Approximately 6·5 million US children live in food-insecure households, meaning that they have restrained access to the types and amounts of foods they usually eat. The nutrient demands of growth and general sub-par dietary intake of US children by age highlight the importance and difficulty of attaining recommended amounts of critical dietary components to promote health and prevent disease. Evaluation of the evidence for a relationship of food insecurity with key dietary outcomes for the specific stages of child growth at 1-5 years, 6-11 years and 12-19 years has not been previously documented. Bradford Hill criteria of strength, consistency and dose-response were applied to aid evaluation. A comprehensive search of original research on US children using food-security assessment measures indexed to January 2017 was completed and identified sixteen studies that evaluated the relationship of food insecurity with key dietary outcomes. Evidence for a strong, consistent and dose-response relationship of food insecurity with lower vegetable intake compared with food security was determined among children aged 1-5 years and strong and consistent evidence of higher added sugar intake among food-insecure children aged 6-11 years compared with food-secure children was apparent. Adolescent-focused evidence was sparse but revealed adolescence as the paediatric age stage where food insecurity has the most potential for negative impact on child dietary intake. A discussion of future research opportunities includes strengthening the evidence through longitudinal study designs, inclusion of additional nutrients of concern, and stronger mitigation of bias and error.

The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.

ARID1A is located in 1p36.11, a region frequently deleted in human cancers. Using a novel method to screen for tumorigenic cDNA sequences, we have identified ARID1A as a presumptive tumor suppressor gene. The transforming ARID1A sequence was an antisense cDNA, and was the product of a genomic rearrangement, as corroborated in the primary breast carcinoma from which the cDNA had been obtained. In further screening, we identified a lung adenocarcinoma cell line with a highly localized homozygous genomic deletion involving the 5' end of ARID1A. These studies provide strong evidence that ARID1A is a tumor suppressor gene. (c) 2007 Wiley-Liss, Inc.