A nonhuman primate model of inherited retinal disease.

Inherited retinal degenerations are a common cause of untreatable blindness worldwide, with retinitis pigmentosa and cone dystrophy affecting approximately 1 in 3500 and 1 in 10,000 individuals, respectively. A major limitation to the development of effective therapies is the lack of availability of animal models that fully replicate the human condition. Particularly for cone disorders, rodent, canine, and feline models with no true macula have substantive limitations. By contrast, the cone-rich macula of a nonhuman primate (NHP) closely mirrors that of the human retina. Consequently, well-defined NHP models of heritable retinal diseases, particularly cone disorders that are predictive of human conditions, are necessary to more efficiently advance new therapies for patients. We have identified 4 related NHPs at the California National Primate Research Center with visual impairment and findings from clinical ophthalmic examination, advanced retinal imaging, and electrophysiology consistent with achromatopsia. Genetic sequencing confirmed a homozygous R565Q missense mutation in the catalytic domain of PDE6C, a cone-specific phototransduction enzyme associated with achromatopsia in humans. Biochemical studies demonstrate that the mutant mRNA is translated into a stable protein that displays normal cellular localization but is unable to hydrolyze cyclic GMP (cGMP). This NHP model of a cone disorder will not only serve as a therapeutic testing ground for achromatopsia gene replacement, but also for optimization of gene editing in the macula and of cone cell replacement in general

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Flag and Boost Theories for Hot Spot Forecasting: An Application of NIJ’s Real-Time Crime Forecasting Algorithm using Colorado Springs Crime Data

By operationalizing two theoretical frameworks, we forecast crime hot spots in Colorado Springs. First, we use a population heterogeneity (flag) framework to find places where the hot spot forecasting is consistently successful over months. Second, we use a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm is implemented in Microsoft Excel®, making it simple to apply and completely transparent. Results shows high accuracy and high efficiency in hot spot forecasting, even if the data set and the type of crime we used in this study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non-serious crime for forecasting are different from each other. We also find that the spatial patterns of forecasted hot spots are different between calls for service and crime event. Future research should consider both flag and boost theories in hot spot forecasting

Crime and Land use in Pittsburgh: A Micro-size Grid-cell Analysis of the Influence of Land-uses on Area Crime

Though substantial amount of research of routine activities/opportunity theory investigated the relationship between land use and crime, very few studies considered various types of land uses at the micro-scale of area. Using 2013 crime data geocoded on the 500-ft2 grid cells overlaid on Pittsburgh, results from multivariate regression models show that certain types of facility such as retail shops, schools and bus stops increase the number of crimes at grid cells. Further results show that, net of the socioeconomic factors, the number of crimes in a grid cell varies both by facility and crime type. However, potential guardianship and target suitability of the facility are not found to have significant influence on the number of crimes in grid cells. Attention to various types of land uses across the city is required to help effective allocation of social control resources against crime

Why Your Bar Has Crime but Not Mine: Resolving the Land Use and Crime – Risky Facility Conflict

Interpretations of two bodies of crime-place research conflict. Land use and crime studies claim particular facilities increase crime. Risky facilities studies show most places of a single type have little or no crime, but a few of that type have a great deal of crime. How can a facility be generally criminogenic and mostly safe? To resolve this conflict, we make use of the fact that a single owner can own multiple facilities and each owner may have consistent management practices in their facilities. We first replicate findings of earlier land use studies with crime and land parcel data from Cincinnati. Second, we cluster land parcels by property owners and re-estimate the land use and crime relationship. The links between land use and crime in the replication decline or disappear after clustering. Findings suggest owners’ place management influences crime at their places, thus resolving the conflict between the two bodies of research

A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting

Real-time crime hot spot forecasting presents challenges to policing. There is a high volume of hot spot misclassifications and a lack of theoretical support for forecasting algorithms, especially in disciplines outside the fields of criminology and criminal justice. Transparency is particularly important as most hot spot forecasting models do not provide their underlying mechanisms. To address these challenges, we operationalize two different theories in our algorithm to forecast crime hot spots over Portland and Cincinnati. First, we use a population heterogeneity framework to find places that are consistent hot spots. Second, we use a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm is implemented in Excel, making it extremely simple to apply and completely transparent. Our forecasting models show high accuracy and high efficiency in hot spot forecasting in both Portland and Cincinnati context. We suggest previously developed hot spot forecasting models need to be reconsidered

Compared to what? Estimating the relative concentration of crime at places using systematic and other reviews

Abstract Background That crime is concentrated at a few places is well established by over 44 studies. This is true whether one examines addresses or street segments. Additionally, crime is concentrated among offenders and victims. Many physical, biological, and social phenomena are concentrated as well. This raises a question: is crime more or less concentrated at places than other phenomena? If it is not, then crime concentration maybe the result of standard ubiquitous processes that operate in nature. If crime is more or is less concentrated than other phenomena, then researchers need to ask why. Methods We synthesize results from three systematic reviews and review other literatures to provide preliminary answers. Results We find that although crime is more concentrated at addresses than other spatial units, this is due to the fact that more addresses have no crime than is true of larger units. When only places with one or more crimes are examined, place crime is no more concentrated than other spatial unit crime. Crime appears to be concentrated at places at about the same level as it is concentrated among offenders or victims. And crime concentration does not appear to be peculiarly concentrated compared to non-crime related phenomena. Conclusions The concentration of crime at places is unexceptional, and should be treated as one manifestation of a general tendency of things to be concentrated

How concentrated is crime at places? A systematic review from 1970 to 2015

Abstract Background Despite the increasing awareness and interests about the importance of crime concentration at places, scholars have not comprehensively synthesized the body of evidence related to this thesis. We conduct a systematic review and meta-analysis of the evidence that crime is concentrated among places. Methods We identified 44 studies that empirically examined crime concentration at place and provided quantitative information sufficient for analysis. We organized data using visual binning and fitted logarithmic curves to the median values of the bins. We examine concentration in two conditions: when all places are studied (prevalence), and when only places with at least one crime are studied (frequency). Results We find that crime is concentrated at a relatively few places in both conditions. We also compared concentration for calls for services to reported crime incidents. Calls for services appear more concentrated than crime at places. Because there are several ways place is defined, we compared different units of analysis. Crime is more concentrated at addresses than other units, including street segments. We compared crime concentration over time and found less concentration in 2000s compared to 1980s and 1990s. We also compared crime concentration between U.S. and non-U.S. countries and found more concentration in U.S. Finally, violent crime is more concentrated than property crime. Conclusions Though we systematically reviewed a comprehensive list of studies, summarizing this literature is problematic. Not only should more systematic reviews be conducted as more research becomes available, but future inquiries should examine other ways of summarizing these studies that could challenge our findings