A phase 2 multi-center, open-label, switch-over trial to evaluate the safety and efficacy of Abcertin® in patients with type 1 Gaucher disease.

Gaucher disease is a lysosomal storage disease for which enzyme replacement therapy has proven to be effective. A switch-over clinical trial was performed to evaluate the efficacy and safety of Abcertin® (ISU Abxis, Seoul, Korea) in subjects with type 1 Gaucher disease who were previously treated with imiglucerase. Five Korean patients with type 1 Gaucher disease were enrolled. Previous doses of imiglucerase ranged from 30 to 55 U/kg every other week. The same dose of Abcertin® was administered to all patients for 24 weeks. Primary efficacy endpoints were changes in hemoglobin levels and platelet counts, and the secondary efficacy endpoints included changes in liver and spleen volumes, serum biomarkers, skeletal status and bone mineral density (BMD). During the study period, no statistically significant changes were observed in all parameters including hemoglobin levels and platelet counts, liver and spleen volumes, skeletal status and BMD. Abcertin® administration was continued in three patients for another 24 weeks as an extension of the study. Hemoglobin levels and platelet counts were maintained in all three patients. In conclusion, the efficacy and safety of Abcertin® are similar to those of imiglucerase, and Abcertin® is an effective therapeutic agent for patients with type 1 Gaucher disease (Clinical Trial Registry No. NCT02053896 at www.clinicaltrials.gov).

Applying modified-data mining techniques to assess public transportation vulnerable urban and suburban city areas.

To guarantee the right to move for residents in areas where public transportation is insufficient, research is needed to identify vulnerable areas and prepare measures. This paper defines the vulnerable regions of public transportation within various city types in Korea. In order to identify appropriate areas to apply the Demand Responsive Transit (DRT), the regions with vulnerability were compared with a specific city (Yangsan-si) which already the DRT system was successfully adopted. To collect monthly bus data, web-data crawling method was performed and processed with coordinating program by matching GPS coordinate. The public transportation demand was predicted for each grid cell size (100 m, 250 m, and 500 m) by different methodologies. Various data mining models based on regression were analyzed to predict bus demand of vulnerable areas. Among models, a modified model was suggested to combine Automated machine learning models for high prediction performance. The modified model outperformed other methods as 0.685 and prediction performance was appropriate at 100 m rectangle grid. Regional characters of DRT bus allocation areas were extracted by K-means clustering method and differentiate urban and suburban types. The findings of this study provide valuable insights into conditions that DRT bus stop can be installed. The urban bus stop areas located in metropolitan cities and the suburban bus stop allocation areas located in countryside. The study results can be used as policy data for the successful introduction to prevent social exclusion and improve resident welfare in the future.

Factors affecting injury severity and the number of vehicles involved in a freeway traffic accident: investigating their heterogeneous effects by facility type using a latent class approach.

The number of vehicles involved in a traffic accident can be representative of the severity of the accident and provide profound insight into the diverse factors affecting severity, which cannot be identified through the victim fatality rate. This paper presents an analysis and comparison between the effects of factors affecting injury severity and the number of involved vehicles. In this study, a latent class model was used to investigate the unobserved heterogeneity of the accident factors. Freeway facility types are latent factors that affect the heterogeneity of the effects of accident factors. The class mainly including accidents at the freeway mainline sections included more injury/fatal accidents and multiple-vehicle accidents and more significant accident factor estimation results than the other class including accidents at the tollgates or ramps. Among these factors, night-time, faults made by the driver, and heavy vehicle accidents were found to increase the accident severity. Investigating accident factors affecting both the injury severity and number of involved vehicles is important as the number of people who are injured or dead is likely to increase when multiple vehicles are involved in the accident.

Effects of advanced warning information systems on secondary crash risk under connected vehicle environment.

This study evaluated the impact of an optimal in-vehicle advanced warning information service in a connected vehicle (CV) environment to prevent secondary crashes. Driving simulation experiments were designed and performed to analyze driving behavior. The forward crash situation was reproduced in a simulated highway environment, and the safety effects were assessed based on simulation data from a driving simulator (DS). To explore and analyze the effectiveness of crash notifications from the advanced warning information system (AWIS) for preventing secondary crashes, this study utilized repeated measures of multivariate analysis of variance (MANOVA), repeated measures of ANOVA, paired t-test, and Wilcoxon signed rank test. The results from this paper indicate that a warning information system was effective to prevent secondary crash risks, in general.

How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis.

The connected and automated vehicle (CAV) technologies have made great progresses. It has been commonly accepted that CV or AV technologies would reduce human errors in driving and benefit traffic safety. However, the answer of how many crashes can be prevented because of CV or AV technologies has not reached a consistent conclusion. In order to quantitatively answer this question, this study used meta-analysis to evaluate the safety effectiveness of nine common and important CV or AV technologies, and tested the safety effectiveness of these technologies for six countries. First, 73 studies about the safety impact of CV or AV technologies were filtered out from 826 CAV-related papers or reports. Second, the safety impacts of these technologies with regard to assistant types and triggering times have been compared. It shows AV technologies can play a more significant role than CV technologies, and the technologies with closer triggering time to collision time have greater safety effectiveness. Third, in the meta-analysis, the random effect model was used to evaluate the safety effectiveness, and the funnel plots and trim-and-fill method were used to evaluate and adjust publication bias, so as to objectively evaluate the safety effectiveness of each technology. Then, according to the crash data of six countries, the comprehensive safety effectiveness and compilation of safety effectiveness of the above technologies were calculated. The results show that if all of technologies were implemented in the six countries, the average number of crashes could be reduced by 3.40 million, among which the India would reduce the most (54.24%). Additionally, different countries should develop different development strategies, e.g., USA should prioritize the development of the lane change warning and intersection warning, the UK should prioritize applications related to intersection warning and rear-end warning. Overall, this study provides comprehensive and quantitative understating of the safety effectiveness of CA or AV technologies and would contribute to government, vehicle companies, and agencies in deciding the development priority of CA or AV technologies.

Endogenous commercial driver's traffic violations and freight truck-involved crashes on mainlines of expressway.

Freight truck-involved crashes result in a high mortality rate and significantly impact logistic costs; therefore, many researchers have analyzed the causes of truck-involved traffic crashes. In the existing literature, it was found that truck-involved crashes are affected by factors such as road geometry, weather, driver and vehicle characteristics, and traffic volume based on a variety of statistical methodologies; however, the endogenous impact resulting from driver traffic violation has not been considered. The goal of the study is to discover the factors influencing freight vehicle crashes and develop more accurate crash probability estimation by explaining the endogenous driver traffic violations. To achieve the purpose of this study, we applied the two-stage residual inclusion (2SRI) approach, a methodology used in the nonlinear regression analysis model for capturing the endogeneity issue. This method improves the accuracy of the model by capturing the unobserved effects of driver traffic violations. From the results, traffic violations were identified to be influenced by the driver's physical condition, as well as driver and vehicle characteristics. Furthermore, variables of driver traffic violations such as improper passing, speeding, and safe distance violation were found to be endogenous in the probability model of freight truck crashes on expressway mainlines.

Time series trends of the safety effects of pavement resurfacing.

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time.

A multicenter, open-label, phase III study of Abcertin in Gaucher disease.

BACKGROUND: Gaucher disease (GD) is caused by a deficiency in the lysosomal enzyme glucocerebrosidase. Enzyme replacement therapy (ERT) is recommended for clinical improvement. METHODS: The efficacy and safety of a new imiglucerase, Abcertin, were assessed in 7 Egyptian patients with treatment-naïve type 1 GD. Each patient was administered a biweekly 60 U/kg dose of Abcertin for 6 months. The primary endpoint was the change in hemoglobin concentration. The secondary endpoints were changes from baseline in platelet counts, spleen and liver volumes, biomarker levels, skeletal parameters, and bone mineral density. RESULTS: The hemoglobin concentration increased by a mean of 1.96 ±â€Š0.91 g/dL (range 1.11-2.80 g/dL) or 20.6% (P = .001). Statistically significant increases in the platelet count and decreases in the spleen volume and biomarker levels were also observed. There were no severe drug-related adverse events. One patient developed anti-imiglucerase antibodies without neutralizing activity. CONCLUSION: Our study results demonstrate the efficacy and safety of Abcertin in patients with type 1 GD. This suggests that Abcertin can be an alternative ERT option for type 1 GD.

Evaluation of safety effectiveness of multiple cross sectional features on urban arterials.

This research evaluates the safety effectiveness of multiple roadway cross-section elements on urban arterials for different crash types and severity levels. In order to consider the nonlinearity of predictors and obtain more reliable estimates, the generalized nonlinear models (GNMs) were developed using 5-years of crash records and roadway characteristics data for urban roadways in Florida. The generalized linear models (GLMs) were also developed to compare model performance. The cross-sectional method was used to develop crash modification factors (CMFs) for various safety treatments. The results from this paper indicated that increasing lane, bike lane, median, and shoulder widths were safety effective to reduce crash frequency. In particular, the CMFs for changes in median and shoulder widths consistently decreased as their widths increased. On the other hand, the safety effects of increasing lane and bike lane widths showed nonlinear variations. It was found that crash rates decrease as the lane width increases until 12ft width and it increases as the lane width exceeds 12ft. The crash rates start to decrease again after 13ft. It was also found that crash rates decreases as the bike lane width increases until 6ft width and it increases as the bike lane width exceeds 6ft. This paper demonstrated that the GNMs clearly captured the nonlinear relationship between crashes and multiple roadway cross-sectional features, which cannot be reflected by the estimated CMFs from the GLMs. Moreover, the GNMs showed better model fitness than GLMs in general. Therefore, in order to estimate more accurate CMFs, the proposed methodology of utilizing the GNMs in the cross-sectional method is recommended over using conventional GLMs when there are nonlinear relationships between the crash rate and roadway characteristics.

Use of empirical and full Bayes before-after approaches to estimate the safety effects of roadside barriers with different crash conditions.

INTRODUCTION: Although many researchers have estimated the crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of prior studies that have explored the variation of CMFs. Thus, the main objectives of this study are: (a) to estimate CMFs for the installation of different types of roadside barriers, and (b) to determine the changes of safety effects for different crash types, severities, and conditions. METHOD: Two observational before-after analyses (i.e. empirical Bayes (EB) and full Bayes (FB) approaches) were utilized in this study to estimate CMFs. To consider the variation of safety effects based on different vehicle, driver, weather, and time of day information, the crashes were categorized based on vehicle size (passenger and heavy), driver age (young, middle, and old), weather condition (normal and rain), and time difference (day time and night time). RESULTS: The results show that the addition of roadside barriers is safety effective in reducing severe crashes for all types and run-off roadway (ROR) crashes. On the other hand, it was found that roadside barriers tend to increase all types of crashes for all severities. The results indicate that the treatment might increase the total number of crashes but it might be helpful in reducing injury and severe crashes. In this study, the variation of CMFs was determined for ROR crashes based on the different vehicle, driver, weather, and time information. PRACTICAL APPLICATIONS: Based on the findings from this study, the variation of CMFs can enhance the reliability of CMFs for different roadway conditions in decision making process. Also, it can be recommended to identify the safety effects of specific treatments for different crash types and severity levels with consideration of the different vehicle, driver, weather, and time of day information.

Assessing the safety effects of multiple roadside treatments using parametric and nonparametric approaches.

This study evaluates the safety effectiveness of multiple roadside elements on roadway segments by estimating crash modification factors (CMFs) using the cross-sectional method. To consider the nonlinearity in crash predictors, the study develops generalized nonlinear models (GNMs) and multivariate adaptive regression splines (MARS) models. The MARS is one of the promising data mining techniques due to its ability to consider the interaction impact of more than one variables and nonlinearity of predictors simultaneously. The CMFs were developed for four roadside elements (driveway density, poles density, distance to poles, and distance to trees) and combined safety effects of multiple treatments were interpreted by the interaction terms from the MARS models. Five years of crash data from 2008 to 2012 were collected for rural undivided four-lane roadways in Florida for different crash types and severity levels. The results show that the safety effects decrease as density of driveways and roadside poles increase. The estimated CMFs also indicate that increasing distance to roadside poles and trees reduces crashes. The study demonstrates that the GNMs show slightly better model fitness than negative binomial (NB) models. Moreover, the MARS models outperformed NB and GNM models due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Therefore, it can be recommended that the CMFs are estimated using MARS when there are nonlinear relationships between crash rate and roadway characteristics, and interaction impacts among multiple treatments.

Development of adjustment functions to assess combined safety effects of multiple treatments on rural two-lane roadways.

Numerous studies have attempted to evaluate the safety effectiveness of specific single treatment on roadways by estimating crash modification factors (CMFs). However, there is a need to also assess safety effects of multiple treatments since multiple treatments are usually simultaneously applied to roadways. Due to the lack of sufficient CMFs of multiple treatments, the Highway Safety Manual (HSM) provides combining method for multiple CMFs. However, it is cautioned in the HSM and related sources that combined safety effect of multiple CMFs may be over or under estimated. Moreover, the literature did not evaluate the accuracy of the combining method using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) to estimate CMFs and crash modification functions (CM Functions) for two single treatments (shoulder rumble strips, widening (1-9ft) shoulder width) and combination (installing shoulder rumble strips+widening shoulder width) using the observational before-after with empirical Bayes (EB) method and (2) to develop adjustment factors and functions to assess combined safety effects of multiple treatments based on the accuracy of the combined CMFs for multiple treatments estimated by the existing combining method. Data was collected for rural two-lane roadways in Florida and Florida-specific safety performance functions (SPFs) were estimated for different crash types and severities. The CM Functions and adjustment functions were developed using linear and nonlinear regression models. The results of before-after with EB method show that the two single treatments and combination are effective in reducing total and SVROR (single vehicle run-off roadway) crashes. The results indicate that the treatments were more safety effective for the roadway segments with narrower original shoulder width in the before period. It was found that although the CMFs for multiple treatments (i.e., combination of two single treatments) were generally lower than CMFs for single treatments, they were getting similar to the roadway segments with wider shoulder width. The findings indicate that the combined safety effects of multiple treatments using HSM combining method are mostly over-estimated and the accuracy of HSM combining method vary based on crash types and severity levels. Therefore, it is recommended to develop and apply the adjustment factors and functions to predict the safety effects of multiple treatments when the HSM combining method is used.

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics.

Although many researchers have estimated crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of studies that explored the heterogeneous effects of roadway characteristics on crash frequency among treated sites. Generally, the CMF estimated by before-after studies represents overall safety effects of the treatment in a fixed value. However, as each treated site has different roadway characteristics, there is a need to assess the variation of CMFs among the treated sites with different roadway characteristics through crash modification functions (CMFunctions). The main objective of this research is to determine relationships between the safety effects of adding a bike lane and the roadway characteristics through (1) evaluation of CMFs for adding a bike lane using observational before-after with empirical Bayes (EB) and cross-sectional methods, and (2) development of simple and full CMFunctions which are describe the CMF in a function of roadway characteristics of the sites. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed. Moreover, socio-economic parameters were collected and included in CMFunctions and SPFs (1) to capture the effects of the variables that represent volume of bicyclists and (2) to identify general relationship between the CMFs and these characteristics. In order to achieve better performance of CMFunctions, data mining techniques were used. The results of both before-after and cross-sectional methods show that adding a bike lane on urban arterials has positive safety effects (i.e., CMF<1) for all crashes and bike crashes. It was found that adding a bike lane is more effective in reducing bike crashes than all crashes. It was also found that the CMFs vary across the sites with different roadway characteristics. In particular, annual average daily traffic (AADT), number of lanes, AADT per lane, median width, bike lane width, and lane width are significant characteristics that affect the variation in safety effects of adding a bike lane. Some socio-economic characteristics such as bike commuter rate and population density also have significant effect on the variation in CMFs. The findings suggest that full CMFunctions showed better model fit than simple CMFuncttions since they account for the heterogeneous effects of multiple roadway and socio-economic characteristics. The proposed CMFunctions provide insights into bike lane design and selection of sites for bike lane installation for reducing crashes.

Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions.

Since a crash modification factor (CMF) represents the overall safety performance of specific treatments in a single fixed value, there is a need to explore the variation of CMFs with different roadway characteristics among treated sites over time. Therefore, in this study, we (1) evaluate the safety performance of a sample of urban four-lane roadway segments that have been widened with one through lane in each direction and (2) determine the relationship between the safety effects and different roadway characteristics over time. Observational before-after analysis with the empirical Bayes (EB) method was assessed in this study to evaluate the safety effects of widening urban four-lane roadways to six-lanes. Moreover, the nonlinearizing link functions were utilized to achieve better performance of crash modification functions (CMFunctions). The CMFunctions were developed using a Bayesian regression method including the estimated nonlinearizing link function to incorporate the changes in safety effects of the treatment over time. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed and used for EB estimation. The results indicated that the conversion of four-lane roadways to six-lane roadways resulted in a crash reduction of 15 percent for total crashes, and 24 percent for injury crashes on urban roadways. The results show that the safety effects vary across the sites with different roadway characteristics. In particular, LOS changes, time changes, and shoulder widths are significant parameters that affect the variation of CMFs. Moreover, it was found that narrowing shoulder and median widths to make space for an extra through lane shows a negative safety impact. It was also found that including the nonlinearizing link functions in developing CMFunctions shows more reliable estimates, if the variation of CMFs with specific parameters has a nonlinear relationship. The findings provide insights into the selection of roadway sites for adding through lanes.

Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models.

This study evaluates the effectiveness of changing lane width in reducing crashes on roadway segments. To consider nonlinear relationships between crash rate and lane width, the study develops generalized nonlinear models (GNMs) using 3-years crash records and road geometry data collected for all roadway segments in Florida. The study also estimates various crash modification factors (CMFs) for different ranges of lane width based on the results of the GNMs. It was found that the crash rate was highest for 12-ft lane and lower for the lane width less than or greater than 12ft. GNMs can extrapolate this nonlinear continuous effect of lane width and estimate the CMFs for any lane width, not only selected lane widths, unlike generalized linear models (GLMs) with categorical variables. The CMFs estimated using GNMs reflect that crashes are less likely to occur for narrower lanes if the lane width is less than 12ft whereas crashes are less likely to occur for wider lanes if the lane width is greater than 12ft. However, these effects varied with the posted speed limits as the effect of interaction between lane width and speed limit was significant. The estimated CMFs show that crashes are less likely to occur for lane widths less than 12ft than the lane widths greater than 12ft if the speed limit is higher than or equal to 40mph. It was also found from the CMFs that crashes at higher severity levels (KABC and KAB) are less likely to occur for lane widths greater or less than 12ft compared to 12-ft lane. The study demonstrates that the CMFs estimated using GNMs clearly reflect variations in crashes with lane width, which cannot be captured by the CMFs estimated using GLMs. Thus, it is recommended that if the relationship between crash rate and lane width is nonlinear, the CMFs are estimated using GNMs.

Estimating safety performance trends over time for treatments at intersections in Florida.

Researchers have put great efforts in quantifying Crash Modification Factors (CMFs) for diversified treatment types. In the Highway Safety Manual (HSM), CMFs have been identified to predict safety effectiveness of converting a stop-controlled to a signal-controlled intersection (signalization) and installing Red Light Running Cameras (RLCs). Previous studies showed that both signalization and adding RLCs reduced angle crashes but increased rear-end crashes. However, some studies showed that CMFs varied over time after the treatment was implemented. Thus, the objective of this study is to investigate trends of CMFs for the signalization and adding RLCs over time. CMFs for the two treatments were measured in each month and 90-day moving windows respectively. The ARMA time series model was applied to predict trends of CMFs over time based on monthly variations in CMFs. The results of the signalization show that the CMFs for rear-end crashes were lower at the early phase after the signalization but gradually increased from the 9th month. On the other hand, the CMFs for angle crashes were higher at the early phase after adding RLCs but decreased after the 9th month and then became stable. It was also found that the CMFs for total and fatal/injury crashes after adding RLCs in the first 18 months were significantly greater than the CMFs in the following 18 months. This indicates that there was a lag effect of the treatments on safety performance. The results of the ARMA model show that the model can better predict trends of the CMFs for the signalization and adding RLCs when the CMFs are calculated in 90-day moving windows compared to the CMFs calculated in each month. In particular, the ARMA model predicted a significant safety effect of the signalization on reducing angle and left-turn crashes in the long term. Thus, it is recommended that the safety effects of the treatment be assessed using the ARMA model based on trends of CMFs in the long term after the implementation of the treatment.

Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways.

As multiple treatments (or countermeasures) are simultaneously applied to roadways, there is a need to assess their combined safety effects. Due to a lack of empirical crash modification factors (CMFs) for multiple treatments, the Highway Safety Manual (HSM) and other related studies developed various methods of combining multiple CMFs for single treatments. However, the literature did not evaluate the accuracy of these methods using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) develop CMFs for two single treatments (shoulder rumble strips, widening shoulder width) and one combined treatment (shoulder rumble strips+widening shoulder width) using before-after and cross-sectional methods and (2) evaluate the accuracy of the combined CMFs for multiple treatments estimated by the existing methods based on actual evaluated combined CMFs. Data was collected for rural multi-lane highways in Florida and four safety performance functions (SPFs) were estimated using 360 reference sites for two crash types (All crashes and Single Vehicle Run-off Roadway (SVROR) crashes) and two severity levels (all severity (KABCO) and injury (KABC)). The results of both before-after and cross-sectional methods show that the two single treatments and the combined treatment produced safety improvement. It was found that safety effects were higher for the roadway segments with shoulder rumble strips and wider shoulder width. It was also found that the treatments were more safety effective (i.e. lower CMF) for the roadway segments with narrower original shoulder width in the before period. However, although CMFs for multiple treatments were generally lower than CMFs for single treatments, they were similar for the roadway segments with shoulder width of 8-12 feet. More specifically, CMFs for single treatments were lower than CMFs for multiple treatments for the roadway segments with shoulder width of 9 feet or higher. Among different methods of combining CMFs, the HSM, Systematic Reduction of Subsequent CMFs, Applying only the most effective CMF, and Weighted average of multiple CMFs (Meta-Analysis) showed good estimates of the combined CMFs for multiple treatments with 2.2% difference between actual and estimated CMFs. The findings suggest that the existing methods of combining multiple CMFs are generally valid but they need to be applied for different crash types and injury levels separately. Lastly, an average of the combined CMFs from the best two methods was closer to the actual CMF than the combined CMF from only one best method. This indicates that it is better not to rely on only one specific existing method of combining CMFs for predicting CMF for multiple treatments.

Beta-synuclein inhibits formation of alpha-synuclein protofibrils: a possible therapeutic strategy against Parkinson's disease.

Parkinson's disease (PD) is an age-associated and progressive movement disorder that is characterized by dopaminergic neuronal loss in the substantia nigra and, at autopsy, by fibrillar alpha-synuclein inclusions, or Lewy bodies. Despite the qualitative correlation between alpha-synuclein fibrils and disease, in vitro biophysical studies strongly suggest that prefibrillar alpha-synuclein oligomers, or protofibrils, are pathogenic. Consistent with this proposal, transgenic mice that express human alpha-synuclein develop a Parkinsonian movement disorder concurrent with nonfibrillar alpha-synuclein inclusions and the loss of dopaminergic terminii. Double-transgenic progeny of these mice that also express human beta-synuclein, a homologue of alpha-synuclein, show significant amelioration of all three phenotypes. We demonstrate here that beta- and gamma-synuclein (a third homologue that is expressed primarily in peripheral neurons) are natively unfolded in monomeric form, but structured in protofibrillar form. Beta-synuclein protofibrils do not bind to or permeabilize synthetic vesicles, unlike protofibrils comprising alpha-synuclein or gamma-synuclein. Significantly, beta-synuclein inhibits the generation of A53T alpha-synuclein protofibrils and fibrils. This finding provides a rationale for the phenotype of the double-transgenic mice and suggests a therapeutic strategy for PD.