Density Labeling Research Articles

Disentangling cobionts and contamination in long-read genomic data using sequence composition.

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

Nucleus subtype classification using inter-modality learning.

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of 0.34 ± 0.15 (prevalence 0.03 ± 0.01) and 0.47 ± 0.1 (prevalence 0.07 ± 0.02) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology.

An exploratory study on dialect density estimation for children and adult's African American Englisha).

This paper evaluates an innovative framework for spoken dialect density prediction on children's and adults' African American English. A speaker's dialect density is defined as the frequency with which dialect-specific language characteristics occur in their speech. Rather than treating the presence or absence of a target dialect in a user's speech as a binary decision, instead, a classifier is trained to predict the level of dialect density to provide a higher degree of specificity in downstream tasks. For this, self-supervised learning representations from HuBERT, handcrafted grammar-based features extracted from ASR transcripts, prosodic features, and other feature sets are experimented with as the input to an XGBoost classifier. Then, the classifier is trained to assign dialect density labels to short recorded utterances. High dialect density level classification accuracy is achieved for child and adult speech and demonstrated robust performance across age and regional varieties of dialect. Additionally, this work is used as a basis for analyzing which acoustic and grammatical cues affect machine perception of dialect.

AG-Meta: Adaptive graph meta-learning via representation consistency over local subgraphs

Graph meta-learning has recently received significantly increased attention by virtue of its potential to extract common and transferable knowledge from learning different tasks on a graph. Existing methods for graph meta-learning usually leverage local subgraphs to transfer subgraph-specific information. However, they inherently face the challenge of imbalanced subgraphs due to inconsistent node density and different label distributions over local subgraphs. This paper proposes an adaptive graph meta-learning framework (AG-Meta) for learning the consistent and transferable representation of a graph in a way that can adapt to imbalanced subgraphs. Specifically, AG-Meta first learns the structural representation of subgraphs with various degrees using an Adaptive Graph Cascade Diffusion Network (AGCDN). AG-Meta then employs a prototype-consistency classifier to produce more accurate transferable inductive representations (also called prototypes) under few-shot settings with different label distributions of a subgraph. In the context of optimizing a model-agnostic meta-learner, a novel metric loss is finally introduced to achieve structural representation and prototype consistency. Extensive experiments are conducted to compare AG-Meta against baselines on five real-world networks, which validates that AG-Meta outperforms the state-of-the-art approaches.

Sodium tanshinone IIA sulfonate inhibits tumor growth via miR-138 upregulation in intermittent hypoxia-induced xenograft mice.

We studied the functions of sodium tanshinone IIA sulfonate (TSA) in inducing tumor growth in obstructive sleep apnea (OSA)-mimicking intermittent hypoxia (IH) xenograft mice and the underlying potential molecular mechanism. RNA sequencing was conducted to screen the differentially expressed microRNAs in cell lines exposed to IH with or without TSA treatment. As part of the 5-week in vivo study, we treated xenograft mice with 8-h IH once daily. TSA and miR-138 inhibitors or mimics were administrated appropriately. In addition, we performed real-time quantitative polymerase chain reaction (RT-PCR), Western blotting, enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), microvessel density (MVD), and terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assays. RNA sequencing and RT-PCR results demonstrated that TSA increased the levels of miR-138 under IH conditions in vitro. TSA reduced the IH-stimulated high levels of hypoxia-induced factor-1α and vascular endothelial growth factor. Furthermore, IH contributed to high tumor migration, invasion, MVD, and low apoptosis. TSA attenuated IH-mediated tumor proliferation, migration, invasion, MVD, and increased apoptosis, whereas miR-138 inhibitor interrupted the effect of TSA on treating IH-induced tumor behaviors. OSA mimicking IH facilitates tumor growth and reduces miR-138 levels. TSA inhibits IH-induced tumor growth by upregulating the expression of miR-138.

The effect of variable labels on deep learning models trained to predict breast density

Purpose. High breast density is associated with reduced efficacy of mammographic screening and increased risk of developing breast cancer. Accurate and reliable automated density estimates can be used for direct risk prediction and passing density related information to further predictive models. Expert reader assessments of density show a strong relationship to cancer risk but also inter-reader variation. The effect of label variability on model performance is important when considering how to utilise automated methods for both research and clinical purposes. Methods. We utilise subsets of images with density labels from the same 13 readers and 12 reader pairs, and train a deep transfer learning model which is used to assess how label variability affects the mapping from representation to prediction. We then create two end-to-end models: one that is trained on averaged labels across the reader pairs and the second that is trained using individual reader scores, with a novel alteration to the objective function. The combination of these two end-to-end models allows us to investigate the effect of label variability on the model representation formed. Results. We show that the trained mappings from representations to labels are altered considerably by the variability of reader scores. Training on labels with distribution variation removed causes the Spearman rank correlation coefficients to rise from 0.751 ± 0.002 to either 0.815 ± 0.026 when averaging across readers or 0.844 ± 0.002 when averaging across images. However, when we train different models to investigate the representation effect we see little difference, with Spearman rank correlation coefficients of 0.846 ± 0.006 and 0.850 ± 0.006 showing no statistically significant difference in the quality of the model representation with regard to density prediction. Conclusions. We show that the mapping between representation and mammographic density prediction is significantly affected by label variability. However, the effect of the label variability on the model representation is limited.

Glutamine energy substrate anaplerosis increases bone density in the Pahenu2 classical PKU mouse in the absence of phenylalanine restriction.

Osteopenia is an under‐investigated clinical presentation of phenylalanine hydroxylase (PAH)‐deficient phenylketonuria (PKU). While osteopenia is not fully penetrant in human PKU, the Pahenu2 mouse is universally osteopenic and ideal to study the phenotype. We determined Pahenu2 mesenchymal stem cells (MSCs) are developmentally impaired in the osteoblast lineage. Moreover, we determined energy dysregulation and oxidative stress contribute to the osteoblast developmental deficit. The MSC preferred substrate glutamine (Gln) was applied to enhance energy homeostasis. In vitro Pahenu2 MSCs, in the context of 1200 μM Phe, respond to Gln with increased in situ alkaline phosphatase activity indicating augmented osteoblast differentiation. Oximetry applied to Pahenu2 MSCs in osteoblast differentiation show Gln energy substrate increases oxygen consumption, specifically maximum respiration and respiratory reserve. For 60 days post‐weaning, Pahenu2 animals received either no intervention (standard lab chow), amino acid defined chow maintaining plasma Phe at ~200 μM, or standard lab chow where ad libitum water was a 2% Gln solution. Bone density was assessed by microcomputed tomography and bone growth assessed by dye labeling. Bone density and dye labeling in Phe‐restricted Pahenu2 was indistinguishable from untreated Pahenu2. Gln energy substrate provided to Pahenu2, in the context of uncontrolled hyperphenylalaninemia, present increased bone density and dye labeling. These data provide further evidence that Pahenu2 MSCs experience a secondary energy deficit that is responsive both in vitro and in vivo to Gln energy substrate and independent of hyperphenylalaninemia. Energy support may have effect to treat human PKU osteopenia and elements of PKU neurologic disease resistant to standard of care systemic Phe reduction. Glutamine energy substrate anaplerosis increased Pahenu2 bone density and improved in vitro MSC function in the context of hyperphenylalaninemia in the classical PKU range.

The Effect of Bangerter Filters on Visual Acuity and Contrast Sensitivity With External Noise.

It is critical to address the relationship between density label of Bangerter filters and expected visual acuity, and how filters modulate contrast sensitivity (CS) at different spatial frequency and external noise levels. In the current study, the monocular visual acuity and CS at ten spatial frequencies and three noise levels were measured in normal subjects wearing no filters, 0.8, 0.4, or 0.2 Bangerter filters. Compared with the baseline condition (no filter worn), Bangerter filters degraded both visual acuity and contrast sensitivity function (CSF) in zero-noise conditions, but the reduction of both visual functions did not correlate with each other at any filter level. In addition, the stronger the filter was, the worse both visual functions became. In contrast, when external noise was present, filters improved the contrast sensitivity at low frequencies but deteriorated it at intermediate and high spatial frequencies. The perceptual template model was used to reveal the corresponding mechanism accounted for filter-induced visual function changes. Although the internal process in visual system should not be affected by the filters, the measurement of parameters was biased. To be specific, (1) the internal additive noise was elevated at all frequencies; (2) the perceptual template was improved at low spatial frequencies but impaired at intermediate spatial frequencies; and (3) the changes in both factors were highly dependent on filter intensity. We conclude that Bangerter filters influence visual acuity and contrast sensitivity differently and that their effect on contrast sensitivity depends on spatial frequency and noise.

Pedestrian Traffic Prediction using Deep Learning

Pedestrian traffic information offers useful insights when developing or maintaining a business.This research combines image processing and machine learning methods to predictpedestrian traffic flowrate and density for up to two days into the future, based on weatherdata, calendar data, and special events. To obtain the traffic flowrate and density, we firstdeveloped a neural network model to predict the number of new people and the total numberof people in each sequence of images captured by a Nova Scotia Webcams camera. Thesecounts of people are used to calculate the pedestrian traffic flowrate and density labels forhourly intervals. These labels are then combined with hourly weather data, calendar data,and special event data from the same period to train a recurrent neural network to predictthe traffic flowrate and density for up to two days in advance.We try two different approaches, CNN-LSTM and dual input CNN to predict the numberof new people and the total number of people from the images and compare how well eachapproach performs. The results show that the dual image input CNN models are moreeffective at predicting the number of new people and the total number of people than the CNN- LSTM models. Tested on independent test sets of images using K-fold cross-validation, theMTL CNN model achieved a test accuracy of 72% for the number of new people and 78%accuracy for the total number of people.

Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

AbstractComplex networks represent various real‐world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real‐world known networks, including Zachary, Dolphins, and COVID‐19 Co‐authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state‐of‐the‐art overlapping community detection algorithms in terms of recall, precision, F‐score and overlapping normalized mutual information.

Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms.

This paper investigates the usefulness of multi-fractal analysis and local binary patterns (LBP) as texture descriptors for classifying mammogram images into different breast density categories. Multi-fractal analysis is also used in the pre-processing step to segment the region of interest (ROI). We use four multi-fractal measures and the LBP method to extract texture features, and to compare their classification performance in experiments. In addition, a feature descriptor combining multi-fractal features and multi-resolution LBP (MLBP) features is proposed and evaluated in this study to improve classification accuracy. An autoencoder network and principal component analysis (PCA) are used for reducing feature redundancy in the classification model. A full field digital mammogram (FFDM) dataset, INBreast, which contains 409 mammogram images, is used in our experiment. BI-RADS density labels given by radiologists are used as the ground truth to evaluate the classification results using the proposed methods. Experimental results show that the proposed feature descriptor based on multi-fractal features and LBP result in higher classification accuracy than using individual texture feature sets.

A stable community detection approach for complex network based on density peak clustering and label propagation

Dividing a network into communities has great benefits in understanding the characteristics of the network. The label propagation algorithm (LPA) is a fast and convenient community detection algorithm. However, the community initialization of LPA does not take advantage of topological information of networks, and its robustness is poor. In this paper, we propose a stable community detection algorithm based on density peak clustering and label propagation (DS-LPA). First, the local density calculation method in density peak clustering algorithm is improved in finding the community center of the network, so as to build a suitable initial community, which can improve the quality of community partition. Then, the label update order is determined reasonably by computing the information transmission power of nodes, and the solutions for multiple candidate labels are provided, which greatly improved the robustness of the algorithm. DS-LPA is compared with other seven algorithms on the synthetic network and real-world networks. NMI, ARI, and modularity are used to evaluate these algorithms. It can be concluded that DS-LPA has a higher performance than most comparison algorithms on synthetic network with ten different mixed parameters by statistical testing. And DS-LPA can quickly calculate the best community partition on different sizes of real-world networks.

A Novel Algorithm for Detecting Pedestrians on Rainy Image.

Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.

3D Magnetic Alignment of Cardiac Cells in Hydrogels.

Tissue engineering aims to repair or replace deficient tissue by delivering constructs that mimic the native in vivo structure. One challenge in cardiac tissue engineering approaches is to achieve intrinsic cardiac organization, particularly the alignment of cardiomyocytes. Here, we propose a strategy for 3D manipulation and alignment of cardiomyocytes by combining magnetism and a hydrogel. The advantage of using magnetic forces is that they act remotely on the cells when these are endowed with magnetization via the internalization of magnetic nanoparticles. The magnetic actuation then allows obtaining, almost instantaneously and before gel transition, an aligned biomimetic cardiac tissue construct. Gel transition enables us to keep the cellular pattern once the magnetic field was removed. This cardiac tissue engineering approach was tested with both H9c2 cell line and primary cardiomyocytes, and with both a synthetic hydrogel and a natural one, Pluronic F-127 and fibrin, respectively. Key parameters of the anisotropic tissue formation were assessed. Hydrogel rheology is provided, and the impact of cell density and magnetic labeling on cell-cell alignment is assessed. Immunofluorescence confirms the presence of several cardiac markers upon chaining, demonstrating the functionality of the tissue-like cell alignment obtained via magnetic actuation.

Optimization and Validation of a Human Ex Vivo Femoral Head Model for Preclinical Cartilage Research and Regenerative Therapies.

ObjectiveArticular cartilage is incapable of effective repair following injury or during osteoarthritis. While there have been developments in cartilage repair technologies, there is a need to advance biologically relevant models for preclinical testing of biomaterial and regenerative therapies. This study describes conditions for the effective ex vivo culture of the whole human femoral head.DesignFresh, viable femoral heads were obtained from femoral neck fractures and cultured for up to 10 weeks in (a) Dulbecco’s modified Eagle’s medium (DMEM); (b) DMEM + mixing; (c) DMEM + 10% human serum (HS); (d) DMEM + 10% HS + mixing. The viability, morphology, volume, and density of fluorescently labelled in situ chondrocytes and cartilage surface roughness were assessed by confocal microscopy. Cartilage histology was studied for glycosaminoglycan content using Alcian blue and collagen content using picrosirius red.ResultsChondrocyte viability remained at >95% in DMEM + 10% HS. In DMEM alone, viability remained high for ~4 weeks and then declined. For the other conditions, superficial zone chondrocyte viability fell to <35% at 10 weeks with deeper zones being relatively unaffected. In DMEM + 10% HS at 10 weeks, the number of chondrocytes possessing cytoplasmic processes increased compared with DMEM (P = 0.017). Alcian blue labeling decreased (P = 0.02) and cartilage thinned (P ≤ 0.05); however, there was no change to surface roughness, chondrocyte density, chondrocyte volume, or picrosirius red labeling (P > 0.05).ConclusionsIn this ex vivo model, chondrocyte viability was maintained in human femoral heads for up to 10 weeks in culture, a novel finding not previously reported. This human model could prove invaluable for the exploration, development, and assessment of preclinical cartilage repair and regenerative therapies.

Study of Microbial Extracellular Vesicles:Separation by Density Gradients, Protection Assays and Labelling for Live Tracking.

Extracellular vesicles (EVs) are produced by all domains of life including Bacteria, Archaea and Eukarya. EVs are critical for cellular physiology and contain varied cargo: virulence factors, cell wall remodeling enzymes, extracellular matrix components and even nucleic acids and metabolites. While various protocols for isolating EVs have been established for mammalian cells, the field is actively developing tools to study EVs in other organisms. In this protocol we describe our methods to perform density gradient purification of EVs in bacterial cells, allowing for separation of EV subpopulations, followed by protection assays for EV cargo characterization. Furthermore, we devised a protocol which incorporates a fluorescent conjugate of fatty acids into EVs, the first to allow live-cell EV tracking to observe release of EVs, including during infection of mammalian cells by pathogenic bacteria. These protocols are powerful tools for EV researchers as they enable the observation of EV release and the study of the mechanisms of their formation and release.

Heavy upright exercise increases ventilation-perfusion mismatch in the basal lung: indirect evidence for interstitial pulmonary edema.

Ventilation-perfusion (V̇a/Q̇) mismatch during exercise may result from interstitial pulmonary edema if increased pulmonary vascular pressure causes fluid efflux into the interstitium. If present, the increased fluid may compress small airways or blood vessels, disrupting V̇a/Q̇ matching, but this is unproven. We hypothesized that V̇a/Q̇ mismatch would be greatest in basal lung following heavy upright exercise, consistent with hydrostatic forces favoring edema accumulation in the gravitationally dependent lung. We applied new tools to reanalyze previously published magnetic resonance imaging data to determine regional V̇a/Q̇ mismatch following 45 min of heavy upright exercise in six athletes (V̇o2max = 61 ± 7 mL·kg-1·min-1). In the supine posture, regional alveolar ventilation and local perfusion were quantified from specific ventilation imaging, proton density, and arterial spin labeling data in a single sagittal slice of the right lung before exercise (PRE), 15 min after exercise (POST), and in recovery 60 min after exercise (REC). Indices of V̇a/Q̇ mismatch [second moments (log scale) of ventilation (LogSDV) and perfusion (LogSDQ) vs. V̇a/Q̇ distributions] were calculated for apical, middle, and basal lung thirds, which represent gravitationally nondependent, middle, and dependent regions, respectively, during upright exercise. LogSDV increased after exercise only in the basal lung (PRE 0.46 ± 0.06, POST 0.57 ± 0.14, REC 0.55 ±0.14, P = 0.01). Similarly, LogSDQ increased only in the basal lung (PRE 0.40 ± 0.06, POST 0.51 ± 0.10, REC 0.44 ± 0.09, P = 0.04). Increased V̇a/Q̇ mismatch in the basal lung after exercise is potentially consistent with interstitial pulmonary edema accumulating in gravitationally dependent lung during exercise.NEW & NOTEWORTHY We reanalyzed previously published MRI data with new tools and found increased ventilation-perfusion mismatch only in the basal lung of athletes following 45 min of cycling exercise. This is consistent with the development of interstitial edema in the gravitationally dependent lung during heavy exercise.

Self-reinforced diffusion for graph-based semi-supervised learning

Graph construction is the essential component of graph-based semi-supervised learning. Gaussian kernel weighted graph is widely used in this field. The problem of using such kind of graphs is that they are susceptible to data noise. They cannot approximate the geodesic distance on the underlying manifold appropriately, especially for high-dimensional data. Diffusion process has shown its effectiveness of learning pair-wise affinities which is equivalent to a graph, due to its capability of revealing the geometry structure of the manifold. However, data density distribution and label information are ignored in this process, limiting its application for semi-supervised problems. To address these issues, we propose a variant of the diffusion process, named Self-Reinforced Diffusion, which can make use of the label information. As for data density distribution, we introduce an intuitive affinity term, called self-affinity, which can well approximate density distributions and can be directly diffused on the graph. Extensive experiments on noisy synthetic data and various real-world data have demonstrated the effectiveness of the proposed method on semi-supervised learning.

PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes, and severe congestion. Many methods only focus on the local appearance features and they cannot handle the aforementioned challenges. In order to tackle them, we propose a perspective crowd counting network (PCC Net), which consists of three parts: 1) density map estimation (DME) focuses on learning very local features of density map estimation; 2) random high-level density classification (R-HDC) extracts global features to predict the coarse density labels of random patches in images; and 3) fore-/background segmentation (FBS) encodes mid-level features to segments the foreground and background. Besides, the Down, Up, Left, and Right (DULR) module is embedded in PCC Net to encode the perspective changes on four directions (DULR). The proposed PCC Net is verified on five mainstream datasets, which achieves the state-of-the-art performance on the one and attains the competitive results on the other four datasets. The source code is available at https://github.com/gjy3035/PCC-Net .

Whole mouse brain reconstruction and registration to a reference atlas with standard histochemical processing of coronal sections.

Advances in molecular neuroanatomical tools have expanded the ability to map in detail connections of specific neuron subtypes in the context of behaviorally driven patterns of neuronal activity. Analysis of such data across the whole mouse brain, registered to a reference atlas, aids in understanding the functional organization of brain circuits related to behavior. A process is described to image mouse brain sections labeled with standard histochemical techniques, reconstruct those images into a whole brain image volume and register those images to the Allen Mouse Brain Common Coordinate Framework. Image analysis tools automate detection of cell bodies and quantification of axon density labeling in the structures in the annotated reference atlas. Examples of analysis are provided for mapping the axonal projections of layer-specific cortical neurons using Cre-dependent AAV vectors and for mapping inputs to such neurons using retrograde transsynaptic tracing with modified rabies viral vectors.