Omega-6支撑身体，Omega-3支撑大脑：均衡摄入对儿童大脑发育的影响（网络首发、推荐阅读）

人体大脑和身体的发育，需要从食物中摄取均衡的营养物质。人类大脑是区分人类和其他动物的特征。食物中的必需脂肪酸是机体组织结构和功能的必要组成部分。Omega-6（O6）亚油酸（LA6）是皮肤组织的组成成分，且是炎症、血栓形成、免疫和其他信号分子的前体；Omega-3（O3）α-亚麻酸（ALA3），特别是其长链代谢产物——二十二碳六烯酸（DHA3），是大脑、视网膜和部分神经组织中的关键组分。从富含LA6脂肪酸（缺乏O3脂肪酸）的植物籽中提取出的廉价而优质油脂，是20世纪的西方国家食品工业生产的主要脂肪来源。在代谢通路中，高浓度的LA6脂肪酸可拮抗O3脂肪酸代谢，造成O3脂肪酸不足，因此，在给怀孕动物的饲料中，只提供富含LA6但缺乏O3脂肪酸的油脂作为唯一的脂肪来源，会导致幼崽大脑发育不良。过去20~30年的研究表明，低含量LA6且含DHA3的油脂可改善大脑的功能。近年来的研究较多集中在营养因素对大脑发育的影响，最新研究数据表明，脂肪酸平衡对营养不良儿童的大脑发育尤为重要。世界卫生组织（WHO）越来越重视大脑的营养健康，通过其下属的食品法典委员会，建议用于治疗严重急性营养不良儿童的即食治疗食品中，使用含有均衡脂肪酸组成/构成的脂肪。同样，脂肪酸均衡对老年人可能也很重要。目前，业界已经有了调整油脂成分的方法，以确保脂肪酸均衡，从而维持人体整个生命周期的大脑健康。     

食物成瘾的研究进展及启示

食物成瘾是一种复杂的慢性疾病，导致人们在日常生活中对某种食物产生过度依赖与过度进食等相关行为特征，近年来已被认为是影响欧美发达国家肥胖症、暴食症、糖尿病等发病率持续升高的关键原因，引起学术界的极大关注，成为研究热点。本文主要介绍食物成瘾的概念、诊断标准，综述了食物成瘾的病理机制、影响因素和应对措施等方面的研究进展，并探讨食物成瘾对我国食品界的启示，为预防食物成瘾，保障公众健康提供有益借鉴。     

A self‐scalable load injection service

Load testing of applications is an important and costly activity for software provider companies. Classical solutions are very difficult to set up statically, and their cost is prohibitive in terms of both human and hardware resources. Virtualized cloud computing platforms provide new opportunities for stressing an application's scalability, by providing a large range of flexible and less expensive (pay‐per‐use model) computation units. On the basis of these advantages, load testing solutions could be provided on demand in the cloud. This paper describes a Benchmark‐as‐a‐Service solution that automatically scales the load injection platform and facilitates its setup according to load profiles. Our approach is based on: (i) virtualization of the benchmarking platform to create self‐scaling injectors; (ii) online calibration to characterize the injector's capacity and impact on the benched application; and (iii) a provisioning solution to appropriately scale the load injection platform ahead of time. We also report experiments on a benchmark illustrating the benefits of this system in terms of cost and resource reductions. Copyright © 2013 John Wiley & Sons, Ltd.     

Sanitary legislation governing Food Services in Brazil

The Technical Regulation of Good Practices for Brazilian Food Services (Regulamento Técnico de Boas Práticas para Serviços de Alimentação do Brasil) can be complemented by sanitary surveillance agencies at the state, district and municipal levels by tailoring it to location-specific requirements. The purpose of the present study was to survey sanitary legislation governing Brazilian Food Services. During the period from November 2011 to November 2012, a survey was conducted with the sanitary surveillance agencies from the various states, capitals and the Federal District. A previously prepared and tested tool was used for data collection, comprising 10 questions. Only 10 (19.2%) of the sanitary codes had been established prior to RDC Resolution n° 216/2004 and were not complementary. Of the legislation cited as complementary, only the states of Santa Catarina, Rio Grande do Sul and São Paulo and the capital of the state of São Paulo truly presented additional and location-specific aspects. No legislation that was specific to hospital kitchens was found. The majority of participating coordination, including the Evaluation List for Good Practices in Food Services, was also verified; however, it had not been published. In conclusion, a gap in sanitary surveillance services exists, which was caused by the lack of location-specific food service regulations.     

Starch: a potential substrate for biohydrogen production

Hydrogen is a clean energy carrier with great potential to be an alternative fuel. Anaerobic hydrogen fermentation seems to be more favorable, since hydrogen is yielded at high rates and various organic waste and wastewater enriched with carbohydrates as substrate result in low cost for hydrogen production. Abundant biomass from various industries could be a source for biohydrogen production where combination of waste treatment and energy production would be an advantage. Carbohydrate‐rich nitrogen‐deficient solid wastes such as starch residues can be used for hydrogen production by using suitable bioprocess technologies. Alternatively, converting biomass into gaseous fuels, such as biohydrogen, is possibly the most efficient way to use these agroindustrial residues. This review summarizes the potential of starch agroindustrial residues as a substrate for biohydrogen production. Types of potential starch agroindustrial residues, recent developments and bio‐processing conditions for biohydrogen production will be discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

Application of the novel Droplet digital PCR technology for identification of meat species

The authenticity and traceability of meat products are issues of primary importance to ensure food safety. Unfortunately, food adulteration (e.g. the addition of inexpensive cuts to minced meat products) and mislabelling (e.g. the inclusion of meat from species other than those declared) happens frequently worldwide. The aim of this study was to apply a droplet digital PCR assay for the detection and quantification (copies μL⁻¹) of the beef, pork, horse, sheep, chicken and turkey in meat products. The analysis conducted on commercial meat showed the presence of traces of DNA from other animal species than those declared. We show that the method is highly sensitive, specific and accurate (accuracy = 100%). This method could be adopted by competent food safety authorities to verify compliance with the labelling of meat products and to ensure quality and safety throughout the meat supply chain, from primary production to consumption.     

Prevalence,enumeration, and characterization of Salmonella isolated from aquatic food products from retail markets in China

This study aimed to estimate the extent and level of Salmonella contamination of aquatic food products in China, and to determine serotype, virulotype, and the antimicrobial resistance profiles of recovered Salmonella isolates. Out of 554 samples collected from July 2011 to May 2014, 86 (15.5%) tested positive for Salmonella. The highest contamination rate occurred in oysters (23.1%, 6/26), followed by freshwater fish (18.6%, 43/231), shrimp (13.0%, 13/100), and saltwater fish (12.2%, 24/197). The contamination levels generally corresponded to a most probable number (MPN)/g of 0.3–10, although one sample exceeded 110 MPN/g. Among the 103 isolates, S. Typhimurium, S. Wandsworth, S. Thompson, and S. Derby were the most prevalent serovars. Sixty-eight isolates (66.0%) were resistant to at least one antimicrobial, and 35 (34.0%) were resistant to more than three. High rates of resistance were observed for tetracycline (35.9%), ampicillin (28.2%), nalidixic acid (26.2%), trimethoprim-sulfamethoxazole (25.2%), chloramphenicol (20.4%) and streptomycin (18.4%). Of note, S. Thompson isolates exhibited resistance to multiple extended-spectrum cephalosporins, ciprofloxacin, and other antimicrobials. PCR analysis of 15 virulence genes showed that ssaQ, mgtC, siiD, sopB, and bcfC were present in all 103 isolates, whereas the remaining loci were variably distributed. S. Typhimurium, S. Enteritidis, and S. Weltevreden isolates exhibited a wider range of pathogenicity determinants compared with the other strains. Our study provides a comprehensive surveillance on prevalence of Salmonella in aquatic food products from China and indicates its potential risk to public health. These data are valuable for epidemiological studies, risk management, and public health strategies.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.