Small molecule correctors of F508del-CFTR discovered by structure-based virtual screening

Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability. In silico structure-based screening was performed at the putative binding-sites and a total of 496 candidate compounds from all three sites were tested in functional assays. A total of 15 compounds, representing diverse chemotypes, were identified as F508del folding correctors. This corresponds to a 3% hit rate, ~tenfold higher than hit rates obtained in corresponding high-throughput screening campaigns. The same binding sites also yielded potentiators and, most notably, compounds with a dual corrector-potentiator activity (dual-acting). Compounds harboring both activity types may prove to be better leads for the development of CF therapeutics than either pure correctors or pure potentiators. To the best of our knowledge this is the first report of structure-based discovery of CFTR modulators.     

Flavor and LHC searches for new physics

Uncovering the physics of electroweak symmetry breaking (EWSB) is the raison-d’etre of the LHC. Flavor questions, it would seem, are of minor relevance for this quest, apart from their role in constraining the possible structure of EWSB physics. In this short review article, we outline, using flavor-dependent slepton physics as an example, how flavor can affect both searches for supersymmetry, and future measurements aimed at understanding the nature of any new discoveries. If the production cross-sections for supersymmetry are relatively low, as indicated by the fact that it has not revealed itself yet in standard searches, the usual assumptions about the superpartner spectra need rethinking. Furthermore, one must consider more intricate searches, such as lepton-based searches, which could be susceptible to flavor effects. We start by reviewing the flavor structure of existing frameworks for mediating supersymmetry breaking, emphasizing flavor-dependent models proposed recently. We use the kinematic endpoints of invariant mass distributions to demonstrate how flavor dependence can impact both searches for supersymmetry and the Inverse Problem. We also discuss methods for measuring small-mass splittings and mixings at the LHC, both in models with a neutralino LSP and in models with a charged slepton (N)LSP.     

Multivalency as a Key Factor for High Activity of Selective Supported Organocatalysts for the Baylis–Hillman Reaction

The polystyrene‐supported N‐alkylimidazole‐based dendritic catalysts for the Baylis–Hillman reaction exhibit one of the strongest beneficial effects of multivalent architecture ever reported for an organocatalyst. The yields in the model reaction of methyl vinyl ketone with p‐nitrobenzaldehyde are more than tripled when a non‐dendritic catalyst is replaced by a second‐ or third‐generation analogue. Moreover, the reaction of the less active substrates will not occur with the non‐dendritic catalyst and will proceed to a significant extent only with the analogous catalysts of higher generations. A substantial additional enhancement of the reaction yield could be achieved by increasing the content of water in the reaction solvent. The plausible cause of the dendritic effect is the assistance of the second, nearby imidazole moiety in the presumably rate‐determining proton transfer in the intermediate adduct, after the first imidazole unit induced the formation of the new carbon–carbon bond.     

Eigenvectors of random graphs: Nodal Domains

We initiate a systematic study of eigenvectors of random graphs. Whereas much is known about eigenvalues of graphs and how they reflect properties of the underlying graph, relatively little is known about the corresponding eigenvectors. Our main focus in this article is on the nodal domains associated with the different eigenfunctions. In the analogous realm of Laplacians of Riemannian manifolds, nodal domains have been the subject of intensive research for well over a hundred years. Graphical nodal domains turn out to have interesting and unexpected properties. Our main theorem asserts that there is a constant c such that for almost every graph G, each eigenfunction of G has at most two large nodal domains, and in addition at most c exceptional vertices outside these primary domains. We also discuss variations of these questions and briefly report on some numerical experiments which, in particular, suggest that almost surely there are just two nodal domains and no exceptional vertices. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 39, 39–58, 2011     

SilverSil: A New Class of Antibacterial Materials of Broad Scope

Consisting of organically modified silica (ORMOSIL) physically doped with Ag nanoparticles, the SilverSil new class of antibacterial materials of broad scope reported herein shows remarkably high and stable activity against representative Gram-positive and Gram-negative bacteria. The low cost, ease of application and excellent health and environmental profile of SilverSil hybrid glassy coatings open the route to their widespread utilization across domestic, hospital, school, industrial and commercial environments and in consumer products.     

Estimating speech spectra for copy synthesis by linear prediction and by hand

Linear prediction is a widely available technique for analyzing acoustic properties of speech, although this method is known to be error-prone. New tests assessed the adequacy of linear prediction estimates by using this method to derive synthesis parameters and testing the intelligibility of the synthetic speech that results. Matched sets of sine-wave sentences were created, one set using uncorrected linear prediction estimates of natural sentences, the other using estimates made by hand. Phoneme restrictions imposed on linguistic properties allowed comparisons between continuous and intermittent voicing, oral or nasal and fricative manner, and unrestricted phonemic variation. Intelligibility tests revealed uniformly good performance with sentences created by hand-estimation and a minimal decrease in intelligibility with estimation by linear prediction due to manner variation with continuous voicing. Poorer performance was observed when linear prediction estimates were used to produce synthetic versions of phonemically unrestricted sentences, but no similar decline was observed with synthetic sentences produced by hand estimation. The results show a substantial intelligibility cost of reliance on uncorrected linear prediction estimates when phonemic variation approaches natural incidence.     

Experimental study of shape transitions and energy scaling in thin non-Euclidean plates

We present the first quantitative measurements of shape and energy variation in non-Euclidean plates. Using environmentally responsive gel, we construct non-Euclidean disks of constant imposed Gaussian curvature, K(tar). We vary the disks' thickness t(0) and measure the dependence of configurations, surface curvature, and energy content on t(0). For K(tar)<0, configurations are of a single wavy mode and undergo a set of bifurcations that leads to their refinement with decreasing thickness. This leads to sharp increase in the amount of surface bending as t(0)→0, and to a slow decay of both bending and stretching energies. Both vary like t(0)(2), compared with t(0)(3) of the bending energy in disks with K(tar)>0.     

Dual subgradient algorithms for large-scale nonsmooth learning problems

“Classical” First Order (FO) algorithms of convex optimization, such as Mirror Descent algorithm or Nesterov’s optimal algorithm of smooth convex optimization, are well known to have optimal (theoretical) complexity estimates which do not depend on the problem dimension. However, to attain the optimality, the domain of the problem should admit a “good proximal setup”. The latter essentially means that (1) the problem domain should satisfy certain geometric conditions of “favorable geometry”, and (2) the practical use of these methods is conditioned by our ability to compute at a moderate cost proximal transformation at each iteration. More often than not these two conditions are satisfied in optimization problems arising in computational learning, what explains why proximal type FO methods recently became methods of choice when solving various learning problems. Yet, they meet their limits in several important problems such as multi-task learning with large number of tasks, where the problem domain does not exhibit favorable geometry, and learning and matrix completion problems with nuclear norm constraint, when the numerical cost of computing proximal transformation becomes prohibitive in large-scale problems. We propose a novel approach to solving nonsmooth optimization problems arising in learning applications where Fenchel-type representation of the objective function is available. The approach is based on applying FO algorithms to the dual problem and using the accuracy certificates supplied by the method to recover the primal solution. While suboptimal in terms of accuracy guaranties, the proposed approach does not rely upon “good proximal setup” for the primal problem but requires the problem domain to admit a Linear Optimization oracle—the ability to efficiently maximize a linear form on the domain of the primal problem.     

H2S Removal from Groundwater by Chemical Free Advanced Oxidation Process Using UV-C/VUV Radiation

Sulfide species may be present in groundwater due to natural processes or due to anthropogenic activity. H₂S contamination poses odor nuisance and may also lead to adverse health effects. Advanced oxidation processes (AOPs) are considered promising treatments for hydrogen-sulfide removal from water, but conventional AOPs usually require continuous chemical dosing, as well as post-treatment, when solid catalysts are applied. Vacuum-UV (VUV) radiation can generate ·OH in situ via water photolysis, initiating chemical-free AOP. The present study investigated the applicability of VUV-based AOP for removal of H₂S both in synthetic solutions and in real groundwater, comparing combined UV-C/VUV and UV-C only radiation in a continuous-flow reactor. In deionized water, H₂S degradation was much faster under the combined radiation, dominated by indirect photolysis, and indicated the formation of sulfite intermediates that convert to sulfate at high radiation doses. Sulfide was efficiently removed from natural groundwater by the two examined lamps, with no clear preference between them. However, in anoxic conditions, common in sulfide-containing groundwater, a small advantage for the combined lamp was observed. These results demonstrate the potential of utilizing VUV-based AOP for treating H₂S contamination in groundwater as a chemical-free treatment, which can be especially attractive to remote small treatment facilities.