Trigeminal nerve morphology in the American alligator : implications for infering sensory potential in extinct species [abstract]

Among the many adaptations of modern crocodilians, one of the most intriguing is their derived sense of face touch, in which numerous trigeminal nerve-innervated dome pressure receptors speckle the face and mandible and sense vibrations and other mechanical stimuli, directing the animal towards, or away from stimuli. However, the morphological features of this system are not well known, and it remains unclear how aspects of the trigeminal system change during ontogeny and how they scale with other cranial and nervous structures

Dual priming oligonucleotide system for the multiplex detection of respiratory viruses and SNP genotyping of CYP2C19 gene

Successful PCR starts with proper priming between an oligonucleotide primer and the template DNA. However, the inevitable risk of mismatched priming cannot be avoided in the currently used primer system, even though considerable time and effort are devoted to primer design and optimization of reaction conditions. Here, we report a novel dual priming oligonucleotide (DPO) which contains two separate priming regions joined by a polydeoxyinosine linker. The linker assumes a bubble-like structure which itself is not involved in priming, but rather delineates the boundary between the two parts of the primer. This structure results in two primer segments with distinct annealing properties: a longer 5′-segment that initiates stable priming, and a short 3′-segment that determines target-specific extension. This DPO-based system is a fundamental tool for blocking extension of non-specifically primed templates, and thereby generates consistently high PCR specificity even under less than optimal PCR conditions. The strength and utility of the DPO system are demonstrated here using multiplex PCR and SNP genotyping PCR

Impact of Job Search Method and Effort On Search Outcomes

Research on the behavior of job seekers has been extensive in the economics discipline. In more recent years this research has been supplemented by insights and contributions from other fields including sociology and human resources management. Since Stiglers pioneering studies in the early 1960s (1961, 1962), the analysis of the economics of job search has been dominated by sequential stopping models" in which job seekers are assumed to sample wage offers sequentially one after another. The job seeker is viewed as deciding whether or not to accept an offer on the basis of the relationship of the wage offer to the individual's reservation wage (McCall, 1970; Mortensen, 1970; Gronau, 1971). Subsequent empirical literature has elaborated on this basic framework by exploring factors affecting search intensity and search outcomes. Among the factors explored have been a variety of financial and nonfinancial resources allocated to job search, the availability of various forms of unemployment in surance (UI) benefits, other forms of non-search related income, and so forth (Blau & Robins, 1986; Blau, 1991; Barron & Gilley, 1979; Barron & Mellow, 1979; Keeley & Robins, 1985). A second, but obviously related body of economics literature has examined job seeker decision making related to choice of job search methods and the impact of various job search methods on employ ment outcomes (Rees , 1966: Rees & Schulz, 1970; Stevens, 1977: Azevedo, 1974; Holzer, 1987: Blau, 1992)

Application of Neural Networks for Estimation of Concrete Strength

The uniaxial compressive strength of concrete is the most widely used criterion in producing concrete. Although testing of the uniaxial compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. At this point, it is too late to make improvements if the test result does not satisfy the required strength. Therefore, the strength estimation before the placement of concrete is highly desirable. This study presents the first effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions. Back-propagation neural networks were developed, trained, and tested using actual data sets of concrete mix proportions provided by two ready-mixed concrete companies. The compressive strengths estimated by the neural networks were verified by laboratory testing results. This study demonstrated that the neural network techniques are effective in estimating the compressive strength of concrete based on the mix proportions. Application of these techniques will contribute significantly to the concrete quality assurance

Guards and Culprits in the Endoplasmic Reticulum: Glucolipotoxicity and β-Cell Failure in Type II Diabetes

The endoplasmic reticulum (ER) is a cellular organelle responsible for multiple important cellular functions including the biosynthesis and folding of newly synthesized proteins destined for secretion, such as insulin. The ER participates in all branches of metabolism, linking nutrient sensing to cellular signaling. Many pathological and physiological factors perturb ER function and induce ER stress. ER stress triggers an adaptive signaling cascade, called the unfolded protein response (UPR), to relieve the stress. The failure of the UPR to resolve ER stress leads to pathological conditions such as β-cell dysfunction and death, and type II diabetes. However, much less is known about the fine details of the control and regulation of the ER response to hyperglycemia (glucotoxicity), hyperlipidemia (lipotoxicity), and the combination of both (glucolipotoxicity). This paper considers recent insights into how the response is regulated, which may provide clues into the mechanism of ER stress-mediated β-cell dysfunction and death during the progression of glucolipotoxicity-induced type II diabetes