Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies

This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.

Brief synaptic inhibition persistently interrupts firing of fast-spiking interneurons

Neurons perform input-output operations that integrate synaptic inputs with intrinsic electrical properties; these operations are generally constrained by the brevity of synaptic events. Here, we report that sustained firing of CA1 hippocampal fast-spiking parvalbumin-expressing interneurons (PV-INs) can be persistently interrupted for several hundred milliseconds following brief GABAAR-mediated inhibition in vitro and in vivo. A single presynaptic neuron could interrupt PV-IN firing, occasionally with a single action potential (AP), and reliably with AP bursts. Experiments and computational modeling reveal that the persistent interruption of firing maintains neurons in a depolarized, quiescent state through a cell-autonomous mechanism. Interrupted PV-INs are strikingly responsive to Schaffer collateral inputs. The persistent interruption of firing provides a disinhibitory circuit mechanism favoring spike generation in CA1 pyramidal cells. Overall, our results demonstrate that neuronal silencing can far outlast brief synaptic inhibition owing to the well-tuned interplay between neurotransmitter release and postsynaptic membrane dynamics, a phenomenon impacting microcircuit function.

Abstract 449: Machine learning models identify histological features that can predict KEAP1 mutations in lung adenocarcinoma

Background: In lung cancer, the KEAP1/NRF2 pathway modulates an anti-tumor response by regulating cellular metabolism and inflammatory processes. Approximately 19% of lung adenocarcinoma (LUAD) tumors have a mutation in KEAP1. Clinically, KEAP1-mutated LUAD has poor prognosis, and there is need for rapid and accurate patient genotyping to inform treatment decisions. Here, we describe machine learning (ML) models that can predict KEAP1 mutation status from tissue histology.

Methods: ML models, pre-trained to identify and quantify areas of tissue (cancer epithelium, cancer stroma, and necrosis), counts of cancer, fibroblast, and immune cells (lymphocytes, macrophages, plasma cells) in non-small cell lung cancer (NSCLC), were applied to 208 hematoxylin and eosin (H&E)-stained whole slide images (WSI) of LUAD from The Cancer Genome Atlas (TCGA) without further training. Genomic analyses indicated that 17% (N=35) of these cases are KEAP1MUT. Human Interpretable Features (HIFs), based on histology predictions, are automatically extracted from the model and provide a quantitative description of the tumor microenvironment of each WSI. Associations between HIFs and KEAP1MUT were determined by univariate analysis followed by false discovery rate (FDR) correction. Hierarchical clustering using cross correlation and combining p-values for HIF groups using an Empirical Brown’s method identified correlations between HIFs. Confounding factor influence was accounted for after positive associations were identified. Independent validation of associations between KEAP1MUT and HIFs was performed using TCGA transcriptomic data to correlate specific mutations with mRNA expression of relevant markers.

Results: ML-models generated 4,443 HIFs from the TCGA LUAD WSI, which were reduced to 2,684 HIFs after removal of outlier HIFs, exclusion of HIFs that are degenerate, have missing features, or are of an absolute value.

KEAP1MUT was significantly associated with 193 HIFs in univariate analyses (p<0.05 after FDR correction), and four groups of HIFs after taking correlations into account (p<0.05 after group-wise FDR correction; 211-264 HIFs per group). 161 HIFs were identified by both methods. Further assessment of the KEAP1MUT -associated HIFs showed that these mutations were correlated with a reduction in macrophages in the tumor microenvironment (TME). This was supported by analysis of transcriptomic data from KEAP1MUT (N=35) and KEAP1WT (N=164) samples, which showed significantly reduced expression of the macrophage marker CD14 (p<0.001) in KEAP1MUT tissue samples.

Conclusions: ML model quantification of TME histological features can generate HIFs that correlate with the KEAP1MUT status of a LUAD biopsy. These results exemplify how ML-powered digital pathology could predict molecular markers directly from standard H&E biopsy slides.

Citation Format: Robert Egger, Martin Ieong, Murray Resnick, Amaro Taylor-Weiner, Victoria Mountain, Ilan Wapinski, Michael Montalto, Andrew Beck, Josie Hayes, Benjamin Glass. Machine learning models identify histological features that can predict KEAP1 mutations in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 449.

Abstract 1922: Application of an interpretable graph neural network to predict gene expression signatures associated with tertiary lymphoid structures in histopathological images

Background: Tertiary lymphoid structures (TLS) are vascularized lymphocyte aggregates in the tumor microenvironment (TME) that correlate with better patient outcomes. Previous studies identified a 12 chemokine gene expression signature associated with disease progression and the type and degree of TLS. These signatures could provide insight important for clinical decision making during pathologic evaluation, but predicting gene expression from whole slide images (WSI) may be impeded by low prediction accuracy and lack of interpretability. Here we report an artificial intelligence (AI)-based, state-of-the-art workflow to predict the 12-chemokine TLS gene signature from lung cancer WSI, and identify histological features relevant to model predictions.

Methods: Models were trained using 538 cases of paired lung cancer WSI and mRNA-seq expression data (The Cancer Genome Atlas). Cell and tissue classifiers, based on convolutional neural networks (CNN) were trained on WSI, and a graph neural network (GNN) model that leverages the relative spatial arrangement of the CNN-identified cells and tissues was used to predict gene expression. GNN predictions of TLS signature genes were compared with the predictions of models trained using hand-crafted, task-specific features (TLS feature models) describing the number, size, and cellular composition of identified TLS. The Pearson correlation coefficient was used to assess the accuracy of GNN and TLS feature model predictions. GNNExplainer1, a tool that simultaneously identifies a subgraph and a subset of node features important for predictions, was applied to interpret the GNN model predictions.

Results: GNN model predictions show reasonable accuracy: GNN models significantly predicted mRNA expression of all 12 genes (p<0.05), and the predicted expression of six genes was moderately correlated with ground-truth measurements (Pearson-r>0.5). The correlation of GNN predictions was higher than that of the TLS feature models for all 12 signature genes. The GNNExplainer identified relevant features including the mean and standard deviation of lymphocyte count, and fraction of lymphocytes in cancer stroma. Subgraphs selected by the GNNExplainer focus on, but extend beyond, regions of human-annotated TLS objects, indicating that TLS may influence gene expression and the TME in regions beyond their immediate vicinity.

Conclusion: Here, we show a comparison of two interpretable AI methods for the prediction of TLS-induced gene expression from WSI. The outperforming GNN-based approach is highly reproducible and accurate, predicting histopathology features relevant to TLS that may be used to inform patient prognosis and treatment. These methods could be applied to predict additional clinically relevant transcriptomic signatures. 1. ​​Ying, R, et al. 2019. arXiv:1903.03894v4

Citation Format: Ciyue Shen, Collin Schlager, Deepta Rajan, Maryam Pouryahya, Mary Lin, Victoria Mountain, Ilan Wapinski, Amaro Taylor-Weiner, Benjamin Glass, Robert Egger, Andrew Beck. Application of an interpretable graph neural network to predict gene expression signatures associated with tertiary lymphoid structures in histopathological images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1922.

Abstract 464: AI-powered segmentation and analysis of nuclei morphology predicts genomic and clinical markers in multiple cancer types

Morphological features of cancer cell nuclei are linked to gene expression signatures and genomic alterations. In addition, pathologists have leveraged nuclear morphology as diagnostic and prognostic markers. To enable the use of nuclear morphology in digital pathology, we developed a pan-tissue, deep-learning-based digital pathology pipeline for exhaustive nucleus detection, instance segmentation, and classification. We collected > 29,000 manual nucleus annotations from hematoxylin and eosin (H&E)-stained pathology images from 21 tumor types at 40x and 20x magnification from The Cancer Genome Atlas (TCGA) project, as well as a proprietary set of H&E-stained tissue biopsies of skin, liver non-alcoholic steatohepatitis (NASH), colon inflammatory bowel disease (IBD), and kidney lupus. Annotations were used to train an object detection and segmentation model for identifying nuclei. Application of the model to held-out test data, including held-out tissue types, demonstrated performance comparable to state-of-the-art models described in the literature (mean Dice score = 0.80, aggregated Jaccard index = 0.60). We deployed our model to segment nuclei in H&E slides from the breast cancer (BRCA, N = 941) and prostate adenocarcinoma (PRAD, N = 457) TCGA cohorts. We extracted interpretable features describing the shape (circularity, eccentricity), size, staining intensity (mean and standard deviation), and texture of each nucleus. Nuclei were assigned as cancer or other cell types using separately trained convolutional neural networks for BRCA and PRAD. We used the mean and standard deviation of each feature sampled from a random subset of cancer nuclei to summarize the nuclear morphology on each slide (mean (range) = 10,068 (5,981-10,452) cancer cells from each BRCA slide; mean (range) = 10,053 (5,029-10,495) cancer cells from each PRAD slide). We used nuclear features to construct random forest classification models for predicting markers of genomic instability and prognosis: whole-genome doubling (WGD) and homologous recombination deficiency (HRD) status separately in BRCA and PRAD, HER2 subtype in BRCA, and Gleason grade in PRAD. Nuclear features were predictive of WGD (area under the receiver operating characteristic curve (AUROC) = 0.78 BRCA, = 0.69 PRAD) and binarized HRD status (AUROC = 0.65 BRCA, = 0.68 PRAD) on held-out test sets. Nuclear features were predictive of HER2-enriched breast cancer vs. other molecular subtypes (AUROC = 0.72), and distinguished between low risk (6) and moderate/high risk (7-10) Gleason grade in PRAD (AUROC = 0.72). In summary, we present a powerful pan-tissue approach for nucleus segmentation and featurization, which enables the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

Citation Format: John Abel, Suyog Jain, Deepta Rajan, Ken Leidal, Harshith Padigela, Aaditya Prakash, Jake Conway, Michael Nercessian, Christian Kirkup, Robert Egger, Ben Trotter, Andrew Beck, Ilan Wapinski, Michael G. Drage, Limin Yu, Amaro Taylor-Weiner. AI-powered segmentation and analysis of nuclei morphology predicts genomic and clinical markers in multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 464.

Using focal cooling to link neural dynamics and behavior

Establishing a causal link between neural function and behavioral output has remained a challenging problem. Commonly used perturbation techniques enable unprecedented control over intrinsic activity patterns and can effectively identify crucial circuit elements important for specific behaviors. However, these approaches may severely disrupt activity, precluding an investigation into the behavioral relevance of moment-to-moment neural dynamics within a specified brain region. Here we discuss the application of mild focal cooling to slow down intrinsic neural circuit activity while preserving its overall structure. Using network modeling and examples from multiple species, we highlight the power and versatility of focal cooling for understanding how neural dynamics control behavior and argue for its wider adoption within the systems neuroscience community.

Local Axonal Conduction Shapes the Spatiotemporal Properties of Neural Sequences

Sequential activation of neurons has been observed during various behavioral and cognitive processes, but the underlying circuit mechanisms remain poorly understood. Here, we investigate premotor sequences in HVC (proper name) of the adult zebra finch forebrain that are central to the performance of the temporally precise courtship song. We use high-density silicon probes to measure song-related population activity, and we compare these observations with predictions from a range of network models. Our results support a circuit architecture in which heterogeneous delays between sequentially active neurons shape the spatiotemporal patterns of HVC premotor neuron activity. We gauge the impact of several delay sources, and we find the primary contributor to be slow conduction through axonal collaterals within HVC, which typically adds between 1 and 7.5 ms for each link within the sequence. Thus, local axonal "delay lines" can play an important role in determining the dynamical repertoire of neural circuits.

Dimensionality-Driven Photoproduction of Massive Dirac Pairs near Threshold in Gapped Graphene Monolayers

Generation of quasiparticle-hole pairs in gapped graphene monolayers in the combined field of two counterpropagating light waves is studied. The process represents an analog of electron-positron pair production from the vacuum of quantum electrodynamics (QED) by the Breit-Wheeler effect. We show, however, that the two-dimensional structure of graphene causes some striking differences between both scenarios. In particular, contrary to the QED case, it allows for nonzero pair production rates at the energy threshold when the Breit-Wheeler reaction proceeds nonlinearly with absorption of three photons.

Weak Measurement Protocols for Majorana Bound State Identification

We propose a continuous weak measurement protocol testing the nonlocality of Majorana bound states through current shot noise correlations. The experimental setup contains a topological superconductor island with three normal-conducting leads weakly coupled to different Majorana states. Putting one lead at finite voltage and measuring the shot noise correlations between the other two (grounded) leads, devices with true Majorana states are distinguished from those without by strong current correlations. The presence of true Majorana states manifests itself in unusually high noise levels or the near absence of noise, depending on the chosen device configuration. Monitoring the noise statistics amounts to a weak continuous measurement of the Majorana qubit and yields information similar to that of a full braiding protocol, but at much lower experimental effort. Our theory can be adapted to different platforms and should allow for the clear identification of Majorana states.

Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers

Pyramidal tract neurons (PTs) represent the major output cell type of the mammalian neocortex. Here, we report the origins of the PTs’ ability to respond to a broad range of stimuli with onset latencies that rival or even precede those of their intracortical input neurons. We find that neurons with extensive horizontally projecting axons cluster around the deep-layer terminal fields of primary thalamocortical axons. The strategic location of these corticocortical neurons results in high convergence of thalamocortical inputs, which drive reliable sensory-evoked responses that precede those in other excitatory cell types. The resultant fast and horizontal stream of excitation provides PTs throughout the cortical area with input that acts to amplify additional inputs from thalamocortical and other intracortical populations. The fast onsets and broadly tuned characteristics of PT responses hence reflect a gating mechanism in the deep layers, which assures that sensory-evoked input can be reliably transformed into cortical output.

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Simulations predict in vivo responses for major output cell type of the neocortex

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Simulations reveal strategy how to test the origins of cortical output empirically

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Manipulations confirm that deep-layer corticocortical neurons gate cortical output

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Gating of cortical output originates from deep-layer thalamocortical input stratum

Giant Shot Noise from Majorana Zero Modes in Topological Trijunctions

The clear-cut experimental identification of Majorana bound states in transport measurements still poses experimental challenges. We here show that the zero-energy Majorana state formed at a junction of three topological superconductor wires is directly responsible for giant shot noise amplitudes, in particular at low voltages and for small contact transparency. The only intrinsic noise limitation comes from the current-induced dephasing rate due to multiple Andreev reflection processes.

First reported implementation of a German-language progress test in an undergraduate dental curriculum: A prospective study

INTRODUCTION

Progress testing is a special form of longitudinal and feedback-oriented assessment. Even though well established in human medical curricula, this is not the case in dental education. The aim was the prospective development and implementation of the first reported German-language Dental Progress Test (DPT) for the undergraduate dental curriculum at the Medical University of Graz, Austria.

MATERIAL AND METHODS

Participation in DPT was compulsory for all dental students in terms 7-12 (years 4-6). Three tests, each consisting of 100 items out of a pool of 375, were administered within 3 consecutive terms in 2016 and 2017. Rasch analyses were used to evaluate the questionnaire and identify misfitting items.

RESULTS

In the item responses, 59.7% were "correct," 27.0% were "false" and 13.3% were answered with "don't know," with similar results at all 3 time points. The assumption of parallel ICC was met (T1: χ²  = 51.071, df = 74, P = .981; T2: χ²  = 57.044, df = 67, P = .802; T3: χ²  = 58.443, df = 72, P = .876) and item difficulties for the thematic fields were similarly distributed across the latent dimensions.

CONCLUSION

The newly introduced DPT is appropriate for testing dental students and is well balanced for the tested target group.

Morphological characterization of HVC projection neurons in the zebra finch (Taeniopygia guttata)

Singing behavior in the adult male zebra finch is dependent upon the activity of a cortical region known as HVC (proper name). The vast majority of HVC projection neurons send primary axons to either the downstream premotor nucleus RA (robust nucleus of the arcopallium, or primary motor cortex) or Area X (basal ganglia), which play important roles in song production or song learning, respectively. In addition to these long-range outputs, HVC neurons also send local axon collaterals throughout that nucleus. Despite their implications for a range of circuit models, these local processes have never been completely reconstructed. Here, we use in vivo single-neuron Neurobiotin fills to examine 40 projection neurons across 31 birds with somatic positions distributed across HVC. We show that HVC_(RA) and HVC_(X) neurons have categorically distinct dendritic fields. Additionally, these cell classes send axon collaterals that are either restricted to a small portion of HVC ("local neurons") or broadly distributed throughout the entire nucleus ("broadcast neurons"). Overall, these processes within HVC offer a structural basis for significant local processing underlying behaviorally relevant population activity.

Relationships between structure, in vivo function and long-range axonal target of cortical pyramidal tract neurons

Pyramidal tract neurons (PTs) represent the major output cell type of the neocortex. To investigate principles of how the results of cortical processing are broadcasted to different downstream targets thus requires experimental approaches, which provide access to the in vivo electrophysiology of PTs, whose subcortical target regions are identified. On the example of rat barrel cortex (vS1), we illustrate that retrograde tracer injections into multiple subcortical structures allow identifying the long-range axonal targets of individual in vivo recorded PTs. Here we report that soma depth and dendritic path lengths within each cortical layer of vS1, as well as spiking patterns during both periods of ongoing activity and during sensory stimulation, reflect the respective subcortical target regions of PTs. We show that these cellular properties result in a structure-function parameter space that allows predicting a PT's subcortical target region, without the need to inject multiple retrograde tracers.The major output cell type of the neocortex - pyramidal tract neurons (PTs) - send axonal projections to various subcortical areas. Here the authors combined in vivo recordings, retrograde tracings, and reconstructions of PTs in rat somatosensory cortex to show that PT structure and activity can predict specific subcortical targets.

EM connectomics reveals axonal target variation in a sequence-generating network

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks.

6π Josephson Effect in Majorana Box Devices

We study Majorana devices featuring a competition between superconductivity and multichannel Kondo physics. Our proposal extends previous work on single-channel Kondo systems to a topologically nontrivial setting of a non-Fermi liquid type, where topological superconductor wires (with gap Δ) represent leads tunnel coupled to a Coulomb-blockaded Majorana box. On the box, a spin degree of freedom with Kondo temperature T_{K} is nonlocally defined in terms of Majorana states. For Δ≫T_{K}, the destruction of Kondo screening by superconductivity implies a 4π-periodic Josephson current-phase relation. Using a strong-coupling analysis in the opposite regime Δ≪T_{K}, we find a 6π-periodic Josephson relation for three leads, with critical current I_{c}≈eΔ^{2}/ℏT_{K}, corresponding to the transfer of fractionalized charges e^{*}=2e/3.

Towards Realistic Implementations of a Majorana Surface Code

Surface codes have emerged as promising candidates for quantum information processing. Building on the previous idea to realize the physical qubits of such systems in terms of Majorana bound states supported by topological semiconductor nanowires, we show that the basic code operations, namely projective stabilizer measurements and qubit manipulations, can be implemented by conventional tunnel conductance probes and charge pumping via single-electron transistors, respectively. The simplicity of the access scheme suggests that a functional code might be in close experimental reach.

Beyond Columnar Organization: Cell Type- and Target Layer-Specific Principles of Horizontal Axon Projection Patterns in Rat Vibrissal Cortex

Vertical thalamocortical afferents give rise to the elementary functional units of sensory cortex, cortical columns. Principles that underlie communication between columns remain however unknown. Here we unravel these by reconstructing in vivo-labeled neurons from all excitatory cell types in the vibrissal part of rat primary somatosensory cortex (vS1). Integrating the morphologies into an exact 3D model of vS1 revealed that the majority of intracortical (IC) axons project far beyond the borders of the principal column. We defined the corresponding innervation volume as the IC-unit. Deconstructing this structural cortical unit into its cell type-specific components, we found asymmetric projections that innervate columns of either the same whisker row or arc, and which subdivide vS1 into 2 orthogonal [supra-]granular and infragranular strata. We show that such organization could be most effective for encoding multi whisker inputs. Communication between columns is thus organized by multiple highly specific horizontal projection patterns, rendering IC-units as the primary structural entities for processing complex sensory stimuli.

Generation of dense statistical connectomes from sparse morphological data

Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and subcellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results.

Multichannel Kondo impurity dynamics in a Majorana device

We study the multichannel Kondo impurity dynamics realized in a mesoscopic superconducting island connected to metallic leads. The effective "impurity spin" is nonlocally realized by Majorana bound states and strongly coupled to lead electrons by non-Fermi liquid correlations. We explore the spin dynamics and its observable ramifications near the low-temperature fixed point. The topological protection of the system raises the perspective to observe multichannel Kondo impurity dynamics in experimentally realistic environments.

3D reconstruction and standardization of the rat vibrissal cortex for precise registration of single neuron morphology

The three-dimensional (3D) structure of neural circuits is commonly studied by reconstructing individual or small groups of neurons in separate preparations. Investigation of structural organization principles or quantification of dendritic and axonal innervation thus requires integration of many reconstructed morphologies into a common reference frame. Here we present a standardized 3D model of the rat vibrissal cortex and introduce an automated registration tool that allows for precise placement of single neuron reconstructions. We (1) developed an automated image processing pipeline to reconstruct 3D anatomical landmarks, i.e., the barrels in Layer 4, the pia and white matter surfaces and the blood vessel pattern from high-resolution images, (2) quantified these landmarks in 12 different rats, (3) generated an average 3D model of the vibrissal cortex and (4) used rigid transformations and stepwise linear scaling to register 94 neuron morphologies, reconstructed from in vivo stainings, to the standardized cortex model. We find that anatomical landmarks vary substantially across the vibrissal cortex within an individual rat. In contrast, the 3D layout of the entire vibrissal cortex remains remarkably preserved across animals. This allows for precise registration of individual neuron reconstructions with approximately 30 µm accuracy. Our approach could be used to reconstruct and standardize other anatomically defined brain areas and may ultimately lead to a precise digital reference atlas of the rat brain.

Majorana single-charge transistor

We study transport through a Coulomb blockaded topologically nontrivial superconducting wire (with Majorana end states) contacted by metallic leads. An exact formula for the current through this interacting Majorana single-charge transistor is derived in terms of wire spectral functions. A comprehensive picture follows from three different approaches. We find Coulomb oscillations with universal halving of the finite-temperature peak conductance under strong blockade conditions, where the valley conductance mainly comes from elastic cotunneling. The nonlinear conductance exhibits finite-voltage sidebands due to anomalous tunneling involving Cooper pair splitting.

Helical Luttinger liquid in topological insulator nanowires

We derive and analyze the effective low-energy theory for interacting electrons in a cylindrical nanowire made of a strong topological insulator. Three different approaches provide a consistent picture for the band structure, where surface states forming inside the bulk gap correspond to one-dimensional bands indexed by total angular momentum. When a half-integer magnetic flux pierces the nanowire, we find a strongly correlated helical Luttinger liquid topologically protected against weak disorder. We describe how transport experiments can detect this state.

Interface solitons excited between a simple lattice and a superlattice

We demonstrate nonlinear surface states at the interface between optically-induced simple (periodic) and super (bi-periodic) photonic lattices. Depending on the excitation condition at the interface, we identify numerically and observe experimentally different types of interface solitons with characteristic phase structures under a self-focusing nonlinearity, including dipole-like "mixed-band" interface gap solitons.

Spin transport and bipolaron density in organic polymers

We present a theory for spin-polarized transport through a generic organic polymer connected to ferromagnetic leads with arbitrary angle θ between their magnetization directions, taking into account the polaron and bipolaron states as effective charge and spin carriers. Within a diffusive description of polaron-bipolaron transport including polaron-bipolaron conversion, we find that the bipolaron density depends on the angle θ. This is remarkable, given the fact that bipolarons are spinless quasiparticles, and opens a new way to probe spin accumulation in organic polymers.

Anomalous Josephson current through a spin-orbit coupled quantum dot

For a general model of a mesoscopic multilevel quantum dot, we determine the necessary conditions for the existence of an anomalous Josephson current with spontaneously broken time-reversal symmetry. They correspond to a finite spin-orbit coupling, a suitably oriented Zeeman field, and the dot being a chiral conductor. We provide analytical expressions for the anomalous supercurrent covering a wide parameter regime.

Optical induction of three-dimensional photonic lattices and enhancement of discrete diffraction

We demonstrate experimentally the formation of three-dimensional (3D) reconfigurable photonic lattices in a bulk nonlinear crystal by employing the optical induction technique. Such 3D lattices are established by inducing 2D square lattices in two orthogonal directions. The induced 3D periodic index structures are monitored by plane-wave guidance and Brillouin zone spectroscopy. Enhanced discrete diffraction due to the waveguide modulation and coupling in 3D lattices is also observed.

Automated three-dimensional detection and counting of neuron somata

We present a novel approach for automated detection of neuron somata. A three-step processing pipeline is described on the example of confocal image stacks of NeuN-stained neurons from rat somato-sensory cortex. It results in a set of position landmarks, representing the midpoints of all neuron somata. In the first step, foreground and background pixels are identified, resulting in a binary image. It is based on local thresholding and compensates for imaging and staining artifacts. Once this pre-processing guarantees a standard image quality, clusters of touching neurons are separated in the second step, using a marker-based watershed approach. A model-based algorithm completes the pipeline. It assumes a dominant neuron population with Gaussian distributed volumes within one microscopic field of view. Remaining larger objects are hence split or treated as a second neuron type. A variation of the processing pipeline is presented, showing that our method can also be used for co-localization of neurons in multi-channel images. As an example, we process 2-channel stacks of NeuN-stained somata, labeling all neurons, counterstained with GAD67, labeling GABAergic interneurons, using an adapted pre-processing step for the second channel. The automatically generated landmark sets are compared to manually placed counterparts. A comparison yields that the deviation in landmark position is negligible and that the difference between the numbers of manually and automatically counted neurons is less than 4%. In consequence, this novel approach for neuron counting is a reliable and objective alternative to manual detection.

Josephson-current-induced conformational switching of a molecular quantum dot

We discuss the behavior of a two-level system coupled to a quantum dot contacted by superconducting source and drain electrodes, representing a simple model for the conformational degree of freedom of a molecular dot or a break junction. The Josephson current is shown to induce conformational changes, including a complete reversal. For small bias voltage, periodic conformational motions induced by Landau-Zener transitions between Andreev states are predicted.

Interaction-induced harmonic frequency mixing in quantum dots

We show that harmonic frequency mixing in quantum dots coupled to two leads under the influence of time-dependent voltages of different frequency is dominated by interaction effects. This offers a unique and direct spectroscopic tool to access correlations, and holds promise for efficient frequency mixing in nanodevices. Explicit results are provided for an Anderson dot and for a molecular level with phonon-mediated interactions.

Magnetic confinement of massless Dirac fermions in graphene

Because of Klein tunneling, electrostatic potentials are unable to confine Dirac electrons. We show that it is possible to confine massless Dirac fermions in a monolayer graphene sheet by inhomogeneous magnetic fields. This allows one to design mesoscopic structures in graphene by magnetic barriers, e.g., quantum dots or quantum point contacts.

Tomonaga-Luttinger liquid and Coulomb blockade in multiwall carbon nanotubes under pressure

We report that the conductance of macroscopic multiwall nanotube (MWNT) bundles under pressure shows power laws in temperature and voltage, as corresponding to a network of bulk-bulk connected Tomonaga-Luttinger liquids (LLs). Contrary to individual MWNTs, where the observed power laws are attributed to Coulomb blockade, the measured ratio for the end and bulk obtained exponents, approximately 2.4, can be accounted for only by LL theory. At temperatures characteristic of interband separation, it increases due to thermal population of the conducting sheets unoccupied bands.

Nonlinear magnetotransport in interacting chiral nanotubes

Nonlinear transport through interacting single-wall nanotubes containing a few impurities is studied theoretically. Extending the Luttinger liquid theory to incorporate trigonal warping and chirality effects, we derive the current contribution Ie even in the applied voltage V and odd in an orbital magnetic field B, which is nonzero only for chiral tubes and in the presence of interactions.

Four-body problem and BEC-BCS crossover in a quasi-one-dimensional cold fermion gas

The four-body problem for an interacting two-species Fermi gas is solved analytically in a confined quasi-one-dimensional geometry, where the two-body atom-atom scattering length a(aa) displays a confinement-induced resonance. We compute the dimer-dimer scattering length a(dd) and show that this quantity completely determines the many-body solution of the associated BEC-BCS crossover phenomenon in terms of bosonic dimers.

Hospital health care resource utilization and costs of colorectal cancer during the first 3-year period following diagnosis in Switzerland

BACKGROUND

In industrialized countries, colorectal cancer is a leading cause of morbidity and mortality. Decisions on colorectal cancer screening are based on cost-effectiveness analyses that rely on colorectal cancer cost studies. Additionally, the study of the resource utilization pattern may lead to cost-saving strategies in the care of colorectal cancer.

AIM

To estimate hospital resource utilization, the use of various therapy modalities and costs of colorectal cancer cases undergoing surgery during the first 3 years following the diagnosis at a Swiss university hospital.

METHODS

Consecutive colorectal cancer patients from 1997 to 1998 were identified using the surgery database of the University Hospital of Basel and followed for a period of 3 years. In-hospital resource utilization and costs were retrieved from the computerized administrative records. Treatment outside of the hospital during the study period constituted an exclusion criterion.

RESULTS

Eighty-three (94%) of 89 patients undergoing surgery for colorectal cancer were included in the study, 58 with colon cancer and 25 with rectal cancer. The average ages were 70.3 and 63.6 years, respectively. Overall, 59% of the patients were treated with surgery alone, 27% also had chemotherapy and 15% received additional chemoradiotherapy. These percentages and resource utilization varied broadly between the two colorectal cancer groups. On average, patients were admitted to the hospital 2.7 times and the hospital length of stay amounted to 35 days. They were visited by doctors 69 times, and examined with colonoscopy, ultrasonography and computerized tomography 2.7, 3.2 and 2.4 times, respectively. Mean costs incurred for rectal cancer (US dollars 40,230) were about 22% higher than for colon cancer patients (US dollars 33,079). Hospitalization and surgical therapy generated the greatest costs. Expenses were highest for the first year and with more severe disease stages at diagnosis.

CONCLUSIONS

Colorectal cancer is an expensive disease. Economic analyses on screening should take into account the large resource utilization and cost variability by performing sensitivity analysis on broad cost ranges. Furthermore, they should consider stage shifting at diagnosis and include stage-specific costs.

Exact results for one-dimensional disordered bosons with strong repulsion

We study one-dimensional disordered bosons with strong repulsive interactions. A Bose-Fermi mapping expresses this problem in terms of noninteracting Anderson-localized fermions, whereby known results for the distribution function of the local density of states, the spectral statistics, and density-density correlations can be transferred to this new domain of applicability. We show that disorder destroys bosonic quasi-long-range order by calculating the momentum distribution, and comment on the experimental observability of these predictions in ultracold atomic gases.

Atom-dimer scattering for confined ultracold fermion gases

We solve the three-body problem of a quasi-one-dimensional ultracold Fermi gas with parabolic confinement length a (perpendicular) and 3D scattering length a. On the two-body level, there is a Feshbach-type resonance at a (perpendicular)/a approximately 1.46, and a dimer state for arbitrary a (perpendicular)/a. The three-body problem is shown to be universal, and described by the atom-dimer scattering length a(ad) and a range parameter b(ad). In the dimer limit a (perpendicular)/a>>1, we find a repulsive zero-range atom-dimer interaction. For a (perpendicular)/a<<-1, however, the potential has long range, with a(ad)>0 and b(ad)>>a(ad). There is no trimer state, and despite a(ad)=0 at a( perpendicular)/a approximately 2.6, there is no resonance enhancement of the interaction.

Josephson current through a nanoscale magnetic quantum dot

We present theoretical results for the equilibrium Josephson current through an Anderson dot tuned into the magnetic regime, using Hirsch-Fye Monte Carlo simulations covering the complete crossover from Kondo-dominated physics to pi junction behavior in a numerically exact way. Within the "magnetic" regime, U/Gamma >> 1 and epsilon0/Gamma < or = 1, the Josephson current is found to depend only on Delta/TK, where Delta is the BCS gap and TK the Kondo temperature. The junction behavior can be classified into four different quantum phases. We describe these behaviors, specify the associated three transition points, and identify a local minimum in the critical current of the junction as a function of Delta/TK.

Evidence for Luttinger-liquid behavior in crossed metallic single-wall nanotubes

Transport measurements through crossed metallic single-wall nanotubes are presented. We observe a zero-bias anomaly in one tube which is suppressed by a current flowing through the other nanotube. These results are compared with a Luttinger-liquid model which takes into account electrostatic tube-tube coupling together with crossing-induced backscattering processes. Explicit solution of a simplified model is able to describe qualitatively the observed experimental data with only one adjustable parameter.

Landauer-type transport theory for interacting quantum wires: application to carbon nanotube y junctions

We propose a Landauerlike theory for nonlinear transport in networks of one-dimensional interacting quantum wires (Luttinger liquids). A concrete example of current experimental focus is given by carbon nanotube Y junctions. Our theory has three basic ingredients that allow one to explicitly solve this transport problem: (i) radiative boundary conditions to describe the coupling to external leads, (ii) the Kirchhoff node rule describing charge conservation, and (iii) density matching conditions at every node.

Spin-orbit coupling and electron spin resonance theory for carbon nanotubes

A theoretical description of electron spin resonance (ESR) in 1D interacting metals is given, with primary emphasis on carbon nanotubes. The spin-orbit coupling is derived, and the resulting ESR spectrum is analyzed using a low-energy field theory. Drastic differences in the ESR spectra of single-wall and multiwall nanotubes are found. For single-wall tubes, the predicted double peak spectrum is linked to spin-charge separation. For multiwall tubes, a single narrow asymmetric peak is expected.

Coulomb drag shot noise in coupled Luttinger liquids

Coulomb drag shot noise has been studied theoretically for 1D interacting electron systems, which are realized, e.g., in single-wall nanotubes. We show that under adiabatic coupling to external leads, the Coulomb drag shot noise of two coupled or crossed nanotubes contains surprising effects, in particular, a complete locking of the shot noise in the tubes. In contrast to Coulomb drag of the average current, the noise locking is based on a symmetry of the underlying Hamiltonian and is not limited to asymptotically small energy scales.

Bulk and boundary zero-bias anomaly in multiwall carbon nanotubes

We compute the tunneling density of states of doped multiwall nanotubes including disorder and electron-electron interactions. A nonconventional Coulomb blockade reflecting nonperturbative Altshuler-Aronov-Lee power-law zero-bias anomalies is found, in accordance with recent experimental results. The presence of a boundary implies a universal doubling of the boundary exponent in the diffusive limit.

Spin transport in interacting quantum wires and carbon nanotubes

We present a general formulation of spin-dependent transport through a clean one-dimensional interacting quantum wire or carbon nanotube, connected to noncollinear ferromagnets via tunnel junctions. The low energy description of each junction is given by a conformally invariant boundary condition representing exchange coupling, in addition to a pair of electron tunneling operators. The effects of the exchange coupling are strongly enhanced by interactions, leading to a dramatic suppression of spin accumulation: a direct signature of spin-charge separation. Finally, backscattering induces nonequilibrium oscillations in the current-voltage relation.

Path-integral Monte Carlo simulations without the sign problem: multilevel blocking approach for effective actions

The multilevel blocking algorithm recently proposed as a possible solution to the sign problem in path-integral Monte Carlo simulations has been extended to systems with long-ranged interactions along the Trotter direction. As an application, results for the real-time quantum dynamics of the spin-boson model are presented.

Current bistability and hysteresis in strongly correlated quantum wires

Nonequilibrium transport properties are determined exactly for an adiabatically contacted single-channel quantum wire containing one impurity. Employing the Luttinger liquid model with interaction parameter g, for very strong interactions g less, similar0.2, and sufficiently low temperatures, we find an S-shaped current-voltage relation. The unstable branch with negative differential conductance gives rise to current oscillations and hysteretic effects. These nonperturbative and nonlinear features appear only out of equilibrium.