A Comprehensive performance evaluation and ranking methodology under a sustainable development perspective

Under industry globalization and the intensely competitive environment, a company's competitiveness must constantly be upgraded in order to achieve the goal of sustainability. Therefore, the correct and valid evaluation of companies’ sustainable performance has become an important issue. The main purpose of this study is to discuss and establish a sustainable performance evaluation criteria and model for companies. First, the measurements of companies’ financial, credit risk, environmental and social responsibility are integrated to create sustainable business performance evaluation criteria. Then, we integrate grey relational analysis and an improved TOPSIS method to construct a sustainable performance evaluation model for companies. In order to verify the findings of this study, we adopt Taiwan's high-tech listed companies as the research object to explore sustainable operating performance and ranking in 2011. The empirical results will help companies to build future business strategies and can also be used as an important reference for investor and bank credit auditing

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm

Agriculture is the foundation of the national economy. Thus, an appropriate tool for forecasting agricultural output is very important for policy making. In this study, both modified background value calculation and use of a genetic algorithm to find the optimal parameters were adopted simultaneously to construct an improved GM(1,1) model (GAIGM(1,1)). The sample period of the forecasting models includes the annual values for the data of Taiwan’s agricultural output from 1998 to 2010. The mean absolute percentage error and the root mean square percentage error are two criteria with which to compare the various forecasting models results. Both in-sample and out-of-sample forecast performance results show that the GAIGM(1,1) model has highly accurate forecasting. Therefore, the GAIGM(1,1) model can raise the forecast accuracy of the GM(1,1) model, and it is suitable for use in modeling and forecasting of agricultural output

Application of EM-AMMI to Analyze Integrated Regional Trial Data over Multiple Years

區域試驗施行於品系產量試驗之後，其目的為確保品系之產量與農藝性狀在不同環境下皆能具有穩定表現。當區域試驗資料的基因與環境交感效應顯著時，可採用AMMI模式對基因與環境交感項做進一步之分析，能提升品系選拔的效率。惟使用AMMI模式分析區域試驗資料時，不允許基因型與環境組合的平均產量有缺值的情形。然而AMMI模式奇異值分解的不允許基因型與環境組合的平均產量有缺值的情形，但多年期多重地區之區域試驗資料通常高度不均衡，限制育種家探討跨年期間基因型與環境的交感。本研究利用EM-AMMI方式進行缺值估計。模擬結果顯示，無論缺值率大小，僅使用第一主成分軸之缺值估計效果為最佳。本研究亦應用EM-AMMI於跨年期毛豆區域試驗資料之品種穩定性比較，希望藉此幫助育種家進行較長期的育種材料評估。 The regional trials aim to ensure the stability of yields and agronomic traits of the targeted lines under various environments. When there exists a significant genotype-by-environment interaction, AMMI model can be applied to improve the efficiency of selection. However, AMMI model is not applicable when the average yields are missing in some genotype and environment combinations, which cannot be avoided when combining multiple regional trial data to study the genotype-by-environment interaction across years. In this study, we adopted Expectation-Maximization-Additive Main Effect and Multiplicative Interaction (EM-AMMI) method to impute the missing average yields. According the simulations, imputation using only the first principle component axis had the most accurate estimates regardless of the missing percentages. We also applied EM-AMMI method to the multiple-season vegetable soybean regional trial dataset and then proceed to AMMI analysis on the imputed dataset. The proposed method may make it possible for the breeders to perform long-term evaluations on their breeding materials

ERF73/HRE1 is involved in H2O2 production via hypoxia-inducible Rboh gene expression in hypoxia signaling

Hypoxia deprives cells of energy and induces severe physical damage in embryophytes. Under hypoxia, the equilibrium between ethylene and H2O2 affects the response of the transcription factor AtERF73/HRE1. To evaluate the role of AtERF73/HRE1 during hypoxia signaling, we used three independent AtERF73/HRE1 knockout lines to detect H2O2 accumulation. The results revealed that under hypoxia, H2O2 accumulation in the AtERF73/HRE1 knockout lines decreased, indicating that AtERF73/HRE1 uses a negative feedback regulation mechanism to influence the production of H2O2 induced through hypoxia signal transduction. Quantitative RT-PCR analyses showed that oxygen deficiency had different effects on the expression of the hypoxia-induced genes Rboh B, D, G, and I in the AtERF73/HRE1 knockout lines. In particular, Rboh B and D expression were increased, whereas Rboh G expression was decreased. The expression of Rboh I was increased at 1 h but decreased at 3 h during hypoxia treatment in the AtERF73/HRE1 knockout lines. Similarly, the transcript levels of antioxidant and hypoxia-induced/ethylene response genes in the AtERF73/HRE1 knockout lines were affected by hypoxic stress, indicating that AtERF73/HRE1 is essential to hypoxia signal transduction in embryophytes. Additionally, in histochemical analysis, AtERF73/HRE1 promoter-induced GUS expression was detected in various plant parts throughout the plant growth process (e.g., leaves, inflorescences, siliques), particularly in the edges of mature leaves and guard cells. Taken together, our results confirm that AtERF73/HRE1 plays a role in H2O2 production by affecting the hypoxia-induced expression of Rboh genes in hypoxia signal transduction