On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Correction: Click activated protodrugs against cancer increase the therapeutic potential of chemotherapy through local capture and activation

Correction for ‘Click activated protodrugs against cancer increase the therapeutic potential of chemotherapy through local capture and activation’ by Kui Wu et al., Chem. Sci., 2021, 12, 1259–1271, DOI: 10.1039/D0SC06099B.

The authors regret that the reference to the bond-breaking bioorthogonal chemistry, termed ‘click-to-release’ was omitted from the original article. In addition, we would like to include a reference describing the synthesis of compound 1, which is an intermediate to the protodrugs described in the original article. These references are listed below as ref. 1 and 2.The Royal Society of Chemistry apologizes for these errors and any consequent inconvenience to authors and readers.     

Lipids from the Marine Alga <Emphasis Type="Italic">Gracilaria verrucosa</Emphasis>

The composition of lipids and fatty acids from the red alga Gracilaria verrucosa, for which a high content of 20:4n-6 acid is typical, was studied. The principal lipids were digalactosyldiacylglycerides, phosphatidylcholines (PC), monogalactosyldiacylglyderides (MGDG), and sulfoquinovosyldiacylglycerides, the fraction of each was approximately the same. Sphingophospholipids, inositephosphoceramides, were identified among the polar lipids. Each lipid class differed in the ratio of fatty acids (FA). The FA of all glycerolipids contained 20:4n-6 acid but its concentration was greatest in MGDG and PC, 67.2% and 56.5% of the acid mass.__________Translated from Khimiya Prirodnykh Soedinenii, No. 3, pp. 230–232, May–June, 2005.     

Room temperature MgI2-catalyzed Friedel–Crafts reaction between electron-rich (het)arenes and ethyl glyoxylate

Magnesium iodide-catalyzed addition of electron-rich (het)arenes to ethyl glyoxylate proceeds at room temperature with high chemoselectivity to afford ethyl 2-(het)aryl- 2-hydroxyacetates in yields up to 95%.     

NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

NIFTy , “Numerical Information Field Theory,” is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high‐dimensional real‐world problems. This paper introduces NIFTy  3, a major upgrade to the original NIFTy  framework. NIFTy  3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n‐dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy  3 is able to handle non‐scalar fields, such as vector or tensor fields. The functionality and performance of the software package is demonstrated with example code, which implements a mock inference inspired by a real‐world algorithm from the realm of information field theory. NIFTy  3 is open‐source software available under the GNU General Public License v3 (GPL‐3) at https://gitlab.mpcdf.mpg.de/ift/NIFTy/tree/NIFTy_3 .     

Experimental and Theoretical Evidence of Spin-Orbit Heavy Atom on the Light Atom 1H NMR Chemical Shifts Induced through H⋅⋅⋅I− Hydrogen Bond

Spin-orbit (SO) heavy-atom on the light-atom (SO-HALA) effect is the largest relativistic effect caused by a heavy atom on its light-atom neighbors, leading, for example, to unexpected NMR chemical shifts of ¹H, ¹³C, and ¹⁵N nuclei. In this study, a combined experimental and theoretical evidence for the SO-HALA effect transmitted through hydrogen bond is presented. Solid-state NMR data for a series of 4-dimethylaminopyridine salts containing I⁻, Br⁻ and Cl⁻ counter ions were obtained experimentally and by theoretical calculations. A comparison of the experimental chemical shifts with those calculated by a standard DFT methodology without the SO contribution to the chemical shifts revealed a remarkable error of the calculated proton chemical shift of a hydrogen atom that is in close contact with the iodide anion. The addition of the relativistic SO correction in the calculations significantly improves overall agreement with the experiment and confirms the propagation of the SO-HALA effect through hydrogen bonds.     

Calorimetric study of poly(2-ethylhexyl acrylate) over the range from T → 0 to 350 K

For the first time, the heat capacity $ C_{\text{p}}^{^\circ } $ of poly(2-ethylhexyl acrylate) has been studied in an adiabatic vacuum calorimeter between 7 and 350 K, the standard thermodynamic functions: heat capacity $ C_{\text{p}}^{^\circ } $ (T), enthalpy H°(T) ? H°(0), entropy S°(T) ? S°(0), Gibbs function G°(T) ? H°(0) have been calculated from T → 0 to 350 K. The energy of combustion Δ_c U of the compound under study has been measured in a calorimeter with a stationary bomb and an isothermal shell. The standard enthalpy of combustion Δ_c H° and thermodynamic parameters of formation—enthalpy Δ_f H°, entropy Δ_f S°, Gibbs function Δ_f G°—at T = 298.15 K have been calculated. The results have been used to calculate the thermodynamic characteristics of 2-ethylhexyl acrylate bulk polymerization into poly(2-ethylhexyl acrylate) over the range from T → 0 to 350 K.     

The Effect of Pea Chloroplast Alignment and Variation of Excitation Wavelength on the Circularly Polarized Chlorophyll Luminescence

Circularly polarized luminescence (CPL) is a powerful technique to study the macroorganization of photosynthetic light-harvesting apparatus in vivo and in vitro. It is particularly useful for monitoring environmental stress induced molecular re-organization of thylakoid membranes in green leaves. The current study focuses on two questions which are important to perform and interpret such experiments: how does CPL depend on the excitation wavelength and how on the orientation of the granal thylakoids. CPL and circular dichroism (CD) of pea chloroplasts were complementarily applied when chloroplasts were either in suspension or trapped in a polyacrylamide gel (PAAG) after alignment in a magnetic field. In contrast to the CD spectrum, the CPL signal was found to be independent of the excitation wavelength in both the Soret and the Qy absorption region for chloroplasts in both suspension and PAAG. The improved resolution of luminescence measurements revealed a relatively small negative CPL band in addition to the previously described large positive band. No effect of photoselection upon excitation on the CPL spectra was detected. The CPL intensity at 690 nm at the edge of the granal thylakoids was found to be higher than at the face of the grana suggesting the CPL anisotropy.