The Vengeful Victim? Assessing the Attitudes of Victims Participating in Restorative Youth Conferencing

Considers the findings of research into the youth conferencing system in Northern Ireland which assessed the validity of the view that victim participation in sentencing decisions would lead to higher sentences with a greater retributive element. Outlines the victims' reasons for participating in the conference, and their descriptions of their experiences. Analyses the elements of the conference plans negotiated with the victims' participation

Comparing the direct and community-mediated effects of disturbance on plant population dynamics: Flooding, herbivory and Mimulus guttatus

Competition, trophic interactions and abiotic disturbances play important roles in governing plant population dynamics, yet few studies have addressed their relative contributions or interacting effects. We used Life Table Response Experiment (LTRE) analysis, coupled with stochastic analyses, to examine how a major abiotic disturbance, flooding, influences the fitness and population growth of a common riparian plant, Mimulus guttatus, and how this effect compares and interacts with that exerted by herbivory. We also extended LTRE analysis to include nested factors, which enabled us to examine differences across experimental sites. These spatial contributions to changes in population growth rate, λ, were compared and contrasted with those derived for year and experimental treatments. Flooding had direct positive impacts on population growth, while protection from herbivory benefited plants in both flooded and non-flooded areas. Spatial variation in plant performance was also substantial, with greater variation across experimental sites than temporal variation across years. Our stochastic analysis revealed that the impact of herbivores on population growth was much greater when the environment fluctuated between years with and without flooding than in more constant environments. Both flooding and herbivory exerted the majority of their impacts on plant performance via changes in adult summer survival. For flooded sites, this was surprising, given the small difference in summer survival between control and herbivore-exclusion treatments, and results from the high sensitivity of population growth to adult survival. The importance of herbivory in flooded sites would have not been discerned had we not considered how adult survival interacts with other stages of the M. guttatus life cycle. Thus, in order to increase ecological understanding associated with shifts in community dynamics, experimental results should be placed in a life-history context. Within disturbance-driven systems, the direct abiotic effects of factors such as flooding play a critical role in determining population dynamics. However, the biotic interactions that change as a consequence of disturbance can have equal and lasting impacts on population growth. © 2006 The Authors

Novel cruzain inhibitors for the treatment of Chagas' disease.

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas' disease

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Life Stage Simulation Analysis: Estimating Vital-Rate Effects on Population Growth for Conservation

We developed a simulation method, known as life-stage simulation analysis (LSA) to measure potential effects of uncertainty and variation in vital rates on population growth (lambda) for purposes of species conservation planning. Under LSA, we specify plausible or hypothesized levels of uncertainty, variation, and covariation in vital rates fur a given population. We use these data under resampling simulations to establish random combinations of vital rates for a large number of matrix replicates and finally summarize results from the matrix replicates to estimate potential effects of each vital rate on lambda in a probability-based context. Estimates of potential effects are based on a variety of summary statistics, such as frequency of replicates having the same vital rate of highest elasticity, difference in elasticity values calculated under simulated conditions vs, elasticities calculated using mean invariant vital rates, percentage of replicates having positive population growth, and variation in lambda explained by variation in each vital rate. To illustrate, we applied LSA to viral rates for two vertebrates: desert tortoise (Gopherus agassizii) and Greater prairie Chicken (Tympanuchus cupido). Results fur the prairie chicken indicated that a single vital rate consistently had greatest effect on population growth. Results for desert tortoise, however, suggested that a variety of life stages could have strong effects on population growth. Additional simulations for the Greater Prairie Chicken under a hypothetical conservation plan also demonstrated that a variety of vital rates could be manipulated to achieve desired population growth. To improve the reliability of inference, we recommend that potential effects of vital rates on lambda be evaluated using a probability-based approach like LSA. LSA is an important complement to other methods that evaluate vital-rate effects on lambda, including classical elasticity analysis, retrospective methods of variance decomposition, and simulation of the effects of environmental stochasticity

Dose, exposure time, and resolution in Serial X-ray Crystallography

The resolution of X-ray diffraction microscopy is limited by the maximum dose that can be delivered prior to sample damage. In the proposed Serial Crystallography method, the damage problem is addressed by distributing the total dose over many identical hydrated macromolecules running continuously in a single-file train across a continuous X-ray beam, and resolution is then limited only by the available molecular and X-ray fluxes and molecular alignment. Orientation of the diffracting molecules is achieved by laser alignment. We evaluate the incident X-ray fluence (energy/area) required to obtain a given resolution from (1) an analytical model, giving the count rate at the maximum scattering angle for a model protein, (2) explicit simulation of diffraction patterns for a GroEL-GroES protein complex, and (3) the frequency cut off of the transfer function following iterative solution of the phase problem, and reconstruction of an electron density map in the projection approximation. These calculations include counting shot noise and multiple starts of the phasing algorithm. The results indicate counting time and the number of proteins needed within the beam at any instant for a given resolution and X-ray flux. We confirm an inverse fourth power dependence of exposure time on resolution, with important implications for all coherent X-ray imaging. We find that multiple single-file protein beams will be needed for sub-nanometer resolution on current third generation synchrotrons, but not on fourth generation designs, where reconstruction of secondary protein structure at a resolution of 0.7 nm should be possible with short exposures.Comment: 19 pages, 7 figures, 1 tabl

Voltage tuning of vibrational mode energies in single-molecule junctions

Vibrational modes of molecules are fundamental properties determined by intramolecular bonding, atomic masses, and molecular geometry, and often serve as important channels for dissipation in nanoscale processes. Although single-molecule junctions have been employed to manipulate electronic structure and related functional properties of molecules, electrical control of vibrational mode energies has remained elusive. Here we use simultaneous transport and surface-enhanced Raman spectroscopy measurements to demonstrate large, reversible, voltage-driven shifts of vibrational mode energies of C60 molecules in gold junctions. C60 mode energies are found to vary approximately quadratically with bias, but in a manner inconsistent with a simple vibrational Stark effect. Our theoretical model suggests instead that the mode shifts are a signature of bias-driven addition of electronic charge to the molecule. These results imply that voltage-controlled tuning of vibrational modes is a general phenomenon at metal-molecule interfaces and is a means of achieving significant shifts in vibrational energies relative to a pure Stark effect.Comment: 23 pages, 4 figures + 12 pages, 7 figures supporting materia

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US