Automation of a Wireless Cotton Module Tracking System for Cotton Fiber Quality Mapping

The ability to map the profit made across a cotton field would enable producers to see in detail where money is being made or lost on their farms. This ability, which requires sitespecific knowledge of yield, fiber quality, and input costs would further enable them to implement precise field management practices to ensure that they receive the highest return possible on each portion of a field and do not waste materials and other inputs throughout the field. Investigators at Texas A&M previously developed a wireless-GPS system that tracks where a module of cotton comes from within a field. This system is a necessary component in mapping fiber quality, which is a major determiner of price and thus profit. Three drawbacks to the previous wireless-GPS system are that (1) a person must manually trigger the system to send wireless communications when a field machine dumps its load of cotton, (2) multiple field machines of the same type (e.g., two cotton pickers) cannot be used simultaneously on the same system within the same field, and (3) no software is available to automatically produce fiber-quality maps after the data are downloaded from the gin. The first two drawbacks, the need for an automatic communication-triggering system and the needed capability for multiple field machines of the same type are the problems addressed in this work. To solve the first problem, a sensing and control system was added to a harvester to automatically indicate when the machine is dumping a basket load of cotton so that wireless messages can be automatically sent from the harvester to subsequent field machines without human intervention. This automated communication-triggering system was incorporated into the existing wireless- GPS system, rigorously field tested, and ultimately proven to operate as designed. Linking data collected with this system together with classing information will enable producers to create fiber-quality maps, and linking fiber-quality maps with yield and input-cost maps will enable them to create profit maps. Additionally, a radio-frequency identification (RFID) system was integrated with the wireless-GPS system to allow for multiple field machines of the same type. The RFID system was also rigorously field tested and proven to operate as designed. Finally, the entire system was field tested as a whole and operated according to design. Thus, the wireless-GPS module tracking system now operates without human intervention and works with multiple field machines of each type, two additional capabilities required for practical use in large farming operations

Objective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73–83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.Canadian Institutes of Health Research (MOP#82940)Sickkids FoundationOntario Research FundNational Science Foundation (U.S.)National Human Genome Research Institute (U.S.) (R01 HG003985