Global comparison of warring groups in 2002–2007: fatalities from targeting civilians vs. fighting battles

Background Warring groups that compete to dominate a civilian population confront contending behavioral options: target civilians or battle the enemy. We aimed to describe degrees to which combatant groups concentrated lethal behavior into intentionally targeting civilians as opposed to engaging in battle with opponents in contemporary armed conflict. Methodology/Principal Findings We identified all 226 formally organized state and non-state groups (i.e. actors) that engaged in lethal armed conflict during 2002–2007: 43 state and 183 non-state. We summed civilians killed by an actor's intentional targeting with civilians and combatants killed in battles in which the actor was involved for total fatalities associated with each actor, indicating overall scale of armed conflict. We used a Civilian Targeting Index (CTI), defined as the proportion of total fatalities caused by intentional targeting of civilians, to measure the concentration of lethal behavior into civilian targeting. We report actor-specific findings and four significant trends: 1.) 61% of all 226 actors (95% CI 55% to 67%) refrained from targeting civilians. 2.) Logistic regression showed actors were more likely to have targeted civilians if conflict duration was three or more years rather than one year. 3.) In the 88 actors that targeted civilians, multiple regressions showed an inverse correlation between CTI values and the total number of fatalities. Conflict duration of three or more years was associated with lower CTI values than conflict duration of one year. 4.) When conflict scale and duration were accounted for, state and non-state actors did not differ. We describe civilian targeting by actors in prolonged conflict. We discuss comparable patterns found in nature and interdisciplinary research. Conclusions/Significance Most warring groups in 2002–2007 did not target civilians. Warring groups that targeted civilians in small-scale, brief conflict concentrated more lethal behavior into targeting civilians, and less into battles, than groups in larger-scale, longer conflict

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

© Springer Nature Switzerland AG 2018. A major problem in the field of peace and conflict studies is to extract events from a variety of news sources. The events need to be coded with an event type and annotated with entities from a domain specific ontology for future retrieval and analysis. The problem is dynamic in nature, characterised by new or changing groups and targets, and the emergence of new types of events. A number of automated event extraction systems exist that detect thousands of events on a daily basis. The resulting datasets, however, lack sufficient coverage of specific domains and suffer from too many duplicated and irrelevant events. Therefore expert event coding and validation is required to ensure sufficient quality and coverage of a conflict. We propose a new framework for semi-automatic rule-based event extraction and coding based on the use of deep syntactic-semantic patterns created from normal user input to an event annotation system. The method is implemented in a prototype Event Coding Assistant that processes news articles to suggest relevant events to a user who can correct or accept the suggestions. Over time as a knowledge base of patterns is built, event extraction accuracy improves and, as shown by analysis of system logs, the workload of the user is decreased

Frontiers in pigment cell and melanoma research.

In this perspective, we identify emerging frontiers in clinical and basic research of melanocyte biology and its associated biomedical disciplines. We describe challenges and opportunities in clinical and basic research of normal and diseased melanocytes that impact current approaches to research in melanoma and the dermatological sciences. We focus on four themes: (1) clinical melanoma research, (2) basic melanoma research, (3) clinical dermatology, and (4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology. Significantly, this document encapsulates important advances in melanocyte and melanoma research including emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, epidemiology, pigment biophysics and chemistry, and evolution