Predicting non-state terrorism worldwide.
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ABSTRACT:
SUBMITTER: Python A
PROVIDER: S-EPMC8324061 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The Global Terrorism Database (GTD) is an open-source database on terrorist incidents around the world since 1970, and it is maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism (START; College Park, Maryland USA), a US Department of Homeland Security Center of Excellence. The consortium reviews media reports to determine if an event meets eligibility to be categorized as a terrorism incident for entry into the database. OBJECTIVE:The objective of this study was to characterize chemical terrorism incidents reported to the GTD and understand more about the kinds of chemical agents used, the associated morbidity and mortality, the geography of incidents, and the intended targets. METHODS:Chemical terrorism incidents from 1970 through 2015 were analyzed by chemical agent category, injury and fatality, geographic region, and target. RESULTS:During the study period, 156,772 terrorism incidents were reported to the GTD, of which 292 (0.19%) met the inclusion criteria for analysis as a chemical terrorism incident. The reported chemical agent categories were: unknown chemical (30.5%); corrosives (23.3%); tear gas/mace (12.3%); unspecified gas (11.6%); cyanide (8.2%); pesticides (5.5%); metals (6.5%); and nerve gas (2.1%). On average, chemical terrorism incidents resulted in 51 injuries (mean range across agents: 2.5-1,622.0) and seven deaths (mean range across agents: 0.0-224.3) per incident. Nerve gas incidents (2.1%) had the highest mean number of injuries (n = 1,622) and fatalities (n = 224) per incident. The highest number of chemical terrorism incidents occurred in South Asia (29.5%), Western Europe (16.8%), and Middle East/North Africa (13.0%). The most common targets were private citizens (19.5%), of which groups of women (22.8%) were often the specific target. Incidents targeting educational institutions often specifically targeted female students or teachers (58.1%). CONCLUSIONS:Chemical terrorism incidents rarely occur; however, the use of certain chemical terrorism agents, for example nerve gas, can cause large mass-causality events that can kill or injure thousands with a single use. Certain regions of the world had higher frequency of chemical terrorism events overall, and also varied in their frequencies of the specific chemical terrorism agent used. Data suggest that morbidity and mortality vary by chemical category and by region. Results may be helpful in developing and optimizing regional chemical terrorism preparedness activities.
| S-EPMC6800028 | biostudies-literature
Project description:By drawing on large-scale online data we are able to construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent companies, while the links model the flow of employees and the associated transfer of know-how across companies. We use network centrality measures to assess, at an early stage, the likelihood of the long-term positive economic performance of a start-up. We find that the start-up network has predictive power and that by using network centrality we can provide valuable recommendations, sometimes doubling the current state of the art performance of venture capital funds. Our network-based approach supports the theory that the position of a start-up within its ecosystem is relevant for its future success, while at the same time it offers an effective complement to the labour-intensive screening processes of venture capital firms. Our results can also enable policy-makers and entrepreneurs to conduct a more objective assessment of the long-term potentials of innovation ecosystems, and to target their interventions accordingly.
| S-EPMC6962148 | biostudies-literature
Project description:BackgroundSuppressing damaging aggregate behaviors such as insurgency, terrorism, and financial panics are important tasks of the state. Each outcome of these aggregate behaviors is an emergent property of a system in which each individual's action depends on a subset of others' actions, given by each individual's network of interactions. Yet there are few explicit comparisons of strategies for suppression, and none that fully incorporate the interdependence of individual behavior.Methods and findingsHere I show that suppression tactics that do not require the removal of individuals from networks of interactions are nearly always more effective than those that do. I find using simulation analysis of a general model of interdependent behavior that the degree to which such less disruptive suppression tactics are superior to more disruptive ones increases in the propensity of individuals to engage in the behavior in question.ConclusionsThus, hearts-and-minds approaches are generally more effective than force in counterterrorism and counterinsurgency, and partial insurance is usually a better tactic than gag rules in quelling financial panics. Differences between suppression tactics are greater when individual incentives to support terrorist or insurgent groups, or susceptibilities to financial panic, are higher. These conclusions have utility for policy-makers seeking to end bloody conflicts and prevent financial panics. As the model also applies to mass protest, its conclusions provide insight as well into the likely effects of different suppression strategies undertaken by authoritarian regimes seeking to hold on to power in the face of mass movements seeking to end them.
| S-EPMC3076430 | biostudies-literature
Project description:The underlying reasons behind modern terrorism are seemingly complex and intangible. Despite diverse causal mechanisms, research has shown that there exists general statistical patterns at the global scale that can shed light on human confrontation behaviour. While many policing and counter-terrorism operations are conducted at a city level, there has been a lack of research in building city-level resolution prediction engines based on statistical patterns. For the first time, the paper shows that there exist general commonalities between global cities under frequent terrorist attacks. By examining over 30 000 geo-tagged terrorism acts over 7000 cities worldwide from 2002 to today, the results show the following. All cities experience attacks A that are uncorrelated to the population and separated by a time interval t that is negative exponentially distributed with a death-toll per attack that follows a power-law distribution. The prediction parameters yield a high confidence of explaining up to 87% of the variations in frequency and 89% in the death-toll data. These findings show that the aggregate statistical behaviour of terror attacks are seemingly random and memoryless for all global cities. They enabled the author to develop a data-driven city-specific prediction system, and we quantify its information-theoretic uncertainty and information loss. Further analysis shows that there appears to be an increase in the uncertainty over the predictability of attacks, challenging our ability to develop effective counter-terrorism capabilities.
| S-EPMC6774967 | biostudies-literature
Project description:This article investigates whether gender imbalance may be conducive to domestic terrorism in developing countries. A female-dominated society may not provide sufficient administration, law, or order to limit domestic terrorism, especially since societies in developing countries primarily turn to males for administration, policing, and paramilitary forces. Other economic considerations support female imbalance resulting in grievance-generated terrorism. Because male dominance may also be linked to terrorism, empirical tests are ultimately needed to support our prediction. Based on panel data for 128 developing countries for 1975 to 2011, we find that female gender imbalance results in more total and domestic terrorist attacks. This female gender imbalance does not affect transnational terrorism in developing countries or domestic and transnational terrorism in developed countries. Further tests show that gender imbalance affects terrorism only when bureaucratic institutions are weak. Many robustness tests support our results.
| S-EPMC5308407 | biostudies-literature
Project description:Unauthorized immigration, already a divisive and controversial subject in American society, was reframed as a grave national security threat after the terrorist attacks on Sept. 11, 2001. Yet, despite substantial public, political and policy attention to the issue of undocumented immigration and terrorism, there has been relatively little empirical assessment of the relationship between unauthorized immigration flows and terrorist activity. We attempt to fill this gap by combining newly developed estimates of the unauthorized population, a novel use of sentencing and prosecutorial data to measure terrorism-related activity, and multiple data sources on the criminological, socioeconomic, and demographic context from all 50 states from 1990 to 2014. We then leverage this unique dataset to examine the longitudinal, macro-level relationship between undocumented immigration and various measures of terrorism. Results from fixed effects negative binomial models suggest that increased undocumented immigration over this period is not associated with terrorist attacks, radicalization, or terrorism prosecutions.
| S-EPMC7926034 | biostudies-literature
Project description: Not available
| S-EPMC2732458 | biostudies-literature
Project description:DNA methylation is an epigenetic modification, influenced by both genetic and environmental variation, that plays a key role in transcriptional regulation and many organismal phenotypes. Although patterns of DNA methylation have been shown to differ between human populations, it remains to be determined how epigenetic diversity relates to the patterns of genetic and gene expression variation at a global scale. Here we measured DNA methylation at 485,000 CpG sites in five diverse human populations, and analyzed these data together with genome-wide genotype and gene expression data. We found that population-specific DNA methylation mirrors genetic variation, and has greater local genetic control than mRNA levels. We estimated the rate of epigenetic divergence between populations, which indicates far greater evolutionary stability of DNA methylation in humans than has been observed in plants. This study provides a deeper understanding of worldwide patterns of human epigenetic diversity, as well as initial estimates of the rate of epigenetic divergence in recent human evolution.
2017-08-01 | GSE101431 | GEO
Project description:BackgroundIt remains unknown how the COVID-19 pandemic has changed neuro-oncology clinical practice, training, and research efforts.MethodsWe performed an international survey of practitioners, scientists, and trainees from 21 neuro-oncology organizations across 6 continents, April 24-May 17, 2020. We assessed clinical practice and research environments, institutional preparedness and support, and perceived impact on patients.ResultsOf 582 respondents, 258 (45%) were US-based and 314 (55%) international. Ninety-four percent of participants reported changes in their clinical practice. Ninety-five percent of respondents converted at least some practice to telemedicine. Ten percent of practitioners felt the need to see patients in person, specifically because of billing concerns and pressure from their institutions. Sixty-seven percent of practitioners suspended enrollment for at least one clinical trial, including 62% suspending phase III trial enrollments. More than 50% believed neuro-oncology patients were at increased risk for COVID-19. Seventy-one percent of clinicians feared for their own personal safety or that of their families, specifically because of their clinical duties; 20% had inadequate personal protective equipment. While 69% reported increased stress, 44% received no psychosocial support from their institutions. Thirty-seven percent had salary reductions and 63% of researchers temporarily closed their laboratories. However, the pandemic created positive changes in perceived patient satisfaction, communication quality, and technology use to deliver care and mediate interactions with other practitioners.ConclusionsThe pandemic has changed treatment schedules and limited investigational treatment options. Institutional lack of support created clinician and researcher anxiety. Communication with patients was satisfactory. We make recommendations to guide clinical and scientific infrastructure moving forward and address the personal challenges of providers and researchers.
| S-EPMC7928618 | biostudies-literature
Project description:Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).
| S-EPMC8633536 | biostudies-literature