Using recency, frequency and monetary variables to predict customer lifetime value with XGBoost

CRM) will continue to gain prominence in the coming years. A commonly used CRM metric called Customer Lifetime Value (CLV) is the value a customer will contribute while they are an active customer. This study investigated the ability of supervised machine learning models constructed with XGBoost to predict future CLV, as well as the likelihood that a customer will drop to a lower CLV in the future. One approach to determining CLV, called the RFM method, is done by isolating recency (R), frequency (F) and (M) monetary values. The produced models used these RFM variables and also assessed if including temporal, product, and other customer transaction information assisted the XGBoost classifier in making better predictions. The classification models were constructed by extracting each customer's RFM values and transaction information from a Fast Mover Consumer Goods dataset. Different variations of CLV were calculated through one- and two-dimensional K-means clustering of the M (Monetary), F and M (Profitability), F and R (Loyalty), as well as the R and M (Burgeoning) variables. Two additional CLV variations were also determined by isolating the M tercile segments and a commonly used weighted-RFM approach. To test the effectiveness of XGBoost in predicting future timeframes, the dataset was divided into three consecutive periods, where the first period formed the features used to predict the target CLV variables in the second and third periods. Models that predicted if CLV dropped to a lower value from the first to the second and from the first to the third periods were also constructed. It was found that the XGBoost models were moderately to highly effective in classifying future CLV in both the second and third periods. The models also effectively predicted if CLV would drop to a lower value in both future periods. The ability to predict future CLV and CLV drop in the second period, was only slightly better than the ability to predict the future CLV in the third period. Models constructed by adding additional temporal, product, and customer transaction information to the RFM values did not improve on those created that used only the RFM values. These findings illustrate the effectiveness of XGBoost as a predictor for future CLV and CLV drop, as well as affirming the efficacy of utilising RFM values to determine future CLV

Organellar carbon metabolism is co-ordinated with distinct developmental phases of secondary xylem

Subcellular compartmentation of plant biosynthetic pathways in the mitochondria and plastids requires coordinated regulation of nuclear encoded genes, and the role of these genes has been largely ignored by wood researchers. In this study, we constructed a targeted systems genetics coexpression network of xylogenesis in Eucalyptus using plastid and mitochondrial carbon metabolic genes and compared the resulting clusters to the aspen xylem developmental series. The constructed network clusters reveal the organization of transcriptional modules regulating subcellular metabolic functions in plastids and mitochondria. Overlapping genes between the plastid and mitochondrial networks implicate the common transcriptional regulation of carbon metabolism during xylem secondary growth. We show that the central processes of organellar carbon metabolism are distinctly coordinated across the developmental stages of wood formation and are specifically associated with primary growth and secondary cell wall deposition. We also demonstrate that, during xylogenesis, plastid-targeted carbon metabolism is partially regulated by the central clock for carbon allocation towards primary and secondary xylem growth, and we discuss these networks in the context of previously established associations with wood-related complex traits. This study provides a new resolution into the integration and transcriptional regulation of plastid- and mitochondrial-localized carbon metabolism during xylogenesis

Maize microarray annotation database

<p>Abstract</p> <p>Background</p> <p>Microarray technology has matured over the past fifteen years into a cost-effective solution with established data analysis protocols for global gene expression profiling. The Agilent-016047 maize 44 K microarray was custom-designed from EST sequences, but only reporter sequences with EST accession numbers are publicly available. The following information is lacking: (a) reporter - gene model match, (b) number of reporters per gene model, (c) potential for cross hybridization, (d) sense/antisense orientation of reporters, (e) position of reporter on B73 genome sequence (for eQTL studies), and (f) functional annotations of genes represented by reporters. To address this, we developed a strategy to annotate the Agilent-016047 maize microarray, and built a publicly accessible annotation database.</p> <p>Description</p> <p>Genomic annotation of the 42,034 reporters on the Agilent-016047 maize microarray was based on BLASTN results of the 60-mer reporter sequences and their corresponding ESTs against the maize B73 RefGen v2 "Working Gene Set" (WGS) predicted transcripts and the genome sequence. The agreement between the EST, WGS transcript and gDNA BLASTN results were used to assign the reporters into six genomic annotation groups. These annotation groups were: (i) "annotation by sense gene model" (23,668 reporters), (ii) "annotation by antisense gene model" (4,330); (iii) "annotation by gDNA" without a WGS transcript hit (1,549); (iv) "annotation by EST", in which case the EST from which the reporter was designed, but not the reporter itself, has a WGS transcript hit (3,390); (v) "ambiguous annotation" (2,608); and (vi) "inconclusive annotation" (6,489). Functional annotations of reporters were obtained by BLASTX and Blast2GO analysis of corresponding WGS transcripts against GenBank.</p> <p>The annotations are available in the Maize Microarray Annotation Database <url>http://MaizeArrayAnnot.bi.up.ac.za/</url>, as well as through a GBrowse annotation file that can be uploaded to the MaizeGDB genome browser as a custom track.</p> <p>The database was used to re-annotate lists of differentially expressed genes reported in case studies of published work using the Agilent-016047 maize microarray. Up to 85% of reporters in each list could be annotated with confidence by a single gene model, however up to 10% of reporters had ambiguous annotations. Overall, more than 57% of reporters gave a measurable signal in tissues as diverse as anthers and leaves.</p> <p>Conclusions</p> <p>The Maize Microarray Annotation Database will assist users of the Agilent-016047 maize microarray in (i) refining gene lists for global expression analysis, and (ii) confirming the annotation of candidate genes before functional studies.</p

Soil Seed Bank Study on Abandoned Fields and Semi-Arid Grassland, South Africa

Up to the 1980’s marginal soils in the central grasslands of South Africa were successfully ploughed for crop production, but unfortunately those soils soon proved to be uneconomical. Due to high input costs, low maize prices, and unreliable rainfall, the Department of Agriculture soon implemented the “soil conversion scheme” to promote the conversion of those ploughed marginal soils to permanent pastures. Regardless of the implementation the soil conversion scheme, many farmers unfortunately just abandoned some of these marginal fields, not establishing permanent pastures. This led to many hectares of unproductive previously cultivated fields, referred to as abandoned fields. It was noted that such abandoned fields do not recover to the same potential as the natural grassland of those areas. Therefore, it was decided to investigate a few such fields at a single location in a semi-arid climate of the Free State province, South Africa. The aim was to gain some knowledge on the dynamics of such disturbed ecosystems and identify the restoration potential of such situations. Amongst others, the soil seed bank was investigated to quantify the soil seed bank potential of abandoned fields, and why climax grass species do not establish voluntarily on these disturbed areas. The main aim was to quantify the differences in the soil seed bank between abandoned fields and natural grasslands, by investigating the research question: are there any climax grass species in the soil seed bank of abandoned fields available for grassland restoration

A comparative study of molecular and morphological methods of describing genetic relationships in traditional Ethiopian highland maize

The comparison of different methods of estimating the genetic diversity could define their usefulness in plant breeding and conservation programs. In this study, a total of 15 morphological traits, eight AFLP-primer combinations and 20 simple sequence repeat (SSR) loci were used (i) to study the morphological and genetic diversity among 62 selected highland maize accessions, (ii) to assess the level of correlation between phenotypic and genetic distances, and (iii) to classify the accessions into groups based on molecular profiles and morphological traits. The analysis of variance of the morphological data revealed significant differences among accessions for all measured traits. The mean morphological dissimilarity (0.3 with a range of 0.1-0.68) was low in comparison to dissimilarity calculated using SSR markers (0.49 with a range 0.27-0.63) and AFLP markers (0.57 with a range 0.32-0.69). The correlation between the morphological dissimilarity matrix and the matrices of genetic dissimilarity based on SSR and AFLP markers was 0.43 and 0.39, respectively (p = 0.001). The correlation between SSRs and AFLPs dissimilarity matrices was 0.67 (p = 0.001). This congruence indicates that both marker systems are equally suited for genetic diversity study of maize accessions. Cluster analysis of morphological and marker distances revealed three groups of maize accessions with distinctive genetic profiles and morphological traits. This information will be useful for collections, conservation and various breeding programs in the highlands of Ethiopia.African Journal of Biotechnology Vol. 4 (7), pp. 586-595, 200