Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene.

Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications. This highlights the importance of understanding the semantics of their outputs. To shed light on this issue, two questions are formulated, (i) do p-tools provide similar predictions? (inner consistency), and (ii) are these predictions consistent with the literature? (outer consistency). To answer these, six p-tools are evaluated with exhaustive SNV datasets from the BRCA1 gene. Two indices, called K a l l and K s t r o n g , are proposed to quantify the inner consistency of pairs of p-tools while the outer consistency is quantified by standard information retrieval metrics. While the inner consistency analysis reveals that most of the p-tools are not consistent with each other, the outer consistency analysis reveals they are characterized by a low prediction performance. Although this result highlights the need of improving the prediction performance of individual p-tools, the inner consistency results pave the way to the systematic design of truly diverse ensembles of p-tools that can overcome the limitations of individual members.

Discrimination of corn from monocotyledonous weeds with ultraviolet (UV) induced fluorescence.

In production agriculture, savings in herbicides can be achieved if weeds can be discriminated from crop, allowing the targeting of weed control to weed-infested areas only. Previous studies demonstrated the potential of ultraviolet (UV) induced fluorescence to discriminate corn from weeds and recently, robust models have been obtained for the discrimination between monocots (including corn) and dicots. Here, we developed a new approach to achieve robust discrimination of monocot weeds from corn. To this end, four corn hybrids (Elite 60T05, Monsanto DKC 26-78, Pioneer 39Y85 (RR), and Syngenta N2555 (Bt, LL)) and four monocot weeds (Digitaria ischaemum (Schreb.) I, Echinochloa crus-galli (L.) Beauv., Panicum capillare (L.), and Setaria glauca (L.) Beauv.) were grown either in a greenhouse or in a growth cabinet and UV (327 nm) induced fluorescence spectra (400 to 755 nm) were measured under controlled or uncontrolled ambient light intensity and temperature. This resulted in three contrasting data sets suitable for testing the robustness of discrimination models. In the blue-green region (400 to 550 nm), the shape of the spectra did not contain any useful information for discrimination. Therefore, the integral of the blue-green region (415 to 455 nm) was used as a normalizing factor for the red fluorescence intensity (670 to 755 nm). The shape of the normalized red fluorescence spectra did not contribute to the discrimination and in the end, only the integral of the normalized red fluorescence intensity was left as a single discriminant variable. Applying a threshold on this variable minimizing the classification error resulted in calibration errors ranging from 14.2% to 15.8%, but this threshold varied largely between data sets. Therefore, to achieve robustness, a model calibration scheme was developed based on the collection of a calibration data set from 75 corn plants. From this set, a new threshold can be estimated as the 85% quantile on the cumulative frequency curve of the integral of the normalized red fluorescence. With this approach the classification error was nearly constant (16.0% to 18.5%), thereby indicating the potential of UV-induced fluorescence to reliably discriminate corn from monocot weeds.

Improved discrimination between monocotyledonous and dicotyledonous plants for weed control based on the blue-green region of ultraviolet-induced fluorescence spectra.

Precision weeding by spot spraying in real time requires sensors to discriminate between weeds and crop without contact. Among the optical based solutions, the ultraviolet (UV) induced fluorescence of the plants appears as a promising alternative. In a first paper, the feasibility of discriminating between corn hybrids, monocotyledonous, and dicotyledonous weeds was demonstrated on the basis of the complete spectra. Some considerations about the different sources of fluorescence oriented the focus to the blue-green fluorescence (BGF) part, ignoring the chlorophyll fluorescence that is inherently more variable in time. This paper investigates the potential of performing weed/crop discrimination on the basis of several large spectral bands in the BGF area. A partial least squares discriminant analysis (PLS-DA) was performed on a set of 1908 spectra of corn and weed plants over 3 years and various growing conditions. The discrimination between monocotyledonous and dicotyledonous plants based on the blue-green fluorescence yielded robust models (classification error between 1.3 and 4.6% for between-year validation). On the basis of the analysis of the PLS-DA model, two large bands were chosen in the blue-green fluorescence zone (400-425 nm and 425-490 nm). A linear discriminant analysis based on the signal from these two bands also provided very robust inter-year results (classification error from 1.5% to 5.2%). The same selection process was applied to discriminate between monocotyledonous weeds and maize but yielded no robust models (up to 50% inter-year error). Further work will be required to solve this problem and provide a complete UV fluorescence based sensor for weed-maize discrimination.

Effects of preprocessing of ultraviolet-induced fluorescence spectra in plant fingerprinting applications.

Preprocessing is an important step in data analysis. Dealing with spectral data, normalization is mandatory in order to compare items collected under various conditions. This paper addresses normalization of frontface fluorescence spectroscopy data where spectra are affected by an unknown multiplicative effect. The usual methods for reducing multiplicative problems are reviewed and a more detailed analysis of the normalization by closure is provided based on data on the fluorescence of plants as a means for plant species fingerprinting. As normalization is essentially the reduction of information, some methods of carrying it out are likely to remove either meaningful or discriminant pieces of information. As a result, it is demonstrated that normalization by closure should be performed using spectral data in a range where the spectra contain no information relevant to the problem at hand. This applies provided that in this range the signal-to-noise ratio is high enough. When the noise level is too high, a compromise should be found between preserving useful information and limiting the amount of noise introduced by the normalization procedure. Even if this study were carried out using fluorescence spectra, the overall process is likely to be applied to other spectral data.

Spontaneous recovery in phacolytic glaucoma.

We report a case of phacolytic glaucoma in which spontaneous absorption of the hypermature lens allowed a patient who refused surgery to recover a normal pressure and satisfactory visual acuity.