Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Candida etchellsii is a predominant strain in Chinese horse bean-chili-paste, a famous and traditional condiment with high salt content. In this study, physiological properties, ester production capacity and gene expression of C. etchellsii under salt stress were investigated, and the results showed that the strain evoked miscellaneous regulations to resist high salt stress. Under salt stress, C. etchellsii would accumulate intracellular glycerol, Na+ and K+ to balance osmotic pressure. Interestingly, the strain showed much stronger capacity to yield ethyl hexanoate and hexyl acetate after 5% and 20% (w/v) NaCl treatment whether in esterification or alcoholysis system. Furthermore, transcriptome data indicated that the genes associated with carbohydrate metabolism and amino acid metabolism had significantly different expression, and their regulations were consistent with the results of physiological analysis. The results obtained in this study could contribute to understand the salt tolerance mechanism of C. etchellsii and provide a theoretical support for its application in traditional food fermentation. 

Salt tolerance and ester production mechanisms of Candida etchellsii in Chinese horse bean-chili-paste

—This paper investigates how to enhance the Multi-access Edge Computing (MEC) systems performance with the aid of device-to-device (D2D) communication computation offloading. By adequately exploiting a novel computation offloading mechanism based on D2D collaboration, users can efficiently share computational resources with each other. However, it is challenging to distinguish valuable information that truly promotes a collaborative decision, as worthless information can hinder collaboration among users. In addition, the transmission of large volumes of information requires high bandwidth and incurs significant latency and computational complexity, resulting in unacceptable costs. In this paper, we propose an efficient D2D-assisted MEC computation offloading framework based on Attention Communication Deep Reinforcement Learning (ACDRL), which simulates the interactions between related entities, including device-to-device collaboration in the horizontal and device-to-edge offloading in the vertical. Secondly, we developed a distributed cooperative reinforcement learning algorithm that includes an attention mechanism that skews computational resources towards active users to avoid unnecessary resource wastage in large-scale MEC systems. Finally, to improve the effectiveness and rationality of cooperation among users, we introduce a communication channel to integrate information from all users in a communication group, thus facilitating cooperative decision-making. The proposed framework is benchmarked, and the experimental results show that the proposed framework can effectively reduce latency and provide valuable insights for practical design compared to other baseline approaches.

Task Computation Offloading for Multi-Access Edge Computing via Attention Communication Deep Reinforcement Learning

Microbial Interactions in Anaerobic Wastewater Treatment

This study tackles the online user allocation problem in mobile edge computing (MEC) systems powered by non-orthogonal multiple access. App vendors need to determine a proper wireless channel in a base station/edge server and sufficient transmit power for every user. We consider a stochastic MEC system where users arrive and depart over time. When an edge server runs out of computing resources, some users will have to wait until the resources become available again, which incurs an allocation delay cost. This cost is often not investigated in many studies, which also do not consider a multi-cell, multi-channel system as we do in this work, due to its complexity. We aim to minimize the allocation delay and transmit power costs, increasing the system&#x2019;s energy efficiency. To achieve this objective while guaranteeing users&#x2019; data rate requirements over time, we adopt the Lyapunov framework to convert this long-term optimization problem into a series of subproblems to be solved in every time slot. To solve the aforementioned subproblems efficiently, we present a distributed game theory-based approach. The proposed algorithm is theoretically evaluated and experimentally demonstrated to outperform several baseline and state-of-the-art methods, highlighting the significance of systematic consideration for both computation and communication aspects of this problem. 

Online User and Power Allocation in Dynamic NOMA-Based Mobile Edge Computing

Cardiovascular disease (CVD) is associated with higher morbidity and mortality rates in patients with chronic kidney disease (CKD). Studies have shown that vascular calcification is a major predictor of CVD. Vascular calcification in the CKD population is associated with various risk factors, and changes in bone and mineral metabolism have been linked to an increased risk of atherosclerosis. Therefore, we aimed to investigate the correlation between vascular calcification and bone metabolism, which is necessary to improve the survival and prognosis of patients with CKD.We included 146 patients with CKD who received coronary artery calcification (CAC) scores at our hospital from May 2017 to November 2018. Spearman rank correlation analysis, Mann-Whitney U test, and Kaplan-Meier method were used to analyze laboratory data and all-cause mortality.In the 146 patients, chronic glomerulonephritis accounted for the most common cause of CKD, at approximately 39.0%. Spearman rank correlation analysis on the factors influencing vascular calcification in patients with CKD showed that CAC score was significantly and positively correlated with C-reactive protein, N-terminal/midregion osteocalcin (N-MID), N-terminal peptide of type 1 procollagen (P1NP), β-cross-linked C-telopeptide of type 1 collagen (β-CTx), and parathyroid hormone (P = 0.0423, P = 0.0432, P = 0.0235, P = 0.0061, P < 0.0001, respectively). Serum calcium levels were positively correlated with N-MID, P1NP, β-CTx, and iPTH (r = 0.19, r = 0.24, r = 0.21, r = 0.21, respectively), and serum phosphorus levels were positively correlated with N-MID, P1NP, β-CTx, and iPTH (r = 0.50, r = 0.37, r = 0.50, r = 0.55, respectively). However, no difference was found in CVC scores among patients with CKD in different stages and receiving different treatments. In the Kaplan-Meier analysis of all-cause hospitalization and mortality rates, patients with CAC > 400 had a higher risk.We found that the primary cause of CKD is glomerulonephritis, and the CAC score is positively correlated with inflammatory and bone metabolism markers, with a higher risk of all-cause mortality and cardiovascular hospitalization when the CAC score is greater than 400. 

/pdf/coronary-artery-calcification-in-patients-with-advanced-1k5ldxcv.pdf

Qiang He

Papers

Salt tolerance and ester production mechanisms of Candida etchellsii in Chinese horse bean-chili-paste

Task Computation Offloading for Multi-Access Edge Computing via Attention Communication Deep Reinforcement Learning

Microbial Interactions in Anaerobic Wastewater Treatment

Online User and Power Allocation in Dynamic NOMA-Based Mobile Edge Computing

Coronary artery calcification in patients with advanced chronic kidney disease