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

We propose a framework to model an operational conversational negation by applying worldly context (prior knowledge) to logical negation in compositional distributional semantics. Given a word, our framework can create its negation that is similar to how humans perceive negation. The framework corrects logical negation to weight meanings closer in the entailment hierarchy more than meanings further apart. The proposed framework is flexible to accommodate different choices of logical negations, compositions, and worldly context generation. In particular, we propose and motivate a new logical negation using matrix inverse. 
We validate the sensibility of our conversational negation framework by performing experiments, leveraging density matrices to encode graded entailment information. We conclude that the combination of subtraction negation and phaser in the basis of the negated word yields the highest Pearson correlation of 0.635 with human ratings.

Conversational Negation using Worldly Context in Compositional Distributional Semantics

Negation in natural language does not follow Boolean logic and is therefore inherently difficult to model. In particular, it takes into account the broader understanding of what is being negated. In previous work, we proposed a framework for negation of words that accounts for `worldly context'. In this paper, we extend that proposal now accounting for the compositional structure inherent in language, within the DisCoCirc framework. We compose the negations of single words to capture the negation of sentences. We also describe how to model the negation of words whose meanings evolve in the text.

Razin A. Shaikh

Papers

Conversational Negation using Worldly Context in Compositional Distributional Semantics

Composing Conversational Negation