A Successful Experience of Traceability System for the Case of Yin Chuan Organic Farm in Taiwan

In Taiwan, the traceability system (TS) of whole food chain is in infantile time. Yin Chuan Organic Farm, constituted by the team of rice production and marketing (TRPM), is the first brand in Taiwan rice market. The leader of TRPM is not only to produce the premising rice quality, but also to implement the (TS) that was introduced by Taiwan government with a great result and could be a successful experience passed to other proprietors. The findings of this paper are listed as following: 1. The TS executed by the case is only emphasized on the production side that needs to build more detailed marketing side. 2. The key successful factor to get sound TS model is to have obvious purposes and system harmony.traceability system, food safety, organic food, analytic hierarchy process (AHP), Agribusiness, Food Consumption/Nutrition/Food Safety,

Microbiota and bile acid profiles in retinoic acid-primed mice that exhibit accelerated liver regeneration.

Background & aimsAll-trans Retinoic acid (RA) regulates hepatic lipid and bile acid homeostasis. Similar to bile acid (BA), RA accelerates partial hepatectomy (PHx)-induced liver regeneration. Because there is a bidirectional regulatory relationship between gut microbiota and BA synthesis, we examined the effect of RA in altering the gut microbial population and BA composition and established their relationship with hepatic biological processes during the active phases of liver regeneration.MethodsC57BL/6 mice were treated with RA orally followed by 2/3 PHx. The roles of RA in shifting gut microbiota and BA profiles as well as hepatocyte metabolism and proliferation were studied.ResultsRA-primed mice exhibited accelerated hepatocyte proliferation revealed by higher numbers of Ki67-positive cells compared to untreated mice. Firmicutes and Bacteroidetes phyla dominated the gut microbial community (>85%) in both control and RA-primed mice after PHx. RA reduced the ratio of Firmicutes to Bacteroidetes, which was associated with a lean phenotype. Consistently, RA-primed mice lacked transient lipid accumulation normally found in regenerating livers. In addition, RA altered BA homeostasis and shifted BA profiles by increasing the ratio of hydrophilic to hydrophobic BAs in regenerating livers. Accordingly, metabolic regulators fibroblast growth factor 21, Sirtuin1, and their downstream targets AMPK and ERK1/2 were more robustly activated in RA-primed than unprimed regenerating livers.ConclusionsPriming mice with RA resulted in a lean microbiota composition and hydrophilic BA profiles, which were associated with facilitated metabolism and enhanced cell proliferation

Employee Reporting in the U.K.: An Empirical Study of Managerial Objectives and Behaviour

Since the 1970's, an increase of interest was shown by researchers in employee reporting, but little empirical attention has been focused on the managerial determinants. This research addresses the need to understand how and why voluntary employee reporting developed, examining its place in the wider organisational and social context. A three-pronged enquiry was carried out in the U.K. during 1981-83, comprising documentary analysis, postal questionnaire survey and personal interviews, to find out managerial objectives and attitudes in employee communications. It traced influences from specific organisational, social and societal factors on employee reporting. Analysis of the qualitative data consistently demonstrated the importance attached by managers to reporting financial information to the employees. Their communication objectives showed a dominant social theme. Employee reporting developments result from strong faith or commitment to communications on the part of management, and their perceptions of specific pressing organisational needs and wider social and moral issues at the time. The study highlights the complexity of managers' perceptions and mixed feelings about the intrinsic value and conflicting social interests in employee information. There remains scope for experimentation on the uses and designs of employee communication systems. This research represents a contribution to the developing interpretive approach in accounting research, and to the debates about corporate accountability to the employees and public policy making

Computational Hybrid Systems for Identifying Prognostic Gene Markers of Lung Cancer

Lung cancer is the most fatal cancer around the world. Current lung cancer prognosis and treatment is based on tumor stage population statistics and could not reliably assess the risk for developing recurrence in individual patients. Biomarkers enable treatment options to be tailored to individual patients based on their tumor molecular characteristics. To date, there is no clinically applied molecular prognostic model for lung cancer. Statistics and feature selection methods identify gene candidates by ranking the association between gene expression and disease outcome, but do not account for the interactions among genes. Computational network methods could model interactions, but have not been used for gene selection due to computational inefficiency. Moreover, the curse of dimensionality in human genome data imposes more computational challenges to these methods.;We proposed two hybrid systems for the identification of prognostic gene signatures for lung cancer using gene expressions measured with DNA microarray. The first hybrid system combined t-tests, Statistical Analysis of Microarray (SAM), and Relief feature selections in multiple gene filtering layers. This combinatorial system identified a 12-gene signature with better prognostic performance than published signatures in treatment selection for stage I and II patients (log-rank P\u3c0.04, Kaplan-Meier analyses). The 12-gene signature is a more significant prognostic factor (hazard ratio=4.19, 95% CI: [2.08, 8.46], P\u3c0.00006) than other clinical covariates. The signature genes were found to be involved in tumorigenesis in functional pathway analyses.;The second proposed system employed a novel computational network model, i.e., implication networks based on prediction logic. This network-based system utilizes gene coexpression networks and concurrent coregulation with signaling pathways for biomarker identification. The first application of the system modeled disease-mediated genome-wide coexpression networks. The entire genomic space were extensively explored and 21 gene signatures were discovered with better prognostic performance than all published signatures in stage I patients not receiving chemotherapy (hazard ratio\u3e1, CPE\u3e0.5, P \u3c 0.05). These signatures could potentially be used for selecting patients for adjuvant chemotherapy. The second application of the system modeled the smoking-mediated coexpression networks and identified a smoking-associated 7-gene signature. The 7-gene signature generated significant prognostication specific to smoking lung cancer patients (log-rank P\u3c0.05, Kaplan-Meier analyses), with implications in diagnostic screening of lung cancer risk in smokers (overall accuracy=74%, P\u3c0.006). The coexpression patterns derived from the implication networks in both applications were successfully validated with molecular interactions reported in the literature (FDR\u3c0.1).;Our studies demonstrated that hybrid systems with multiple gene selection layers outperform traditional methods. Moreover, implication networks could efficiently model genome-scale disease-mediated coexpression networks and crosstalk with signaling pathways, leading to the identification of clinically important gene signatures