The online competition between pro- and anti-vaccination views.

Distrust in scientific expertise1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2-4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9-11. There is a lack of understanding about how this distrust evolves at the system level13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.

Shockwavelike Behavior across Social Media.

Online communities featuring "anti-X" hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwavelike solutions explain how, why, and when such activity rises from "out-of-nowhere," and show how it can be delayed, reshaped, and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.

Generalized Gelation Theory Describes Onset of Online Extremist Support.

We introduce a generalized form of gelation theory that incorporates individual heterogeneity and show that it can explain the asynchronous, sudden appearance and growth of online extremist groups supporting ISIS (so-called Islamic State) that emerged globally post-2014. The theory predicts how heterogeneity impacts their onset times and growth profiles and suggests that online extremist groups present a broad distribution of heterogeneity-dependent aggregation mechanisms centered around homophily. The good agreement between the theory and empirical data suggests that existing strategies aiming to defeat online extremism under the assumption that it is driven by a few "bad apples" are misguided. More generally, this generalized theory should apply to a range of real-world systems featuring aggregation among heterogeneous objects.

Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors.

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

New understanding of multidrug efflux and permeation in antibiotic resistance, persistence, and heteroresistance.

Antibiotics effective against Gram-negative ESKAPE pathogens are a critical area of unmet need. Infections caused by these pathogens are not only difficult to treat but finding new therapies to overcome Gram-negative resistance is also a challenge. There are not enough antibiotics in development that target the most dangerous pathogens and there are not enough novel drugs in the pipeline. The major obstacle in the antibiotic discovery pipeline is the lack of understanding of how to breach antibiotic permeability barriers of Gram-negative pathogens. These barriers are created by active efflux pumps acting across both the inner and the outer membranes. Overproduction of efflux pumps alone or together with either modification of the outer membrane or antibiotic-inactivating enzymes and target mutations contribute to clinical levels of antibiotics resistance. Recent efforts have generated significant advances in the rationalization of compound efflux and permeation across the cell envelopes of Gram-negative pathogens. Combined with earlier studies and novel mathematical models, these efforts have led to a multilevel understanding of how antibiotics permeate these barriers and how multidrug efflux and permeation contribute to the development of antibiotic resistance and heteroresistance. Here, we discuss the new developments in this area.

Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to understand the structure and dynamics of this complex pathogen. The spike glycoprotein of SARS-CoV-2 is a significant target for immunogens as it is the means by which the virus enters human cells, while simultaneously sporting mutations responsible for immune escape. These functional and escape processes are regulated by complex molecular-level interactions. Our study presents quantitative insights on domain and residue contributions to allosteric communication, immune evasion, and local- and global-level control of functions through the derivation of a weighted graph representation from all-atom MD simulations. Focusing on the ancestral form and the D614G-variant, we provide evidence of the utility of our approach by guiding the selection of a mutation that alters the spike's stability. Taken together, the network approach serves as a valuable tool to evaluate communication "hot-spots" in proteins to guide design of stable immunogens.

Organic Zener diodes: tunneling across the gap in organic semiconductor materials.

Organic Zener diodes with a precisely adjustable reverse breakdown from -3 to -15 V without any influence on the forward current-voltage curve are realized. This is accomplished by controlling the width of the charge depletion zone in a pin-diode with an accuracy of one nanometer independently of the doping concentration and the thickness of the intrinsic layer. The breakdown effect with its exponential current voltage behavior and a weak temperature dependence is explained by a tunneling mechanism across the highest occupied molecular orbital-lowest unoccupied molecular orbital gap of neighboring molecules. The experimental data are confirmed by a minimal Hamiltonian model approach, including coherent tunneling and incoherent hopping processes as possible charge transport pathways through the effective device region.

Microscopic Approach to Intrinsic Antibiotic Resistance.

The emergence of multidrug resistance in Gram-negative pathogens is critically determined by the interplay between efflux pumps activity and low permeation outer membrane. Although phenotypic heterogeneity in isogenic cells is recognized as a key factor of treatment failure, a mathematical framework able to integrate growth dynamics and single-cell heterogeneity in antimicrobial resistance, remains absent. Here we provide such framework that bridges single-cell and colony scales in the context of bacterial survival and efficacy against drugs. Using experimental inputs, our approach produces testable outputs and reveals nontrivial collective effects with key implications for fitness and survival of the colony. This framework provides a mathematical tool to test stress response strategies in organisms that can potentially guide experiments in natural and synthetic cellular systems.

Predictive Rules of Efflux Inhibition and Avoidance in Pseudomonas aeruginosa.

Antibiotic-resistant bacteria rapidly spread in clinical and natural environments and challenge our modern lifestyle. A major component of defense against antibiotics in Gram-negative bacteria is a drug permeation barrier created by active efflux across the outer membrane. We identified molecular determinants defining the propensity of small peptidomimetic molecules to avoid and inhibit efflux pumps in Pseudomonas aeruginosa, a human pathogen notorious for its antibiotic resistance. Combining experimental and computational protocols, we mapped the fate of the compounds from structure-activity relationships through their dynamic behavior in solution, permeation across both the inner and outer membranes, and interaction with MexB, the major efflux transporter of P. aeruginosa We identified predictors of efflux avoidance and inhibition and demonstrated their power by using a library of traditional antibiotics and compound series and by generating new inhibitors of MexB. The identified predictors will enable the discovery and optimization of antibacterial agents suitable for treatment of P. aeruginosa infections.IMPORTANCE Efflux pump avoidance and inhibition are desired properties for the optimization of antibacterial activities against Gram-negative bacteria. However, molecular and physicochemical interactions defining the interface between compounds and efflux pumps remain poorly understood. We identified properties that correlate with efflux avoidance and inhibition, are predictive of similar features in structurally diverse compounds, and allow researchers to distinguish between efflux substrates, inhibitors, and avoiders in P. aeruginosa The developed predictive models are based on the descriptors representative of different clusters comprising a physically intuitive combination of properties. Molecular shape (represented by acylindricity), amphiphilicity (anisotropic polarizability), aromaticity (number of aromatic rings), and the partition coefficient (LogD) are physicochemical predictors of efflux inhibitors, whereas interactions with Pro668 and Leu674 residues of MexB distinguish between inhibitors/substrates and efflux avoiders. The predictive models and efflux rules are applicable to compounds with unrelated chemical scaffolds and pave the way for development of compounds with the desired efflux interface properties.

Getting closer to the goal by being less capable.

Understanding how systems with many semi-autonomous parts reach a desired target is a key question in biology (e.g., Drosophila larvae seeking food), engineering (e.g., driverless navigation), medicine (e.g., reliable movement for brain-damaged individuals), and socioeconomics (e.g., bottom-up goal-driven human organizations). Centralized systems perform better with better components. Here, we show, by contrast, that a decentralized entity is more efficient at reaching a target when its components are less capable. Our findings reproduce experimental results for a living organism, predict that autonomous vehicles may perform better with simpler components, offer a fresh explanation for why biological evolution jumped from decentralized to centralized design, suggest how efficient movement might be achieved despite damaged centralized function, and provide a formula predicting the optimum capability of a system's components so that it comes as close as possible to its target or goal.

Individual heterogeneity generating explosive system network dynamics.

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Impact of body mass index on the predictive capacity of the COPD-6 device in the detection of airflow obstruction. / Impacto del índice de masa corporal sobre la capacidad predictiva del dispositivo COPD-6 en la detección de obstrucción al flujo aéreo.

BACKGROUND AND OBJECTIVE: The portable COPD-6 device has been validated as a screening tool for airflow obstruction through the quantification of the FEV1/FEV6 ratio. To date, however, the impact of body mass index (BMI) on its ability to predict airflow obstruction has not been evaluated. The aim of the study was to assess the predictive ability of COPD-6 to detect airflow obstruction based on the patient's BMI. MATERIAL AND METHOD: A prospective and open cohort study in which 223 subjects who underwent conventional spirometry and COPD-6 were included. The area under the curve ROC (AUC) of FEV1/FEV6 was analysed in the detection of obstruction for all patients in addition to BMI (BMI<30kg/m2 and BMI≥30kg/m2). Sensitivity and specificity, negative and positive predictive value as well as likelihood ratio were calculated to determine the cut-off point of COPD-6 FEV1/FEV6 ratio with greater predictive capacity. RESULTS: The COPD-6 allows ruling out airflow obstruction with AUC of the estimated ROC curve of 88% (95% CI 83-93). The cut-off point FEV1/FEV6 of 0.74-0.76 shows the best predictive capacity. However, this capacity is altered according to BMI with an increase in false positives in subjects with BMI≥30kg/ m2 when using the same cut-off point for the whole sample. CONCLUSION: The choice of cut-off point FEV1/FEV6 for the detection of obstruction should be adjusted to the patient's BMI.

Cardiovascular comorbidity in patients with chronic obstructive pulmonary disease in the Canary Islands (CCECAN study). / Comorbilidad cardiovascular en pacientes con enfermedad pulmonar obstructiva crónica en Canarias (estudio CCECAN).

INTRODUCTION: Numerous studies have shown a high prevalence of cardiovascular disease in patients with chronic obstructive pulmonary disease (COPD). The aim of this study was to analyse the prevalence of cardiovascular risk factors and comorbidity in a Canary Islands population diagnosed with COPD, and compared it with data from the general population. PATIENTS AND METHODS: A cross-sectional study was carried out in 300 patients with COPD and 524 subjects without respiratory disease (control group). The two groups were compared using standard bivariate methods. Logistic regression models were used to estimate the cardiovascular risks in COPD patients compared to control group. RESULTS: Patients with COPD showed a high prevalence of hypertension (72%), dyslipidaemia (73%), obesity (41%), diabetes type 2 (39%), and sleep apnoea syndrome (30%) from mild stages of the disease (GOLD 2009). There was a 22% prevalence of cardiac arrhythmia, 16% of ischaemic heart disease, 16% heart failure, 12% peripheral vascular disease, and 8% cerebrovascular disease. Compared to the control group, patients with COPD had a higher risk of dyslipidaemia (OR 3.24, 95% CI; 2.21-4.75), diabetes type 2 (OR 1.52, 95% CI; 1.01-2,28), and ischaemic heart disease (OR 2.34, 95% CI; 1.22-4.49). In the case of dyslipidaemia, an increased risk was obtained when adjusted for age, gender, and consumption of tobacco (OR 5.04, 95% CI; 2.36-10.74). CONCLUSIONS: Patients with COPD resident in the Canary Islands have a high prevalence of hypertension, dyslipidaemia, ischaemic heart disease, and cardiac arrhythmia. Compared to general population, patients with COPD have a significant increase in the risk of dyslipidaemia.

Atypical viral dynamics from transport through popular places.

The flux of visitors through popular places undoubtedly influences viral spreading-from H1N1 and Zika viruses spreading through physical spaces such as airports, to rumors and ideas spreading through online spaces such as chat rooms and social media. However, there is a lack of understanding of the types of viral dynamics that can result. Here we present a minimal dynamical model that focuses on the time-dependent interplay between the mobility through and the occupancy of such spaces. Our generic model permits analytic analysis while producing a rich diversity of infection profiles in terms of their shapes, durations, and intensities. The general features of these theoretical profiles compare well to real-world data of recent social contagion phenomena.

Open source data reveals connection between online and on-street protest activity.

There is enormous interest in inferring features of human behavior in the real world from potential digital footprints created online - particularly at the collective level, where the sheer volume of online activity may indicate some changing mood within the population regarding a particular topic. Civil unrest is a prime example, involving the spontaneous appearance of large crowds of otherwise unrelated people on the street on a certain day. While indicators of brewing protests might be gleaned from individual online communications or account content (e.g. Twitter, Facebook) societal concerns regarding privacy can make such probing a politically delicate issue. Here we show that instead, a simple low-level indicator of civil unrest can be obtained from online data at the aggregate level through Google Trends or similar tools. Our study covers countries across Latin America during 2011-2014 in which diverse civil unrest events took place. In each case, we find that the combination of the volume and momentum of searches from Google Trends surrounding pairs of simple keywords, tailored for the specific cultural setting, provide good indicators of periods of civil unrest. This proof-of-concept study motivates the search for more geographically specific indicators based on geo-located searches at the urban level.

Women's connectivity in extreme networks.

A popular stereotype is that women will play more minor roles than men as environments become more dangerous and aggressive. Our analysis of new longitudinal data sets from offline and online operational networks [for example, ISIS (Islamic State)] shows that although men dominate numerically, women emerge with superior network connectivity that can benefit the underlying system's robustness and survival. Our observations suggest new female-centric approaches that could be used to affect such networks. They also raise questions about how individual contributions in high-pressure systems are evaluated.

Internal character dictates transition dynamics between isolation and cohesive grouping.

We show that accounting for internal character among interacting heterogeneous entities generates rich transition behavior between isolation and cohesive dynamical grouping. Our analytical and numerical calculations reveal different critical points arising for different character-dependent grouping mechanisms. These critical points move in opposite directions as the population's diversity decreases. Our analytical theory may help explain why a particular class of universality is so common in the real world, despite the fundamental differences in the underlying entities. It also correctly predicts the nonmonotonic temporal variation in connectivity observed recently in one such system.

Extreme alien light allows survival of terrestrial bacteria.

Photosynthetic organisms provide a crucial coupling between the Sun's energy and metabolic processes supporting life on Earth. Searches for extraterrestrial life focus on seeking planets with similar incident light intensities and environments. However the impact of abnormal photon arrival times has not been considered. Here we present the counterintuitive result that broad classes of extreme alien light could support terrestrial bacterial life whereas sources more similar to our Sun might not. Our detailed microscopic model uses state-of-the-art empirical inputs including Atomic Force Microscopy (AFM) images. It predicts a highly nonlinear survivability for the basic lifeform Rsp. Photometricum whereby toxic photon feeds get converted into a benign metabolic energy supply by an interplay between the membrane's spatial structure and temporal excitation processes. More generally, our work suggests a new handle for manipulating terrestrial photosynthesis using currently-available extreme value statistics photon sources.

Simple mathematical law benchmarks human confrontations.

Many high-profile societal problems involve an individual or group repeatedly attacking another - from child-parent disputes, sexual violence against women, civil unrest, violent conflicts and acts of terror, to current cyber-attacks on national infrastructure and ultrafast cyber-trades attacking stockholders. There is an urgent need to quantify the likely severity and timing of such future acts, shed light on likely perpetrators, and identify intervention strategies. Here we present a combined analysis of multiple datasets across all these domains which account for >100,000 events, and show that a simple mathematical law can benchmark them all. We derive this benchmark and interpret it, using a minimal mechanistic model grounded by state-of-the-art fieldwork. Our findings provide quantitative predictions concerning future attacks; a tool to help detect common perpetrators and abnormal behaviors; insight into the trajectory of a 'lone wolf'; identification of a critical threshold for spreading a message or idea among perpetrators; an intervention strategy to erode the most lethal clusters; and more broadly, a quantitative starting point for cross-disciplinary theorizing about human aggression at the individual and group level, in both real and online worlds.

Nueva clasificación de las fracturas expuestas. Experiencia de 5,207 casos en el Hospital de Traumatología ®Victorio de la Fuente Narváez¼. Parte 1 / A new classificaction for open fractures as a result of the experience of 5,207 cases in the ®Victori De la fuente Narváez¼ Trauma Hospital. Part 1

Este es el primero de una serie de reportes acerca de fracturas expuestas y se presenta la estadística de 5,207 casos de fracturas expuestas de 5,072 pacientes atendidos de enero de 1991 a diciembre de 1997 en el Hospital de Traumatología ®Victorio de la Fuente Narváez¼ de la Ciudad de México. Se trata de un estudio retrospectivo, longitudinal, descriptivo, observacional. Presenta una nueva clasificación de fracturas expuestas que está basado en la clasificación de Gustilo, la cual se modificó porque en nuestro medio no es aplicable a la totalidad de los pacientes. Toma en cuenta la lesión de las partes blandas, el grado de energía absorbida por el hueso manifestada a través del trazo de fractura y el grado de contaminación. La clasificación consta de cuatro grupos en un total de diez tipos, se agrega además un tipo especial que son las que se producen en pacientes inmunodeprimidos y en aquéllos que tienen insuficiencia vascular previa; cada grupo tiene caractetísticas propias y los tipos están dados por los aspectos de la lesión, y también da una idea del tratamiento y del pronóstico. Asimismo hace posible la correlación del tipo de la fractura con la aparición de complicaciones (infecciones, pseudoartrosisi y amputaciones). Se presenta el protocolo general del manejo de las fracturas expuestas. Los resultados obtenidos muestra resultados satisfactorios en general. El porcentaje de infección es de 4.4 por ciento en general; se presenta el desglose de la infección por tipo de exposición, la psuedoartrosis se presentó en 5.8 por ciento, el porcentaje de amputaciones varía según el tipo de exposición. Hemos encontrado que nuestra clasificación tiene una correlación entre el tipo de exposición y el pronóstico de la fractura