2D Extensions Research Articles

Development and Characterization of an In Silico Database of In Vitro Tested Antiviral Compounds

Antivirals are substances that inhibit viral infection or virus replication, acting as drugs to treat viral diseases. However, due the diversity of pathogens, it is important to seek new antivirals. Among the options, repositioning already approved drugs is a cheaper and faster strategy when compared to classical research and development methods. Since there is a lack of compiled and standardized data on these drugs, this work aims to build a database of in vitro antiviral tests. Thus, the compounds and their information were obtained through publications of in vitro methods for testing antiviral drugs, we created six databases covering SMILES, MOL, SDF 2D, MOL2, PDB and PDBQT extensions, classified by the presence and/or absence of antiviral activity. Each file contains its IUPAC name and structural data in up to three dimensions.

How graphenic are graphynes? Evidence for low-lying correlated gapped states in graphynes.

Graphynes can be structurally envisioned as 2D extensions to graphene, whereby linearly bonded carbon linkages increase the distance between trigonal carbon nodes. Many graphynes have been predicted to exhibit a Dirac-like semimetallic (SEM) graphenic electronic structure, which could potentially make them competitive with graphene for applications. Currently, most graphynes remain as attractive synthetic targets, and their properties are still unconfirmed. Here, we demonstrate that the electronic structure of hexagonal α-graphyne is analogous to that of biaxially strained graphene. By comparison with accurate quantum Monte Carlo results on strained graphene, we show that the relative energetic stability of electronic states in this correlated 2D system can be captured by density functional theory (DFT) calculations using carefully tailored hybrid functionals. Our tuned hybrid DFT approach confirms that α-graphyne has a low energy correlated Mott-like antiferromagnetic insulating (AFI) state, which competes with the SEM state. Our work shows that the AFI-SEM crossover in α-graphyne could be tunable by in-plane biaxial strain. Applying our approach to other graphynes shows that they should also exhibit correlated AFI states, which could be dominant even at zero strain. Calculations using an onsite Coulombic repulsive term (i.e., DFT + U) also confirm the predictions of our hybrid DFT calculations. Overall, our work strongly suggests that graphynes are not as graphenic (i.e., Dirac-like) as often previously predicted by DFT calculations using standard generalized gradient approximation functionals. However, due to the greater electronic versatility (e.g., tunable semiconducting bandgaps and accessible spin polarized states) implied by our study, graphynes could have novel device applications that are complementary to those of graphene.

SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing

SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.

The Empirical Watershed Wavelet.

The empirical wavelet transform is an adaptive multi-resolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain. However, existing 2D extensions are constrained by the shape of the detected partitioning. In this paper, we provide theoretical results that permits us to build 2D empirical wavelet filters based on an arbitrary partitioning of the frequency domain. We also propose an algorithm to detect such partitioning from an image spectrum by combining a scale-space representation to estimate the position of dominant harmonic modes and a watershed transform to find the boundaries of the different supports making the expected partition. This whole process allows us to define the empirical watershed wavelet transform. We illustrate the effectiveness and the advantages of such adaptive transform, first visually on toy images, and next on both unsupervised texture segmentation and image deconvolution applications.

Dimensional Control of Assembling Metal Chalcogenide Clusters

AbstractInvited for the cover of this issue is the group of Colin Nuckolls from Columbia University, New York, USA. The cover image shows the metal chalcogenide core building block and its 1D, 2D, and 3D extensions.

Probing CO and N2 Snow Surfaces in Protoplanetary Disks with N2H+ Emission

Abstract Snowlines of major volatiles regulate the gas and solid C/N/O ratios in the planet-forming midplanes of protoplanetary disks. Snow surfaces are the 2D extensions of snowlines in the outer disk regions, where radiative heating results in an increasing temperature with disk height. CO and N2 are two of the most abundant carriers of C, N, and O. N2H+ can be used to probe the snow surfaces of both molecules, because it is destroyed by CO and formed from N2. Here we present Atacama Large Millimeter/submillimeter Array (ALMA) observations of N2H+ at ∼0.″2–0.″4 resolution in the disks around LkCa 15, GM Aur, DM Tau, V4046 Sgr, AS 209, and IM Lup. We find two distinctive emission morphologies: N2H+ is either present in a bright, narrow ring surrounded by extended tenuous emission, or in a broad ring. These emission patterns can be explained by two different kinds of vertical temperature structures. Bright, narrow N2H+ rings are expected in disks with a thick Vertically Isothermal Region above the Midplane (VIRaM) layer (LkCa 15, GM Aur, DM Tau) where the N2H+ emission peaks between the CO and N2 snowlines. Broad N2H+ rings come from disks with a thin VIRaM layer (V4046 Sgr, AS 209, IM Lup). We use a simple model to extract the first sets of CO and N2 snowline pairs and corresponding freeze-out temperatures toward the disks with a thick VIRaM layer. The results reveal a range of N2 and CO snowline radii toward stars of similar spectral type, demonstrating the need for empirically determined snowlines in disks.

Analysis of Retinoic Acid-induced Neural Differentiation of Mouse Embryonic Stem Cells in Two and Three-dimensional Embryoid Bodies.

Mouse embryonic stem cells (ESCs) isolated from the inner mass of the blastocyst (typically at day E3.5), can be used as in vitro model system for studying early embryonic development. In the absence of leukemia inhibitory factor (LIF), ESCs differentiate by default into neural precursor cells. They can be amassed into a three dimensional (3D) spherical aggregate termed embryoid body (EB) due to its similarity to the early stage embryo. EBs can be seeded on fibronectin-coated coverslips, where they expand by growing two dimensional (2D) extensions, or implanted in 3D collagen matrices where they continue growing as spheroids, and differentiate into the three germ layers: endodermal, mesodermal, and ectodermal. The 3D collagen culture mimics the in vivo environment more closely than the 2D EBs. The 2D EB culture facilitates analysis by immunofluorescence and immunoblotting to track differentiation. We have developed a two-step neural differentiation protocol. In the first step, EBs are generated by the hanging-drop technique, and, simultaneously, are induced to differentiate by exposure to retinoic acid (RA). In the second step, neural differentiation proceeds in a 2D or 3D format in the absence of RA.

Electric field distribution and voltage breakdown modeling for any PN junction

Purpose – Scientists and engineers have been solving Poisson’s equation in PN junctions following two approaches: analytical solving or numerical methods. Although several efforts have been accomplished to offer accurate and fast analyses of the electric field distribution as a function of voltage bias and doping profiles, so far none achieved an analytic or semi-analytic solution to describe neither a double diffused PN junction nor a general case for any doping profile. The paper aims to discuss these issues. Design/methodology/approach – In this work, a double Gaussian doping distribution is first considered. However, such a doping profile leads to an implicit problem where Poisson’s equation cannot be solved analytically. A method is introduced and successfully applied, and compared to a finite element analysis. The approach is then generalized, where any doping profile can be considered. 2D and 3D extensions are also presented, when symmetries occur for the doping profile. Findings – These results and the approach here presented offer an efficient and accurate alternative to numerical methods for the modeling and simulation of mathematical equations arising in physics of semiconductor devices. Research limitations/implications – A general 3D extension in the case where no symmetry exists can be considered for further developments. Practical implications – The paper strongly simplify and ease the optimization and design of any PN junction. Originality/value – This paper provides a novel method for electric field distribution analysis.

Comprehensive knowledge base of two- and three-dimensional activity cliffs for medicinal and computational chemistry

Activity cliffs are formed by pairs or groups of structurally similar or analogous active compounds with large differences in potency. They can be defined in two or three dimensions by comparing graph-based molecular representations or compound binding modes, respectively. Through systematic analysis of publicly available compound activity data and ligand-target X-ray structures we have in a series of studies determined all currently available two- and three-dimensional activity cliffs (2D- and 3D-cliffs, respectively). Furthermore, we have systematically searched for 2D extensions of 3D-cliffs. Herein, we specify different categories of activity cliffs we have explored and introduce an open access data deposition in ZENODO (doi: 10.5281/zenodo.18490) that makes the entire knowledge base of current activity cliffs freely available in an organized form.

Multivariate and 2D Extensions of Singular Spectrum Analysis with theRssaPackage

Implementation of multivariate and 2D extensions of Singular Spectrum Analysis (SSA) by means of the R-package Rssa is considered. The extensions include MSSA for simultaneous analysis and forecasting of several time series and 2D-SSA for analysis of digital images. A new extension of 2D-SSA analysis called Shaped 2D-SSA is introduced for analysis of images of arbitrary shape, not necessary rectangular. It is shown that implementation of Shaped 2D-SSA can serve as a base for implementation of MSSA and other generalizations. Efficient implementation of operations with Hankel and Hankel-block-Hankel matrices through FFT is suggested. Examples with code fragments in R, which explain the methodology and demonstrate the proper use of Rssa, are presented.

Simple yet effective color principal and discriminant feature extraction for representing and recognizing color images

In this paper, we investigate the problem of extracting two-dimensional color principal and discriminant features for understanding color images. Specifically, two simple yet effective color image feature extraction criteria, called Color Principal Component Analysis (ColorPCA) and Color Linear Discriminant Analysis (ColorLDA), are proposed for color image analysis. The presented criteria can preserve color and topology information of pixels in images, and extract features directly from color images in an efficient manner by eigen-decomposing a single eigen-problem. In modeling the criteria, color image scatter matrices are defined. Like PCA, LDA and their two-dimensional (2D) extensions, our methods only need to choose the number of projection vectors. More importantly, the matrices to be eigen-decomposed in our criteria have the same size as 2DPCA and 2DLDA that are very efficient. To achieve an orthogonal projection matrix, trace ratio ColorLDA is also presented. We also present the alternative versions of our approaches for feature learning through mining row or column information of the images. Extensive simulations on benchmark datasets are conducted to evaluate our algorithms. The investigated cases demonstrate the effectiveness and efficiency of our techniques, compared with other most related state-of-the-art 1D and 2D criteria.

Low complexity and high accuracy angle of arrival estimation using eigenvalue decomposition with extension to 2D AOA and power estimation

In this paper, an angle of arrival (AOA) estimator is presented. Accurate AOA estimation is very crucial for many applications such as wireless positioning and signal enhancement using space processing techniques. The proposed estimator is based upon applying the eigenvalue decomposition (EVD) method on the crosscorrelation matrix of the received signals at two sides of the antenna array doublets. The proposed method is named the eigenvalue-decomposition-based AOA (EDBA) estimator. In comparison with the ESPRIT algorithm, the EDBA has less complexity because the decomposition in the EDBA method is performed only once and on a smaller matrix dimension than that in the ESPRIT algorithm where the decomposition is performed twice. The other advantage is that the EDBA method has better performance than the ESPRIT algorithm. The EDBA is also extended to two-dimensional (2D) AOA estimation with automatic pairing in two ways. The first one performs the 2D AOA estimation by considering the eigenvalues of the crosscorrelation matrix to estimate the azimuth angles and their corresponding eigenvectors to estimate their corresponding elevation angles. Thus, the 2D AOA estimation is performed with automatic pairing and without the need for any pairing or searching techniques. The first 2D extension of the EDBA is named the EDBA-2D estimator. Another 2D AOA estimator that is based upon the EDBA method is also presented and named the Two-EDBA estimator. This second 2D estimator performs the pairing between the azimuth and elevation angles using the alignment of the eigenvalues' magnitudes. An additional advantage for the EDBA estimator is the fact that it provides an estimate for the received signal power paired automatically with its corresponding AOA estimate. Simulations of the proposed EDBA method and its 2D extensions with the signals' power estimation are shown to assess their performance.

Some New Delay Integral Inequalities in Two Independent Variables on Time Scales

Some new Gronwall‐Bellman‐type delay integral inequalities in two independent variables on time scales are established, which can be used as a handy tool in the research of boundedness of solutions of delay dynamic equations on time scales. Some of the established results are 2D extensions of several known results in the literature, while some results unify existing continuous and discrete analysis.

A review of model applications for structured soils: a) Water flow and tracer transport

Although it has many positive effects, soil structure may adversely affect the filtering function of the vadose zone that protects natural water resources from various sources of pollution. Physically based models have been developed to analyze the impacts of preferential water flow (PF) and physical non-equilibrium (PNE) solute transport on soil and water resources. This review compiles results published over the past decade on the application of such models for simulating PF and PNE non-reactive tracer transport for scales ranging from the soil column to the catchment area. Recent progress has been made in characterizing the hydraulically relevant soil structures, dynamic flow conditions, inverse parameter and uncertainty estimations, independent model parameterizations, stochastic descriptions of soil heterogeneity, and 2D or 3D extensions of PNE models. Two-region models are most widely used across all scales; as a stand-alone approach to be used up to the field scale, or as a component of distributed, larger scale models. Studies at all scales suggest that inverse identification of parameters related to PF is generally not possible based on a hydrograph alone. Information on flux-averaged and spatially distributed local resident concentrations is jointly required for quantifying PNE transport. At the column and soil profile scale, model predictions of PF are becoming increasingly realistic through the implementation of the 3D soil structure as derived from hydrogeophysical and tracer techniques. At the field scale, integrating effects of the soil structure and its spatial variability has been attempted by combining 1D PNE approaches with stochastic parameter sampling. At the catchment area scale, the scarcity of data makes validation of PF related model components a task yet to be accomplished. The quest for easily measurable proxy variables, as ‘the missing link’ between soil structure and model parameters, continues in order to improve the practical predictive capability of PF–PNE models. A follow-up paper complementing this manuscript reviews model applications involving non-equilibrium transport of pesticides, as representatives of reactive solutes.

Ratio of Fourth to Second Fingertip Extensions in Relation to Serum Estradiol and Testosterone Levels in Men and Women

Sex differences in second (2D) and fourth (4D) fingertip extensions relative to the middle fingertip and 4D:2D fingertip extension ratios were studied in men and women. Body height positively correlated with index fingertip extensions, not with ring fingertip extensions, nor with their ratio. Mean 2D extension (both hands) was smaller in women than men; mean 4D extension (right hand) was smaller in men than women; 4D:2D fingertip extension ratios from both hands were larger in women than men. Serum estradiol concentration negatively correlated with 2D extensions for both hands (no significant correlation with 4D extensions), but positively correlated with 4D:2D extension ratios for both hands. Serum testosterone concentration positively correlated with 2D extensions of both hands (no significant correlation with 4D extension), but negatively correlated with 4D:2D extension ratios for both hands. These relations were also studied in men and women separately. It was concluded that the 4D:2D extension ratio was greater in women than in men; 2D and 4D extensions and 4D:2D extension ratios may be determined prenatally by sex hormones; fingertip extensions may be predictive of adult and prenatal sex hormone levels.

RATIO OF FOURTH TO SECOND FINGERTIP EXTENSIONS IN RELATION TO SERUM ESTRADIOL AND TESTOSTERONE LEVELS IN MEN AND WOMEN

-Sex differences in second (2D) and fourth (4D) fingertip extensions relative to the middle fingertip and 4D:2D fingertip extension ratios were studied in men and women. Body height positively correlated with index fingertip extensions, not with ring fingertip extensions, nor with their ratio. Mean 2D extension (both hands) was smaller in women than men; mean 4D extension (right hand) was smaller in men than women; 4D:2D fingertip extension ratios from both hands were larger in women than men. Serum estradiol concentration negatively correlated with 2D extensions for both hands (no significant correlation with 4D extensions), but positively correlated with 4D:2D extension ratios for both hands. Serum testosterone concentration positively correlated with 2D extensions of both hands (no significant correlation with 4D extension), but negatively correlated with 4D:2D extension ratios for both hands. These relations were also studied in men and women separately. It was concluded that the 4D:2D extension ratio was greater in women than in men; 2D and 4D extensions and 4D:2D extension ratios may be determined prenatally by sex hormones; fingertip extensions may be predictive of adult and prenatal sex hormone levels.

Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition

The nonlinear normalization (NLN) method based on line density equalization is popularly used in handwritten Chinese character recognition. To overcome the insufficient shape restoration capability of one-dimensional NLN, a pseudo two-dimensional NLN (P2DNLN) method has been proposed and has yielded higher recognition accuracy. The P2DNLN method, however, is very computationally expensive because of the line density blurring of each row/column. In this paper, we propose a new pseudo 2D normalization method using line density projection interpolation (LDPI), which partitions the line density map into soft strips and generate 2D coordinate mapping function by interpolating the 1D coordinate functions that are obtained by equalizing the line density projections of these strips. The LDPI method adds little computational overhead to one-dimensional NLN yet performs comparably well with P2DNLN. We also apply this strategy to extending other normalization methods, including line density projection fitting, centroid-boundary alignment, moment, and bi-moment methods. The latter three methods are directly based on character image instead of line density map. Their 2D extensions provide real-time computation and high recognition accuracy, and are potentially applicable to gray-scale images and online trajectories.

1H 13C two-dimensional chemical shift correlation spectroscopy using DEPT

The DEPT sequence (I) is readily extended to two-dimensional (2D) heteronuclear NMR applications, a number of which we describe briefly. In more detail, we show that it can be extended to ‘H-13C chemical shift correlation spectroscopy, by including appropriate incremented delay periods, but without additional pulses. The method has several valuable advantages over the normal form (2) of ‘H-13C chemical shift correlation spectroscopy. First, provided phase-sensitive 2D software is available, separate CH3, CH2, and CH 2D subspectra, i.e., 2D spectrum editing, may be achieved. Second, phase errors are fully refocused using the DEPT sequence and thus also the 2D DEPT sequence. This enables phase correction in both dimensions, thus improving lineshapes by eliminating the need to do a magnitude calculation as is normally done. Third, the normal form of heteronuclear chemical shift correlation spectroscopy may also be improved by the addition of refocusing pulses so that phase errors are refocused. The resulting 7 or 8 pulse sequences (sequences [C] and [B] of Ref. (3)) may be considered to be 2D extensions of the INEPT (4) sequence. Thus it is also possible to achieve 2D spectrum editing with these methods in the same way as one-dimensional editing using INEPT (3). But the 2D DEPT sequence described here contains fewer pulses and is also more accurate for spectrum editing, just as DEPT is more accurate than INEPT for one-dimensional editing (1).