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This study applies both quantitative and qualitative analyses to the energy policies and energy security situation in China between 1980 and 2010. Two primary objectives are achieved in this study. The first objective is to examine how the energy security situation in China has evolved in the past 30 years. This study establishes a quantitative framework that shows that China’s energy security situation has not improved during the economic reform. The second objective of the study is to explore qualitatively why the energy security situation has not improved. To answer the “why” question, the study opens up a new perspective by analysing the relationship between energy security and energy policies from the macroeconomic reform perspective. This study discusses major reforms that took place over 30 years. It finds that China’s macroeconomic reform has restricted the formation of China’s energy policies and determined its energy security situation. In essence, China’s energy policies were only a reaction to the macroeconomic measures. They were not originally intended to improve energy security, but were passive reactions to China’s macroeconomic reform. This explains why China did not improve its energy security situation despite 30 years of reform.

Despite unprecedented energy consumption rates and environmental sustainability threats, China has seen fast economic growth in the past 30 years of reform. Within this context, this study applies both quantitative and qualitative analyses to the energy security situation in China between 1980 and 2010. Two primary objectives are achieved in this study. The first objective is to examine how the energy security situation in China has evolved in the past 30 years. This study establishes a quantitative 4-As framework and identifies 20 indicators incorporated in the framework based on a comprehensive concept of energy security which covers availability of resources, applicability of technologies, acceptability by society and affordability of prices (the 4-As). The framework analysis shows that China’s energy security situation has not improved during the economic reform. Its enlarging appetite for energy resources has made the quantitative value of the availability dimension decline. The acceptability dimension deteriorated along with the energy utilization, only improving a little recently. The affordability dimension improved after it hit the bottom in 1995. Although the country’s technology continued to progress, the improvement of the applicability dimension cannot restore the overall energy security status to the early reform period level. The emphasis on energy policies during the 30 years of reform appears to have affected the energy security status directly. The second objective of the study is to explore qualitatively why the energy security situation has not improved. To answer the “why” question, the study opens up a new perspective by analysing the relationship between energy security and energy policies from the macroeconomic reform perspective. This study discusses major reforms that took place over 30 years. It is found that China’s macroeconomic reform has restricted the formation of China’s energy policies and determined its energy security situation. In essence, China’s energy policies are only a reaction to the macroeconomic measures. In other words, China’s energy policies are not originally intended to improve energy security, but passive reactions to China’s macroeconomic reform. This explains why China did not improve its energy security situation despite 30 years of reform. Doctor of Philosophy

Klebsiella oxytoca Rs-5 isolated with ACC (1-aminocyclopropane-1-carboxylate) deaminase activity as the sole nitrogen source could obviously promote cotton seedling growth under salt stress and produce phytohormone indole-3-acetic acid (IAA). The amount of IAA produced by the strain Rs-5 was measured, and the effect of IAA on cotton growth under salt stress was studied. Different treatments were set to treat cotton seeds with fermentation broth containing strain Rs-5 (FB), strain Rs-5, fermentation broth with bacteria removed (FB-NB), fermentation broth without bacteria or IAA (FB-NB-NI) and single IAA solutions (SI) according to the IAA concentration after strain Rs-5 culturing of 48, 72 and 120 h. The germination rate, dry weight, plant height, root length and malondialdehyde (MDA), proline and endogenous IAA content in roots were determined. The results showed that both IAA produced by strain Rs-5 and the strain were effective in promoting cotton growth under salt stress. The growth and ability to resist salt stress of cotton seedlings were increased with the enhancement of IAA concentration. The treatment of FB containing bacteria and IAA at 120 h obtained the best state of cotton growth, when the IAA content was the highest in the fermentation broth (42.14 μg·L(-1)). The germination rate, dry weight, plant height and root length were increased by 29.4%, 24.3%, 27.2% and 27.2% , respectively, compared to the saline control. The strain Rs-5 and/or IAA could obviously reduce the MDA and proline content and increase the endogenous IAA content in cotton seedlings. However, the efficacy of other components in the fermentation broth was inconspicuous.

To observe the microscopic characteristic of the bonding interface between resin cement and dentin before and after using Nd: YAG laser, and evaluate the effects of the Nd: YAG laser on the shear bond strength of 3 resin cements to dentin.30 human premolars were cut into nearly equal halves, prepared to expose the dentin and randomly divided into two groups: Laser group and control group. The teeth in the laser group were processed with pulse Nd: YAG laser (0.8 W, 10 Hz) for 25 s, and then the teeth were bonded using 3 resin cements (RelyX ARC, Panavia F and RelyX Unicem) to dentin. All teeth in the control group were directly filled with 3 cements. The shear bond strength of specimens was measured. Each fractured specimen was examined under dental microscope. And 6 new human premolars were prepared into standard bonding specimens. After 3 teeth processed with pulse Nd: YAG laser, all teeth were bonded using 3 resin cements. To observe the microscopic characteristics of the bonding interface between resin cements and dentin.Nd: YAG laser on dentin surface could increase bond strength of Panavia F and RelyX Unicem (P0.05). And the laser could decrease bond strength of RelyX ARC (P0.05). Most of the fractured adhesive dentin surfaces were adhesive failure modes. Scanning electron microscope (SEM) observation demonstrated that Nd: YAG laser irradiation on dentin surface could decrease the hybrid layer and resin tags between the resin cements (RelyX ARC and Panavia F) and dentin. The interface between RelyX Unicem and dentin showed no obvious change, and resin tags were not observed.Nd: YAG laser on dentin surface can increase bond strength of Panavia F and RelyX Unicem. Therefore, they can be used together in clinic.

This chapter presents a quantifiable definition of energy security and the various indicators generally used to measure it. These indicators are then applied to Association of Southeast Asian Nations (ASEAN) countries in order to evaluate their past energy security policies and activities, which include cross-border infrastructure building and hydropower exploitation projects in the region. ASEAN’s efforts at enhancing energy security are analysed relative to its efforts for climate change mitigation, as energy security and climate change have profound impacts on each other. The chapter also suggests alternative approaches for addressing issues related to energy security and climate change in the region.

Application efficacy of the biological seed coating agent (BSCA) prepared with polyvinyl alcohol, sodium carboxymethyl celullose and Na-bentonite and having plant growth-promoting rhizobacteria (PGPR) Rs-5 and BCL-8, was investigated on cotton plants in the field. The results showed that the germination rate of cotton seeds treated with BSCA was increased by 11.3%, but the incidence rate of cotton seedlings was decreased by 10.8%, while other growth factors such as plant height, fresh weight, dry weight and leaf area of cotton seedlings were increased by 14.4%, 19.1%, 25.7% and 47.4% compared to treated with chemical seed coating agent (CSCA), respectively. Further analysis demonstrated that enzymatic activity of PAL, POD, PPO, SOD and photosynthetic pigment contents were improved to a certain extent as well, while MDA activity was declined. Moreover, the ionic uptake of Ca2+, Mg2+ and K+ in cotton was enhanced, while Na+ absorption was reduced in BSCA compared to CSCA. The yield of cotton seeds treated with BSCA was increased up to 10.5% in comparison with CSCA treatment. Hence, our results revealed that the use of BSCA could not only alleviate the adverse effects of salinity but also has a promising plant growth promoting potential in salinized soil.

Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them., Author Summary An ontology represents the concepts and their interrelation within a knowledge domain. Several ontologies have been developed in biomedicine, which provide standardized vocabularies to describe diseases, genes and gene products, physiological phenotypes, anatomical structures, and many other phenomena. Scientists use them to encode the results of complex experiments and observations and to perform integrative analysis to discover new knowledge. A remaining challenge in ontology development is how to evaluate an ontology's representation of knowledge within its scientific domain. Building on classic measures from information retrieval, we introduce a family of metrics including breadth and depth that capture the conceptual coverage and parsimony of an ontology. We test these measures using (1) four commonly used medical ontologies in relation to a corpus of medical documents and (2) seven popular English thesauri (ontologies of synonyms) with respect to text from medicine, news, and novels. Results demonstrate that both medical ontologies and English thesauri have a small overlap in concepts and relations. Our methods suggest efforts to tighten the fit between ontologies and biomedical knowledge.

Natural compounds represent a significant source for the development of novel medicines. Finding the target proteins for a natural compound is the most important step towards understanding its molecular mechanism for therapeutic usage. In fact, the search for target proteins could be considered the first step of the drug discovery and development pipeline. While experimental determination of compound-protein interactions remains very challenging, effective in silico approaches have been developed and have demonstrated appealing advantages, including their low-cost and capability to scale up easily. The goal of this article is to provide an introduction to in silico search for drug targets of natural compounds. I first review currently available natural compounds databases and human gene/protein databases, and the rapidly emerging databases for known drug-target interactions. These resources provide the 'materials' for in silico approaches and define the gold standard of 'positives' for evaluating them. I then introduce three classes of computational methods for target identification of natural compounds, namely molecular docking, quantitative structure-activity relationship (QSAR) modeling, and data mining and integrative analysis. Use of these methods is explained using real examples, and the advantages and disadvantages of each method are compared. As these state-of-the-art methods continue to mature amid significant challenges, this field appears poised for a period of significant growth, with untold benefits to drug discovery and natural product development.

The formulas were derived for the calculation of the permittivity of dielectric measured in TE 109 mode rectangular cavity and TE 011 mode cylindrical cavity. A testing system based on scalar network analyzer was adopted to measure the same sheet dielectric by the two cavities above respectively. The testing software based on LABVIEW was developed. The results showed that the measuring error was less than 3.8% and repeatability error was than 1.1% by perturbation method in rectangular cavity, while those are 0.4% and 0.3%, by using cylindrical cavity. The automatic measurement of permittivity of dielectric sheet can be achieved by perturbation method in cylindrical cavity.

Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies.

Summary Objective: The discovery of new targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Conventional target identification approaches are disease-dependent, which require heavy experimental workload and comprehensive domain knowledge. In this work, we propose that a disease-independent property of proteins, “drug-target likeness”, can be explored to facilitate the genomic scale target screening in the post-genomic age. Methods: ASupport Vector Machine (SVM) classifier was trained to recognize target and non-target protein sequences compiled from the Therapeutic Target Database, Drug Bank, and PFam. Protein sequences are encoded by theircomposition, transition and distribution features of residues and Gaussian kernel function was used in SVM classification. Results: SVM with a fine-tuned kernel width records 66.4 ± 5.1% of sensitivity and 97.2 ± 0.6% of specificity, corresponding to an overall target prediction accuracy of 94.4 ± 0.8%. Conclusions: Though primitive, these results suggest that, similar to the “drug likeness” for small chemicals, their binding partners, drug targets, also display shared features which are reflected in their sequences and can be captured bystatistical learning approaches. Further research on how to accurately and interpretably measure the likeness of protein being a drug target is promising. Inspired bythe progress of “drug likeness” studies, advances in protein descriptors, statistical learning algorithms and more comprehensive and accurate gold-standard data set from disease biology research may help to further define the “drug-target likeness” property of proteins.

It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.

Understanding of cellular processes and underlying molecular events requires knowledge about different aspects of molecular interactions, networks of molecules and pathways in addition to the sequence, structure and function of individual molecules involved. Databases of interacting molecules, pathways and related chemical reaction equations have been developed. The kinetic data for these interactions, which is important for mechanistic investigation, quantitative study and simulation of cellular processes and events, is not provided in the existing databases. We introduce a new database of Kinetic Data of Bio-molecular Interactions (KDBI) aimed at providing experimentally determined kinetic data of protein-protein, protein-RNA, protein-DNA, protein-ligand, RNA-ligand, DNA-ligand binding or reaction events described in the literature. KDBI contains information about binding or reaction event, participating molecules (name, synonyms, molecular formula, classification, SWISS-PROT AC or CAS number), binding or reaction equation, kinetic data and related references. The kinetic data is in terms of one or a combination of the following quantities as given in the literature of a particular event: association/dissociation or on/off rate constant, first/second/third/. order rate constant, equilibrium rate constant, catalytic rate constant, equilibrium association/dissociation constant, inhibition constant and binding affinity constant. Each entry can be retrieved through protein or nucleic acid or ligand name, SWISS-PROT AC number, ligand CAS number and full-text search of a binding or reaction event. KDBI currently contains 8273 entries of biomolecular binding or reaction events involving 1380 proteins, 143 nucleic acids and 1395 small molecules. Hyperlinks are provided for accessing references in Medline and available 3D structures in PDB and NDB. This database can be accessed at http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp.