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[前沿资讯] Computer program aids food safety experts with pathogen testing 进入全文


An innovative computer program could be a big help for food safety professionals working to keep production facilities free of food-borne pathogens. Cornell University scientists have developed a computer program, Environmental Monitoring With an Agent-Based Model of Listeria (EnABLe), to simulate the most likely locations in a processing facility where the deadly food-borne pathogen Listeria monocytogenes might be found. Food safety managers may then test those areas for the bacteria's presence, adding an important tool to prevent food contamination and human exposure to the pathogen through tainted food. The computer model, which is described in the Jan. 24 issue of Scientific Reports, has the potential to be modified for a wide range of microbes and locations. "The goal is to build a decision-support tool for control of any pathogen in any complex environment," said Renata Ivanek, associate professor in the Department of Population Medicine and Diagnostic Sciences and senior author of the paper. The study was funded by the Frozen Food Foundation through a grant to Martin Wiedmann, professor of food science, who is also a co-author of the paper. The researchers, including first author Claire Zoellner, a postdoctoral research associate in Ivanek's lab, want to eventually apply the framework to identifying contamination from pathogens that cause hospital-acquired infections in veterinary hospitals or E. coli bacteria in fruit and vegetable processing plants. Food safety professionals at processing facilities keep regular schedules for pathogen testing. They rely on their own expertise and knowledge of the building to determine where to swab for samples. "Whenever we have an environment that is complex, we always have to rely on expert opinion and general rules for this system, or this company, but what we're trying to offer is a way to make this more quantitative and systematic by creating this digital reality," Ivanek said. For the system to work, Zoellner, Ivanek and colleagues entered all relevant data into the model - including historical perspectives, expert feedback, details of the equipment used and its cleaning schedule, the jobs people do, and materials and people who enter from outside the facility. "A computer model like EnABLe connects those data to help answer questions related to changes in contamination risks, potential sources of contamination and approaches for risk mitigation and management," Zoellner said. "A single person could never keep track of all that information, but if we run this model on a computer, we can have in one iteration a distribution of Listeria across equipment after one week. And every time you run it, it will be different and collectively predict a range of possible outcomes," Ivanek said. The paper describes a model system that traces Listeria species on equipment and surfaces in a cold-smoked salmon facility. Simulations revealed contamination dynamics and risks for Listeria contamination on equipment surfaces. Furthermore, the insights gained from seeing patterns in the areas where Listeria is predicted can inform the design of food processing plants and Listeria-monitoring programs. In the future, the model will be applied to frozen food facilities.

[前沿资讯] Internet of Things Collaborative caps successful first year with additional $2.2M grant 进入全文


The Internet of Things Collaborative (IoTC), a partnership between Case Western Reserve and Cleveland State universities, is bringing together industrial, governmental, educational, neighborhood and non-profit entities in the region to harness IoT's vast potential. To continue building on the IoTC's early successes, the Cleveland Foundation has awarded another $2.2 million, one-year grant to the collaborative, which was created in 2017 to position Cleveland as a leader in digital innovation. The new funding follows $2 million in grants the foundation awarded the previous two years to help establish the IoTC and attract top academicians and create research labs for the initiative. The Internet of Things refers to the massive interconnected network of devices and serves as the technology framework for blockchain and other future digital innovations. The IoTC is focusing especially on four sectors in Northeast Ohio: manufacturing, health, energy and municipal infrastructure. According to some industry estimates, the number of IoT-related devices is expected to exceed 30 billion by 2020, and the economic impact of related projects is predicted to reach as much as $6 trillion worldwide within five years. "Our continued support of the IoT Collaborative is indicative of the first-year success of the unprecedented partnership between Case Western Reserve University and Cleveland State University," said Leon Wilson, the Cleveland Foundation's chief of digital innovation and chief information officer. "Investing in our research universities to drive Cleveland's future in the digital economy is a proven model for economic transformation and makes it possible for our public sector to embrace technology in ways that enhance quality of life for Cleveland residents." Late last year, the IoTC awarded five pilot grants to research teams at both universities as seed money for wide-ranging projects. Separately, CWRU and CSU have also funded additional IoT pilot projects from internal sources.Such financial support has allowed the IoTC to:(1)Launch a bimonthly thought leadership series to examine industry innovations in IoT.(2)Influence the research and development of an "Industrial IoT Roadmap" by Team NEO (funded by the Burton D. Morgan Foundation), designed to help manufacturers deploy and integrate "smart manufacturing"--also known as "Industry 4.0"-- technologies, including addressing talent development. (3)Work on two neighborhood-based demonstration projects. (4)Add faculty, staff--including those with industry experience--and consultants to the IoTC team. Included were two new faculty members and three new staff members to support CWRU's Institute for Smart Secure and Connected Systems (ISAACS), the body coordinating CWRU's involvement in the IoTC. CSU is recruiting two new faculty members and two staff members to support its Center for IoT Innovation (CITI). (5)Leverage the foundation's investment by raising $6.6 million in additional philanthropic awards for ISSACS and CITI. CWRU has designated 10 endowed chairs for ISSACS-associated faculty, representing more than $20 million in endowments, which generates more than $800,000 annually for faculty to use for students, equipment, conferences and other expenses.

[统计数据] The Production Quantity of Mutton in the World(FAOSTAT, 2017) 进入全文


FAO统计了2017年世界各个国家/地区的羊肉总产量,项目选择为Meat, goat和Meat, sheep,部分数据见下表,全部数据请参见全文。

[统计数据] 2013年-2017年中国城乡居民肉类人均消费量 进入全文

布瑞克农产品数据库;; 国家统计局


[学术文献] Biosensors for pathogen surveillance 进入全文


Biologists, chemists, and physicists are collaborating to develop highly sensitive and specific biosensors for pathogen detection in the food, healthcare, and environmental sectors. Those novel biosensors allow quick detection and are thus expected to solve the issues of the emergence of highly virulent or antibiotic-resistant pathogens. This article reviews different types of biosensors used for pathogen detection, classified based on the type of transducer used. Optical biosensors integrate labeled means, e.g., fluorophores, quantum dots, and carbon dots to overcome photobleaching. Surface plasmon resonance is also used for enhanced sensitivity. Mechanical biosensors with piezoelectric crystals and cantilevers are adapted for the detection of food pathogens without sample preparation or labels. Conventional methods using electrodes for the measurement of electrochemical changes with differential pulse voltammetry or impedance spectroscopy are fast and highly sensitive. Immunosensors are developed for pathogen detection at trace levels using sample enrichment, signal amplification, and new visual detection techniques.

[学术文献] Sensing Technologies for Precision Phenotyping in Vegetable Crops: Current Status and Future Challenges 进入全文


Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with "omics" disciplines will also be discussed.



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