Variations in drought tolerance components and their association with yield components in finger millet (Eleusine coracana).

Finger millet has gained considerable attention worldwide due to its nutritional and health benefits. Being a rainfed crop of semiarid and arid regions, drought is one of the major constraints to its yield stabilisation. To address this, a set of 38 accessions of finger millet were evaluated in both field and mini-lysimeters under both well-watered (WW) and water-stressed (WS) conditions. The objectives of the study were to identify the range of variations for yield components, water-use (WU) and transpiration efficiency (TE) and to examine the potential of the mini-lysimeter system in assessing the genotypic performance in the field conditions. Approximately 2-fold variations in shoot biomass and ~9-fold variations in grain yield under WS conditions were observed. Reproductive growth was more sensitive to WS than the vegetative growth. Our results indicate that in addition to yield potential under WW conditions, WU followed by TE were the other two major contributors toward shoot biomass, whereas, HI followed by TE were the major contributors toward grain yield under WS. The close association between the yield components recorded in the field and in mini-lysimeters suggests that the lysimetric system has the great potential to reflect the genotypic performance under field conditions. Regression analyses suggest that HI explained almost all the variations in grain yield under WW conditions, whereas under WS treatment, next to HI, both TE and WU had also contributed significantly to grain yield. The absence of interrelationship between WU and TE suggests that both these components contribute independently toward the yield components under WW or WS conditions. The accessions with higher shoot biomass and grain yield extract much more water during the post-anthesis stages than the poor performers under WS. Results also suggests that higher WU contributed more towards shoot biomass and higher TE contributed more towards grain yield by improving the harvest index.

Serum prognostic biomarkers in head and neck cancer patients.

OBJECTIVES/HYPOTHESIS: A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). STUDY DESIGN: Prospective cohort study. METHODS: A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient's serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. RESULTS: Poor overall survival was associated with African Americans (hazard ratio [HR] for death = 2.61; 95% confidence interval [CI]: 1.58-4.33; P = .000), advanced stage (HR = 2.79; 95% CI: 1.40-5.57; P = .004), and recurrent disease (HR = 6.66; 95% CI: 2.54-17.44; P = .000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. CONCLUSIONS: The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted.

Stay-green quantitative trait loci's effects on water extraction, transpiration efficiency and seed yield depend on recipient parent background.

A stay-green phenotype enhances the adaptation of sorghum (Sorghum bicolor (L.) Moench) to terminal drought, although the mechanisms leading to its expression remain unclear. Differences in tillering and leaf area at anthesis, transpiration efficiency (TE), water extraction, harvest index (HI) and yield under terminal drought and fully irrigated conditions were assessed in 29 introgression lines (IL) targeting stay-green quantitative trait loci (QTLs) Stg1, Stg2, Stg3, Stg4, StgA and StgB in an S35 background, and 16 IL targeting Stg1, Stg3, Stg4 and StgB in an R16 background. TE was increased by StgB in the R16 background, whereas there was no effect in the S35 background. Water extraction was increased by Stg1 in the S35 background but not in R16. StgB modified the proportion of water extracted before and after anthesis in the S35 background. While tillering and leaf area at anthesis were decreased by Stg1 and Stg3 in S35, there was no effect in R16. Yield data under fully irrigated conditions showed higher tiller grain yield in Stg1, Stg2 and Stg3 ILs. Although yield differences were mostly explained by HI variation, the yield variation unexplained by HI was closely related to TE in S35 (R2=0.29) and R16 (R2=0.72), and was closely related to total water extracted in S35 (R2=0.41) but not in R16. These data indicate the potential for several stay-green QTLs to affect traits related to plant water use. However, these effects depend on the interaction between the genetic background and individual QTLs.

Autoantibody approach for serum-based detection of head and neck cancer.

Currently, no effective tool exists for screening or early diagnosis of head and neck squamous cell carcinoma (HNSCC). Here, we describe an approach for cancer detection based on analysis of patterns of serum immunoreactivity against a panel of biomarkers selected using microarray-based serologic profiling and specialized bioinformatics. We biopanned phage display libraries derived from three different HNSCC tissues to generate 5,133 selectively cloned tumor antigens. Based on their differential immunoreactivity on protein microarrays against serum immunoglobulins from 39 cancer and 41 control patients, we reduced the number of clones to 1,021. The performance of a neural network model (Multilayer Perceptron) for cancer classification on a data set of 80 HNSCC and 78 control samples was assessed using 10-fold cross-validation repeated 100 times. A panel of 130 clones was found to be adequate for building a classifier with sufficient sensitivity and specificity. Using these 130 markers on a completely new and independent set of 80 samples, an accuracy of 84.9% with sensitivity of 79.8% and specificity of 90.1% was achieved. Similar performance was achieved by reshuffling of the data set and by using other classification models. The performance of this classification approach represents a significant improvement over current diagnostic accuracy (sensitivity of 37% to 46% and specificity of 24%) in the primary care setting. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay with sufficient sensitivity and specificity suitable for detection of early-stage HNSCC in high-risk populations.