IL2. Tools for the Computational Design of Asymmetric Catalysts

The development of new catalysts for asymmetric organic reactions typically proceeds by the experimental screening of potential new designs. This requires the synthesis and testing of many catalysts that will ultimately prove ineffective. We envision an alternative approach to catalyst development driven by the computational screening of virtual libraries of potential designs. Toward this end, I will describe a Perl module (AaronTools) that provides functions for building, manipulating, and analyzing molecular and supramolecular structures, creating input files and parsing output files from popular electronic structure packages, and interacting with common batch queuing systems. I will also introduce a computational toolkit built using these tools (AARON: An Automated Reaction Optimizer for New catalysts) that automates the optimization of the 100s of transition state and intermediate structures needed to predict stereoselectivities of complex asymmetric organic reactions.[1-2] Illustrative examples of the application of AARON will be presented,[3] along with a brief summary of the latest developments.[4

Dimensionality effects on transition state resonances for H+DH and D+HD reactive collisions

The transition state resonances of the title reactions have been studied on the accurate double many-body expansion (DMBE) potential energy surface for H3 using two-dimensional (2D) and three-dimensional (3D) time-dependent wave packet propagation methods. It is shown that the resonance energies are strongly associated with the vibrational threshold states of the molecular fragment obtained upon dissociation, both in the 2D and 3D calculations. However, although the two systems have the same threshold states, the resonance widths for the 2D D+HD collisions are found to be considerably narrower than for the 3D ones, while no such phenomenon is observed for H+DH. As a result, we have concluded that there is an apparent dimensional effect on the resonance widths of the heavy-light-heavy (HLH) reaction.http://www.sciencedirect.com/science/article/B6TGT-3Y6H16X-B/1/4253a179e22a333236b87dd9066f7e7

Role of the anterior insula in task-level control and focal attention

In humans, the anterior insula (aI) has been the topic of considerable research and ascribed a vast number of functional properties by way of neuroimaging and lesion studies. Here, we argue that the aI, at least in part, plays a role in domain-general attentional control and highlight studies (Dosenbach et al. 2006; Dosenbach et al. 2007) supporting this view. Additionally, we discuss a study (Ploran et al. 2007) that implicates aI in processes related to the capture of focal attention. Task-level control and focal attention may or may not reflect information processing supported by a single functional area (within the aI). Therefore, we apply a novel technique (Cohen et al. 2008) that utilizes resting state functional connectivity MRI (rs-fcMRI) to determine whether separable regions exist within the aI. rs-fcMRI mapping suggests that the ventral portion of the aI is distinguishable from more dorsal/anterior regions, which are themselves distinct from more posterior parts of the aI. When these regions are applied to functional MRI (fMRI) data, the ventral and dorsal/anterior regions support processes potentially related to both task-level control and focal attention, whereas the more posterior aI regions did not. These findings suggest that there exists some functional heterogeneity within aI that may subserve related but distinct types of higher-order cognitive processing

Photoreactive Crystalline Quasiracemates

Rationally designed racemic and quasiracemic sulfonamidecinnamic acids assemble to give hydrogen-bonded dimers with coplanar alignment of neighboring olefins. The quasiracemate phase contains near inversion-related motifs with chemically distinct components forming supramolecular heterodimers that undergo asymmetric photodimerization

Photoreactive Crystalline Quasiracemates

Rationally designed racemic and quasiracemic sulfonamidecinnamic acids assemble to give hydrogen-bonded dimers with coplanar alignment of neighboring olefins. The quasiracemate phase contains near inversion-related motifs with chemically distinct components forming supramolecular heterodimers that undergo asymmetric photodimerization

Evidence accumulation and the moment of recognition: dissociating perceptual recognition processes using fMRI

Decision making can be conceptualized as the culmination of an integrative process in which evidence supporting different response options accumulates gradually over time. We used functional magnetic resonance imaging to investigate brain activity leading up to and during decisions about perceptual object identity. Pictures were revealed gradually and subjects signaled the time of recognition (TR) with a button press. We examined the time course of TR-dependent activity to determine how brain regions tracked the timing of recognition. In several occipital regions, activity increased primarily as stimulus information increased, suggesting a role in lower-level sensory processing. In inferior temporal, frontal, and parietal regions, a gradual buildup in activity peaking in correspondence with TR suggested that these regions participated in the accumulation of evidence supporting object identity. In medial frontal cortex, anterior insula/frontal operculum, and thalamus, activity remained near baseline until TR, suggesting a relation to the moment of recognition or the decision itself. The findings dissociate neural processes that function in concert during perceptual recognition decisions

The Douglas-Fir Genome Sequence Reveals Specialization of the Photosynthetic Apparatus in Pinaceae.

A reference genome sequence for Pseudotsuga menziesii var. menziesii (Mirb.) Franco (Coastal Douglas-fir) is reported, thus providing a reference sequence for a third genus of the family Pinaceae. The contiguity and quality of the genome assembly far exceeds that of other conifer reference genome sequences (contig N50 = 44,136 bp and scaffold N50 = 340,704 bp). Incremental improvements in sequencing and assembly technologies are in part responsible for the higher quality reference genome, but it may also be due to a slightly lower exact repeat content in Douglas-fir vs. pine and spruce. Comparative genome annotation with angiosperm species reveals gene-family expansion and contraction in Douglas-fir and other conifers which may account for some of the major morphological and physiological differences between the two major plant groups. Notable differences in the size of the NDH-complex gene family and genes underlying the functional basis of shade tolerance/intolerance were observed. This reference genome sequence not only provides an important resource for Douglas-fir breeders and geneticists but also sheds additional light on the evolutionary processes that have led to the divergence of modern angiosperms from the more ancient gymnosperms

Metabolomic Investigation of Tenderness and Aging Response in Beef Longissimus Steaks

A study was conducted to identify molecular changes reflective of beef tenderness variation and tenderization during postmortem aging. Carcasses (U.S. Select) were selected to represent extremes in tenderness (n = 20; 10 per class). Two pairs of adjacent longissimus lumborum steaks from each strip loin were blocked by location and assigned to each aging time (2, 7, 14, or 28 d postmortem). One steak from each pair was designated for slice shear force determination and the other was used for sarcomere length, western blotting for desmin, and non-targeted LC- and GC–MS metabolite profiling. Tough steaks had higher (P < 0.001) slice shear force values than tender steaks, and increasing aging time decreased (P < 0.001) slice shear force values. Tender steaks had a greater (P < 10–4) proportion of desmin degraded than tough steaks, and increasing aging time increased (P < 10–22) desmin degradation in steaks from both classes. From 2,562 profiled metabolites, 102 metabolites were included in the final analysis after statistical screening. Twenty-eight metabolites could be annotated and loosely categorized into amino acids/peptides (n = 16), metabolism intermediates (n = 7), glycosides (n = 4), and fatty acids and phospholipids (n = 3). Amino acids were primarily associated with desmin degradation. Increased glucose levels were strongly associated to the tender classification and moderately associated to increased proteolysis, while increased glucose-6-phosphate was strongly related to the tender class but was related to decreased proteolysis. Increased malic acid was strongly associated to the tough classification, increased slice shear force, and decreased proteolysis. Increased levels of 3-phosphoglyceric acid and glycerol-3-phosphate was moderately associated with increased slice shear force and decreased proteolysis. These data indicate that accumulation of amino acids during aging is strongly related to postmortem proteolysis and may provide evidence of the fate of proteins degraded postmortem. Measures of glucose, glucose-6-phosphate, and malic acid concentrations may provide a metabolic fingerprint indicative of tenderness differences in beef longissimus

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef