Evaluation and application of dye tracers in Karst terrain for determining aquifer characteristics

As ground water becomes increasingly important as a source of water in the United States, reliable information on the hydrological characteristics of carbonate aquifers must be obtained in order to develop them intelligently. Various tracers have been used in the past to determine if two or more points are connected hydrologically. Today there exists a number of excellent dye tracers and tracer methods have been developed in unconsolidated and clastic aquifers for determining hydrological characteristics. This paper reviews the various dye tracers and methods which might be applied in a carbonate karst terrain using wells as injection and sampling points. Fluorescein, Rhodamine, and Pontacyl Pink are evaluated. Effective porosities of the Gasconade, Roubidoux, and Jefferson City Formations were determined from field samples to estimate the amount of water which might be stored and transmitted through these rocks. In field investigations, salt, fluorescein, and Rhodamine were used as tracers during pumping tests at three sites. No background fluorescene was detected in any of the well or spring samples. No connection was established between any wells because none of the tracers placed in the injection wells was detected at any of the observation wells. A point dilution curve was obtained for one injection well --Abstract, page ii

The Czech Economic Transition: Exploring Options Using a Macrosectoral Model

The processes that will drive the next stage of the Czech transition are likely to be similar to those promoting real convergence in the EU cohesion countries. We draw on previous modelling research on the cohesion economies to construct and calibrate a small macrosectoral model of the Czech Republic that serves to highlight key policy issues facing CEE-country decision-makers. Four scenarios are then explored by simulation: the first projects the current pattern of disequilibrium wage setting into the future, while a second looks at the consequences of labour market reform. The other scenarios highlight some of the differences between policy strategies based on indigenous versus FDI-driven export-led growth.

Engendering a market orientation: exploring the invisible role of leaders’ personal values

With the emergence of a ‘second generation of market orientation’, the topic has become a rich field for researchers to cultivate. Though marketing scholars have turned their attention to the critical role top leaders play in shaping and creating a market oriented organization, no research to date has considered the impact of leaders’ personal values in the process of engendering a market orientation. We argue that personal values, the primary driver of motivation, fundamentally determine human behavior. The objective of this study is to fill the gap in the current literature by systematically exploring the relationship between the two constructs. In this paper, a series of propositions is derived which suggests that leaders with different sets of personal values tend to emphasize different dynamics of market orientation. Consequently, we postulate that personal values play an invisible yet powerful role in impeding or facilitating the development of a market orientation. This research leads to an important implication for managers: a more balanced market-orientated approach can be achieved if managers become more aware of the personal values they possess

Atypical miRNA expression in temporal cortex associated with dysregulation of immune, cell cycle, and other pathways in autism spectrum disorders.

BackgroundAutism spectrum disorders (ASDs) likely involve dysregulation of multiple genes related to brain function and development. Abnormalities in individual regulatory small non-coding RNA (sncRNA), including microRNA (miRNA), could have profound effects upon multiple functional pathways. We assessed whether a brain region associated with core social impairments in ASD, the superior temporal sulcus (STS), would evidence greater transcriptional dysregulation of sncRNA than adjacent, yet functionally distinct, primary auditory cortex (PAC).MethodsWe measured sncRNA expression levels in 34 samples of postmortem brain from STS and PAC to find differentially expressed sncRNA in ASD compared with control cases. For differentially expressed miRNA, we further analyzed their predicted mRNA targets and carried out functional over-representation analysis of KEGG pathways to examine their functional significance and to compare our findings to reported alterations in ASD gene expression.ResultsTwo mature miRNAs (miR-4753-5p and miR-1) were differentially expressed in ASD relative to control in STS and four (miR-664-3p, miR-4709-3p, miR-4742-3p, and miR-297) in PAC. In both regions, miRNA were functionally related to various nervous system, cell cycle, and canonical signaling pathways, including PI3K-Akt signaling, previously implicated in ASD. Immune pathways were only disrupted in STS. snoRNA and pre-miRNA were also differentially expressed in ASD brain.ConclusionsAlterations in sncRNA may underlie dysregulation of molecular pathways implicated in autism. sncRNA transcriptional abnormalities in ASD were apparent in STS and in PAC, a brain region not directly associated with core behavioral impairments. Disruption of miRNA in immune pathways, frequently implicated in ASD, was unique to STS

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density function is discontinuous with respect to, while further providing runtime checks for boundary crossings. This enables the introduction of new inference engines that are able to exploit gradient information, while remaining efficient for models which are not everywhere differentiable. We demonstrate this ability by incorporating a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this language has a density with measure zero discontinuities to maintain the validity of the inference engine.Comment: Published in the proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS