Hybrid Kinetics Embedding Framework for Dynamic PET Reconstruction

In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solutions can be categorized in two groups in the way the temporal dynamics is modeled: data-driven approaches use spatiotemporal neural networks to learn the temporal dynamics of tracer kinetics from data, which relies heavily on data supervision; physics-based approaches leverage \textit{a priori} tracer kinetic models to focus on inferring their parameters, which relies heavily on the accuracy of the prior kinetic model. In this paper, we marry the strengths of these two approaches in a hybrid kinetics embedding (HyKE-Net) framework for dynamic PET reconstruction. We first introduce a novel \textit{hybrid} model of tracer kinetics consisting of a physics-based function augmented by a neural component to account for its gap to data-generating tracer kinetics, both identifiable from data. We then embed this hybrid model at the latent space of an encoding-decoding framework to enable both supervised and unsupervised identification of the hybrid kinetics and thereby dynamic PET reconstruction. Through both phantom and real-data experiments, we demonstrate the benefits of HyKE-Net -- especially in unsupervised reconstructions -- over existing physics-based and data-driven baselines as well as its ablated formulations where the embedded tracer kinetics are purely physics-based, purely neural, or hybrid but with a non-adaptable neural component.Comment: Under Revie

Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework

Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many abstract latent representations may reconstruct observations, yet do they guarantee an adequate identification of the governing dynamics? This paper investigates this challenge from two angles: the use of physics inductive bias specific to the data being modeled, and a learn-to-identify strategy that separates forecasting objectives from the data used for the identification. We combine these two strategies in a novel framework for unsupervised meta-learning of hybrid latent dynamics (Meta-HyLaD) with: 1) a latent dynamic function that hybridize known mathematical expressions of prior physics with neural functions describing its unknown errors, and 2) a meta-learning formulation to learn to separately identify both components of the hybrid dynamics. Through extensive experiments on five physics and one biomedical systems, we provide strong evidence for the benefits of Meta-HyLaD to integrate rich prior knowledge while identifying their gap to observed data.Comment: Under Revie

Preparation and Characterization of Superhydrophobic Modification of Polyvinylidene Fluoride Membrane by Dip-Coating

The superhydrophobicity polyvinylidene fluoride (PVDF) membranes were modified via reducing surface energy by dip-coating perfluoroalkyl methacrylic copolymer (Zonyl 8740) onto the membranes prepared on mat glass. The chemical component of the unmodified and modified PVDF membranes surface was investigated by ATR-FTIR. Morphology and hydrophobicity of the unmodified and modified PVDF membranes were examined by scanning electronic microscopy and water contact angle, respectively. The effects of concentration of Zonyl 8740, coating time, conditions of heat treatment on hydrophobic capability of PVDF membranes were investigated. The results showed that the water contact angle increased from 141˚ to 151˚ by the dip-coating modification, therefore getting superhydrophobic PVDF membrane. Moreover, the porosity and the morphology of modified PVDF membrane were unchanged by the dip-coating modification. This results suggested that the hydrophobicty stability of the modified PVDF membrane was also good

Effects of the TLR4/Myd88/NF-κB Signaling Pathway on NLRP3 Inflammasome in Coronary Microembolization-Induced Myocardial Injury

Background/Aims: Coronary microembolization (CME) is a common complication of acute coronary syndrome (ACS) and percutaneous coronary intervention (PCI); Myocardial inflammation, caused by CME, is the main cause of cardiac injury. TLR4/MyD88/NF-κB signaling plays an important role in the development of myocardial inflammation, but its effects on CME remain unclear. To assess the cardiac protective effects of TAK-242 (TLR4 specific inhibitor) on CME-induced myocardial injury, and explore the underlying mechanism. Methods: Cardiac function, serum c-troponin I level, microinfarct were examined by cardiac ultrasound, myocardial enzyme assessment, HBFP staining. The levels of TLR4/MyD88/NF-κB signaling and NLRP3 inflammasome pathway were detected by ELISA, qRT-PCR and western blot. Results: The results showed inflammatory responses in the myocardium after CME, with increased expression levels of pro-inflammatory factors TNF-α, IL-1β and IL-18. Meanwhile, TLR4/MyD88/NF-κB signaling and the NLRP3 inflammasome were involved in the inflammatory process. TAK-242 administration before CME effectively inhibited the inflammatory response in the rat myocardium after CME and reduced myocardial injury, mainly by inhibiting TLR4/ MyD88/NF-κB signaling and reducing NLRP3 inflammasome activation. In addition, in vitro assays with neonatal rat cardiomyocytes further confirmed that TLR4/MyD88/NF-κB signaling was significantly activated in the inflammatory response of LPS-induced cardiomyocytes, via activation of the NLRP3 inflammasome. Inhibition of TLR4/MyD88/NF-κB signaling resulted in increased survival of cardiomyocytes mainly by reducing the release of inflammatory cytokines and decreasing NLRP3 inflammasome activation. Conclusions: TLR4/MyD88/NF-κB signaling participates in the inflammatory response of the myocardium after CME, activating the NLRP3 inflammasome, promoting the inflammatory cascade, and aggravating myocardial injury. Blocking TLR4/MyD88/NF-κB signaling may help reduce myocardial injury and improve cardiac function after CME

Applications of CRISPR/Cas Technology to Research the Synthetic Genomics of Yeast

The whole genome projects open the prelude to the diversity and complexity of biological genome by generating immense data. For the sake of exploring the riddle of the genome, scientists around the world have dedicated themselves in annotating for these massive data. However, searching for the exact and valuable information is like looking for a needle in a haystack. Advances in gene editing technology have allowed researchers to precisely manipulate the targeted functional genes in the genome by the state-of-the-art gene-editing tools, so as to facilitate the studies involving the fields of biology, agriculture, food industry, medicine, environment and healthcare in a more convenient way. As a sort of pioneer editing devices, the CRISPR/Cas systems having various versatile homologs and variants, now are rapidly giving impetus to the development of synthetic genomics and synthetic biology. Firstly, in the chapter, we will present the classification, structural and functional diversity of CRISPR/Cas systems. Then we will emphasize the applications in synthetic genome of yeast (Saccharomyces cerevisiae) using CRISPR/Cas technology based on year order. Finally, the summary and prospection of synthetic genomics as well as synthetic biotechnology based on CRISPR/Cas systems and their further utilizations in yeast are narrated

Subgroup Economic Analysis for Glioblastoma in a Health Resource-Limited Setting

BACKGROUND: The aim of this research was to evaluate the economic outcomes of radiotherapy (RT), temozolomide (TMZ) and nitrosourea (NT) strategies for glioblastoma patients with different prognostic factors. METHODOLOGY/PRINCIPAL FINDINGS: A Markov model was developed to track monthly patient transitions. Transition probabilities and utilities were derived primarily from published reports. Costs were estimated from the perspective of the Chinese healthcare system. The survival data with different prognostic factors were simulated using Weibull survival models. Costs over a 5-year period and quality-adjusted life years (QALYs) were estimated. Probabilistic sensitivity and one-way analyses were performed. The baseline analysis in the overall cohort showed that the TMZ strategy increased the cost and QALY relative to the RT strategy by $25,328.4 and 0.29, respectively; and the TMZ strategy increased the cost and QALY relative to the NT strategy by$23,906.5 and 0.25, respectively. Therefore, the incremental cost effectiveness ratio (ICER) per additional QALY of the TMZ strategy, relative to the RT strategy and the NT strategy, amounts to $87,940.6 and$94,968.3, respectively. Subgroups with more favorable prognostic factors achieved more health benefits with improved ICERs. Probabilistic sensitivity analyses confirmed that the TMZ strategy was not cost-effective. In general, the results were most sensitive to the cost of TMZ, which indicates that better outcomes could be achieved by decreasing the cost of TMZ. CONCLUSIONS/SIGNIFICANCE: In health resource-limited settings, TMZ is not a cost-effective option for glioblastoma patients. Selecting patients with more favorable prognostic factors increases the likelihood of cost-effectiveness

Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain

High-quality and fast reconstructions are essential for the clinical application of positron emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed for PET image reconstruction directly from the sinogram domain to achieve high-quality and high-speed reconstruction at the same time. In this framework, conditional generative adversarial networks are constructed to learn a mapping from sinogram data to a reconstructed image and to generate a well-trained model. The network consists of a generator that utilizes the U-net structure and a whole-image strategy discriminator, which are alternately trained. Simulation experiments are conducted to validate the performance of the algorithm in terms of reconstruction accuracy, reconstruction efficiency, and robustness. Real patient data and Sprague Dawley rat data were used to verify the performance of the proposed method under complex conditions. The experimental results demonstrate the superior performance of the proposed method in terms of image quality, reconstruction speed, and robustness

Dysbiotic alteration in the fecal microbiota of patients with polycystic ovary syndrome

ABSTRACT Polycystic ovary syndrome (PCOS) is a common disease associated with high androgen and infertility. The gut microbiota plays an important role in metabolic diseases including obesity, hyperglycemia, and fatty liver. Although the gut microbiota has been associated with PCOS, little is known about the gut microbial structure and function in individuals with PCOS from Northeast China. In this study, 17 PCOS individuals and 17 age-matched healthy individuals were recruited for community structure and function analysis of the gut microbiota. The results showed that PCOS individuals have reduced diversity and richness of the gut microbiota compared with healthy individuals. Beta diversity analysis showed that the community structure of the gut microbiota of individuals with PCOS was significantly separated from healthy individuals. At the phylum level, PCOS individuals have reduced Firmicutes and Bacteroidota and increased Actinobacteriota and Proteobacteria compared with healthy individuals. At the family and genus levels, the composition of the gut microbiota between PCOS patients and healthy individuals was also significantly different. In addition, PICRUSt2 showed that individuals with PCOS have different microbial functions in the gut compared with healthy individuals. We finally confirmed that Bifidobacterium was enriched in the fecal samples of PCOS patients, while other 11 genera including Bacteroides, UCG_002, Eubacterium__coprostanoligenes_group_unclassified, Dialister, Firmicutes_unclassified, Ruminococcus, Alistipes, Christensenellaceae_R_7_group, Clostridia_UCG_014_unclassified, Roseburia, and Lachnospiraceae_unclassified were depleted compared with healthy individuals. These results indicate that individuals with PCOS have altered community structure and functions of the gut microbiota, which suggests that targeting the gut microbiota might be a potential strategy for PCOS intervention.IMPORTANCEGut microbiota plays a critical role in the development of PCOS. There is a complex and close interaction between PCOS and gut microbiota. The relationship between the pathogenesis and pathophysiological processes of PCOS and the structure and function of the gut microbiota needs further investigation

A Driving Power Supply for Piezoelectric Transducers Based on an Improved LC Matching Network

In the ultrasonic welding system, the ultrasonic power supply drives the piezoelectric transducer to work in the resonant state to realize the conversion of electrical energy into mechanical energy. In order to obtain stable ultrasonic energy and ensure welding quality, this paper designs a driving power supply based on an improved LC matching network with two functions, frequency tracking and power regulation. First, in order to analyze the dynamic branch of the piezoelectric transducer, we propose an improved LC matching network, in which three voltage RMS values are used to analyze the dynamic branch and discriminate the series resonant frequency. Further, the driving power system is designed using the three RMS voltage values as feedback. A fuzzy control method is used for frequency tracking. The double closed-loop control method of the power outer loop and the current inner loop is used for power regulation. Through MATLAB software simulation and experimental testing, it is verified that the power supply can effectively track the series resonant frequency and control the power while being continuously adjustable. This study has promising applications in ultrasonic welding technology with complex loads