VisGenome: visualization of single and comparative genome representations

VisGenome visualizes single and comparative representations for the rat, the mouse and the human chromosomes at different levels of detail. The tool offers smooth zooming and panning which is more flexible than seen in other browsers. It presents information available in Ensembl for single chromosomes, as well as homologies (orthologue predictions including ortholog one2one, apparent ortholog one2one, ortholog many2many) for any two chromosomes from different species. The application can query supporting data from Ensembl by invoking a link in a browser

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species.

Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

Identifying Stored-Grain Insects Using Near-Infrared Spectroscopy

Proper identification of insects in grain storage facilities is critical for predicting development of pest populations and for making management decisions. However, many stored-grain insect pests are difficult to identify, even for trained personnel. We examined the possibility that near-infrared (NIR) spectroscopy could be used for taxonomic purposes based on the premise that every species may have a unique chemical composition. Tests were conducted with 11 species of beetles commonly associated with stored grain. Spectra from individual insects were collected by using a near-infrared diode-array spectrometer. Calibrations were developed by using partial least squares analysis and neural networks. The neural networks calibration correctly identified .99% of test insects as primary or secondary pests and correctly identified .95% of test insects to genus. Evidence indicates that absorption characteristics of cuticular lipids may contribute to the classification of these species. We believe that this technology could be used for rapid, automated identification of many other organisms

Detection of insect fragments in wheat flour by near-infrared spectroscopy

Insect fragments in commercial wheat flour are a major concern to the milling industry because consumers expect high quality and wholesome products at the retail level. Thus, the US Food and Drug Administration (FDA) has established a defect action level of 75 insect fragments per 50 g of flour. Millers routinely test their wheat flour to comply with this federal requirement and to deliver sound flour to their consumers. The current standard flotation method for detecting fragments in flour is expensive and labor intensive. Therefore, we examined the possible use of a rapid, near-infrared spectroscopy (NIRS) method for detecting insect fragments in wheat flour. We also compared the sensitivity and accuracy of the NIRS method with that of the current standard flotation method. Fragment counts with both techniques were significantly correlated with the actual number of fragments present in flour samples. However, the flotation method was more sensitive than the NIRS method with fragment counts below the FDA defect action level. We were unable to predict whether the number of fragments in a sample exceeded the FDA action level with our NIRS instrumentation. However, we were able to predict accurately whether flour samples contained less than or more than 130 fragments. Although current NIRS instruments are unable to detect insect fragments at the FDA action level, this method should be re-examined in the future because NIRS technology is rapidly improving

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species.

BACKGROUND: Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. METHODS: Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. RESULTS: The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. CONCLUSION: RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

Genetic analysis of feed quality and seed weight of sorghum inbred lines and hybrids using analytical methods and NIRS

Eight lines of grain sorghum and their F1 hybrids were evaluated for contents of crude protein (CP), fat (FAT), and starch (STA); protein digestibility (PD); and in vitro dry matter disappearance (IVDMD). The effect of seed weight (SW) on these traits and the potential use of near infrared reflectance spectroscopy (NIRS) to predict them also were investigated. The male lines included three normal-seeded lines (TX2737, TX435, and P954063) and two largeseeded lines (PL-1 and Eastin1). The female lines included commonU.S. seed parent lines (Wheatland, Redlan, and SA3042). The lines and their hybrids were grown under dryland conditions at Kansas State University experiment fields in Ashland and Belleville, Kansas, in 1999. The experiments were conducted using a randomized complete block design with four replications at each location. The effect of genotype was significant for all measured traits. The male parent lines were highly variable and expressed high levels of genetic variation in combining ability for CP, PD, STA, and SW. The female parents were genetically more uniform; however, significant general combining ability effects were noted for PD and SW. Significant negative correlations were noted between CP and STA and between SW and STA. Significant positive correlations were found between CP and SW and between FAT and IVDMD. Crude protein content was predicted accurately by NIRS. Fat content and IVDMD could not be predicted by NIRS. The NIRS equations based on ground samples were more accurate than those based on whole-seed samples

Near-infrared spectroscopy as a complementary age grading and species identification tool for African malaria vectors

Near-infrared spectroscopy (NIRS) was recently applied to age-grade and differentiate laboratory reared Anopheles gambiae sensu strico and Anopheles arabiensis sibling species of Anopheles gambiae sensu lato complex. In this study, we report further on the accuracy of this tool for simultaneously estimating the age class and differentiating the morphologically indistinguishable An. gambiae s.s. and An. arabiensis from semi-field releases and wild populations. Nine different ages (1, 3, 5, 7, 9, 11, 12, 14, 16 d) of An. arabiensis and eight different ages (1, 3, 5, 7, 9, 10, 11, 12 d) of An. gambiae s.s. maintained in 250 × 60 × 40 cm cages within a semi-field large-cage system and 105 wild-caught female An. gambiae s.l., were included in this study. NIRS classified female An. arabiensis and An. gambiae s.s. maintained in semi-field cages as <7 d old or ≥7 d old with 89% (n = 377) and 78% (n = 327) accuracy, respectively, and differentiated them with 89% (n = 704) accuracy. Wild caught An. gambiae s.l. were identified with 90% accuracy (n = 105) whereas their predicted ages were consistent with the expected mean chronological ages of the physiological age categories determined by dissections. These findings have importance for monitoring control programmes where reduction in the proportion of older mosquitoes that have the ability to transmit malaria is an important outcome

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species

BACKGROUND: Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. METHODS: Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. RESULTS: The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. CONCLUSION: RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended