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.     

Radiochemical Synthesis of 4-[18F]FluorobenzylAzide and Its Conjugation with EGFR-Specific Aptamers

Central nervous system tumors related to gliomas are of neuroectodermal origin and cover about 30% of all primary brain tumors. Glioma is not susceptible to any therapy and surgical attack remains one of the main approaches to its treatment. Preoperative tumor imaging methods, such as positron emission tomography (PET), are currently used to distinguish malignant tissue to increase the accuracy of glioma removal. However, PET is lacking a specific visualization of cells possessing certain molecular markers. Here, we report an application of aptamers to enhancing specificity in imaging tumor cells bearing the epidermal growth factor receptor (EGFR). Glioblastoma is characterized by increased EGFR expression, as well as mutations of this receptor associated with active division, migration, and adhesion of tumor cells. Since 2021, EGFR has been included into the WHO classification of gliomas as a molecular genetic marker. To obtain conjugates of aptamers GR20 and GOL1-specific to EGFR, a 4-[¹⁸F]fluorobenzylazide radiotracer was used as a synthon. For the production of the synthon, a method of automatic synthesis on an Eckert & Ziegler research module was adapted and modified using spirocyclic iodonium ylide as a precursor. Conjugation of 4-[¹⁸F]fluorobenzylazide and alkyne-modified aptamers was carried out using Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) with/without the TBTA ligand. As a result, it was possible to obtain ¹⁸F-labelled conjugates with 97% radiochemical purity for [¹⁸F]FB-GR20 and 98% for [¹⁸F]FB-GOL1. The obtained conjugates can be used for further studies in PET analysis on model animals with grafted glioblastoma.     

Metal ion binding by a bicyclic diamide: deep UV Raman spectroscopic characterization

Deep UV resonance Raman spectroscopy was used for characterizing ligand-metal ion complexes. The obtained results demonstrated a strong intrinsic sensitivity and selectivity of a Raman spectroscopic signature of a bicyclic diamide, a novel chelating agent for lanthanides and actinides (Lumetta, G. J.; Rapko, B. M.; Garza, P. A.; Hay, B. P.; Gilbertson, R. D.; Weakley, T. J. R.; Hutchison, J. E. J. Am. Chem. Soc. 2002, 124, 5644). Molecular modeling, which included structure optimization and calculation of Raman frequencies and resonance intensities, allowed for assigning all strong Raman bands of the bicyclic diamide as well as predicting the band shifts observed because of complex formation with metal ions. A comparative analysis of Raman spectra and the results of the molecular modeling could be used for elucidating the structure of complexes in solution.     

Recent Advances on the Total Syntheses of Communesin Alkaloids and Perophoramidine

The communesin alkaloids are a diverse family of Penicillium‐derived alkaloids. Their caged‐polycyclic structure and intriguing biological profiles have made these natural products attractive targets for total synthesis. Similarly, the ascidian‐derived alkaloid, perophoramidine, is structurally related to the communesins and has also become a popular target for total synthesis. This review serves to summarize the many elegant approaches that have been developed to access the communesin alkaloids and perophoramidine. Likewise, strategies to access the communesin ring system are reviewed.     

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.     

Raman microspectroscopic chemical mapping and chemometric classification for the identification of gunshot residue on adhesive tape

A novel approach utilizing automated Raman microspectroscopic mapping for gunshot residue (GSR) detection was investigated. A well-established technique for GSR recovery (tape lifting) was utilized for GSR particle collection. Uncontaminated samples of the substrate (tape), organic GSR (OGSR), and inorganic GSR (IGSR) particles were characterized to generate three respective Raman spectroscopic training sets. Automated Raman mapping was used to rapidly collect spectra over areas of the tape substrate populated with GSR particles. Raman spectra collected from the maps were classified against the training sets via partial least squares discriminant analysis (PLS-DA) to determine if GSR was present. We report the application of Raman chemical mapping as a proof of concept for the positive detection of GSR particles of varying morphologies. The estimated size of GSR particles, which could be readily detected by this method, is about 3.4 μm. The efficiency of the classification was quantitated with rates of true positives and negatives. Validation studies scrutinizing the practicality of this approach as a viable tool for potential forensics investigations are currently in progress.