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Bacterial blight of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of high nitrogen (N) responsive diseases. Rice plants became more disease resistant with decreasing N suggesting that the crosstalk between disease resistance and N utilization pathways might exist. However, the co-regulatory components in such crosstalk have not been elucidated. Here, we comparatively analyzed the gene expression profiling of rice under Xoo inoculation, low N treatment, or a combination of both stresses, and identified the differentially-expressed genes (DEGs) in overlapping responses. These DEGs were involved in different biological processes, including innate immunity and nitrogen metabolism. The randomly-selected DEGs expression was validated by quantitative real-time PCR assays. Temporal expression of six genes from different functional categories suggested that N condition was the dominant factor when both stresses were present. These DEGs identified provide novel insights into the coordinated regulatory mechanism in biotic and abiotic stress responses in rice.

Chetty, Friedman, and Rockoff (2014a, b) study value-added (VA) measures of teacher effectiveness. CFR (2014a) exploits teacher switching as a quasi-experiment, concluding that student sorting creates negligible bias in VA scores. CFR (2014b) finds VA scores are useful proxies for teachers' effects on students' long-run outcomes. I successfully reproduce each in North Carolina data. But I find that the quasi-experiment is invalid, as teacher switching is correlated with changes in student preparedness. Adjusting for this, I find moderate bias in VA scores, perhaps 10-35 percent as large, in variance terms, as teachers' causal effects. Long-run results are sensitive to controls and cannot support strong conclusions.

BackgroundObservational studies have reported a modest association between obesity and risk of ovarian cancer; however, whether it is also associated with survival and whether this association varies for the different histologic subtypes are not clear. We undertook an international collaborative analysis to assess the association between body mass index (BMI), assessed shortly before diagnosis, progression-free survival (PFS), ovarian cancer-specific survival and overall survival (OS) among women with invasive ovarian cancer.MethodsWe used original data from 21 studies, which included 12 390 women with ovarian carcinoma. We combined study-specific adjusted hazard ratios (HRs) using random-effects models to estimate pooled HRs (pHR). We further explored associations by histologic subtype.ResultsOverall, 6715 (54%) deaths occurred during follow-up. A significant OS disadvantage was observed for women who were obese (BMI: 30-34.9, pHR: 1.10 (95% confidence intervals (CIs): 0.99-1.23); BMI: ⩾35, pHR: 1.12 (95% CI: 1.01-1.25)). Results were similar for PFS and ovarian cancer-specific survival. In analyses stratified by histologic subtype, associations were strongest for women with low-grade serous (pHR: 1.12 per 5 kg m(-2)) and endometrioid subtypes (pHR: 1.08 per 5 kg m(-2)), and more modest for the high-grade serous (pHR: 1.04 per 5 kg m(-2)) subtype, but only the association with high-grade serous cancers was significant.ConclusionsHigher BMI is associated with adverse survival among the majority of women with ovarian cancer.

There is a growing consensus that evidence of teachers' contributions to student learning should be a component of teacher evaluation systems, along with evidence about the quality of teachers' practice. Value-added models (VAMs), designed to evaluate student test score gains from one year to the next are often promoted as tools to accomplish this goal. However, current research suggests that VAM ratings are not sufficiently reliable or valid to support high-stakes, individual-level decisions about teachers. Other tools for teacher evaluation have shown greater success in measuring and improving teaching, especially those that examine teachers' practices in relation to professional standards.

Racial segregation is a defining feature of urban neighborhoods in the United States. A large body of social science research has established that black children raised in more segregated areas have worse outcomes' including lower levels of completed education' lower test scores' lower marriage rates' lower employment and earnings' and higher crime rates (e.g.' Massey and Denton 1993; Cutler and Glaeser 1997). Though researchers still do not agree about the extent to which the observed correlations between segregation and these outcomes are causal' a major goal of public policy over the past four decades has been to reduce racial segregation in neighborhoods' schools' and workplaces. The efficacy of integration policies depends critically on the underlying forces that have led to and sustained segregation. While institutional and legal forces played an important part in enforcing segregation in the Jim Crow era' many analysts have argued that the preferences of white families for neighborhoods with a lower fraction of minority residents are the driving force in explaining segregation today (e.g.' Cutler and Glaeser 1997). In a highly influential contribution' Schelling (1971) showed that even when most whites have relatively weak preferences for lower minority shares' social interactions in preferences are likely to lead to a fully segregated equilibrium. In Schelling's model (and in more recent theoretical studies' including Brock and Durlauf 2001 and Glaeser and Scheinkman 2003)' a given neighborhood can have multiple equilibria. Holding constant conditions in the rest of the city' the neighborhood could either be (nearly) 100 percent white' nearly 100 percent minority' or a mixture. Importantly' however' in Schelling's formulation the mixed equilibrium is inherently unstable: Adding a few extra minority families sets off a chain of departures by whites that only ends once all the white families have left. Likewise' adding a few white families sets off a chain of departures by minority families that ultimately lead to an all-white neighborhood. In this chapter we use data on the evolution of census tracts from 1970 until 2000 to investigate whether integrated neighborhoods are sustainable in the long run' or whether they are inherently unstable and destined to become either 100 percent minority or 100 percent white. Our analysis builds on a companion paper (Card' Mas' and Rothstein 2008b; hereafter CMR)' in which we found that most major metropolitan areas are characterized by a city-specific "tipping point'" a level of the minority share in a neighborhood that once exceeded sets off a rapid exodus of the white population. To illustrate this finding' Figure 14.1 plots mean percentage changes in the white population of Chicago census tracts from 1970 to 1980 against the tract's minority share in 1970.1 The graph shows clear evidence of a critical threshold at around a 5 percent minority share: neighborhoods with 1970 minority shares below this threshold experienced gains in their white populations over the next decade' while those with initial shares above the threshold experienced substantial outflows. These patterns hold on average for a broad sample of U.S. cities in each of the past three decades. Most common understandings of neighborhood tipping envision a transition from virtually all-white composition to virtually 100 percent minority. This is certainly the historical experience. Northern cities had relatively low numbers of racial minorities in 1940' but as African Americans migrated from the South' many neighborhoods within these cities tipped from all-white to nearly all-black. This process has been interpreted by many analysts as evidence of the inherent instability in integrated neighborhoods predicted by Schelling's model. According to this interpretation' the mixed neighborhoods observed today (say with a 10 or 15% minority share) are in the process of transitioning to an all-minority status. Nevertheless' a class of alternative models-including the one developed in CMR-suggests that mixed neighborhoods can survive in the long run' so long as the minority share does not exceed a critical tipping point. In these alternative models' the tipping point is not a "knife edge" temporary equilibrium that is destined to fail. Rather' the tipping point represents a boundary point. Neighborhoods with minority shares below this level can remain integrated; but once the tipping point is exceeded' the neighborhood will quickly move to a nearly 100% minority equilibrium. The distinction between these views of tipping is quite important for policy purposes. Under Schelling's model' planners hoping to create and maintain vital integrated neighborhoods must fight continuously against market forces' which are always pulling the neighborhood toward complete segregation. By contrast' under the alternative models' a neighborhood can remain stable with a moderate minority share. The alternative models provide a justification for policies meant to encourage racial and ethnic diversity in neighborhoods. If integrated neighborhoods are inherently unstable' however' these efforts are likely to have little long run effect on the degree of racial segregation in a city. We attempt to distinguish between alternative models of tipping by investigating whether integrated neighborhoods with minority shares below the tipping point tend to experience rapid minority flight (as predicted by Schelling's original model) or whether they can remain integrated over several decades. The answer to this question is of growing importance because tipping points appear to have risen. If neighborhoods below the tipping point are stable' increases in the tipping point can lead to increasingly integrated neighborhoods' all else equal. CMR documented average tipping points in the range of a 13 percent minority share over the 1970-1990 period' with slight increases over time. This contrasts sharply with earlier experience' where neighborhoods in many cities seemed prone to tip in response to even a small (1 or 2 percent) minority presence. Applying the same methods as in CMR' we estimated the tipping points for three large Midwestern cities (Chicago' Cleveland' and Detroit) for the 1940-1970 period. Figure 14.2 shows the evolution of the tipping points in these cities since 1940.2 In two of the three cities' the tipping point was near zero in 1940 and 1950 (in the third' Cleveland' it was near 10 percent in 1940 but fell to near zero in 1950)' and in each case it rose substantially by 1970 and farther in the later years. Although 1940 and 1950 tipping points are not available for other cities' the figure also shows that the average tipping point across all large cities in the country was around 12 percent in 1970 and rose somewhat over the next two decades. Changes in tipping points have been accompanied by dramatic changes in the cross-sectional distribution of minority shares across census tracts. Figure 14.3 shows the distribution of tract minority shares for the pooled sample of tracts from the three cities in 1950' 1970' 1980' and 1990. In 1950' this distribution is highly bimodal' with many allwhite neighborhoods' a few all-minority (almost entirely black) neighborhoods' and essentially no integrated neighborhoods. This distribution would be expected from a tipping point at a very low minority share. In more recent decades' we see two key changes. First' there are more neighborhoods with very high minority shares' as each city's black (and more recently Hispanic) population expanded over the second half of the twentieth century. Second' we increasingly see neighborhoods with intermediate minority shares' neither all-white nor all-minority. Many of these integrated neighborhoods have minority shares below the (now higher) tipping points. The histograms suggest the possibility that neighborhoods below the tipping points might be stable' though because they represent only cross-sections they are also consistent with instability of integrated tracts. In what follows we present a series of tests for the stability of neighborhoods with minority shares below the tipping points identified by CMR. We focus on the 1970 tipping point. As indicated in Figure 14.2' 1970 seems to represent the beginning of the modern era for this sort of analysis' with tipping points that resemble those seen in the 1980s and 1990s more closely than they do the lower tipping points observed in the 1940s and 1950s.3 Importantly for our purposes' a focus on 1970 allows us to observe neighborhoods' outcomes over a thirty-year period. We examine the racial/ethnic composition of census tracts in 1980' 1990' and 2000' relating this to a tract's location relative to the 1970 tipping point. Overall' we conclude that tipping is one-sided: while neighborhoods with minority shares above the 1970 tipping point appear to move toward high minority concentrations in later decades' those that remain below the tipping point are more stable' and show no indication of substantial minority flight. Copyright © 2011 University of Pennsylvania Press. All rights reserved.

Growing concerns over the inadequate achievement of U.S. students have led to proposals to reward good teachers and penalize (or fire) bad ones. The leading method for assessing teacher quality is "value added" modeling (VAM), which decomposes students' test scores into components attributed to student heterogeneity and to teacher quality. Implicit in the VAM approach are strong assumptions about the nature of the educational production function and the assignment of students to classrooms. In this paper, I develop falsification tests for three widely used VAM specifications, based on the idea that future teachers cannot influence students' past achievement. In data from North Carolina, each of the VAMs' exclusion restrictions is dramatically violated. In particular, these models indicate large "effects" of fifth grade teachers on fourth grade test score gains. I also find that conventional measures of individual teachers' value added fade out very quickly and are at best weakly related to long-run effects. I discuss implications for the use of VAMs as personnel tools. © 2010 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

The EITC is intended to encourage work. But EITC-induced increases in labor supply may drive wages down. I simulate the economic incidence of the EITC. In each scenario that I consider, a large portion of low-income single mothers' EITC payments is captured by employers through reduced wages. Workers who are EITC ineligible also see wage declines. By contrast, a traditional Negative Income Tax (NIT) discourages work, and so induces large transfers from employers to their workers. With my preferred parameters, $1 in EITC spending increases after-tax incomes by $0.73, while $1 spent on the NIT yields $1.39.

Data from college admissions tests can provide a valuable measure of student achievement, but the non-representativeness of test-takers is an important concern. We examine selectivity bias in both state-level and school-level SAT and ACT averages. The degree of selectivity may differ importantly across and within schools, and across and within states. To identify within-state selectivity, we use a control function approach that conditions on scores from a representative test. Estimates indicate strong selectivity of test-takers in "ACT states," where most college-bound students take the ACT, and much less selectivity in SAT states. To identify within- and between-school selectivity, we take advantage of a policy reform in Illinois that made taking the ACT a graduation requirement. Estimates based on this policy change indicate substantial positive selection into test participation both across and within schools. Despite this, school-level averages of observed scores are extremely highly correlated with average latent scores, as across-school variation in sample selectivity is small relative to the underlying signal. As a result, in most contexts the use of observed school mean test scores in place of latent means understates the degree of between-school variation in achievement but is otherwise unlikely to lead to misleading conclusions. © 2008 Elsevier Ltd.

Racial segregation is often blamed for some of the achievement gap between blacks and whites. We study the effects of school and neighborhood segregation on the relative SAT scores of black students across different metropolitan areas, using large microdata samples for the 1998-2001 test cohorts. Our models include detailed controls for the family background of individual test-takers, school-level controls for selective participation in the test, and city-level controls for racial composition, income, and region. We find robust evidence that the black-white test score gap is higher in more segregated cities. Holding constant family background and other factors, a shift from a highly segregated city to a nearly integrated city closes about one-quarter of the raw black-white gap in SAT scores. Specifications that distinguish between school and neighborhood segregation suggest that neighborhood segregation has a consistently negative impact while school segregation has no independent effect, though we cannot reject equality of the two effects. Additional tests indicate that much of the effect of neighborhood segregation operates through neighbors' incomes, not through race per se. Data on enrollment in honors courses suggest that within-school segregation increases when schools are more highly integrated, potentially offsetting the benefits of school desegregation and accounting for our findings. © 2007.

This paper calls for the establishment of a comprehensive academic and theological center to be created and located at a prestigious secular university in the United States. As the first of its kind in North America, it should be affiliated with both American Muslim and Jewish institutions. Modeled on similar Jewish-Christian centers, its mission will be to foster both a neutral ground for dialogue and the development of a theology of Jewish-Muslim coexistence. [ABSTRACT FROM AUTHOR]