Population genetics of the spotted seahorse (Hippocampus kuda) in Thai waters : implications for conservation

A population genetics approach was used to investigate the genetic diversity of the spotted seahorse (Hippocampus kuda) in Thai waters; specifically, the degree of genetic differentiation and species evolution was inferred from sequence analysis of 353 bp of the mitochondrial (mt)DNA control region. The data were then used to identify discrete populations in Thai waters for effective conservation and management. Spotted seahorses were collected from 4 regions on the east and west coasts of the Gulf of Thailand and a geographically separated region in the Andaman Sea. Of the 101 mtDNA sequences analyzed, 7 haplotypes were identified, 5 of which were shared among individuals from the east and west coasts of the Gulf of Thailand. The remaining haplotypes were restricted to individuals from the Andaman Sea. Nucleotide and haplotype diversities were similar within the Gulf of Thailand samples, whereas diversity was lower in the Andaman Sea sample. Genetic differentiation appeared between pairs of samples from the Gulf of Thailand and Andaman Sea (FST, p &lt; 0.0001). A large genetic variance appeared among the 2 population groups (94.46%, &Phi;CT = 0.94464, p &lt; 0.01). A Neighbor-joining tree indicated that individuals from the Gulf of Thailand and Andaman Sea formed 2 phylogenetically distinct groups, which were segregated into different population-based clades. While results reported here indicate that populations from the Gulf of Thailand and Andaman Sea should be treated as separate conservation units, a larger sample size from the Andaman Sea is required to confirm this genetic partitioning and low level of diversity observed in the present study.<br /

China's rise and the 'Chinese dream' in international relations theory

The rise of China/East Asia and the perceived decline of the US/West pose an emerging question about how international relations (IR) theory should respond to this change. Increasingly, there have been heated discussions among Chinese IR academics over a desirable Chinese contribution to IR theory (IRT), particularly the possibility of building a distinctive Chinese IRT. Inevitably, this drive towards theorizing from a Chinese perspective also creates a backlash among not only Western but also other Chinese scholars as they question the ‘nationalistic’ if not ‘hegemonic’ discourse of the scholarship. Drawing on the sociology of scientific knowledge framework, this article examines the linkages between the vibrant dynamics of the Chinese theoretical debates and the actual practices of Chinese scholars in realizing their claims. It suggests that this investigation can serve as a springboard into a better appreciation of the theory–practice and power–knowledge relationships in the context of Chinese IR

Relationship Between Obesity and Periodontal Status in Vietnamese Patients

This study aims to investigate periodontal status, and the relationship between obesity and periodontal status in patients who first visited the Institute of Traditional Medicine, Ho Chi Minh City, Vietnam. 118 patients aged 18 or older, including 56 obese subjects (BMI&ge;27.5, mean age: 33.8, males: 11, females: 45) and 62 non-obese subjects (BMI&lt;27.5, mean age: 34.3, males: 4, females: 58) were enrolled for a period of 5 months from February 2014 to June 2014. The information on socio-demographic characteristics and dental habits were collected by questionnaire. Periodontal status (PLI, GI, BOP, PD, CAL) was examined and the anthropometric index was measured. There was significantly higher prevalence of periodontitis (39.3%) in the obese group than the non-obese group (16.4%). Means of GI, BOP, PD, and CAL in obese subjects were significantly higher than those in non-obese subjects. Significantly higher percentages of subjects who had lower education, visited dental offices, scaled and polished their teeth regularly were in the non-obese group than in the obese group. Multiple logistic regression analysis revealed that age (OR=3.10), routine of dental visit (OR=3.34) and obesity (OR=2.79) were risk factors significantly related to periodontitis. Periodontal status in obese subjects was poorer than non-obese subjects. Obesity might be the risk factor for periodontitis in Vietnamese patients

Manifesting individuality as a Heideggerian approach to Toni Morrison\u27s Trilogy .

Within our self-defining quest to create and uncreate ourselves and our place in the world is a discourse that encompasses both our life and our literature

Edge Assignment and Data Valuation in Federated Learning

Federated Learning (FL) is a recent Machine Learning method for training with private data separately stored in local machines without gathering them into one place for central learning. It was born to address the following challenges when applying Machine Learning in practice: (1) Communication cost: Most real-world data that can be useful for training are locally collected; to bring them all to one place for central learning can be expensive, especially in real-time learning applications when time is of the essence, for example, predicting the next word when texting on a smartphone; and (2) Privacy protection: Many applications must protect data privacy, such as those in the healthcare field; the private data can only be seen by its local owner and as such the learning may only use a content-hiding representation of this data, which is much less informative. To fulfill FL’s promise, this dissertation addresses three important problems regarding the need for good training data, system scalability, and uncertainty robustness: 1. The effectiveness of FL depends critically on the quality of the local training data. We should not only incentivize participants who have good training data but also minimize the effect of bad training data on the overall learning procedure. The first problem of my research is to determine a score to value a participant’s contribution. My approach is to compute such a score based on Shapley Value (SV), a concept of cooperative game theory for profit allocation in a coalition game. In this direction, the main challenge is due to the exponential time complexity of the SV computation, which is further complicated by the iterative manner of the FL learning algorithm. I propose a fast and effective valuation method that overcomes this challenge. 2. On scalability, FL depends on a central server for repeated aggregation of local training models, which is prone to become a performance bottleneck. A reasonable approach is to combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. The second problem of my research is to optimize this tradeoff. This dissertation shows that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. Specifically, I propose an assignment solution that is especially useful for the case of non-IID training data which is well-known to hinder today’s FL performance. 3. FL participants may decide on their own what devices they run on, their computing capabilities, and how often they communicate the training model with the aggregation server. The workloads incurred by them are therefore time-varying, and unpredictably. The server capacities are finite and can vary too. The third problem of my research is to compute an edge server assignment that is robust to such dynamics and uncertainties. I propose a stochastic approach to solving this problem