The transcription factor PU.1 occupies a central role in controlling myeloid and early B-cell development, and its correct lineage-specific expression is critical for the differentiation choice of hematopoietic progenitors. However, little is known of how this tissue-specific pattern is established. We previously identified an upstream regulatory cis element whose targeted deletion in mice decreases PU.1 expression and causes leukemia. We show here that the upstream regulatory cis element alone is insufficient to confer physiologic PU.1 expression in mice but requires the cooperation with other, previously unidentified elements. Using a combination of transgenic studies, global chromatin assays, and detailed molecular analyses we present evidence that PU.1 is regulated by a novel mechanism involving cross talk between different cis elements together with lineage-restricted autoregulation. In this model, PU.1 regulates its expression in B cells and macrophages by differentially associating with cell type–specific transcription factors at one of its cis-regulatory elements to establish differential activity patterns at other elements.

Gneiting (2002) proposed a nonseparable covariance model for spatial-temporal data. In the present paper we show that in certain circumstances his model possesses a counterintuitive dimple. In some cases, the magnitude of the dimple can be nontrivial.

(2013). Geostatistics: modeling spatial uncertainty, second edition. Journal of Applied Statistics: Vol. 40, No. 4, pp. 923-923.

The purpose of this article is to identify food deserts using a geographic information system (GIS)-based multicriteria decision-making (MCDM) approach in the city of Tehran. We have found that, compared to technocratic methods, GIS-based MCDM and taking into account people or their agents' opinions in the food deserts analysis leads to different results. Whereas measuring food deserts based on the distance to large retail food stores indicated that a large part of the northern neighborhoods of Tehran do not have access to large food stores, identifying the food desert through the GIS-based MCDM approach revealed that northern neighborhoods of Tehran have relatively good access to healthy, affordable food. In addition, results indicated that individual factors have a more effective role than environmental factors in food accessibility. Food accessibility analysis revealed that more than 26.6 percent of Tehran's people (2,049,796) are living in very low and low food accessibility areas. Accordingly, to achieve a relatively healthy and inclusive food environment, establishment of a food council, development of mobile food markets and farmers' markets, extension of public transport, enhancement of food literacy, and community-based development of small full-service grocery stores, especially in southern and western sections of Tehran, should be pursued. In addition, addressing Tehran's food desert issues would be incomplete without due attention to the wider political and economic environment.

Climate change is one of the greatest challenges in the 21st century and the agriculture sector is very vulnerable to this phenomenon. Since wheat is the most important cereal crop in Iran, we aim to analyze the potential impact of climatic variables (temperature and precipitation) on rainfed wheat productivity in Hamedan Province, Iran. For this purpose, generalized additive models have been used to model yields of rainfed wheat based on climatic variables during 2004–2012. Then, based on sensitivity of rainfed wheat to temperature and precipitation in this period, we predict the potential effects of climate change on rainfed wheat yield under the IPCC SRES A1FI and B1 climate change scenarios. Results suggest that yields of rainfed wheat would decrease in all Hamedan's counties primarily because of decreasing October to June precipitation and higher temperature. As a result, it is predicted that the yield of rainfed wheat in Hamedan under the A1F1 and B1 scenarios will fall by 41.3% and 20.6%, respectively, in the 2080s. In other words, according to the A1F1 scenario, in the 2080s, Hamedan Province's rainfed wheat production will decline from 1090 kg/ha to 639 kg/ha and under the B1 scenario to 865 kg/ha.

A reproducing kernel Hilbert space (RKHS) is a Hilbert space of functions on an index set E, say, for which function evaluation is continuous in the Hilbert norm. One of the classic applications of such spaces is to show that the solution of a certain penalised least squares problem is given by a smoothing spline. However, the formulation of this problem in an RKHS setting involves several arbitrary choices. In this paper, we propose the use of a semi-RKHS (SRKHS), which provides a more natural setting for the smoothing spline solution. In addition, a systematic study is made of the properties of an SRKHS. It is well known that there is a one-to-one correspondence between an RKHS and positive semi-definite (psd) function on E, which in turn can be viewed as the covariance function of a stochastic process on E. In this paper, we extend this result to show that there is a one-to-one correspondence between an SRKHS and conditionally positive semi-definite (cpsd) function on E, which in turn defines the covariance behaviour of certain increments on E. Further, it is shown how optimal smoothing in the functional setting corresponds to optimal prediction in the stochastic process setting.