Hit-and-run Research Articles

Hit-and-run or hit-and-stay? Unintended effects of a stricter BAC limit.

Although they comprise a relatively small subset of all traffic deaths, hit-and-run (HR) fatalities are both contemptible and preventable. We analyze longitudinal data from 1982 to 2008 to examine the effects of blood alcohol concentration (BAC) laws on HR traffic fatalities. Our results suggest that lower BAC limits may have an unintended consequence of increasing HR fatalities, whereas a similar effect is absent for non-HR fatalities. Specifically, we find that the adoption of a 0.08 BAC limit is associated to an 8.3% increase in HR fatalities. This unintended effect is more pronounced in urban areas and during weekends, which are typical settings for HR incidents.

Enhanced conformational exploration of protein loops using a global parameterization of the backbone geometry.

Flexible loops are paramount to protein functions, with action modes ranging from localized dynamics contributing to the free energy of the system, to large amplitude conformational changes accounting for the repositioning whole secondary structure elements or protein domains. However, generating diverse and low energy loops remains a difficult problem. This work introduces a novel paradigm to sample loop conformations, in the spirit of the hit-and-run (HAR) Markov chain Monte Carlo technique. The algorithm uses a decomposition of the loop into tripeptides, and a novel characterization of necessary conditions for Tripeptide Loop Closure to admit solutions. Denoting m the number of tripeptides, the algorithm works in an angular space of dimension 12 m. In this space, the hyper-surfaces associated with the aforementioned necessary conditions are used to run a HAR-like sampling technique. On classical loop cases up to 15 amino acids, our parameter free method compares favorably to previous work, generating more diverse conformational ensembles. We also report experiments on a 30 amino acids long loop, a size not processed in any previous work.

Improved polytope volume calculations based on Hamiltonian Monte Carlo with boundary reflections and sweet arithmetics

Computing the volume of a high dimensional polytope is a fundamental problem in geometry, also connected to the calculation of densities of states in statistical physics, and a central building block of such algorithms is the method used to sample a target probability distribution. This paper studies Hamiltonian Monte Carlo (HMC) with reflections on the boundary of a domain, providing an enhanced alternative to Hit-and-run (HAR) to sample a target distribution restricted to the polytope. We make three contributions. First, we provide a convergence bound, paving the way to more precise mixing time analysis. Second, we present a robust implementation based on multi-precision arithmetic, a mandatory ingredient to guarantee exact predicates and robust constructions. We however allow controlled failures to happen, introducing the Sweeten Exact Geometric Computing (SEGC) paradigm. Third, we use our HMC random walk to perform H-polytope volume calculations, using it as an alternative to HAR within the volume algorithm by Cousins and Vempala. The systematic tests conducted up to dimension $n$ = 100 on the cube, the isotropic and the standard simplex show that HMC significantly outperforms HAR both in terms of accuracy and running time. Additional tests show that calculations may be handled up to dimension $n$ = 500. These tests also establish that multiprecision is mandatory to avoid exits from the polytope.

Discovering novel cancer bio-markers in acquired lapatinib resistance using Bayesian methods.

Signalling transduction pathways (STPs) are commonly hijacked by many cancers for their growth and malignancy, but demystifying their underlying mechanisms is difficult. Here, we developed methodologies with a fully Bayesian approach in discovering novel driver bio-markers in aberrant STPs given high-throughput gene expression (GE) data. This project, namely 'PathTurbEr' (Pathway Perturbation Driver) uses the GE dataset derived from the lapatinib (an EGFR/HER dual inhibitor) sensitive and resistant samples from breast cancer cell lines (SKBR3). Differential expression analysis revealed 512 differentially expressed genes (DEGs) and their pathway enrichment revealed 13 highly perturbed singalling pathways in lapatinib resistance, including PI3K-AKT, Chemokine, Hippo and TGF-$\beta $ singalling pathways. Next, the aberration in TGF-$\beta $ STP was modelled as a causal Bayesian network (BN) using three MCMC sampling methods, i.e. Neighbourhood sampler (NS) and Hit-and-Run (HAR) sampler that potentially yield robust inference with lower chances of getting stuck at local optima and faster convergence compared to other state-of-art methods. Next, we examined the structural features of the optimal BN as a statistical process that generates the global structure using $p_1$-model, a special class of Exponential Random Graph Models (ERGMs), and MCMC methods for their hyper-parameter sampling. This step enabled key drivers identification that drive the aberration within the perturbed BN structure of STP, and yielded 34, 34 and 23 perturbation driver genes out of 80 constituent genes of three perturbed STP models of TGF-$\beta $ signalling inferred by NS, HAR and MH sampling methods, respectively. Functional-relevance and disease-relevance analyses suggested their significant associations with breast cancer progression/resistance.

A skewed logistic model of two-unit bicycle-vehicle hit-and-run crashes

Objective Hit-and-run behavior in crashes is a severe offense worldwide because the identification and emergency rescue of any injured road user is delayed. A motorist’s run from the crash scene is especially serious for a cyclist who would be more prone to be physically injured in a bicycle-vehicle (BV) crash. The objective of this paper is to explore potential risk factors that contribute to the hit-and-run (HR) behavior of a driver after a two-unit BV collision. Methods The data used in this study are extracted from traffic crash records in the city of Durham, North Carolina in 2007–2014. This study uses the skewed logistic (Scobit) model to account for the skewness of the dependent variable (i.e., HR) in the dataset. Results The Likelihood ratio test, AIC and BIC results show that the Scobit model is preferred to the standard binary logistic model for modeling a driver’s decision to run from a two-unit BV crash scene. Estimation results indicate that, the driver’s tendency to run from a crash scene without reporting it in Durham increases if the bicyclist is a teenager or an adult, a drunk-driving or a speeding driver is involved, when the crash happens at night (19:00–6:59), on a local street, or when the automobile overtakes the bicycle. HR behavior will decrease if the cyclist is drunk, an SUV is involved, or the bicyclist fails to yield. Conclusions The findings of this study are important and useful when developing countermeasures to prevent BV-HR crashes and to improve cycling safety.

Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment

Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes

A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average.

Regulating Road Traffic Violation by Youth in India: A Therapeutic Jurisprudential Approach

Traffic Violations (except grave offences like hit-and–run cases) are considered as petty violations of laws in Indian context. The punishments prescribed for traffic rule violations stem out majorly from Motor Vehicles Act,1988 (amended in 1994) and partly from the provisions of Indian Penal Code. But such sentences may remain limited to pecuniary fines or jail terms for a minimum period. In most cases, the offender may not have to undergo the later especially when the provision provides option between pecuniary fines and jail term by including “or” in the penal portion. This article claims that such nature of punishment may not serve actual purpose of traffic violation penology because the offender may get away only with pecuniary fines and may repeat the offence since he/she may know that he/she may always get away with ‘just fines’. This article offers solution for this from Therapeutic Jurisprudential perspectives using the Safe driving center model proposed by Winnick & Perez(2009).

Identifying factors related to a hit-and-run after a vehicle-bicycle collision

Abstract Purpose Understanding bicycle-vehicle collisions that result in hit-and-run (HAR) behavior is an important concern for multiple disciplines, such as public health, transportation safety, enforcement, and affected individuals. If bicyclists are injured, this issue has implications for expedient access to medical care and for protection from the financial burden of associated injury and property costs. This study aimed to identify significant predictors of vehicle-bicycle HARs, the results of which help inform interdisciplinary prevention interventions. Method Data were collected from Boston Police Department bicycle collision reports for 2009–2012. The data identified whether a collision was a HAR and other predictor variables including road and bicyclist characteristics. The probability of a HAR was fit to selected variables through logistic regression models. Effects of the predictors were reported as odds ratios. Results Police reported data from Boston, Massachusetts were examined for the years 2009 to 2012. Of the 1646 bike-vehicle collisions, 6% (n=93) resulted in a HAR and 80% (n=1309) involved an injury to the bicyclist. Controlling for all other variables, the odds of a HAR did not differ when the bicyclist was injured versus not injured or male versus female. The odds of a HAR were 2.40 (95% CI: 1.31, 4.23) times more likely when the vehicle was a taxi versus another type of vehicle, 1.65 (95% CI: 1.08, 2.54) times more likely during night as during daylight hours, and 1.74 (95%: 1.07, 2.66) times more likely during the weekend versus during the week. The interactions of male-by-injured, taxi-by-injured, and night-by-weekend were nonsignificant. Conclusion The probability of a HAR partially depends on time, day of the week, and whether the vehicle type was a taxi. We discuss implications for policies and interventions aimed at preventing this type of collision and crime.

Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects

ABSTRACTObjective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR.Methods: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model.Results: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime.Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.

Economic Shocks and Rebel Tactics: Evidence from Colombia

Why do rebels vary their tactics? Some insurgents employ terrorism and hit-and run attacks; others wage conventional wars against state rivals. Although recent scholarship studies how the technologies of rebellion differ across insurgencies, we have few explanations for why rebel tactics vary within conflicts. I argue rebels’ tactical choices reflect the incentives and imperatives of three constraints: economic opportunities of non-combatants, state strength, and rebel capacity. To test if these constraints shape the character of internal violence, I study microdata on rebel violence in Colombia and exploit plausibly random shocks to local income from coffee, oil and coca production. I find evidence that local economic shocks substantially affect rebel tactics. Specifically, when government forces benefit from local windfalls and economic opportunities for civilians improve, insurgents favor irregular tactics. On the other hand, when rebels are strengthened, they favor conventional tactics. These results are robust to accounting for numerous potential sources of bias, including atmospheric dispersion of illicit crop herbicides, violence spillovers from drug trafficking, and foreign military aid shocks. The main findings also support my claim that economic constraints bind rebel tactics, challenge prominent theories of insurgency, and are relevant to the comparative study of political violence, with important implications for scholars and policy makers.

Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding.

The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR) Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA), the second on linear programming (LP) and finally we employ the Lovazs ellipsoid method (LEM). Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.

Notes on ‘Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis’

In our previous work published in this journal, we showed how the Hit-And-Run (HAR) procedure enables efficient sampling of criteria weights from a space formed by restricting a simplex with arbitrary linear inequality constraints. In this short communication, we note that the method for generating a basis of the sampling space can be generalized to also handle arbitrary linear equality constraints. This enables the application of HAR to sampling spaces that do not coincide with the simplex, thereby allowing the combined use of imprecise and precise preference statements. In addition, it has come to our attention that one of the methods we proposed for generating a starting point for the Markov chain was flawed. To correct this, we provide an alternative method that is guaranteed to produce a starting point that lies within the interior of the sampling space.

Dust from collisions: A way to probe the composition of exo-planets?

In order to link infrared observations of dust formed during planet formation in debris disks to mid-infrared spectroscopic data of planetary materials from differentiated terrestrial and asteroidal bodies, we obtained absorption spectra of a representative suite of terrestrial crustal and mantle materials, and of typical martian meteorites.A series of debris disk spectra characterized by a strong feature in the 9.0–9.5μm range (HD23514, HD15407a, HD172555 and HD165014), is comparable to materials that underwent shock, collision or high temperature events. These are amorphous materials such as tektites, SiO2-glass, obsidian, and highly shocked shergottites as well as inclusions from mesosiderites (group A).A second group (BD+20307, Beta Pictoris, HD145263, ID8, HD113766, HD69830, P1121, and Eta Corvi) have strong pyroxene and olivine bands in the 9–12μm range and is very similar to ultramafic rocks (e.g. harzburgite, dunite) (group B).This could indicate the occurrence of differentiated materials similar to those in our Solar System in these other systems.However, mixing of projectile and target material, as well as that of crustal and mantle material has to be taken into account in large scale events like hit-and-run and giant collisions or even large-scale planetary impacts. This could explain the olivine-dominated dust of group B.The crustal-type material of group A would possibly require the stripping of upper layers by grazing-style hit-and run encounters or high energy events like evaporation/condensation in giant collisions. In tidal disruptions or the involvement of predominantly icy/water bodies the resulting mineral dust would originate mainly in one of the involved planetesimals. This could allow attributing the observed composition to a specific body (such as e.g. Eta Corvi).

Dynamics of Bcl-xL in Water and Membrane: Molecular Simulations

The Bcl2 family of proteins is capable of switching the apoptotic machinery by directly controlling the release of apoptotic factors from the mitochondrial outer membrane. They have ‘pro’ and ‘anti’-apoptotic subgroups of proteins which antagonize each other’s function; however a detailed atomistic understanding of their mechanisms based on the dynamical events, particularly in the membrane, is lacking. Using molecular dynamics simulations totaling 1.6µs we outline the major differences between the conformational dynamics in water and in membrane. Using implicit models of solvent and membrane, the simulated results reveal a picture that is in agreement with the ‘hit-and run’ concept which states that BH3-only peptides displace the tail (which acts as a pseudo substrate of the protein itself) from its binding pocket; this helps the membrane association of the protein after which the BH3 peptide becomes free. From simulations, Bcl-xL appears to be auto-inhibited by its C-terminal tail that embeds into and covers the hydrophobic binding pocket. However the tail is unable to energetically compete with BH3-peptides in water. In contrast, in the membrane, neither the tail nor the BH3-peptides are stable in the binding pocket and appear to be easily dissociated off as the pocket expands in response to the hydrophobic environment. This renders the binding pocket large and open, thus receptive to interactions with other protein partners. Principal components of the motions are dramatically different in the aqueous and in the membrane environments and provide clues regarding the conformational transitions that Bcl-xL undergoes in the membrane, in agreement with the biochemical data.