Chickpea (Cicer arietinum L.) is grown on low input marginal lands and represents an important component of the subsistence farming. It is the second most important grain legume globally cultivated on an area of 13.20 million hectare (Mha) with an annual production of 11.62 million tons (Mt; FAOSTAT 2011). The global demand for chickpea in 2020 is projected to be 17.0 Mt (up from the current 8.6 Mt; Abate et al. 2012). It is mostly grown on residual moisture from monsoon rains on the Indian subcontinent and semi-arid regions of Sub-Saharan Africa (SSA). India is the largest producer and consumer of chickpea. Among various kinds of abiotic (salinity, heat) stresses affecting the chickpea production, drought stress particularly at the end of the growing season is a major constraint to chickpea production and yield stability in arid and semi-arid regions of the world (see Krishnamurthy et al. 2010). Drought causes substantial annual yield losses up to 50 % in chickpea and the productivity remained constant for the past six decades (Ahmad et al. 2005; see Varshney et al. 2010). With predicted climate change scenarios and continuous population explosion, there is a great need to develop high-yielding chickpea varieties with improved drought tolerance (Krishnamurthy et al. 2013a).
In addition to the root traits, another important trait for drought tolerance is water-use efficiency (WUE) or TE (Passioura 1977; Kashiwagi et al. 2013). Carbon isotope discrimination (δ13C) is considered the best method to screen germplasm for WUE. While a range of reports are available on correlation between δ13C and TE, a positive correlation was found between δ13C and TE under drought stress environments in chickpea (Kashiwagi et al. 2006). Furthermore, irrespective of root traits or TE, yield and yield component traits and harvest index (HI) are always considered the most reliable traits for breeding for drought tolerance.
To understand the genetic and molecular basis of drought tolerance, developed genetic maps and extensive phenotyping data generated on both RIL populations were analyzed in details for identification of both main-effect QTLs as well as the QTLs showing epistatic interactions.
Towards understanding complexity of drought tolerance in chickpea, a few expression and functional genomics (Varshney et al. 2009; Deokar et al. 2011) and physiological (Zaman-Allah et al. 2011) studies were conducted in recent past; however, the genetics and molecular mechanisms for drought tolerance is still not well understood. This study reports genetics-based dissection of drought tolerance after generating and analyzing extensive phenotyping and genotyping data on two segregating populations.
In any breeding program, the traits to be considered as potential selection targets for improving yield under water-limited conditions must be genetically correlated with yield, and should have a greater H 2 than yield itself (Blum 2011). As mentioned earlier, root traits are drought avoidance traits, phenological traits (DF and DM) are drought escape traits and WUE or TE is drought tolerance traits. Improving any one or combination of these traits will improve yield under drought conditions (Gaur et al. 2008). Of course, yield and yield-related traits like HI under drought conditions are the ultimate targets in a breeding program (Krishnamurthy et al. 2013b).
Color Settings Files (CSF), for Use with Adobe CC/CS Applications. Included in this download are CSF files for GRACoL 2013, SWOP 2103, as well as a complete family of 7 G7 based datasets for different substrates and printing processes (ISO 15339/CGATS 21)
Includes Idealliance GRACoL 2013 Adobe Color Settings Files for use with Adobe CC/CS Applications. Idealliance 2013 Proof Verifier for use with GRACoL 2013, SWOP 2013 and as well as a complete family of 7 G7 based datasets for different substrates and printing processes (ISO 15339/CGATS 21). Also includes 2013 Substrate Relative Calculator for use with GRACoL 2013, SWOP 2013 and as well as a complete family of 7 G7 based datasets for different substrates and printing processes (ISO 15339/CGATS 21). Revision 2 now available, revised 2017.
Idealliance 2013 Proof Verifier for use with GRACoL 2013, SWOP 2013 and as well as a complete family of 7 G7 based datasets for different substrates and printing processes (ISO 15339/CGATS 21). This download contains a spreadsheet that allows for manual verification of Idealliance print conditions.
Overall, our study showed a lower prevalence of burnout than would be expected from recent Australian data. Burnout in GP registrars is also strongly linked with general intolerance of uncertainty. Resilience was also lower than might be expected. Resilience was linked to high compassion satisfaction, low burnout, and a higher tolerance of both general and clinical uncertainty.
Mouse in vivo models have been developed to further confirm the physiological relevance of the tolerance process and have helped confirm in vitro-obtained data [23, 24] and address the contribution of various cell types to the overall ET outcome . However, an elegant work based on genomics has pointed out how mouse models reproduce weakly inflammatory responses in humans . It does not mean that results obtained from murine models are not useful; rather, it means that these very models should fit the symptoms and etiology of human diseases, in order to provide pathophysiological relevant data .
We obtained a total of 564 complete data sets (return rate 90.1%) from medical students and 29 questionnaires (return rate 96.7%) from general practitioners. In relation to the reference groups defined by Reis (1997), medical students had poor ambiguity tolerance on all three scales. No differences were found between those in the first and the sixth academic years, although we did observe gender-specific differences in ambiguity tolerance. We found no differences in ambiguity tolerance between general practitioners and medical students.
Employment in the health care industry is characterized by novelty, complexity, and sometimes insolubility . Thus, physicians may encounter very complex situations, as they tend to patients whose treatments and diagnoses reflect a wide continuum of ambiguity. As Geller (2013) summarized, physicians who have a low tolerance of ambiguity are more likely to recall mammograms , increase patient charges , withhold negative genetic test results , fear malpractice litigation, and thus engage in defensive practice , experience discomfort in the context of death and grief , exhibit greater test-ordering tendencies, and demonstrate failure to comply with evidence-based guidelines .
Tolerance for ambiguity also plays an important role on the attitudes and behaviors of medical students. A considerable body of literature exists regarding the tolerance level of ambiguity of medical students. Consequentially, the following traits have been associated with a low tolerance of ambiguity in this population: negative attitudes toward the underserved [12, 13] and fear of making mistakes . Conversely, higher tolerance of ambiguity has been associated with greater leadership abilities in medical students  as well as increased willingness to practice in rural areas [16, 17]. It is possible that the way students deal with ambiguity is malleable . Geller (2013) has attempted to explain why: medical students with a high tolerance of ambiguity entering medical school are drawn to uncertainties characterized by medicine and thus have the opportunity to further develop their ambiguity - related communication and decision-making skills. These students would then have the opportunity to further develop their ambiguity-related communication and decision-making skills. The result is a positive feedback loop in which the tolerance of ambiguity increases in these students . In a similar manner, a negative feedback loop may operate for students with a low ambiguity tolerance, as they may tend to avoid ambiguous situations and thus become even less tolerant . By assessing and evaluating the tolerance of ambiguity among medical students, it may be possible to determine whether this trait is stable, and whether tolerance can be taught and/or developed. Although several studies have compared ambiguity levels across several cohorts of students, a thorough and systematic literature review failed to uncover a study in which tolerance of ambiguity was compared across all years of medical school.
Acquired data were entered and analyzed using the statistical software IBM Statistics SPSS 21 and R version 3.0.1 (R Core Team, 2013). We used an unpaired two-sided t-test to assess the difference between male and female students as well as the difference between medical students and general practitioners. We used an F-test to assess the differences between students in different academic years (we conducted each test separately to obtain a score for OE, SC, and PR). Prior to our analysis, we checked for normal distribution and homoscedasticity. The local significance level was set to 0.05 for each test. No adjustment for multiple testing was performed. 1e1e36bf2d