Universality of political corruption networks

Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades. We show that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefcients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals. We propose a simple model that not only reproduces these empirical properties but reveals also that corruption networks operate near a critical recidivism rate below which the network is entirely fragmented and above which it is overly connected. Our research thus indicates that actions focused on decreasing corruption recidivism may substantially mitigate this type of organized crime

Machine learning partners in criminal networks

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among diferent types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with signifcant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior

Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are capable to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.Comment: 14 two-column pages, 5 figure

State of the Art in the Studies on Crotamine, a Cell Penetrating Peptide from South American Rattlesnake

Animal venoms comprise a naturally selected cocktail of bioactive peptides/proteins and other molecules, each of which playing a defined role thanks to the highly specific interactions with diverse molecular targets found in the prey. Research focused on isolation, structural, and functional characterizations of novel natural biologics (bioactive peptides/proteins from natural sources) has a long way to go through from the basic science to clinical applications. Herein, we overview the structural and functional characteristics of the myoneurotoxin crotamine, firstly isolated from the South American rattlesnake venom. Crotamine is the first venom peptide classified as a natural cell penetrating and antimicrobial peptide (CPP and AMP) with a more pronounced antifungal activity. in contrast to other known natural CPPs and AMPs, crotamine demonstrates a wide spectrum of biological activities with potential biotechnological and therapeutic values. More recent studies have demonstrated the selective in vitro anticancer activity of crotamine. in vivo, using a murine melanoma model, it was shown that crotamine delays tumor implantation, inhibits tumor cells proliferation, and also increases the survival of mice engrafted with subcutaneous melanoma. the structural and functional properties and also the possible biotechnological applications of minimized molecules derived from crotamine are also discussed.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Inst Butantan, Genet Lab, BR-05503900 São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Farmacol, São Paulo, BrazilUniv Fed Ceara, Labomar Inst Ciencias Mar, Fortaleza, CE, BrazilUniv Estado Amazonas, Manaus, AM, BrazilCBA, Lab Bioquim & Biol Mol, Manaus, AM, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Farmacol, São Paulo, BrazilWeb of Scienc

Hexagonal Hybrid Bismuthene by Molecular Interface Engineering

High-quality devices based on layered heterostructures are typically built from materials obtained by complex solid-state physical approaches or laborious mechanical exfoliation and transfer. Meanwhile, wet-chemically synthesized materials commonly suffer from surface residuals and intrinsic defects. Here, we synthesize using an unprecedented colloidal photocatalyzed, one-pot redox reaction a few-layers bismuth hybrid of “electronic grade” structural quality. Intriguingly, the material presents a sulfur-alkyl-functionalized reconstructed surface that prevents it from oxidation and leads to a tuned electronic structure that results from the altered arrangement of the surface. The metallic behavior of the hybrid is supported by ab initio predictions and room temperature transport measurements of individual nanoflakes. Our findings indicate how surface reconstructions in two-dimensional (2D) systems can promote unexpected properties that can pave the way to new functionalities and devices. Moreover, this scalable synthetic process opens new avenues for applications in plasmonics or electronic (and spintronic) device fabrication. Beyond electronics, this 2D hybrid material may be of interest in organic catalysis, biomedicine, or energy storage and conversion.This work has been supported by the European Union (ERC-2018-StG 804110-2D-PnictoChem & and ERC Proof of Concept Grant 101101079-2D4H2 to G.A.; ERC-2021-StG 101042680 2D-SMARTiES awarded to J.J.B.), the Spanish MICINN (PID2019-111742GA-I00, PID2020–115100GB–I00, MRR/PDC2022-133997-I00, TED2021-131347B-I00, and Excellence Unit María de Maeztu CEX2019-000919-M), and the Generalitat Valenciana (CIDEGENT/2018/001, CIDEGENT/2018/005, and CDEIGENT/2019/022). Financial support by Severo Ochoa centre of excellence program (CEX2021–001230–S) is gratefully acknowledged. M.K. and H.B.W. acknowledge support by the Deutsche Forschungsgemeinschaft (DFG), under Projektnummer 182849149 (SFB 953, projects B08 and B13). Electron microscopy work carried out at UCM (M.V., G.S.S.) sponsored by MICINN PID2021-122980OB-C51 and Comunidad de Madrid MAD2D-CM-UCM3. G.S.S. acknowledges financial support from Spanish MCI Grant Nos. RTI2018-099054-J-I00 (MCI/AEI/FEDER, UE) and IJC2018-038164-I. C.D. and Y.M.E. thank the cluster of excellence 3DMM2O funded by DFG under Germany’s Excellence Strategy – 2082/1 – 390761711 for financial support. The authors thank Lukas Grünwald and Erich Müller for helpful discussions. A.M.R. thanks the Spanish MIU (Grant No FPU21/04195). A.S.-D. thanks the Universidad de Valencia, for an ‘Atracción del talento’ predoctoral grant. F.G.-P. thanks ITQ, UPV–CSIC for concession of a contract (PAID 01-18)

New Results from the Cryogenic Dark Matter Search Experiment

Using improved Ge and Si detectors, better neutron shielding, and increased counting time, the Cryogenic Dark Matter Search (CDMS) experiment has obtained stricter limits on the cross section of weakly interacting massive particles (WIMPs) elastically scattering from nuclei. Increased discrimination against electromagnetic backgrounds and reduction of neutron flux confirm WIMP-candidate events previously detected by CDMS were consistent with neutrons and give limits on spin-independent WIMP interactions which are >2X lower than previous CDMS results for high WIMP mass, and which exclude new parameter space for WIMPs with mass between 8-20 GeV/c^2.Comment: 4 pages, 4 figure

Rarity of monodominance in hyperdiverse Amazonian forests.

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors

Diversity of lactic acid bacteria of the bioethanol process

<p>Abstract</p> <p>Background</p> <p>Bacteria may compete with yeast for nutrients during bioethanol production process, potentially causing economic losses. This is the first study aiming at the quantification and identification of Lactic Acid Bacteria (LAB) present in the bioethanol industrial processes in different distilleries of Brazil.</p> <p>Results</p> <p>A total of 489 LAB isolates were obtained from four distilleries in 2007 and 2008. The abundance of LAB in the fermentation tanks varied between 6.0 × 10⁵and 8.9 × 10⁸CFUs/mL. Crude sugar cane juice contained 7.4 × 10⁷to 6.0 × 10⁸LAB CFUs. Most of the LAB isolates belonged to the genus <it>Lactobacillus </it>according to rRNA operon enzyme restriction profiles. A variety of <it>Lactobacillus </it>species occurred throughout the bioethanol process, but the most frequently found species towards the end of the harvest season were <it>L. fermentum </it>and <it>L. vini</it>. The different rep-PCR patterns indicate the co-occurrence of distinct populations of the species <it>L. fermentum </it>and <it>L. vini</it>, suggesting a great intraspecific diversity. Representative isolates of both species had the ability to grow in medium containing up to 10% ethanol, suggesting selection of ethanol tolerant bacteria throughout the process.</p> <p>Conclusions</p> <p>This study served as a first survey of the LAB diversity in the bioethanol process in Brazil. The abundance and diversity of LAB suggest that they have a significant impact in the bioethanol process.</p