In contrast to these descriptions of Sassen’s work, I am asserting, as Jonathan Crary has argued, that instead of focusing on the representation, the focus is situated on the observer and the historical construction of knowledge, thus making the observer part of a wider institutional, social, and technological relation. With the onset of modernity, philosophical, scientific, and aesthetic discourses overlapped with mechanical devices. The general reorganization of vision and historical construction was produced in which vision was discussed, controlled, and incarnated in cultural and scientific practices. 3 This was then followed by the development of optical devices, making photography only secondary to vision, aiding to any photochemical fixation and imagination that functioned to reinforce a subjective vision: “Ideas of things and events in the world were never copies of an external reality, but rather the outcome of an interactional process within the subject in which ideas (Vorstellungen) underwent operations of fusion, fading, inhibition, and blending (Verschmelzung) with other previous or simultaneous occurring ideas or ‘presentation.’ The mind does not reflect the truth but rather extracts it from an ongoing process involving the collision and merging of ideas.” 4 This subjective vision was immediately incorporated into the new regimes of power and knowledge that was then justified, calculated and abstracted on the basis of the new discipline of physiology in the late nineteenth
Part III: Chapter 5: Preparation of various o, o'-Methylene Bridged Bisphenols used for making -ve Tone Photoresists. This chapter deals with the preparation of Bisphenol A and o, o'-methylene bridged bisphenols 20-22 (chart 4 & Chart 5) and their importance. Bispenol A is a very important raw material for the synthesis of epoxy resins and other polymers, and key intermediate for -ve tone photoresists compounds. Photoresists are widely used in electronic industry for the past several decades. The trend in usage of photoresists is still increasing. The basic fundamental principle involved in the photoresists is the photochemical generation of reactive intermediate in a polymer environment and its subsequent chemical reaction or physical change leading to resist. These photoresists have become "Strategic Materials" for INDIA.
Structural determinants of flexibility for the ternary assemblies-The modest change in the orientation of FEN1 from the original crystallographic position could be explained by two intrin- sic features of the complexes that permit structural adaptation: (i) the dfDNA substrate is kinked(192,202) by ~100˚and features a moderately flexible single stranded region; and (ii) a pronounced tilt is observed between the axis of the upstream DNA duplex and the plane of the PCNA or 9-1-1 ring (Figure 6.4). The dsDNA fragment goes through the plane of the sliding clamp ring at a sharp tilt angle: 17.34±2.87˚(s.d) for PCNA and 27.19±1.96˚(s.d) for 9-1-1. In- teractions between the dsDNA phosphodiester backbone and basic residues lining the inner sur- face of the clamps are responsible for the observed binding mode in the two ternary complexes and will be discussed later in greater detail. Our findings carry strong parallels to previous com- putational work (206) on the association of PCNA with dsDNA, which arrived at a similar mark- edly asymmetric model for the PCNA/dsDNA assembly. Conventional models for PCNA encir- cling DNA entail that the clamp is perpendicular to the DNA axis, rendering all three PCNA binding sites equivalent. In contrast, recent evidence has collectively suggested that sliding clamps bind DNA asymmetrically. The breaking of the threefold symmetry of the clamp upon DNA binding offers an ideal mechanism for handoff of protein partners. Experimentally, the x- ray structure of the bacterial β-clamp-DNA complex (207) revealed dsDNA passing through the clamp at a sharp angle of 22°. More recently, EM analysis of DNA ligase-PCNA-DNA and Pol- β/PCNA/DNA assemblies showed that dsDNA was tilted by 16° and 13°, respectively (208,209).
discussed. 51-52 Each configuration carried a weight of e β∆V(r) , where ∆V(r) is the difference between the modified and unmodified potentials for that particular configuration. The probability distributions obtained from the aMD simulations were therefore reweighted using e β∆V(r) , in order to ca lculate the distribution on the unmodified potential, p(ξ), along one or two degrees of freedom, ξ. The free energy profiles were estimated using –RTln[p(ξ)]. The probability distributions of all of the 2D free energy profiles were calculated using a bin size of 20°x20°. Each bin was incremented by the weight of the configuration (frame), e β∆V(r) , whenever ξ of the configurat ion (frame) fell inside the bin. In normal MD, which is equivalent to ∆V( r) = 0 for all r , one would normally increment each bin by 1 whenever the ξ of the configuration falls within the confines of the bin. The 2D free energy profiles were plotted using the MATLAB program. The plots were normalized such that regions with the lowest free energy (most sampled regions) were assigned 0 kcal/mol and colored deep blue, and the free energies of the rest of the plot were relative to the lowest free energy regions, colored from blue to red. Un-sampled regions were also assigned the highest free energy and colored deep red. Five independent simulations of the Pin1-substrate complex were carried, each for 260 ns, for a total of 1.3 µs of simulation time. Also, ten independent simulations of the free substrate in solution were carried out, each for 260 ns, for a total of 2.6 µs of simulation time.
could not add more predictive power when the fluency and the accuracy measures were already in the model. The “best” models for the medium and lower familiarity tasks were exactly the same, consisting of, in the order of importance, the measures of fluency, syntactic complexity, lexical sophistication, and accuracy. As Table 5.8 displays, the b and β values for the each of the predictors in the “best” models for these two tasks were also almost the same or were very close across the two tasks, showing their approximately equal importance in predicting scores on the two tasks. The measure of lexical diversity did not turn out to be an important predictor for writing scores across these two tasks. The above analysis shows that the measures of fluency and accuracy were important predictors of scores on the higher familiarity task while all the linguistic complexity measures were not, and that all the CAF predictors in the analysis, except for the measure of lexical diversity, were important predictors of scores on the medium and lower familiarity tasks. With the differences noted, there was however one main similarity in the predictors in the “best” models across the three topic familiarity tasks: the fluency measure was the most important predictor, although its importance for the higher familiarity task was much more pronounced, as can be seen in the larger b and β values. It can also be observed that the importance of fluency dropped when the cognitive complexity of the tasks increased along the topic familiarity dimension.