Detection of chemical and biological agents plays a fundamental role in biomedical, forensic and environmental sciences1–4 as well as in anti bioterrorism applications.5–7 The development of highly sensitive, cost effective, miniature sensors is therefore in high demand which requires advanced technology coupled with fundamental knowledge in chemistry, biology and material sciences.8–13

In general, sensors feature two functional components: a recognition element to provide selective/specific binding with the target analytes and a transducer component for signaling the binding event. An efficient sensor relies heavily on these two essential components for the recognition process in terms of response time, signal to noise (S/N) ratio, selectivity and limits of detection (LOD).14,15 Therefore, designing sensors with higher efficacy depends on the development of novel materials to improve both the recognition and transduction processes. Nanomaterials feature unique physicochemical properties that can be of great utility in creating new recognition and transduction processes for chemical and biological sensors15–27 as well as improving the S/N ratio by miniaturization of the sensor elements.28

Gold nanoparticles (AuNPs) possess distinct physical and chemical attributes that make them excellent scaffolds for the fabrication of novel chemical and biological sensors (Figure 1).29–36 First, AuNPs can be synthesized in a straightforward manner and can be made highly stable. Second, they possess unique optoelectronic properties. Third, they provide high surface-to-volume ratio with excellent biocompatibility using appropriate ligands.30 Fourth, these properties of AuNPs can be readily tuned varying their size, shape and the surrounding chemical environment. For example, the binding event between recognition element and the analyte can alter physicochemical properties of transducer AuNPs, such as plasmon resonance absorption, conductivity, redox behavior, etc. that in turn can generate a detectable response signal. Finally, AuNPs offer a suitable platform for multi-functionalization with a wide range of organic or biological ligands for the selective binding and detection of small molecules and biological targets.30–32,36 Each of these attributes of AuNPs has allowed researchers to develop novel sensing strategies with improved sensitivity, stability and selectivity. In the last decade of research, the advent of AuNP as a sensory element provided us a broad spectrum of innovative approaches for the detection of metal ions, small molecules, proteins, nucleic acids, malignant cells, etc. in a rapid and efficient manner.37



Figure 1

Physical properties of AuNPs and schematic illustration of an AuNP-based detection system.



In this current review, we have highlighted the several synthetic routes and properties of AuNPs that make them excellent probes for different sensing strategies. Furthermore, we will discuss various sensing strategies and major advances in the last two decades of research utilizing AuNPs in the detection of variety of target analytes including metal ions, organic molecules, proteins, nucleic acids, and microorganisms.

/pdf/gold-nanoparticles-in-chemical-and-biological-sensing-5e2qojkdzn.pdf

Gold nanoparticles in chemical and biological sensing.

We consider four problems connected by the common thread of geometry. The first three arise in applications that apriori do not involve geometry but this turns out to be the right language for visualizing and analyzing them. In the fourth, we generalize some well known results in geometry to the topological plane. The techniques we use come from probability and topology. First, we consider two algorithms that work well in practice but the theoretical mechanism behind whose success is not very well understood. Greedy routing is a routing mechanism that is commonly used in wireless sensor networks. While routing on the Internet uses standard established protocols, routing in ad-hoc networks with little structure (like sensor networks) is more difficult. Practitioners have devised algorithms that work well in practice, however there were no known theoretical guarantees. We provide the first such result in this area by showing that greedy routing can be made to work on Planar triangulations. Linear Programming is a technique for optimizing a linear function subject to linear constraints. Simplex Algorithms are a family of algorithms that have proven quite successful in solving Linear Programs in practice. However, examples of Linear Programs on which these algorithms are very inefficient have been obtained by researchers. In or-

Computational geometry.

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

/pdf/computational-methods-in-drug-discovery-3sww6kbvd5.pdf

Computational Methods in Drug Discovery

The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

Funnels, Pathways and the Energy Landscape of Protein Folding: A Synthesis

Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.

/pdf/fpocket-an-open-source-platform-for-ligand-pocket-detection-3w687djtrt.pdf

Fpocket: An open source platform for ligand pocket detection

A protein consists of linearly combined amino acids via peptide bonds, and an amino acid consists of atoms. It is known that the geometric structure of a protein is the primary factor which determines the functions of the protein. Given the atomic complex of a protein, one of the most important geometric structures of a protein is its molecular surface since this distinguishes between the interior and exterior of the protein and plays an important role in protein folding, docking, interactions between proteins, and other functions. This paper presents an algorithm for the precise and efficient computation of the molecular surface of a protein, using a recently proposed geometric construct called the @b-shape based on the Voronoi diagram of atoms in a protein. Given a Voronoi diagram of atoms, based on the Euclidean distance from the atom surfaces, the proposed algorithm first computes the @b-shape with an appropriate sized probe. Then, the molecular surface is computed by employing a blending operation on the atomic complex of the protein. In this paper, it is also shown that for a given Voronoi diagram of atoms, the multiple molecular surfaces can be computed by using various sized probes.

Molecular surfaces on proteins via beta shapes

Colloidal Au-amplified surface plasmon resonance (SPR), like traditional SPR, is typically used to detect binding events on a thin noble metal film. The two major concerns in developing colloidal Au-amplified SPR lie in 1) the instability, manifested as a change in morphology following immersion in organic solvents and aqueous solutions, and 2) the uncontrollable interparticle distance, determining probe spacing and inducing steric hindrance between neighboring probe molecules. This may introduce uncertainties into such detecting techniques, degrade the sensitivity, and become the barricade hampering colloidal Au-based transducers from applications in sensing. In this paper, colloidal Au-amplified SPR transducers are produced by using ultrathin Au/Al 2 O 3 nanocomposite films via a radio frequency magnetron co-sputtering method. Deposited Au/Al 2 O 3 nanocomposite films exhibit superior stability, and average interparticle distances between Au nanoparticles with similar average sizes can be tuned by changing surface coverage. These characteristics are ascribed to the spacer function and rim confinement of dielectric Al 2 O 3 and highlight their advantages for application in optimal nanoparticle-amplified SPR, especially when the probe size is smaller than the target molecule size. This importance is demonstrated here for the binding of protein (streptavidin) targets to the probe (biotin) surface. In this case, the dielectric matrix Al 2 O 3 is a main contributor, behaving as a spacer, tuning the concentration of Au nanoparticles, and manipulating the average interparticle distance, and thus guaranteeing an appropriate number of biotin molecules and expected near-field coupling to obtain optimal sensing performance.

Highly Stable Au Nanoparticles with Tunable Spacing and Their Potential Application in Surface Plasmon Resonance Biosensors

3D printing, also called additive manufacturing, has been increasingly popular and printing efficiency has become more critical. To print artifacts faster with less material, thus leading to lighter and cheaper printed products, various types of void structureshave been designed and engineered inside of shape models. In this paper, we present a novel method for generating support-free elliptic hollowing for 3D shapes which can entirely avoid additional supporting structures. To achieve this, we perform the ellipse hollowing in one of the cross sectional polygons and then extrude the hollowed ellipses to the other parallel cross sections. To efficiently pack the ellipses in the polygon, we construct the Voronoi diagram of ellipses to reason the free-space around the ellipses and other geometric features by taking advantage of the available algorithm for the efficient and robust construction of the Voronoi diagram of circles. We demonstrate the effectiveness and feasibility of our proposed method by designing and printing support-free hollow for various 3D shapes using Poretron , the program which computes the hollow by embedding appropriate APIs of the Voronoi Diagram Machine library that is freely available from Voronoi Diagram Research Center. It takes a 3D mesh model and produces an STL file which can be either fed into a 3D printer or postprocessed.

pdf/support-free-hollowing-for-3d-printing-via-voronoi-diagram-1tvfb3pwrh.pdf

Support-Free Hollowing for 3D Printing via Voronoi Diagram of Ellipses

The Voronoi diagram of a point set has been extensively used in various disciplines ever since it was first proposed. Its application realms have been even further extended to estimate the shape of point clouds when Edelsbrunner and Mucke introduced the concept of @a-shape based on the Delaunay triangulation of a point set. In this paper, we present the theory of @b-shape for a set of three-dimensional spheres as the generalization of the well-known @a-shape for a set of points. The proposed @b-shape fully accounts for the size differences among spheres and therefore it is more appropriate for the efficient and correct solution for applications in biological systems such as proteins. Once the Voronoi diagram of spheres is given, the corresponding @b-shape can be efficiently constructed and various geometric computations on the sphere complex can be efficiently and correctly performed. It turns out that many important problems in biological systems such as proteins can be easily solved via the Voronoi diagram of atoms in proteins and @b-shapes transformed from the Voronoi diagram.

Three-dimensional beta shapes

The proximity and topology among particles are often the most important factor for understanding the spatial structure of particles. Reasoning the morphological structure of molecules and reconstructing a surface from a point set are examples where proximity among particles is important. Traditionally, the Voronoi diagram of points, the power diagram, the Delaunay triangulation, and the regular triangulation, etc. have been used for understanding proximity among particles. In this paper, we present the theory of the @b-shape and the @b-complex and the corresponding algorithms for reasoning proximity among a set of spherical particles, both using the quasi-triangulation which is the dual of the Voronoi diagram of spheres. Given the Voronoi diagram of spheres, we first transform the Voronoi diagram to the quasi-triangulation. Then, we compute some intervals called @b-intervals for the singular, regular, and interior states of each simplex in the quasi-triangulation. From the sorted set of simplexes, the @b-shape and the @b-complex corresponding to a particular value of @b can be found efficiently. Given the Voronoi diagram of spheres, the quasi-triangulation can be obtained in O(m) time in the worst case, where m represents the number of simplexes in the quasi-triangulation. Then, the @b-intervals for all simplexes in the quasi-triangulation can also be computed in O(m) time in the worst case. After sorting the simplexes using the low bound values of the @b-intervals of each simplex in O(mlogm) time, the @b-shape and the @b-complex can be computed in O(logm+k) time in the worst case by a binary search followed by a sequential search in the neighborhood, where k represents the number of simplexes in the @b-shape or the @b-complex. The presented theory of the @b-shape and the @b-complex will be equally useful for diverse areas such as structural biology, computer graphics, geometric modelling, computational geometry, CAD, physics, and chemistry, where the core hurdle lies in determining the proximity among spherical particles.

Three-dimensional beta-shapes and beta-complexes via quasi-triangulation

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