Specific Labeling Research Articles

Generation of functional neurons from adult human mucosal olfactory ensheathing glia by direct lineage conversion

A recent approach to promote central nervous system (CNS) regeneration after injury or disease is direct conversion of somatic cells to neurons. This is achieved by transduction of viral vectors that express neurogenic transcription factors. In this work we propose adult human mucosal olfactory ensheathing glia (hmOEG) as a candidate for direct reprogramming to neurons due to its accessibility and to its well-characterized neuroregenerative capacity. After induction of hmOEG with the single neurogenic transcription factor NEUROD1, the cells under study exhibited morphological and immunolabeling neuronal features, fired action potentials and expressed glutamatergic and GABAergic markers. In addition, after engraftment of transduced hmOEG cells in the mouse hippocampus, these cells showed specific neuronal labeling. Thereby, if we add to the neuroregenerative capacity of hmOEG cultures the conversion to neurons of a fraction of their population through reprogramming techniques, the engraftment of hmOEG and hmOEG-induced neurons could be a procedure to enhance neural repair after central nervous system injury.

Tracing the Chromatin: From 3C to Live-Cell Imaging.

Chromatin organization plays a key role in gene regulation throughout the cell cycle. Understanding the dynamics governing the accessibility of chromatin is crucial for insight into mechanisms of gene regulation, DNA replication, and cell division. Extensive research has been done to track chromatin dynamics to explain how cells function and how diseases develop, in the hope of this knowledge leading to future therapeutics utilizing proteins or drugs that modify the accessibility or expression of disease-related genes. Traditional methods for studying the movement of chromatin throughout the cell relied on cross-linking spatially adjacent sections or hybridizing fluorescent probes to chromosomal loci and then constructing dynamic models from the static data collected at different time points. While these traditional methods are fruitful in understanding fundamental aspects of chromatin organization, they are limited by their invasive sample preparation protocols and diffraction-limited microscope resolution. These limitations have been challenged by modern methods based on high- or super-resolution microscopy and specific labeling techniques derived from gene targeting tools. These modern methods are more sensitive and less invasive than traditional methods, therefore allowing researchers to track chromosomal organization, compactness, and even the distance or rate of chromatin domain movement in detail and real time. This review highlights a selection of recently developed methods of chromatin tracking and their applications in fixed and live cells.

Targeting Patient-Derived Orthotopic Gastric Cancers with a Fluorescent Humanized Anti-CEA Antibody

BackgroundGastric cancer poses a major diagnostic and therapeutic challenge as surgical resection provides the only opportunity for a cure. Specific labeling of gastric cancer could distinguish resectable and nonresectable disease and facilitate an R0 resection, which could improve survival.MethodsTwo patient-derived gastric cancer lines, KG8 and KG10, were established from surgical specimens of two patients who underwent gastrectomy for gastric adenocarcinoma. Harvested tumor fragments were implanted into the greater curvature of the stomach to establish patient-derived orthotopic xenograft (PDOX) models. M5A (humanized anti-CEA antibody) or IgG control antibodies were conjugated with the near-infrared dye IRDye800CW. Mice received 50 µg of M5A-IR800 or 50 µg of IgG-IR800 intravenously and were imaged after 72 hr. Fluorescence imaging was performed by using the LI-COR Pearl Imaging System. A tumor-to-background ratio (TBR) was calculated by dividing the mean fluorescence intensity of the tumor versus adjacent stomach tissue.ResultsM5A-IR800 administration resulted in bright labeling of both KG8 and K10 tumors. In the KG8 PDOX models, the TBR for M5A-IR800 was 5.85 (SE ± 1.64) compared with IgG-IR800 at 0.70 (SE ± 0.17). The K10 PDOX models had a TBR of 3.71 (SE ± 0.73) for M5A-IR800 compared with 0.66 (SE ± 0.12) for IgG-IR800.ConclusionsHumanized anti-CEA (M5A) antibodies conjugated to fluorescent dyes provide bright and specific labeling of gastric cancer PDOX models. This tumor-specific fluorescent antibody is a promising potential clinical tool to detect the extent of disease for the determination of resectability as well as to visualize tumor margins during gastric cancer resection.

Thickness-Resolved Electrochemiluminescence Microscopy of Extracellular Matrix at Tumor Tissues for Rapid Cancer Diagnosis.

The traditional recognition of extracellular matrix (ECM) at tissue sections relies on the time-consuming immunofluorescence that could not meet the demand of rapid diagnosis. Herein, we introduce a thickness-resolved electrochemiluminescence (ECL) microscopy to image thin-layer ECM at tissue sections for fast histopathological analysis. The unique surface-confined ECL mechanism enables to unveil the diversity and complexity of multiple tissue structures with varying thicknesses. Notably, the short lifetimes and the limited diffusion of electrogenerated coreactant radicals combined with their chemical reactivity result in a 2-fold increase in ECL intensity on ECM structures compared to the remaining tissue, enabling ECM visualization without specific labeling. The further quantitation of the ECM localization within tissue sections furnishes crucial insights into tumor progression and, more importantly, differentiates carcinoma and paracancerous tissues from patients in less than 30 min. Moreover, the reported electrochemistry-based microscopy is a dynamic approach allowing to investigate the transport, tortuosity, and trafficking properties through the tissues. This thickness-resolved recognition strategy not only opens new avenues for imaging complex samples but also holds promise for expediting tissue pathologic diagnosis, offering a more automated protocol with enhanced quantitative data compared to current intraoperative pathology methods.

U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery

Semantic segmentation of remote sensing imagery stands as a fundamental task within the domains of both remote sensing and computer vision. Its objective is to generate a comprehensive pixel-wise segmentation map of an image, assigning a specific label to each pixel. This facilitates in-depth analysis and comprehension of the Earth’s surface. In this paper, we propose an approach for enhancing semantic segmentation performance by employing an ensemble of U-Net models with three different backbone networks: Multi-Axis Vision Transformer, ConvFormer, and EfficientNet. The final segmentation maps are generated through a geometric mean ensemble method, leveraging the diverse representations learned by each backbone network. The effectiveness of the base U-Net models and the proposed ensemble is evaluated on multiple datasets commonly used for semantic segmentation tasks in remote sensing imagery, including LandCover.ai, LoveDA, INRIA, UAVid, and ISPRS Potsdam datasets. Our experimental results demonstrate that the proposed approach achieves state-of-the-art performance, showcasing its effectiveness and robustness in accurately capturing the semantic information embedded within remote sensing images.

Nucleoside Cation Radicals: Generation, Radical-Induced Hydrogen Atom Migrations, and Ribose Ring Cleavage in the Gas Phase.

Nucleoside ions that were furnished on ribose with a 2'-O-acetyl radical group were generated in the gas phase by multistep collision-induced dissociation of precursor ions tagged with radical initiator groups, and their chemistry was investigated in the gas phase. 2'-O-Acetyladenosine cation radicals were found to undergo hydrogen transfer to the acetoxyl radical from the ribose ring positions that were elucidated using specific deuterium labeling of 1'-H, 2'-H, and 4'-H and in the N-H and O-H exchangeable positions, favoring 4'-H transfer. Ion structures and transition-state energies were calculated by a combination of Born-Oppenheimer molecular dynamics and density functional theory and used to obtain unimolecular rate constants for competitive hydrogen transfer and loss of the acetoxyl radical. Migrations to the acetoxyl radical of ribose hydrogens 1'-H, 2'-H, 3'-H, and 4'-H were all exothermic, but product formation was kinetically controlled. Both Rice-Ramsperger-Kassel-Marcus (RRKM) and transition-state theory (TST) calculations indicated preferential migration of 4'-H in a qualitative agreement with the deuterium labeling results. The hydrogen migrations displayed substantial isotope effects that along with quantum tunneling affected the relative rate constants and reaction branching ratios. UV-vis action spectroscopy indicated that the cation radicals from 2'-O-acetyladenosine consisted of a mixture of isomers. Radical-driven dissociations were also observed for protonated guanosine, cytosine, and thymidine conjugates. However, for those nucleoside ions and cation radicals, the dissociations were dominated by the loss of the nucleobase or formation of protonated nucleobase ions.

Bimodal deep learning model for subjectively enhanced emotion classification in films

This research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio–video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented. The insights gained from these subjective evaluations facilitated the creation of a comprehensive database of movie excerpts. This database was subsequently employed to both train and evaluate the efficacy of deep learning models. The latter half of the study shifts focus to the intelligent analysis of audio and video signals that form film excerpts. This involved exploring diverse methodologies for parameterizing these signals. Models that demonstrated the highest accuracy on the test dataset on audio/video only were amalgamated to forge a bimodal model, which integrates both audio and video signals for emotion classification. The bimodal model exhibited superior accuracy in tests compared to a model that solely relied on video signal classification. This enhancement in performance was achieved with only a marginal increase in the complexity and the number of parameters within the model.

Masked self‐supervised pre‐training model for EEG‐based emotion recognition

AbstractElectroencephalogram (EEG), as a tool capable of objectively recording brain electrical signals during emotional expression, has been extensively utilized. Current technology heavily relies on datasets, with its performance being limited by the size of the dataset and the accuracy of its annotations. At the same time, unsupervised learning and contrastive learning methods largely depend on the feature distribution within datasets, thus requiring training tailored to specific datasets for optimal results. However, the collection of EEG signals is influenced by factors such as equipment, settings, individuals, and experimental procedures, resulting in significant variability. Consequently, the effectiveness of models is heavily dependent on dataset collection efforts conducted under stringent objective conditions. To address these challenges, we introduce a novel approach: employing a self‐supervised pre‐training model, to process data across different datasets. This model is capable of operating effectively across multiple datasets. The model conducts self‐supervised pre‐training without the need for direct access to specific emotion category labels, enabling it to pre‐train and extract universally useful features without predefined downstream tasks. To tackle the issue of semantic expression confusion, we employed a masked prediction model that guides the model to generate richer semantic information through learning bidirectional feature combinations in sequence. Addressing challenges such as significant differences in data distribution, we introduced adaptive clustering techniques that manage by generating pseudo‐labels across multiple categories. The model is capable of enhancing the expression of hidden features in intermediate layers during the self‐supervised training process, enabling it to learn common hidden features across different datasets. This study, by constructing a hybrid dataset and conducting extensive experiments, demonstrated two key findings: (1) our model performs best on multiple evaluation metrics; (2) the model can effectively integrate critical features from different datasets, significantly enhancing the accuracy of emotion recognition.

DECLAREd: A Polytime LTLf Fragment

This paper considers a specification rewriting meachanism for a specific fragment of Linear Temporal Logic for Finite traces, DECLAREd, working through an equational logic and rewriting mechanism under customary practitioner assumptions from the Business Process Management literature. By rewriting the specification into an equivalent formula which might be easier to compute, we aim to streamline current state-of-the-art temporal artificial intelligence algorithms working on temporal logic. As this specification rewriting mechanism is ultimately also able to determine with the provided specification is a tautology (always true formula) or a formula containing a temporal contradiction, by detecting the necessity of a specific activity label to be both present and absent within a log, this implies that the proved mechanism is ultimately a SAT-solver for DECLAREd. We prove for the first time, to the best of our knowledge, that this fragment is a polytime fragment of LTLf, while all the previously-investigated fragments or extensions of such a language were in polyspace. We test these considerations over formal synthesis (Lydia), SAT-Solvers (AALTAF) and formal verification (KnoBAB) algorithms, where formal verification can be also run on top of a relational database and can be therefore expressed in terms of relational query answering. We show that all these benefit from the aforementioned assumptions, as running their tasks over a rewritten equivalent specification will improve their running times, thus motivating the pressing need of this approach for practical temporal artificial intelligence scenarios. We validate such claims by testing such algorithms over a Cybersecurity dataset.

Nanoscale cellular organization of viral RNA and proteins in SARS-CoV-2 replication organelles

The SARS-CoV-2 viral infection transforms host cells and produces special organelles in many ways, and we focus on the replication organelles, the sites of replication of viral genomic RNA (vgRNA). To date, the precise cellular localization of key RNA molecules and replication intermediates has been elusive in electron microscopy studies. We use super-resolution fluorescence microscopy and specific labeling to reveal the nanoscopic organization of replication organelles that contain numerous vgRNA molecules along with the replication enzymes and clusters of viral double-stranded RNA (dsRNA). We show that the replication organelles are organized differently at early and late stages of infection. Surprisingly, vgRNA accumulates into distinct globular clusters in the cytoplasmic perinuclear region, which grow and accommodate more vgRNA molecules as infection time increases. The localization of endoplasmic reticulum (ER) markers and nsp3 (a component of the double-membrane vesicle, DMV) at the periphery of the vgRNA clusters suggests that replication organelles are encapsulated into DMVs, which have membranes derived from the host ER. These organelles merge into larger vesicle packets as infection advances. Precise co-imaging of the nanoscale cellular organization of vgRNA, dsRNA, and viral proteins in replication organelles of SARS-CoV-2 may inform therapeutic approaches that target viral replication and associated processes.

Deep learning guided prediction modeling of dengue virus evolving serotype

Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63–65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.

Improving CNN-based semantic segmentation on structurally similar data using contrastive graph convolutional networks

Structurally similar data exist in most practical semantic segmentation applications. For example, objects can appear identical or positionally similar in many images, such as video frames. Objects with structural similarity in data samples can confuse deep neural networks (DNNs) in semantic segmentation applications. These challenges often lead to lower pixel classification accuracy of natural object segmentation. This study proposes a novel approach (S2-GCN) that enhances CNN-based semantic segmentation for structurally similar data using a contrastive graph convolutional network (GCN). By selecting specific label pairs and developing a customized GCN branch parallel to an encoder-decoder backbone, our method significantly improves accuracy, IoU, and F1-score, by up to 8 %, as demonstrated through an extensive evaluation of five datasets. Our findings show that the proposed method effectively addresses the structural similarity problem of CNN-based semantic segmentation and can be applied to a wide range of practical applications.

Learning hierarchical embedding space for image-text matching

There are two mainstream strategies for image-text matching at present. The one, termed as joint embedding learning, aims to model the semantic information of both image and sentence in a shared feature subspace, which facilitates the measurement of semantic similarity but only focuses on global alignment relationship. To explore the local semantic relationship more fully, the other one, termed as metric learning, aims to learn a complex similarity function to directly output score of each image-text pair. However, it significantly suffers from more computation burden at retrieval stage. In this paper, we propose a hierarchically joint embedding model to incorporate the local semantic relationship into a joint embedding learning framework. The proposed method learns the shared local and global embedding spaces simultaneously, and models the joint local embedding space with respect to specific local similarity labels which are easy to access from the lexical information of corpus. Unlike the methods based on metric learning, we can prepare the fixed representations of both images and sentences by concatenating the normalized local and global representations, which makes it feasible to perform the efficient retrieval. And experiments show that the proposed model can achieve competitive performance when compared to the existing joint embedding learning models on two publicly available datasets Flickr30k and MS-COCO.

Impact of different dose reduction criteria on dose assignment of current DOACs and related outcomes: an analysis from 4-year data of the ETNA-AF Europe programme

Abstract Background Direct oral anticoagulants (DOACs) are the standard treatment for stroke prevention in patients with atrial fibrillation (AF). Pharmacological characteristics and dose reduction criteria of the four currently available DOACs are different. Several studies have reported frequent use of non-recommended doses of DOACs associated with worse clinical outcomes. Data from real-world studies, such as the edoxaban Treatment in routiNe clinical prActice (ETNA)-AF, may help assessing the size of this phenomenon and its impact on clinical outcomes. Purpose To evaluate the distribution of dose reduction criteria and explore associated outcomes in a large, unselected, real-world population using 4-year data from the ETNA-AF EU study. Methods The ETNA-AF EU is a prospective, observational, study to evaluate safety and effectiveness of edoxaban in patients with AF from Europe. Patients with available data regarding the SmPC dose reduction criteria of the four currently available DOACs were included. Patients with a creatinine clearance <30 mL/min were excluded. Patients were classified by dose reduction criteria for the different DOACs (i.e., full dose for all [FULL], reduced dose for all [REDUCED], patients not included in previous groups [INTERMEDIATE]). Results We included 12,866 patients from the ETNA-AF EU study. According to the SmPC dose reduction criteria, the percentage of patients that would be eligible for a dose reduction in the ETNA population was highly heterogenous across different DOACS: 5.7% for apixaban, 16.8% for rivaroxaban, 23.8% for edoxaban and 52.9% for dabigatran. Moreover, differences in the dose reduction criteria included in each SmPC was reflected by the distribution of patients in the three pre-specified groups. Among the 11,003 assignable patients, 42.4% would receive a full dose, 5.1% would receive a reduced dose and the largest percentage, 52.5%, was in the INTERMEDIATE group, which would receive either a full or reduced dose of any DOAC depending on the selected agent. Of the patients included in the INTERMEDIATE group, 65.2% received a full dose of edoxaban due to its specific labelling. Interestingly, the INTERMEDIATE group included the vast majority of patients known to be at risk of adverse events: very elderly (i.e., ≥85 yrs; 76.5%), frail as per investigator (73.3%), history of previous stroke (60.4%) or history of bleeding (61.3%). Clinical outcomes at four years were significantly higher in both the INTERMEDIATE and the REDUCED groups compared to the FULL group, with the only exception of haemorrhagic stroke (Table). Conclusion(s): According to the different dose reduction criteria of currently available DOACs, notable percentage of patients could receive either a full or a reduced dose depending on the DOAC chosen by the physician. Considering that the chosen dose of the DOAC has an impact on clinical outcomes, patients in the INTERMEDIATE group need careful assessment, which warrants further analysis.

Functionalized Calcium Carbonate-Based Microparticles as a Versatile Tool for Targeted Drug Delivery and Cancer Treatment.

Nano- and microparticles are increasingly widely used in biomedical research and applications, particularly as specific labels and targeted delivery vehicles. Silica has long been considered the best material for such vehicles, but it has some disadvantages limiting its potential, such as the proneness of silica-based carriers to spontaneous drug release. Calcium carbonate (CaCO3) is an emerging alternative, being an easily available, cost-effective, and biocompatible material with high porosity and surface reactivity, which makes it an attractive choice for targeted drug delivery. CaCO3 particles are used in this field in the form of either bare CaCO3 microbeads or core/shell microparticles representing polymer-coated CaCO3 cores. In addition, they serve as removable templates for obtaining hollow polymer microcapsules. Each of these types of particles has its specific advantages in terms of biomedical applications. CaCO3 microbeads are primarily used due to their capacity for carrying pharmaceutics, whereas core/shell systems ensure better protection of the drug-loaded core from the environment. Hollow polymer capsules are particularly attractive because they can encapsulate large amounts of pharmaceutical agents and can be so designed as to release their contents in the target site in response to specific stimuli. This review focuses first on the chemistry of the CaCO3 cores, core/shell microbeads, and polymer microcapsules. Then, systems using these structures for the delivery of therapeutic agents, including drugs, proteins, and DNA, are outlined. The results of the systematic analysis of available data are presented. They show that the encapsulation of various therapeutic agents in CaCO3-based microbeads or polymer microcapsules is a promising technique of drug delivery, especially in cancer therapy, enhancing drug bioavailability and specific targeting of cancer cells while reducing side effects. To date, research in CaCO3-based microparticles and polymer microcapsules assembled on CaCO3 templates has mainly dealt with their properties in vitro, whereas their in vivo behavior still remains poorly studied. However, the enormous potential of these highly biocompatible carriers for in vivo applications is undoubted. This last issue is addressed in depth in the Conclusions and Outlook sections of the review.

210 Sleeping beauties: A daily time budget for individually housed research pigs at 6-d of age

Abstract The pre-wean period of development is difficult to manage and can impact the success of subsequent production stages. Behavior observations can provide insight into the health and welfare status of animals and allow for efficient and timely management interventions. A validated time budget can be utilized as a baseline for further comparisons and the detection of changes in behavior over time. In this project, 24-h of video data for 21 individually housed research pigs at 6 d of age was evaluated using a validated behavior ethogram. The behavior ethogram contained 5 main categories (inactive, consumption, exploratory, social, and other) and 23 behavior labels. The ethogram was applied using a validated sampling strategy, which included 5-min continuous sampling intervals at the start of each hour for active behaviors and 5-min instantaneous sampling intervals for inactive behaviors. The data from each behavior observation was quantified into durations and presented as percentage of observations spent performing a specific behavior label within the observation period. The daily time budget revealed the following time occupation across the 5 main behavior categories: inactive, 98.60% (lying, 85.43%; not lying, 13.14%); consumption, 0.30%; exploratory, 0.97%; social, 0.01%, and other, 0.12%. As expected for very young pigs, most of the day was inactive, with almost 21 h lying. The most performed behavior was “lying socially”, and this behavior represented 38.48% of the overall daily time budget. The remaining 46.95% of time spent lying was spent “lying sternal”, “lying lateral”, or “lying on an object” in the pen. The pigs are housed individually, but they are able to see, hear, and smell a neighboring pig through a plexiglass barrier. The behavior observation “lying socially” is defined as follows: “The piglet is lying (sternal or lateral) touching the plexiglass barrier. The piglet in the adjacent pen is lying directly on the other side of the barrier, and the piglets would be touching each other if the barrier was not in place.” This observation provides insight into the importance of social resting for young pigs.

Podocyte glycocalyx plays an essential role in the function of GFB and the development of albumin leakage

The role of the cell surface layer, glycocalyx is emerging in various areas of cell biology including signaling, metabolism, and immune response. Although a decrease of podocyte glycocalyx depth in glomerular pathologies has been reported before, the specific role of specialized glycocalyx between the podocyte foot processes and the glomerular basement membrane (GBM) in the function and dysfunction of the glomerular filtration barrier (GFB) is elusive. Here we aimed to develop podocyte or endothelial glycocalyx targeting strategies to gain mechanistic understanding of the specific roles of the glycocalyx in the function of GFB and in the development of albumin leakage. Our overall hypothesis is that the specialized podocyte glycocalyx forms an important additional layer of the GFB and changes in the structure and composition of podocyte glycocalyx leads to GFB dysfunction and the development of albumin leakage. New genetic mouse models were generated by the knock-in of pCAG-Lox-STOP-Lox-EGFP-Sdc1 to generate animals with Cre-dependent expression of a syndecan-1 (Sdc1) fusion protein with N-terminal, extracellular GFP. Sdc1-GFPflox animals were crossed with either Podocin-Cre or Cdh5-Cre mice for the specific genetic labeling of the glycocalyx in podocytes or endothelial cells, respectively. Intravital multiphoton microscopy (MPM) was used to measure changes in the depth of glycocalyx and corresponding changes in GFB function in vivo over time. Endoglycosidase enzymes, such as hyaluronidase (50U/animal), heparinase (0.7 U/mouse), and chondroitinase (1 U/mouse) were used to acutely modify the composition of glycocalyx. Alexa-680 conjugated wheat germ agglutinin (WGA) lectin was used to label the glycosaminoglycan (GAG) component of the glycocalyx; Alexa-594 conjugated albumin was used to visualize GFB permeability. Classic phenotyping of Pod and Cdh5 Sdc1-GFP animals showed no significant difference in terms of systolic blood pressure, glomerular filtration rate, body and kidney weight between the Sdc1-GFP and wild type littermates. The co-localization of the genetic GFP and anti-podocin immunofluorescence co-staining confirmed the podocyte-specific expression of Sdc1-GFP. In vivo MPM imaging of Pod-Sdc1-GFP mouse kidneys revealed intense linear labeling of the podocyte cell surface including the apical and basolateral surfaces of the cell body as well as the surfaces of primary, secondary, and foot processes. Interestingly, the thickness and fluorescence intensity of Sdc1-GFP labeled glycocalyx was greater on the basal surface of the podocyte at the GBM as compared to the apical surface implying the role of glycocalyx in the function of GFB. In addition, labeling of the basolateral surface of the podocyte cell body and podocyte foot processes enabled the visualization of sub-podocyte space. Acute injection of endoglycosidase enzymes significantly (3-fold) reduced Sdc1-GFP fluorescence intensity and the thickness of the linear labeling suggesting the acute shedding of podocyte glycocalyx. Importantly, increased albumin permeability of the GFB was observed simultaneously with the shedding of podocyte glycocalyx, indicating the disruption of GFB charge and size-selective functions. In conclusion, the specialized podocyte glycocalyx at the GBM may function as an important new layer of the GFB and may play an important role in the disruption of GFB functions in disease. ASN Carl W. Gottschalk Research Scholar Grant University of Southern California Dean's Pilot Project Award DK123564. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

An International and Multidisciplinary Consensus on the Labeling of Spatial Neglect Using a Modified Delphi Method

Survivors of neurologic injury (most commonly stroke or traumatic brain injury) frequently experience a disorder in which contralesionally positioned objects or the contralesional features of individual objects are often left unattended or underappreciated. The disorder is known by >200 unique labels in the literature, which potentially causes confusion for patients and their families, complicates literature searches for researchers and clinicians, and promotes a fractionated conceptualization of the disorder. The objective of this Delphi was to determine if consensus (≥75% agreement) could be reached by an international and multidisciplinary panel of researchers and clinicians with expertise on the topic.To accomplish this aim, we used a modified Delphi method in which 66 researchers and/or clinicians with expertise on the topic completed at least 1 of 4 iterative rounds of surveys. Per the Delphi method, panelists were provided with results from each round prior to responding to the survey in the subsequent round with the explicit intention of achieving consensus. The panel ultimately reached consensus that the disorder should be consistently labeled spatial neglect. Based on the consensus reached by our expert panel, we recommend that researchers and clinicians use the label spatial neglect when describing the disorder in general and more specific labels pertaining to subtypes of the disorder when appropriate.

Identification of Spinal Afferent Innervation in the Rat Heart: Atria and Ventricles: Anterograde Tracing

The spinal afferent innervation of the heart regulates cardiac functions by sending sensory information from the dorsal root ganglia (DRG) to the brain. However, the distribution and morphology of spinal afferents in the whole heart are not well characterized. The challenges here are: 1) diffculty in surgically accessing the upper thoracic DRG to label only cardiac spinal afferents. 2) handling flat-mounts of whole atria and ventricles (thickness ~850 μm) without using sections or partial tissues that could disrupt the continuity of axonal structures. To overcome these challenges, we injected anterograde tracer dextran biotin (DB) into the left DRG (C8-T3) of male SD rats (3-5 months) and allowed them to recover for 14 days for tracer transportation. Flat-mounts of the left and right atria and ventricles were prepared, and DAB (3,3′-Diaminobenzidine) stained, followed by imaging, tracing, and digitization using the Neurolucida system. Our findings are: In the atria, the DB-labeled axons entered the atria potentially through the left precaval vein (LPCV), with predominant innervation of the left side of the atrial wall and extended their projection towards the auricles, middle region of the atrium, and pulmonary veins. Most of the spinal afferent axons were present in the myocardial layer. Varicose DB-labeled axons were also observed on cardiac targets at different regions, and distinct morphological structures were found. In the cardiac muscle, DB-labeled axons ran mainly in the direction of muscle fibers. Also, varicose axons ran along the small vasculature on the atria. Spinal afferents formed varicose contacts with individual principal neurons within some intrinsic cardiac ganglia (ICG). In the ventricles, DB-labeled axons were present in the left and right ventricles, which ramified and expressed spherical-like varicosities on the cardiac muscle. In both the atria and ventricles, spinal afferent axons formed simple, branching, and complex terminal structures. Thus, our work, for the first time, successfully labeled, traced, and digitized the spinal cardiac afferents in the flat-mounts of whole atria and ventricles, which will provide a foundation for specific labeling of cardiac spinal afferent fibers. In addition, it will lead to the future study of topographical cardiac spinal afferent innervation, functional studies, and their remodeling in pathological conditions. This study was supported by NIH HEAL/SPARC U01 NS113867-01, NIH R15 R15HL137143-01A1, and UCF dissertation completion Fellowships. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

IDG-SemiAD: An Immune Detector Generation-Based Collaborative Learning Scheme for Semi-supervised Anomaly Detection in Industrial Cyber-physical Systems

Anomaly detection is a critical line of defense to ensure the network security of industrial cyber-physical systems. However, a significant issue in the anomaly detection is the insufficient labels of anomaly classes. With emergence of the new and unknown network attacks, accurately labeling these attacks can be a costly task. The issue of inadequate labeling may negatively impact the detection performance of many existing anomaly detection methods. To meet this gap, this paper proposes a semi-supervised collaborative learning paradigm called IDG-SemiAD, based on an immune detector generation algorithm. First, we design an immune detector generation algorithm based on a chaos map to generate abnormal samples from self-samples. Then, these abnormal samples are combined with self-samples and given specific labels to form a new training set. Finally, the LightGBM classifier is used for training and detection. Experiments on the widely used public dataset BATADAL show that the proposed IDG-SemiAD outperforms the classical v-detector method in terms of recall and f-score, with improvements of 8.2% and 8%, respectively, and outperforms deep learning-based anomaly detection methods, with a maximum improvements of up to 89.7% and 59.5% respectively.