primary tissue samples with variable TILs scores (moderate to high), taken from patients who showed complete response to the NACT treatment (n=6) and non-responding group (n=8) determined by H&amp;E stained slides from our cohort, were further stained for immune cells (Pan-B cell, Pan-T cells, Pan-macrophages) and T cells (CD4; Helper T cell marker, CD8; Cytotoxic T cell marker), FOXP; Regularly CD4+ cell marker) and PanCK (Pan-tumor marker) and counterstained with DAPI for multiplex imaging. Multispectral images for these slides were obtained using the Leica Aperio Versa system. Parameters such as individual cell types of identification and scoring, spatial proximity to tumor cells, and individual cell types will be determined by HALO software using the High plex module. Results A total of 14 TNBC samples were stained for the T-cell subtypes panel and Immune cell subtype s panel to determine the TIME. DAPI stain was used for nuclear counting based the parameters such as threshold intensity, nuclear size, roundness, and aggressiveness. The cytoplasmic size was selected as 2um beyond the outside nuclear boundary. Using Halo-Indica software, around 50000-300000 cells/tissue were evaluated across all samples from mIF whole slide scans of biopsy samples. Cell count was validated by cross-checking individual ROIs and manual cell count using Fiji. A Highplex module was used to identify individual signal positivity for each marker based on threshold intensity and cell completeness percent. Preliminary data showed a higher proportion of CD8+ cells and macrophages population are spatially closer to the tumor cells in Responders compared to non-responders. Conclusion Immune profiles are distinct among responding and non-responding patients in the Indian TNBC cohort. The association of these profiles with long-term survival in patients and various clinical and pathological parameters will help us determine the clinical relevance of these profiles and identify distinct immune subgroups as prognostic in Indian TNBCs. Citation Format: Pooja Vaid, Devaki Kelkar, LS Shashidhara, C B Koppiker, Madhura Kulkarni. Immune cell infiltrate profiles in primary Triple Negative Breast Cancer and co-relation with treatment response and survival outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-25-03.

Implementing an electromagnetic tracking navigation system improves the precision of endoscopic transgastric necrosectomy in an ex vivo model

Endoscopic transgastric necrosectomy is crucial in the management of complications resulting from necrotizing pancreatitis. However, both real-time and visual-spatial information is lacking during the procedure, thereby jeopardizing a precise positioning of the endoscope. We conducted a proof-of-concept study with the aim of overcoming these technical difficulties. For this purpose, a three-dimensional (3D) phantom of a stomach and pancreatic necroses was 3D-printed based on spatial information from individual patient CT scans and subsequently integrated into a silicone torso. An electromagnetic (EM) sensor was adjusted inside the endoscope´s working channel. A software interface enabled real time visualization. The accuracy of this novel assistant system was tested ex vivo by four experienced interventional endoscopists who were supposed to reach seven targets inside the phantom in six different experimental runs of simulated endoscopic transgastric necrosectomy. Supported by endoscopic camera view combined with real-time 3D visualization, all endoscopists reached the targets with a targeting error ranging between 2.6 and 6.5 mm in a maximum of eight minutes. In summary, the EM tracking system might increase efficacy and safety of endoscopic transgastric necrosectomy at the experimental level by enhancing visualization. Yet, a broader feasibility study and further technical improvements are mandatory before aiming at implementation into clinical setting.

Abstract PO2-25-05: Spatial multiplexing of protein biomarkers for immune cell profiling of the tumor microenvironment with ChipCytometry

Abstract Understanding the spatial distribution of key immune cell populations is critical in advancing our understanding of cancer and the development of novel therapeutics. Historically, the spatial analysis of the tumor microenvironment (TME) has been limited to relatively low-plex immunohistochemical (IHC) or immunofluorescent (IF) assays, which were inadequate for deep immune cell profiling of the TME. Here, we present the analysis of fresh-frozen human breast tissue using spatial multiplexing technology called ChipCytometry, which combines iterative immuno-fluorescent staining using open-source antibodies for quantitative measure of virtually unlimited protein biomarkers on the same sample and high dynamic range imaging to facilitate quantitative phenotyping with single-cell resolution. In addition to traditional pathology review of the high-plex dataset, subsequent image analysis can be done using open-source or commercial software for advanced spatial analyses, including cluster analysis, neighborhood analysis and advanced data visualization. The results show precise expression levels for each of the protein biomarkers in the assay for each individual cell within the sample while maintaining spatial information about each cell. Dozens of immune cell subtypes were identified and quantified based on protein expression profiles. Spatial analysis of the samples revealed quantifiable heterogeneity of immune cell infiltration within the tumor samples, demonstrating the utility of the ChipCytometry platform for in-depth immune profiling in clinical samples. The ChipCytometry platform enables simultaneous detection of multiple protein biomarkers on a single tissue section for deep immune cell profiling in the TME. Combined with the single-cell spatial information, such datasets provide an opportunity for the discovery of new complex multiplexed biomarker signatures to inform therapeutic development and personalized medicine. Citation Format: Benton Berigan. Spatial multiplexing of protein biomarkers for immune cell profiling of the tumor microenvironment with ChipCytometry [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-25-05.

Latent Representation Learning for Geospatial Entities

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This paper presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces both transductive (GeoNN-T) and inductive (GeoNN-I) models, ensuring effective encoding of geospatial features and scalability with entity changes. Extensive experiments demonstrate GeoNN’s effectiveness in location-sensitive superpixel-based graphs and real-world points of interest, outperforming baselines across various evaluation measures.

Deep learning neural networks with input processing for vibration-based bearing fault diagnosis under imbalanced data conditions

Deep learning (DL) networks, such as convolutional neural networks (CNNs) and long short-term memory (LSTM), have gained popularity for bearing fault diagnosis utilizing raw vibration signals. However, their accuracy and stability are compromised when facing imbalanced real-world datasets. This research investigates the impact of imbalanced datasets and explores the potential of signal processing techniques on network inputs compared to the direct use of raw vibration signals. The DL techniques studied include LSTM, one-dimensional CNN, and two-dimensional (2D) CNN, and a novel hybrid 2DCNNLSTM algorithm, incorporating signal processing methods such as Fourier transform and continuous wavelet transform while maintaining nearly equal parameters and the same base architecture. The proposed hybrid 2DCNNLSTM algorithm combines the strengths of LSTM and CNN, allowing for improved bearing diagnosis by capturing both spatial and temporal information in vibration signals. The proposed 2DCNNLSTM algorithm also considers multi-channel input augmenting raw vibration signal, mean, and variance channels to extract meaningful features and enhance classification efficiency. The publicly available Case Western Reserve University benchmark-bearing test rig dataset with ten fault classes, the Paderborn University dataset with three fault classes, and NASA Centre for Intelligent Maintenance Systems bearing datasets with five fault classes are utilized to test the proposed deep learning networks’ accuracy, effectiveness, robustness, and stability. The studies reveal that the hybrid 2DCNNLSTM-based networks outperform both CNN and LSTM networks, even without input processing. Further, utilizing multi-channel input by augmenting the 2D raw signal with mean and variance value channels proves to be more efficient in handling imbalanced and complex datasets while employing a 2DCNNLSTM-based network.

Assessment for the Sustainable Development of Components of the Tourism and Recreational Potential of Rural Areas of the Aktobe Oblast of the Republic of Kazakhstan

The assessment of sustainable tourism development in the rural areas of the Aktobe oblast of Kazakhstan involved thoroughly analyzing multiple dimensions. Environmental, socio-economic, and cultural sustainability aspects were considered to comprehensively understand the region’s tourism potential. The study began by evaluating the available tourism resources in rural Aktobe. This included assessing natural attractions such as landscapes, wildlife, and geological features, as well as cultural heritage sites and infrastructure like accommodation facilities and transportation networks. A crucial aspect of the study was to analyze the environmental impact of tourism activities in rural areas. This involved evaluating the effects on ecosystems and natural resources. The measures for conserving these resources were also identified. Another focus was on the socio-cultural aspects of tourism development. The study aimed to preserve local traditions, cultural heritage, and community identity amidst tourism growth. Strategies for achieving socio-cultural sustainability were devised. Ranking methods were employed to identify key factors influencing rural tourism development. These methods helped prioritize areas for improvement and resource allocation. A balanced approach was adopted to assess the interaction between different dimensions of sustainability. This ensured that environmental, economic, and socio-cultural aspects were considered equally to achieve overall sustainable tourism development. ArcGIS 10 was used for data analysis and visualization. Maps and charts were created to represent spatial and statistical information, aiding in identifying trends and patterns. The study findings were crucial for identifying priority areas for infrastructure development and formulating strategies and programs for rural tourism promotion. The study aimed to ensure that tourism development aligns with the principles of sustainable development, benefiting both the local communities and the environment. The study provided valuable insights into the current status of rural tourism in Aktobe oblast and offered recommendations for sustainable development, contributing to the region’s long-term prosperity.

Attentional adversarial training for few-shot medical image segmentation without annotations.

Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a scarcity of annotated labels, making it difficult to train a robust and generalized model. Few-shot learning has the potential to predict new classes that are unseen in training with a few annotations. In this study, a novel few-shot semantic segmentation framework named prototype-based generative adversarial network (PG-Net) is proposed for medical image segmentation without annotations. The proposed PG-Net consists of two subnetworks: the prototype-based segmentation network (P-Net) and the guided evaluation network (G-Net). On one hand, the P-Net as a generator focuses on extracting multi-scale features and local spatial information in order to produce refined predictions with discriminative context between foreground and background. On the other hand, the G-Net as a discriminator, which employs an attention mechanism, further distills the relation knowledge between support and query, and contributes to P-Net producing segmentation masks of query with more similar distributions as support. Hence, the PG-Net can enhance segmentation quality by an adversarial training strategy. Compared to the state-of-the-art (SOTA) few-shot segmentation methods, comparative experiments demonstrate that the proposed PG-Net provides noticeably more robust and prominent generalization ability on different medical image modality datasets, including an abdominal Computed Tomography (CT) dataset and an abdominal Magnetic Resonance Imaging (MRI) dataset.

Integrated Mechanism of Immune Response Modulation by Arctium Lappa L. Fructans Based on Microbiome and Metabolomics Technologies.

Arctium lappa L. is widely consumed for its various biological effects, and polysaccharides are its main functional components. The present study aimed to evaluate the immunoregulatory effects of the main polysaccharides from burdock (ALP-1) and reveal the underlying mechanisms. ALP-1 consisted of fructose and glucose (14.57:1) and had a molecular weight of 2757 Da, with typical characteristics of (1 → 2)-linked linear fructans. Oral intake of ALP-1 significantly increased the number of colonic goblet cells, serum immunoglobulin A and immunoglobulin G levels, and fecal secretory immunoglobulin A content as well as up-regulated antioxidant enzymes and increased short chain fatty acid production. In addition, ALP-1 administration regulated pro/anti-inflammatory cytokines (i.e., interleukin (IL)-1β, IL-6, tumor necrosis factor-α, interferon-γ, and IL-10), intestinal microbiota structure, and the spatial information on key metabolites. Some gut-microbiota-mediated metabolic processes were also significantly altered. These results indicated that ALP-1 could exert beneficial effects on immune responses and intestinal health in healthy mice.

CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

A joint model based on graph and deep learning for hyperspectral anomaly detection

Through the years, graph theory has gradually been applied in hyperspectral image (HSI) processing. The graph theory method does not need to consider the structural characteristics of the dataset and has achieved satisfactory application results. However, the current graph theory methods applied in HSI processing mainly focus on spectral characteristics. This paper proposes a joint model based on graph and deep learning (JGD) for hyperspectral anomaly detection (AD). The proposed JGD consists of a spectral sub-model using graph and a spatial sub-model using deep learning. In the spectral sub-model, graph Fourier transform (GFT) is used, and the reconstruction errors between the original HSI and the HSI after GFT are mapped to fractional Fourier domain (FrFD) for AD results, which mainly makes use of the global spectral information. In the spatial sub-model, stacked autoencoders (SAEs) and an adaptive algorithm are combined to obtain AD results, which mainly use local spatial information and spectral information. These two sub-models are combined through the differential fusion method to form the JGD, which fully utilizes spectral and spatial information of hyperspectral images (HSIs). Compared with seven other algorithms on four real HSIs, the proposed JGD shows superior AD performance.

A Magnetic Particle Imaging Approach for Minimally Invasive Imaging and Sensing With Implantable Bioelectronic Circuits.

Minimally-invasive and biocompatible implantable bioelectronic circuits are used for long-term monitoring of physiological processes in the body. However, there is a lack of methods that can cheaply and conveniently image the device within the body while simultaneously extracting sensor information. Magnetic Particle Imaging (MPI) with zero background signal, high contrast, and high sensitivity with quantitative images is ideal for this challenge because the magnetic signal is not absorbed with increasing tissue depth and incurs no radiation dose. We show how to easily modify common implantable devices to be imaged by MPI by encapsulating and magnetically-coupling magnetic nanoparticles (SPIOs) to the device circuit. These modified implantable devices not only provide spatial information via MPI, but also couple to our handheld MPI reader to transmit sensor information by modulating harmonic signals from magnetic nanoparticles via switching or frequency-shifting with resistive or capacitive sensors. This paper provides proof-of-concept of an optimized MPI imaging technique for implantable devices to extract spatial information as well as other information transmitted by the implanted circuit (such as biosensing) via encoding in the magnetic particle spectrum. The 4D images present 3D position and a changing color tone in response to a variable biometric. Biophysical sensing via bioelectronic circuits that take advantage of the unique imaging properties of MPI may enable a wide range of minimally invasive applications in biomedicine and diagnosis.

Conditioned preferences: Gated by experience, context, and endocrine systems

Central to the navigation of an ever-changing environment is the ability to form positive associations with places and conspecifics. The functions of location and social conditioned preferences are often studied independently, limiting our understanding of their interplay. Furthermore, a de-emphasis on natural functions of conditioned preferences has led to neurobiological interpretations separated from ecological context. By adopting a naturalistic and ethological perspective, we uncover complexities underlying the expression of conditioned preferences. Development of conditioned preferences is a combination of motivation, reward, associative learning, and context, including for social and spatial environments. Both social- and location-dependent reward-responsive behaviors and their conditioning rely on internal state-gating mechanisms that include neuroendocrine and hormone systems such as opioids, dopamine, testosterone, estradiol, and oxytocin. Such reinforced behavior emerges from mechanisms integrating past experience and current social and environmental conditions. Moreover, social context, environmental stimuli, and internal state gate and modulate motivation and learning via associative reward, shaping the conditioning process. We highlight research incorporating these concepts, focusing on the integration of social neuroendocrine mechanisms and behavioral conditioning. We explore three paradigms: 1) conditioned place preference, 2) conditioned social preference, and 3) social conditioned place preference. We highlight nonclassical species to emphasize the naturalistic applications of these conditioned preferences. To fully appreciate the complex integration of spatial and social information, future research must identify neural networks where endocrine systems exert influence on such behaviors. Such research promises to provide valuable insights into conditioned preferences within a broader naturalistic context.