Distinguishing monosaccharide stereo- and structural isomers with TOF-SIMS and multivariate statistical analysis

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is utilized to examine the mass spectra and fragmentation patterns of seven isomeric monosaccharides. Multivariate statistical analysis techniques, including principal component analysis (PCA), allow discrimination of the extremely similar mass spectra of stereoisomers. Furthermore, PCA identifies those fragment peaks that vary significantly between spectra. Heavy isotope studies confirm that these peaks are indeed sugar fragments, allow identification of the fragments, and provide clues to the fragmentation pathways. Excellent reproducibility is shown by multiple experiments performed over time and on separate samples. This study demonstrates the combined selectivity and discrimination power of TOF-SIMS and PCA and suggests new applications of the technique including differentiation of subtle chemical changes in biological samples that may provide insights into cellular processes, disease progress, and disease diagnosis.     

Social "facilitation" as challenge and threat.

The authors conducted an experiment to test a theoretical explanation of social facilitation based on the biopsychosocial model of challenge and threat. Participants mastered 1 of 2 tasks and subsequently performed either the mastered (i.e., well-learned) or the unlearned task either alone or with an audience while cardiovascular responses were recorded. Cardiovascular responses of participants performing a well-learned task in the presence of others fit the challenge pattern (i.e., increased cardiac response and decreased vascular resistance), whereas cardiovascular responses of participants performing an unlearned task in the presence of others fit the threat pattern (i.e., increased cardiac response and increased vascular resistance), confirming the authors' hypotheses and the applicability of the biopsychosocial model of challenge and threat to explain these results. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Functionalized Porous Aromatic Frameworks as High‐Performance Adsorbents for the Rapid Removal of Boric Acid from Water

This study demonstrates that functionalized, highly porous polymers are promising for the adsorptive capture of boric acid, a neutral contaminant that is difficult to remove from seawater using conventional reverse osmosis membranes. Appending N‐methyl‐d‐glucamine (NMDG) to the pore walls of high‐surface‐area porous aromatic frameworks (PAFs) yields the adsorbents PAF‐1‐NMDG and P2‐NMDG in a simple two‐step synthesis. The boron‐selective PAFs demonstrate adsorption capacities that are up to 70% higher than those of a commercial boron‐selective resin, Amberlite IRA743, and markedly faster adsorption rates, owing to their higher NMDG loadings and greater porosities relative to the resin. Remarkably, PAF‐1‐NMDG is able to reduce the boron concentration in synthetic seawater from 2.91 to <0.5 ppm in less than 3 min at an adsorbent loading of only 0.3 mg mL^?1. The boron adsorption rate constants of both frameworks, determined via a pseudo‐second‐order rate model, represent the highest values reported in the literature—in most cases orders of magnitude higher than those of other boron‐selective adsorbents. The frameworks can also be readily regenerated via mild acid/base treatment and maintain constant boron adsorption capacities for at least 10 regeneration cycles. These results highlight the numerous advantages of PAFs over traditional porous polymers in water treatment applications.     

Fast Similarity Search for Learned Metrics

We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships well. To allow sublinear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality makes it infeasible to learn an explicit transformation over the feature dimensions. We demonstrate the approach applied to a variety of image data sets, as well as a systems data set. The learned metrics improve accuracy relative to commonly used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.     

'Severe' wear challenge to 'as-cast' and 'double heat-treated' large-diameter metal-on-metal hip bearings

The wear generation of double-heat-treated and as-cast large-diameter metal-on-metal (MOM) hip bearings was investigated using standard- and 'severe'-gait simulations. The test hypothesis was that double heat treatment would change MOM hip wear compared with the as-cast condition. Two groups of high-carbon MOM bearings of 40 mm diameter were manufactured and subjected to either hot isostatic pressing (HIP) and solution annealing (SA) or no heat treatment (as cast). The results showed no statistical difference between the two groups under both running-in and steady state conditions. Even under the most 'severe'-gait simulation published to date, the mean volumetric wear rates were 2.9 and 3.9 mm3 per 10(6) cycles for the HIP-SA and as-cast bearings respectively, showing a ten-fold increase in wear compared with walking. These differences were not statistically different; therefore our hypothesis was negated. Changes in alloy microstructure do not appear to influence the wear behaviour of high-carbon cast MOM articulations with similar chemical compositions. This is in sharp contrast with the published significance of bearing diameter and radial clearance on the wear of MOM hip bearings.     

Organizational decision-making during COVID-19: A qualitative analysis of the organizational decision-making system in the United States during COVID-19

This study sought to understand COVID-19-related organizational decisions were made across sectors. To gain this understanding, we conducted semi-structured interviews with organizational decision-makers in North Carolina about their experiences responding to COVID-19. Conventional content analysis was used to analyse the context, inputs, and processes involved in decision-making. Between October 2020 and February 2021, we interviewed 44 decision-makers from the following sectors: business (n = 4), community non-profit (n = 3), county government (n = 4), healthcare (n = 5), local public health (n = 5), public safety (n = 7), religious (n = 6), education (n = 7) and transportation (n = 3). We found that during the pandemic, organizations looked to scientific authorities, the decisions of peer organizations, data about COVID-19, and their own experience with prior crises. Interpretation of inputs was informed by current political events, societal trends, and organization mission. Decision-makers had to account for divergent internal opinions and community behaviour. To navigate inputs and contextual factors, organizations decentralized decision-making authority, formed auxiliary decision-making bodies, learned to resolve internal conflicts, learned in real time from their crisis response, and routinely communicated decisions with their communities. In conclusion, aligned with systems and contingency theories of decision-making, decision-making during COVID-19 depended on an organization's ‘fit’ within the specifics of their existing system and their ability to orient the dynamics of that system to their own goals.     

Kernelized locality-sensitive hashing

Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several data sets, and show that it enables accurate and fast performance for several vision problems, including example-based object classification, local feature matching, and content-based retrieval.     

Identification of Phenazine-Based MEMO1 Small-Molecule Inhibitors: Virtual Screening,Fluorescence Polarization Validation,and Inhibition of Breast Cancer Migration

Phosphorylation-dependent protein–protein interactions play a significant role in biological signaling pathways; therefore, small molecules that are capable of influencing these interactions can be valuable research tools and have potential as pharmaceutical agents. MEMO1 (mediator of ErbB2-cell driven motility) is a phosphotyrosine-binding protein that interacts with a variety of protein partners and has been found to be upregulated in breast cancer patients. Herein, we report the first small-molecule inhibitors of MEMO1 interactions identified through a virtual screening platform and validated in a competitive fluorescence polarization assay. Initial structure–activity relationships have been investigated for these phenazine-core inhibitors and the binding sites have been postulated using molecular dynamics simulations. The most potent biochemical inhibitor is capable of disrupting the large protein interface with a K_I of 2.7 μm . In addition, the most promising phenazine core compounds slow the migration of breast cancer cell lines in a scratch assay.     

Characterizing the MTF in 3D for a Quantized SPECT Camera Having Arbitrary Trajectories

The emergence of application-specific 3D tomographic small animal and dedicated breast imaging systems has stimulated the development of simple methods to quantify the spatial resolution or Modulation Transfer Function (MTF) of the system in three dimensions. Locally determined MTFs, obtained from line source measurements at specific locations, can characterize spatial variations in the system resolution and can help correct for such variations. In this study, a method is described to measure the MTF in 3D for a compact SPECT system that uses a 16 × 20 cm(2) CZT-based compact gamma camera and 3D positioning gantry capable of moving in different trajectories. Image data are acquired for a novel phantom consisting of three radioactivity-filled capillary tubes, positioned nearly orthogonally to each other. These images provide simultaneous measurements of the local MTF along three dimensions of the reconstructed imaged volume. The usefulness of this approach is shown by characterizing the MTF at different locations in the reconstructed imaged 3D volume using various (1) energy windows; (2) iterative reconstruction parameters including number of iterations, voxel size, and number of projection views; (3) simple and complex 3D orbital trajectories including simple vertical axis of rotation, simple tilt, complex circle-plus-arc, and complex sinusoids projected onto a hemisphere; and (4) object shapes in the camera's field of view. Results indicate that the method using the novel phantom can provide information on spatial resolution effects caused by system design, sampling, energy windows, reconstruction parameters, novel 3D orbital trajectories, and object shapes. Based on these measurements that are useful for dedicated tomographic breast imaging, it was shown that there were small variations in the MTF in 3D for various energy windows and reconstruction parameters. However, complex trajectories that uniformly sample the breast volume of interest were quantitatively shown to have slightly better spatial resolution performance than more simple orbits.     

Evolution of Texture in Rhabdophane-Derived Monazite Coatings

Microstructure and texture development of fiber coatings of rhabdophane-sol-derived monazite was studied. As-deposited textures and orientation relationships during phase transformations were determined by TEM. Monazite coatings had a crystallographic texture relict from that of as-deposited rhabdophane, with layers of rod-shaped particles that changed orientation by 90° across layers. Heat treatment at 1200°C of minicomposites with these coated fibers caused considerable monazite grain coarsening, and disappearance of the texture.