다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Chronic inflammation is characterized by continuous recruitment and activation of immune cells such as monocytes in response to a persistent stimulus. Production of proinflammatory mediators by monocytes leads to tissue damage and perpetuates the inflammatory response. However, the mechanism(s) responsible for the sustained influx of monocytes in chronic inflammation are not well defined. In chronic peritonitis induced by pristane, the persistent recruitment of Ly6C(hi) inflammatory monocytes into the peritoneum was abolished in type I interferon (IFN-I) receptor-deficient mice but was unaffected by the absence of IFN-gamma, tumor necrosis factor-alpha, interleukin-6, or interleukin-1. IFN-I signaling stimulated the production of chemokines (CCL2, CCL7, and CCL12) that recruited Ly6C(hi) monocytes via interactions with the chemokine receptor CCR2. Interestingly, after 2,6,10,14-tetramethylpentadecane treatment, the rapid turnover of inflammatory monocytes in the inflamed peritoneum was associated with a lack of differentiation into Ly6C(lo) monocytes/macrophages, a more mature subset with enhanced phagocytic capacity. In contrast, Ly6C(hi) monocytes differentiated normally into Ly6C(lo) cells in IFN-I receptor-deficient mice. The effects of IFN-I were specific for monocytes as granulocyte migration was unaffected in the absence of IFN-I signaling. Taken together, our findings reveal a novel role of IFN-I in promoting the recruitment of inflammatory monocytes via the chemokine receptor CCR2. Continuous monocyte recruitment and the lack of terminal differentiation induced by IFN-I may help sustain the chronic inflammatory response.

Type-I interferon modulates monocyte recruitment and maturation in chronic inflammation

We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of ∼120,000 single cell instances we showed the accuracy of classification of PBMC call of ∼ 90%. Optimization techniques and the addition of new high-quality data sets for model training are expected to improve PBMC subtype classification accuracy.

Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology. Here we provide an overview of the state of single-cell genomics methods and an outlook on the use of single-cell techniques to decipher the adaptive and innate components of immunity.

Single-cell transcriptomics to explore the immune system in health and disease

The ZX-calculus is a universal graphical language for qubit quantum computation, meaning that every linear map between qubits can be expressed in the ZX-calculus. Furthermore, it is a complete graphical rewrite system: any equation involving linear maps that is derivable in the Hilbert space formalism for quantum theory can also be derived in the calculus by rewriting. It has widespread usage within quantum industry and academia for a variety of tasks such as quantum circuit optimisation, error-correction, and education.The ZW-calculus is an alternative universal graphical language that is also complete for qubit quantum computing. In fact, its completeness was used to prove that the ZX-calculus is universally complete. This calculus has advanced how quantum circuits are compiled into photonic hardware architectures in the industry.Recently, by combining these two calculi, a new calculus has emerged for qubit quantum computation, the ZXW-calculus. Using this calculus, graphical-differentiation, -integration, and -exponentiation were made possible, thus enabling the development of novel techniques in the domains of quantum machine learning and quantum chemistry.Here, we generalise the ZXW-calculus to arbitrary finite dimensions, that is, to qudits. Moreover, we prove that this graphical rewrite system is complete for any finite dimension. This is the first completeness result for any universal graphical language beyond qubits.

Completeness for arbitrary finite dimensions of ZXW-calculus, a unifying calculus

This paper develops practical summation techniques in ZXW calculus to reason about quantum dynamics, such as unitary time evolution. First we give a direct representation of a wide class of sums of linear operators, including arbitrary qubits Hamiltonians, in ZXW calculus. As an application, we demonstrate the linearity of the Schr¨odinger equation and give a diagrammatic representation of the Hamiltonian in Greene-Diniz et al [14], which is the ﬁrst paper that models carbon capture using quantum computing. We then use the Cayley-Hamilton theorem to show in principle how to exponentiate arbitrary qubits Hamiltonians in ZXW calculus. Finally, we develop practical techniques and show how to do Taylor expansion and Trotterization diagrammatically for Hamiltonian simulation. This sets up the framework for using ZXW calculus to the problems in quantum chemistry and condensed matter physics.

How to sum and exponentiate Hamiltonians in ZXW calculus

Single cell transcriptomics (SCT) reveals cellular patterns that are masked and hidden in bulk RNA experiments. We analyzed 100 human SCT data sets for summary patterns that quantify gene expression per individual cell as well as per gene. Peripheral Blood Mononuclear Cells (PBMCs) show patterns different to those of cancer cell lines, stem cells, embryonic stem cells and other cell types. The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.

Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types

In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data. The model is inspired by, and closely related to, the Beta-VAE model of concepts, but is designed to be more closely connected with language, so that the names of concepts form part of the graphical model. We provide evidence that our model -- which we call the Conceptual VAE -- is able to learn interpretable conceptual representations from simple images of coloured shapes together with the corresponding concept labels. We also show how the model can be used as a concept classifier, and how it can be adapted to learn from fewer labels per instance. Finally, we formally relate our model to Gardenfors' theory of conceptual spaces, showing how the Gaussians we use to represent concepts can be formalised in terms of"fuzzy concepts"in such a space.

Razin A. Shaikh

Papers

Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks

Completeness for arbitrary finite dimensions of ZXW-calculus, a unifying calculus

How to sum and exponentiate Hamiltonians in ZXW calculus

Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types

The Conceptual VAE