Studies on Category Prediction of Ovarian Cancers Based on Magnetic Resonance Images

Ovarian cancer is the gynecological malignant tumor with low early diagnosis rate and high mortality. Ovarian epithelial cancer (OEC) is the most common subtype of ovarian cancer. Pathologically, OEC is divided into two subtypes: Type I and Type II. These two subtypes of OEC have different biological characteristics and treatment response. Therefore, it is important to accurately categorize these two groups of patients and provide the reference for clinicians in designing treatment plans. In the current magnetic resonance (MR) examination, the diagnoses given by the radiologists are largely based on individual judgment and not sufficiently accurate. Because of the low accuracy of the results and the risk of suffering Type II OEC, most patients will undertake the fine-needle aspiration, which may cause harm to patients’ bodies. Therefore, there is need for the method for OEC subtype classification based on MR images. This thesis proposes the automatic diagnosis system of ovarian cancer based on the combination of deep learning and radiomics. The method utilizes four common useful sequences for ovarian cancer diagnosis: sagittal fat-suppressed T2WI (Sag-fs-T2WI), coronal T2WI (Cor-T2WI), axial T1WI (Axi-T1WI), and apparent diffusion coefficient map (ADC) to establish a multi-sequence diagnostic model. The system starts with the segmentation of the ovarian tumors, and then obtains the radiomic features from lesion parts together with the network features. Selected Features are used to build model to predict the malignancy of ovarian cancers, the subtype of OEC and the survival condition. Bi-atten-ResUnet is proposed in this thesis as the segmentation model. The network is established on the basis of U-Net with adopting Residual block and non-local attention module. It preserves the classic encoder/decoder architecture in the U-Net network. The encoder part is reconstructed by the pretrained ResNet to make use of transfer learning knowledge, and bi-non-local attention modules are added to the decoder part on each level. The application of these techniques enhances the network’s performance in segmentation tasks. The model achieves 0.918, 0.905, 0.831, and 0.820 Dice coefficient respectively in segmenting on four MR sequences. After the segmentation work, the thesis proposes a diagnostic model with three steps: quantitative description feature extraction, feature selection, and establishment of prediction models. First, radiomic features and network features are obtained. Then iterative sparse representation (ISR) method is adopted as the feature selection to reduce the redundancy and correlation. The selected features are used to establish a predictive model, and support vector machine (SVM) is used as the classifier. The model achieves an AUC of 0.967 in distinguishing between benign and malignant ovarian tumors. For discriminating Type I and Type II OEC, the model yields an AUC of 0.823. In the survival prediction, patients categorized in high risk group are more likely to have poor prognosis with hazard ratio 4.169

Substrate Recognition and Mechanistic Studies of Protein N-Terminal Methyltransferase 1

The methylation at the α-N-terminal amines of proteins that start with a canonical motif X-P-K (X=A/P/S) has been a known modification for nearly four decades. In 2010, protein α-N-terminal methyltransferase 1 (NTMT1/NRMT1) was identified as the first enzyme responsible for this modification. NTMT2 was discovered as a second member belonging to this family, but it was reported as a mono-methylase. The identification of RCC1, retinoblastoma (Rb) protein, centromere protein-A/B (CENP-A/B), and DNA damaged-binding protein 2 (DDB2) as new NTMT1 substrates revealed NTMT1’s biological significance in mitosis, cell-cycle regulation, centromere formation, and damaged DNA repair, respectively. Although significant progress had been made, a clear understanding of how NTMT1 recognizes substrates remains to be determined. Also, there is no specific small molecule inhibitor for NTMT1. To fill these gaps, we first established a fluorescence-based assay for kinetic characterization of NTMT1. Subsequently, ternary complex crystal structures of NTMT1 were obtained to illustrate the structural basis for enzyme-substrate interactions. The structures of the enzyme-substrate complex coupled with mutagenesis, binding, and enzymatic studies demonstrated the key elements involved in interaction with its substrates. In the meantime, we utilized computational studies and fluorescence assays for novel small molecule discovery. Lastly, we closely monitored the substrates’ methylation progression by NTMT1 and NTMT2 in parallel using a MALDI-MS based assay. Our results indicated that NTMT1 follows a Bi-Bi mechanism, and its methylation proceeds in a distributive pattern. Furthermore, NTMT1 was identified has broad substrate specificity beyond its canonical motif X-P-K (X=A/P/S), since X can be any amino acid except D/E and the third amino acids can also be R. We had also discovered an inhibitor that targets the substrate binding site of NTMT1 with IC50 = 7 µM. Lastly, our methylation progression studies has demonstrated that NTMT2 can also di-, tri-methylate certain substrates although its methylation rate is lower than NTMT1. Overall, this project has laid the foundation for further investigation of N-terminal methylation in terms of functions, mechanisms, and inhibitor design

Multi-hop relaying using energy harvesting

In this letter, the performance of multi-hop relaying using energy harvesting is evaluated. Both amplify-and-forward and decode-and-forward relaying protocols are considered. The evaluation is conducted for time-switching energy harvesting as well as power-splitting energy harvesting. The largest number of hops given an initial amount of energy from the source node is calculated. Numerical results show that, in order to extend the network coverage using multi-hop relaying, time-switching is a better option than power splitting and in some cases, decode-and-forward also supports more hops than amplify-and-forward

Performance evaluation of heterogeneous wireless information and power networks

In this study, the performance of downlink simultaneous wireless information and power transfer (SWIPT) networks over Nakagami-m fading is analysed. The SWIPT network is modelled as a two-tier heterogeneous network, where one tier is the information transmission network and the other is the power transmission network. The seamless integration enables both data and energy to be transferred from access points to the users. Using the stochastic geometry theory, the expressions for outage probability at the information receiver are derived in decoupled and integrated SWIPT networks. Also, the average harvested energy at the power receiver is derived assuming a non-linear energy harvesting model. Simulation results validate the analytical expressions and the impacts of various system parameters on the SWITP performance are investigated

Study on Force Schemes in Pseudopotential Lattice Boltzmann Model for Two-Phase Flows

Multiphase flows are very important in industrial application. In present study, the force schemes in the pseudopotential LBM for two-phase flows have been compared in detail and the force schemes include Shan-Chen, EDM, MED, and Guo’s schemes. Numerical simulations confirm that all four schemes are consistent with the Laplace law. For Shan-Chen scheme, the smaller τ is, the smaller the surface tension is. However, for other schemes, τ has no effect on surface tension. When 0.6<τ≤1, the achieved density ratio will reduce as τ reduces. During this range of τ, the maximum density ratio of EDM scheme will be greater than that of other schemes. For a constant T, the curves of the maximum spurious currents (u′) has a minimum value which is corresponding to τ′ except for EDM schemes. In the region of τ′<τ≤1, u′ will reduce as τ decreases. On the other hand, in the area of τ≤τ′, u′ will increase as τ decreases. However, for EDM scheme, u′ will increase as τ increases

STTAR: A Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip Systems

Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts the input and output buffer lengths of each router with traffic load information to limit routing resources in overheated areas and control the rate of temperature rise. Second, STTAR adopts a scoring strategy based on temperature and the number of free slots in the buffer to avoid data packets being transmitted to high-temperature areas and congested areas and to improve the rationality of selecting routing output nodes. In our experiments, the proposed scoring Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) scheme can increase the throughput by about 14.98% to 47.90% and reduce the delay by about 10.80% to 35.36% compared with the previous works

Effect of strong time-varying transmission distance on LEO satellite-terrestrial deliveries

In this paper, we investigate the effect of the strong time-varying transmission distance on the performance of the low-earth orbit (LEO) satellite-terrestrial transmission (STT) system. We propose a new analytical framework using finite-state Markov channel (FSMC) model and time discretization method. Moreover, to demonstrate the applications of the proposed framework, the performances of two adaptive transmissions, rate-adaptive transmission (RAT) and power-adaptive transmission (PAT) schemes, are evaluated for the cases when the transmit power or the transmission rate at the LEO satellite is fixed. Closed-form expressions for the throughput, energy efficiency (EE), and delay outage rate (DOR) of the considered systems are derived and verified, which are capable of addressing the capacity, energy efficiency, and outage rate performance of the considered LEO STT scenarios with the proposed analytical framework

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field