Laser Ablation Machine for Labeling Cryogenically-Frozen Vials

    公开(公告)号:US20170312852A1

    公开(公告)日:2017-11-02

    申请号:US15499127

    申请日:2017-04-27

    Applicant: MERIAL INC.

    Abstract: The invention relates to a machine for labeling “blank-labeled” cryogenically-frozen vials or ampoules, which contain heat-labile biological materials, and to which a laser-light sensitive material had been applied prior to freezing. Accordingly, the machine has been designed to maintain the integrity of the biological materials throughout all phases of the labeling process. The machine generally comprises a master control system; a programmable user interface; a frame; cryogenic freezer assemblies, for keeping the vials at the required low temperatures; an infeed assembly, configured to receive and position blank-labeled cryogenic vials; a cryostatic labeling/quality control tunnel, wherein the vials are maintained at the required temperature, labeled by laser ablation, and checked for quality; and, an outfeed assembly. The machine further comprises a means for transporting the vials from the infeed assembly to the tunnel, and from the tunnel to the outfeed assembly. Vials labeled according to the instant disclosure are ultimately manually or automatically loaded into cryogenic shipping containers.

    Methods of Providing Grass Growth and Nematode Suitability Indices to Assist Determination of Timing of Parasiticide Treatment

    公开(公告)号:US20170181403A1

    公开(公告)日:2017-06-29

    申请号:US15378729

    申请日:2016-12-14

    Applicant: MERIAL, INC.

    Abstract: The present invention builds and conveys to users in need thereof a Grass Growth Index (GGI) and a Nematode Suitability Index (NSI), to assist the determination of whether and when to administer parasiticides to grazing livestock, such as bovine, ovine and caprine animals. In particular, the invention relates to a computer-implemented method whereby GGI and NSI are calculated based upon both user-supplied information (e.g. grass/forage type and location data) and user-independent information (e.g. weather and environmental condition data associated with the location over time, and grass and nematode growth parameters). Importantly, the disclosed methods provide users with information as to when and where their herds of grazing animals may be at the greatest risk of parasite infestation.

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