Field Inhomogeneity Compensation in High Field Magnetic Resonance Imaging (MRI)

This thesis concentrates on the reduction of field (both main field B0 and RF field B1) inhomogeneity in MRI, especially at high B0 field. B0 and B1 field inhomogeneity are major hindrances in high B0 field MRI applications. B1 inhomogeneity will lead to spatially varying signal intensity in the MR images. B0 inhomogeneity produces blurring, distortion and signal loss at tissue interfaces. B0 artifacts are usually termed off-resonance or susceptibility artifacts. None of the existing methods can perfectly correct these inhomogeneity artifacts.This thesis aims at developing three-dimensional (3D) tailored RF (TRF) pulses to mitigate these artifacts. A current limitation in the use of 3D TRF techniques, however, is that pulses are often too long for practical clinical applications. Multiple transmission techniques are proposed to decrease pulse lengths and provide an inherent correction for B1 inhomogeneity. Shorter pulses are also more robust to profile distortions from susceptibility effects.Specifically, slice-selective 3D TRF pulses for multiple (or ¡°parallel¡±) transmitters were designed and validated in uniform phantom and human brain experiments at 3 Tesla. A pseudo-transmit sensitivity encoding (¡°transmit SENSE¡±) method was introduced using a body coil transmitter and multiple receivers to mimic the real parallel transmitter experiment. The kz-direction was controlled by fast switching of gradients in a fashion similar to Echo planar imaging (EPI). The transverse plane (kx-ky) was sampled sparsely with hexagonal trajectories, and accelerated with the transmit SENSE method. The transmit SENSE 3D TRF pulses reduced the B1 inhomogeneity compared to standard SINC pulses in human brain scans. The undersampled transmit SENSE pulses were only 4.3ms long and could excite a 5mm thick slice, which is very promising for clinical applications. Furthermore, these pulses are shown by numerical simulation to have promise in correcting through-plane susceptibility artifacts

An Equivariant Generalization of McDuff's Theorem

In 1976, Kan and Thurston proved the theorem that any path-connected space $X$ is homology equivalent to the classifying space of some discrete group $G$. In 1979, McDuff proved a homotopy version of it: any path-connected space $X$ has the same weak homotopy type as the classifying space of some discrete monoid $M$. In 1984, Fiedorowicz reproved McDuff's theorem using a largely categorical construction. In this paper we will generalize Fiedorowicz's proof of McDuff's theorem to the equivariant case. Precisely, we will prove that any $G$-connected space $X$ with a $G$-fixed basepoint $x_0$ has the same weak homotopy type as the classifying space of some discrete $G$-monoid

Sampled in Pairs and Driven by Text: A New Graph Embedding Framework

In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs with rich texts: (i) sampling efficiency: deriving from the training objective of RW-based models (e.g., DeepWalk and node2vec), we show that RW-based models are likely to generate large amounts of redundant training samples due to three main drawbacks. (ii) text utilization: these models have difficulty in dealing with zero-shot scenarios where graph embedding models have to infer graph structures directly from texts. To solve these problems, we propose a novel framework, namely Text-driven Graph Embedding with Pairs Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling strategy of RW, being able to reduce ~99% training samples while preserving competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an inductive graph embedding approach, to generate node embeddings from texts. Since each node contains rich texts, TGE is able to generate high-quality embeddings and provide reasonable predictions on existence of links to unseen nodes. We evaluate TGE-PS on several real-world datasets, and experiment results demonstrate that TGE-PS produces state-of-the-art results on both traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019

Access tunnel engineering to optimize the catalytic cycle of carbohydrate hydrolases with buried active site

The active site of many enzymes is buried inside the protein core and is connected with the surrounding solvent by access tunnels. An emerging approach to optimize these enzymes properties is the engineering of structural features governing the exchange of ligands between the active sites and bulk solvent. However, it is still challenging to redesign the access tunnels of enzymes catalyzing biopolymers like carbohydrate hydrolases because of the extremely complicated substrate structure. In this study, structure-guided saturated mutagenesis was performed to reconstruct all three access tunnels of xylanase S7-xyl from Bacillus halodurans S7, which results in a mutant 254-RL1 with 3.4-fold increase in specific activity. Structural comparison and kinetic analysis revealed that products egress is the rate-limiting step in the catalytic cycle of S7-xyl. The products release tunnel in S7-xyl was experimentally validated, and not the tunnel radius but the length determining the products release efficiency. Application assessment showed that relieving the inhibition of reducing sugars on mutant 254-RL1 could accelerate the hydrolysis efficiency of cellulase on different pretreated lignocellulose materials, representing a good candidate in enzyme cocktails for lignocellulose biodegradation. In addition, the same strategy was successfully utilized to improve the specific activities of three other xylanases with buried active site, suggesting the general application of tunnel engineering to optimize carbohydrate hydrolases with buried active site

Blocking neutrophil integrin activation prevents ischemia-reperfusion injury.

Neutrophil recruitment, mediated by β2 integrins, combats pyogenic infections but also plays a key role in ischemia-reperfusion injury and other inflammatory disorders. Talin induces allosteric rearrangements in integrins that increase affinity for ligands (activation). Talin also links integrins to actin and other proteins that enable formation of adhesions. Structural studies have identified a talin1 mutant (L325R) that perturbs activation without impairing talin's capacity to link integrins to actin and other proteins. Here, we found that mice engineered to express only talin1(L325R) in myeloid cells were protected from renal ischemia-reperfusion injury. Dissection of neutrophil function in vitro and in vivo revealed that talin1(L325R) neutrophils had markedly impaired chemokine-induced, β2 integrin-mediated arrest, spreading, and migration. Surprisingly, talin1(L325R) neutrophils exhibited normal selectin-induced, β2 integrin-mediated slow rolling, in sharp contrast to the defective slow rolling of neutrophils lacking talin1 or expressing a talin1 mutant (W359A) that blocks talin interaction with integrins. These studies reveal the importance of talin-mediated activation of integrins for renal ischemia-reperfusion injury. They further show that neutrophil arrest requires talin recruitment to and activation of integrins. However, although neutrophil slow rolling requires talin recruitment to integrins, talin-mediated integrin activation is dispensable

An Exploratory Study on Fairness-Aware Design Decision-Making

With advances in machine learning (ML) and big data analytics, data-driven predictive models play an essential role in supporting a wide range of simple and complex decision-making processes. However, historical data embedded with unfairness may unintentionally reinforce discrimination towards minority groups when using data-driven decision-support technologies. In this paper, we quantify unfairness and analyze its impact in the context of data-driven engineering design using the Adult Income dataset. First, we introduce a fairness-aware design concept. Subsequently, we introduce standard definitions and statistical measures of fairness to the engineering design research. Then, we use the outcomes from two supervised ML models, Logistic Regression and CatBoost classifiers, to conduct the Disparate Impact and fair-test analyses to quantify any unfairness present in the data and decision outcomes. Based on the results, we highlight the importance of considering fairness in product design and marketing, and the consequences, if there is a loss of fairness

PO-277 Nitric oxide generation in red blood cells induced by exercise

Objective Vascular endothelial nitric oxide synthase (NOS) is considered to be the main enzyme source for NO production in blood vessels, and studies have shown that RBC may also express NOS and produce NO. The purpose of this study was to summarize the expression of NOS in vascular red blood cells caused by changes in hemodynamics, and to improve the bioavailability of NO, and to lay a theoretical foundation for exploring the mechanism of exercise to improve vasodilation. Methods A literature review method was used to analyze related studies on exercise and RBC-NOS published in recent years. Results Intravascular NO is one of the most important vascular signaling molecules, which has the function of relaxing blood vessels. NO is produced during the conversion of L-arginine into L-citrulline, which is mainly dependent on the regulation of vascular eNOS. RBC can express NOS under certain action, and RBC-NOS is mainly located on RBC membrane and cytoplasm; The regulatory mechanisms of RBC-NOS and eNOS have similarities and differences: RBC-NOS and eNOS are both dependent on Ca2+ regulation and phosphorylation of Serine 1177  via the PI3K pathway; however, since red blood cells do not have nuclei, endoplasmic reticulum and Golgi, they do not have other mechanisms of action of eNOS. Therefore, the vascular endothelium is not the only source of NO production. Red blood cells, white blood cells and platelets can produce NO. The amount of NO produced by red blood cells is significantly higher than that of white blood cells and platelets，it is another major source of NO production in blood vessels.The level of wall shear stress is the main determinant of NOS expression in blood vessels: On the one hand, exercise training can cause hemodynamic changes, increased shear stress, and induce changes in eNOS and RBC-NOS levels, increase NO bioavailability, and participate in the regulation of vasodilation.On the other hand, moderate-intensity exercise causes NO produced by RBC to increase red blood cell deformability and participate in vascular regulation. Conclusions 1.Erythrocyte is an enzyme source that relies on hemodynamics to release NO from the blood vessel wall. It is regulated by Ca2+ and phosphorylates ser1177 through the PI3K pathway to participate in the regulation of the body. 2.Hemodynamic changes caused by exercise training can simultaneously induce the expression of eNOS and RBC-NOS, increase the bioavailability of NO, and jointly mediate vasodilation

Performance analysis and comparison of PoW, PoS and DAG based blockchains

In the blockchain, the consensus mechanism plays a key role in maintaining the security and legitimation of contents recorded in the blocks. Various blockchain consensus mechanisms have been proposed. However, there is no technical analysis and comparison as a guideline to determine which type of consensus mechanism should be adopted in a specific scenario/application. To this end, this work investigates three mainstream consensus mechanisms in the blockchain, namely, Proof of Work (PoW), Proof of Stake (PoS), and Direct Acyclic Graph (DAG), and derives their performances in terms of the average time to generate a new block, the confirmation delay, the Transaction Per Second (TPS) and the confirmation failure probability. The results show that the consensus process is affected by both network resource (computation power/coin age, buffer size) and network load conditions. In addition, it shows that PoW and PoS are more sensitive to the change of network resource while DAG is more sensitive to network load conditions

Consumers’ Attitudes towards Online Advertising: A Model of Personalization, Informativeness, Privacy Concern and Flow Experience

Online personalized advertising has been widely adopted in China in the recent years, leading to both positive and negative results. This study endeavors to examine the impact of perceived personalization of online advertising on consumers’ attitudes toward advertising. A total of 472 questionnaires were administered and analyzed using Structural Equation Modeling. The results show that perceived personalization exerts a positive impact through perceived informativeness, and a negative impact through privacy concerns. However, the positive effect was determined to be stronger and thus perceived personalization had an overall positive impact. Additionally, the results showed that both effects were mediated by flow experience. The practical and theoretical implications of the findings were discussed