Zhi Xiao

Li Wang

Fuhao Mo
email: fuhaomo@hnu.edu.cn

Xiaojiang Lv

Chunhui Yang

Influences of impact scenarios and vehicle front-end design on head injury risk of motorcyclist

Keywords: Impact, Motorcyclist, Head injury, Vehicle front end shape, Multi-body system

Motorcycle to vehicle collision is one of the most common accidents in the world and usually leads to serious or fatal head injuries to motorcyclists. This study aims to investigate the influences of impact scenarios and vehicle front-end design parameters on head injury risk of the motorcyclist. Five general vehicle types and different impact scenarios were selected for a parametric analysis. Impact scenarios were set according to ISO 13232 regulation considering impact angles and impact speeds. Five vehicle types of Sedan, MPV (Multi-Purpose Vehicle), SUV (Sport Utility Vehicle), EV (Electric Vehicle) and 1-Box vehicle were included. HIC 15 (Head Injury Criterion), head angular acceleration and CSDM (Cumulative Strain Damage Measure) were calculated to evaluate head injury risk of the motorcyclist. The results show that the critical impact speed for HIC 15 and head angular acceleration was around 15 m/s, while the critical speed for CSDM was approximately 10 m/s. Impact angle of 45° show extremely high injury risk to the motorcyclist head. Bonnet leading edge height and its combination with other parameter presented high influences on motorcyclist head injuries, and the increasing the bonnet leading edge height can potentially reduce head injury risk of motorcyclists. In summary, the present research results provide some theoretic bases for determining the test speed in motorcycle-vehicle crash regulation and design consideration for typical vehicle front end shape.

Introduction

About 1.25 million people are killed on the roads every year, which leads to an economical loss of more than 50 billion dollars in each year (WHO, 2016). Among the lives lost to road traffic accidents, motorcyclists account for 14.6% of total road-user fatalities in European Union, 12.1% in Australia, 9.4% in the USA and 9.2% of total traffic deaths in Japan (WHO, 2016).

Head injury is one of the most common injuries occurred in motorcyclist accidents and is estimated to be the cause of death in more than 50% of the motorcyclist fatalities [START_REF] Chevalier | Study of the possible relationships between tramway front-end geometry and pedestrian injury risk[END_REF][START_REF] Macleod | An evidence-based review: Helmet efficacy to reduce head injury and mortality in motorcycle crashes: EAST practice management guidelines[END_REF][START_REF] Raslavičius | Assessment of bicycle-car accidents under four different types of collision[END_REF]. And one-third of these victims present solo head injuries [START_REF] Macleod | An evidence-based review: Helmet efficacy to reduce head injury and mortality in motorcycle crashes: EAST practice management guidelines[END_REF]. The epidemiological investigation also shows that most of motorcyclist accidents are frequently involved in the crashes with the passenger cars [START_REF] Berg | Motorcycle crash tests-an overview[END_REF]. Thus, it is important to study the head injury risk of motorcyclist during the impacts against vehicle.

Comprehensive studies on the impact safety of motorcyclist, cyclist and pedestrian are available in open literatures mainly through in-depth accident investigation along with field data [START_REF] Berg | Analysis of the passive safety of motorcycles using accident investigations and crash tests[END_REF][START_REF] Serre | The motorcyclist impact against a light vehicle: Epidemiological, accidentological and biomechanic analysis[END_REF] , numerical simulation [START_REF] Chinn | COST 327 Motorcycle Safety Helmets[END_REF][START_REF] Mukherjee | Motorcycle-car side impact simulation[END_REF][START_REF] Toma | Assessing motorcycle crash-related head injuries using finite element simulations[END_REF][START_REF] Xu | Simulative investigation on head injuries of electric self-balancing scooter riders subject to ground impact[END_REF] and mechanical test [START_REF] Schaper | Motorcycle collisions with passenger cars-analyses of impact mechanism, kinematics, and effectiveness of full face safety helmets[END_REF]. It was revealed by computational and experimental methods that impact speed [START_REF] Cialdai | Motorcycle-to-car impact: Influence of the mass of the rider in the calculation of the relative impact velocity[END_REF]Simms and Wood, 2006b), impact angle [START_REF] Nie | A study of fatality risk and head dynamic response of cyclist and pedestrian based on passenger car accident data analysis and simulations[END_REF][START_REF] Serre | Numerical analysis of the impact between a ptw rider and a car in different accident configuration motorcycle-car side impact simulation[END_REF]) , vehicle shapes (Li et al., 2017a;Li et al., 2017b) and impact location [START_REF] Mukherjee | Motorcycle-car side impact simulation[END_REF][START_REF] Schaper | Motorcycle collisions with passenger cars-analyses of impact mechanism, kinematics, and effectiveness of full face safety helmets[END_REF][START_REF] Serre | The motorcyclist impact against a light vehicle: Epidemiological, accidentological and biomechanic analysis[END_REF] have great influences on the head injuries. However, the studies on motorcyclist head injuries are still insufficient. Effects of impact scenarios and vehicle front-end shape design on motorcyclist injuries are still not fully clarified. In addition, head injuries are evaluated mainly by HIC (Head Injury Criterion) based on head linear acceleration in previous studies, while head angular acceleration was the primary reason related to diffuse axonal injury (DAI) and subdural hematoma (SDH) of the human head according to previous pedestrian safety investigations [START_REF] Kleiven | Influence of direction and duration of impacts to the human head evaluated using the finite element method[END_REF][START_REF] Zhang | A proposed injury threshold for mild traumatic brain injury[END_REF]. The relation of motorcyclist head injuries to head angular acceleration was rarely reported. Therefore, it is important to analyze the head injuries caused by angular acceleration in motorcycle accidents.

The objective of the present study is to investigate the influences of different impact scenarios and vehicle front-end design on head injury risk of motorcyclists during motorcycle-vehicle impact environments in consideration of both head linear acceleration and angular acceleration.

Five general vehicle types, different impact speeds of both motorcycles and vehicles, and different impact angles were selected for a parametric analysis. HIC 15 index calculated from head linear acceleration data, head angular acceleration and CSDM (Cumulative Strain Damage Measure) calculated from both head angular velocity and acceleration were adopted to evaluate head injury risk of the motorcyclists during various impact conditions.

Materials and Methods

Vehicle models

To globally evaluate the performance of vehicle front-end design on head injury risk of the motorcyclist, five common vehicle types were selected including a sedan, a MPV ( multipurpose vehicle), a SUV ( sport utility vehicle), a EV (electric vehicle) and a 1-Box vehicle. All multi-body vehicle models were established according to the finite element models of corresponding vehicles.

The establishment process of the vehicle and motorcycle-vehicle crash model is presented in Fig. 1. Simulations of the rigid impactor colliding with different part of FE vehicle models were performed to acquire the contact stiffness of each ellipsoid. Through the simulations, the acceleration history curves of impactor at different regions were obtained. Then, referring to the method used by Rooij et al. [START_REF] Rooij | Pedestrian crash reconstruction using multi-body modeling with geometrically detailed, validated vehicle models and advanced pedestrian injury criteria[END_REF], these acceleration history curves were transferred to stiffness curves. Typical stiffness curves of different impact regions are shown in Appendix Fig. A1. The front bumper of the vehicle was tested with a motorcycle tire shaped impactor weighted of 117 kg at an impact speed of 100 km/h. While the hood, hood edge, windshield and A-pillar were tested by an EEVC adult headform perpendicular to the surface of the impact points at a speed of 40 km/h. Fig. 1 The modeling and analysis process of the motorcycle-vehicle impact accidents Among them, the sedan and MPV models were developed based on Toyota Yaris and Dodge Grand Caravan FE models created by the National Crash Analysis Center (NCAC) (NHTSA).

The SUV, EV and 1-Box vehicle models were built according to Changfeng Liebao, and Dongfeng EJ02, GM Wuling N300 vehicle FE models from Chinese automotive companies.

Each vehicle model consists of one rigid body with several ellipsoid surfaces to describe the vehicle shape. To compare design variance of different vehicle types, four main shape parameters of the aforementioned vehicles were measured and summarized as shown in Fig. 2.

Bonnet leading edge height (H), bonnet length (L), bonnet angle (α), and windshield angle (θ) were included. SUV presents an extremely long bonnet and high bonnet leading edge, while 1-Box presents the largest bonnet angle.

Motorcycle and motorcyclist models

The establishment process of the motorcycle and motorcyclist models is shown in Fig. 3. The motorcycle model was established according to the parameters of Jialing JH150-6-a Chinese mainstream brand. The total mass of the motorcycle is 117 kg. The MBS motorcycle model consists of six rigid bodies, such as frame, headstock, upper front fork, lower front fork, front wheel and rear wheel respectively. The frame is connected to the reference space by a free joint.

For simulating the steering behavior, the upper front fork is connected to the headstock by a rotational joint. The lower front fork is also linked to the upper front fork by a rotational joint for modeling the bending behavior of the front fork during the impacts. Each wheel is linked to the frame by a rotational joint. Each rigid body is made up with several ellipsoids to represent the shape of the motorcycle. The contact stiffness of motorcycle components is defined according to the previous study [START_REF] Chinn | COST 327 Motorcycle Safety Helmets[END_REF], as shown in Fig. A2. The MBS model of 50th percentile male pedestrian in MADYMO database was selected for motorcyclist modeling which has been globally employed for cyclist and motorcyclist impact analysis as well as pedestrian impact [START_REF] Ballesteros | Pedestrian injuries and vehicle type in Maryland, 1995-1999[END_REF][START_REF] Han | Analysis of pedestrian kinematics and ground impact in traffic accidents using video records[END_REF]. The human model consists of 52 rigid components that represent head, neck, chest, abdomen, hip, upper and lower extremities of a human body. All the components are connected together as a tree structure by different types of joints. The driving posture is adjusted by setting DOFs (Degree Of Freedom) of joints (Fig. 3). A helmet is attached to the motorcyclist in all simulation cases.

The helmet is represented by a rigid body with two ellipsoids. The rigid body of the helmet was connected to the motorcyclist head by a bracket joint. The helmet stiffness is defined according to the drop test results of the AGV K3 helmet, as shown in Fig. A3.

Impact scenarios

In order to analyze the motorcyclist responses during various motorcycle-vehicle impact scenarios, different impact angles and speeds of motorcycle and vehicles were used in the simulation matrix refer to ISO 13232 regulation [START_REF] Ballesteros | Pedestrian injuries and vehicle type in Maryland, 1995-1999[END_REF][START_REF] Han | Analysis of pedestrian kinematics and ground impact in traffic accidents using video records[END_REF]. As shown in Fig. 4 and Table 1, three impact angles including 0°, 45° and 90°, and corresponding impact speeds were selected. Considering impact angles of 0° and 45°, the motorcycle speed was defined from 0 m/s to13.4 m/s. The lower motorcycle speeds for 90° impact were set as 0 m/s, 2.25 m/s, 4.5 m/s, and 6.75 m/s, because the motorcyclist head would not collide with the vehicle if the motorcycle speed is too large in this type of impact situation. In all impact angles, the vehicle impact speeds were set from 3.3 m/s to 13.4 m/s. The simulation matrix was shown in Table 1. Contacts were defined to simulate the interactions between the ground, motorcycle, motorcyclist and vehicles. Including five vehicle types, a total of 240 simulations were conducted in this simulation matrix design. In addition, despite of the importance of head ground impact (Simms and Wood, 2006;[START_REF] Shang | Detailed assessment of pedestrian ground contact injuries observed from in-depth accident data[END_REF][START_REF] Han | Analysis of pedestrian kinematics and ground impact in traffic accidents using video records[END_REF], only the head injuries caused by colliding with the vehicle were investigated in this study.

Injury evaluation indexes

To assess head injury risk during motorcycle-vehicle impact accidents, it is necessary to choose suitable head injury evaluation indexes. First, HIC 15 was used to evaluate head injuries. HIC 15 was originally defined by Versace in 1971 and was adopted by Federal Motor Vehicle Safety Standards (FMVSS) as head injury criterion with the safety margin of HIC 15 =700 [START_REF] Chen | FEA of helmet-head injury protection based on motorcycle accident reconstruction[END_REF][START_REF] Mchenry | Head injury criterion and the ATB[END_REF]. The HIC 15 value of 700 represents the 5% risk of severe head injury [START_REF] Mertz | Injury risk curves for children and adults in frontal and rear collisions[END_REF]. HIC is defined as Equation (1):

2 1 2.5 2 1 2 1 1 max ( ) ( ) t t HIC a t dt t t t t            
where t 1 and t 2 are the initial and final time points of the interval during which HIC attains a maximum value and t 2 -t 1 ≤ 15 ms, a is the resultant head acceleration. However, HIC 15 is calculated from translational acceleration of head gravity center without considering of angular acceleration.

It was suggested that the contusion was likely to arise and the bridging vein can be ruptured if the maximal angular acceleration exceeded 4500 rad/s 2 [START_REF] Löwenhielm | Mathematical simulation of gliding contusions[END_REF]. Following this threshold, head angular acceleration was studied in this study. Based on the head angular velocity and angular acceleration, the CSDM was also estimated by Equation (2) indicated by Gabler et al.'s Study (Gabler et al., 2018). DAI in the present study based on the previous studies [START_REF] Gabler | Development of a metric for predicting brain strain responses using head kinematics[END_REF].

* * * * * 2 ) i i i i i CSDM         / ( + ( -)*e

Results

General kinematic responses of motorcycle-vehicle impacts

Three typical impact scenarios of motorcycle-sedan impacts were selected to analyze the general kinematic response of motorcyclist, as shown in Fig. 5. In the first case (Case1), the motorcycle velocity and vehicle velocity were 13.4 m/s and 6.7 m/s respectively, according to scenario 114 in the standard of ISO 13232, while the impact angle was set to 0°. The second case (Case2) was set according to scenario 114 in the standard of ISO 13232, with impact angle (1)

(2) of 45°, motorcycle velocity of 13.4 m/s and vehicle velocity of 6.7 m/s. The third case (Case3) was defined according to scenario 143 in the standard of ISO 13232, with impact angle of 90°, motorcycle velocity of 0 m/s and vehicle velocity of 9.8 m/s respectively. In Case1, the motorcycle tire contacts with the vehicle bumper first and the relative velocity between motorcycle and vehicle starts to decrease. At the time of 54 ms, the knees and legs start to contact with the vehicle bumper and the motorcycle handles, hence the motorcyclist is decelerated and rotates forward around the vehicle front structure. Around 90 ms, the motorcycle rebounds reward, while the motorcyclist still moves forward and the head directly collides with the windshield at the time of 138 ms. In Case2, after that the motorcycle tire starts to contact with the vehicle bumper, the motorcyclist rotates with the motorcycle around z-axis and the rear part of the motorcycle becomes closer to the vehicle. The left lower leg first contacts with the vehicle bumper at 69 ms and then the head impacts with the windshield at 119 ms. In Case3, it can be noted that the shoulder of the motorcyclist first contacts with the bonnet, then the head impacts with the windscreen at 154 ms. 4. In Case1, after the motorcycle tire impacts with the vehicle bumper, the head-vehicle relative velocity of xdirection starts to decrease because of the friction force between the pelvis and motorcycle. At the time of 54 ms when the lower extremities begin to contact with the vehicle bumper, the relative velocities in x-and z-directions increase slightly due to the rotation of motorcyclist around vehicle and then decreases fast until 100 ms. The relative velocity in x-direction decreases dramatically and the velocity in z-direction increases after the head impacts with windshield at 138 ms. In Case2, the head-vehicle relative velocities in x-and z-directions are similar to Case1, except that the relative velocity in y-direction is not negligible. In Case3, the head-vehicle relative velocity in x-direction decreases gradually almost in the whole impact period, due to the torso is stiffer and hard to deform in side impact. The relative velocity in zdirection decreases fast after the left lower extremity contacts with the vehicle, and increases after the head impacted with the vehicle at 154 ms. It is worth noting that in Case 1 and Case 2, when the legs impact with the vehicle bumper, the torso will rotate forward to the vehicle. This will lead to an increase of relative velocity between the head and vehicle before the head contacts with the vehicle. While in Case 3, the impact velocity is relatively lower and the legs are restricted by the motorcycle which obstructs the rotation of the torso, therefore the relative velocity does not increase before the head contacts with the vehicle.

Categories of kinematics

Referring to Crocetta 's analysis method [START_REF] Crocetta | The influence of vehicle front-end design on pedestrian ground impact[END_REF], Table 2 illustrates the kinematic gestures categorized by quantifying the head impact position. Such a criterion is used for classification in consideration of the indirect effects of impact position on head injury risk.

Category 1 represents the head impacts with the bonnet leading edge. Category 2 means the head impacts with the vehicle bonnet. The head impact with the transition region between the vehicle bonnet and the windshield is classified as Category 3. And, Category 4, 5 and 6 represent the motorcyclist head impacts with the vehicle windshield, A pillar and wheel fender, respectively. The proportion of each impact category is shown in Fig. 7 within the parametric study of 5 vehicle types, 4 different vehicle speeds, and 4 different motorcycle speeds. Most of the simulation cases show the impact region located on the vehicle bonnet, windshield or transition region between them, as indicated by the distribution of C2, C3 and C4. Sum of C1, C5 and C6 only accounts for 9.09% of the simulation group. It is noted that C3 impact region generally has a higher stiffness due to the windshield wiper. This category presents a large proportion of 21.21%. Compared with pedestrian-vehicle impact, this region should be highly concerned in motorcycle-vehicle impact.

Correlation of the kinematic categories to the vehicle types and impact scenarios

As shown in Fig. 8, the impact locations of the head to the vehicle are significantly influenced by the vehicle type, namely the shape of the vehicle front end. Motorcycle to 1-Box, EV, sedan and MPV impact groups show that the impacts on the windshields occupy the largest portion.

Almost all the head impact points on SUV are located on the bonnet, which can be attributed to its longer and higher bonnet as compared with the other vehicles. While for 1-Box vehicle, motorcyclist head often impacts with the windshield because of its shorter bonnet. Focusing on impact angles (Fig. 8a-c), it seems that C5 group only occurs in 45° cases, C6 group mainly occurs in 90° cases. Regarding impact velocities (Fig. 8d-e), both vehicle and motorcycle impact velocities show similar influences on the head impact locations. With the increase of the impact velocity, the portion of head impact location on the bonnet gradually decreases while that on the windshield gradually increases. C1 group only occurs when the motorcycle velocity is 0 and vehicle velocity is below 6.7 m/s. C5 and C6 groups seem to happen during accidents of high vehicle impact velocities and low motorcycle velocities.

Typical resultant head acceleration (RHA) and injury index calculation

Three typical impact scenarios of motorcycle-sedan impacts described in section 3. In addition, the relationships between the vehicle speeds and the average values of head injury indexes are illustrated in Fig. A4. In some scenarios, the head injuries were not studied because the motorcyclist head didn't impact with the vehicles directly. Considering different impact angles and different vehicle types, these index values all increase with the increasing of impact velocities. The relationships between the motorcycle speed and the average values of head injury indexes are presented in Fig. A5. In the situation with the impact angle of 0° and 45°, the index values increase with the increasing motorcycle speed. While in 90° cases, non-significant influence of the motorcycle speed on head injury indexes can be found.

Influences of impact angles on head injury risk

The relationships between the impact angles and the averaged values of HIC 15 , head angular acceleration and CSDM are illustrated in Fig. 11a-c

Influences of vehicle shape parameters on head injury risk

In order to analyze the sensitivity of each parameter on head injury risk, regression analysis was implemented for the simulation results. Four vehicle front-end shape parameters described in section 2.1 along with motorcycle speed v 1 , vehicle speed v 2 and impact angle β were normalized as shown in Equation ( 3) and the regression model is shown in Equation ( 4).

(3)

x i = x i,0 -x min x max -x min (4) ε + x ∑ x g + x ∑ x f + x ∑ x e + x ∑ x d + x ∑ x c + x ∑ x b + ∑ x a = y 6 + i 1 1 = i i i 5 + i 2 1 = i i i 4 + i 3 1 = i i i 3 + i 4 1 = i i i 2 + i 5 1 = i i i 1 + i 6 1 = i i i 7 1 = i i i
where x i,0 represents the original values of four vehicle front-end shape parameters, motorcycle speed v 1 , vehicle speed v 2 and impact angle β of each simulation case; x i is the normalized value of x i,0 ; x i,max and x i,min are the maximum and minimum values of x i in all the cases; y is the head injury values, a i , b i , c i , d i , e i , f i and g i are the regression coefficients; ε is the regression error.

The R-values of regression models for HIC 15 , angular acceleration and CSDM are 0.845, 0.877 and 0.928, respectively. This means that the regression model can be used to analyze the influence of the variables on injury results. The regression coefficients of different vehicle design parameters are shown in Fig. 12. We can note that the coefficients have a similar trend for the three injury indexes. But the coefficients of angular acceleration and CSDM are larger than those of HIC 15 . The higher coefficient value represents the higher influence of design parameter on the corresponding injury index. Hence, the vehicle shape variables influence the angular acceleration and CSDM much greater than HIC 15 . Considering the single design parameter, bonnet leading edge height H presents the largest influence on the three injury indexes. During the regression analysis, the P-values of variables were output, as shown in Table 3. Some variables are not used in the regression analysis because of their low significance, and their P-values are not shown in this table. From Table 3, it can also be found that bonnet leading edge height H presents the largest influence on the injury indexes among the four shape design parameters, and H•L, H•α and α•θ also show higher significance. Furthermore, Fig. 13 shows the influence of each single design parameter on HIC 15 , head angular acceleration and CSDM values. The results show that the index values of H=0.936, L=0.471, α=33° and θ=42° cases are evidently higher than those of the other cases. When checking the head-vehicle impact speeds, the corresponding high head impact speeds can be found. In this case, it is believed that the head impact speed against vehicle can be responsible for these results. The smaller bonnet length L represents the earlier time of the head begins to contact with the vehicle, so it would lead to higher head-vehicle impact speed and higher head injury indexes. That can be one reason for the higher sensitivity of head injury risk to the bonnet length L. When comparing the present results with the previous studies on pedestiran head injuries, significant differences were noted. While the previous studies indicated that reducing bonnet height [START_REF] Shang | Detailed assessment of pedestrian ground contact injuries observed from in-depth accident data[END_REF][START_REF] Crocetta | The influence of vehicle front-end design on pedestrian ground impact[END_REF] and increasing windshield angle (Lyons and Simms, 2012) can improve a vehicle's performance in protecting the 20 pedestrian head considering head-ground impact, the present results on motrocylist head injuries show uncertain trends with obvious fluctuations of injury indexes concering these parameters' change. While [START_REF] Han | Effects of vehicle impact velocity, vehicle front-end shapes on pedestrian injury risk[END_REF] and Li et al. (2017b) studies showed that a shorter bonnet was preferable for pedestrian protection, the present study show a trend of decreasing bonnet length can increase head injury risk. These differences can be further studied by considering both head-vehicle and head-ground impacts of motorcyclists. Based on the sensitivity analysis, the trend of decreasing the bonnet angle and increasing the bonnet length can potentially reduce head injury risk of motorcyclists. In addition, the present study was focused on the frontal impact of motorcyle-vehicle as well as interactions between motorcyclists and vehicles. All these limited further understanding on head injuries of motorcyclists. Impacts in other directions as well as head injuries due to ground impacts should be also investigated in the future.

H L α θ H • L H • α H • θ L • α L • θ α • θ v 1 v 2 β H • v 1 H • v 2 H • β L • v 1 L • v 2 L • β α • v 1 α • v 2 α • β θ • v 1 θ • v 2 θ • β v 1 • v 2 v 1 • β v 2 • β
v 1 v 2 β H•v 1 H•v 2 H•β L•v 1 L•v 2 L•β α•v
α•v 2 α•β θ•v 1 θ•v 2 θ•β v 1 •v 2 v 1 •β v 2 •

Fig. 2

 2 Fig. 2 Multi-body models and front-end shape parameters of different vehicle types

Fig. 3

 3 Fig.3The MBS models of the motorcycle and motorcyclist

  Impact scenarios of motorcyclist, motorcycle and vehicle

Fig. 5

 5 Fig. 5 Kinematics of different impact scenarios During the impact process of the 3 cases, the relative speeds of head and vehicle in global

Fig. 6

 6 Fig. 6 History curves of head-vehicle relative velocities

Fig. 8

 8 Fig.8Occurrences of the 6 kinematic categories for different vehicle types and impact scenarios

Fig. 10

 10 Fig. 9 Head linear and angular acceleration curves

.Fig. 11

 11 Fig. 11 Relations of the impact angle with the average values of the head injury indexes and relative headvehicle impact speed

  Fig. 12 also shows the coupling effects of different parameters. The coefficients of combined parameters, such as H•L, H•α and α•θ generally show obviously large influence on head injury results.

Fig. 12

 12 Fig. 12 Regression analysis of vehicle front end shape parameters on head injury indexes

Fig. 13

 13 Fig. 13 Relations of vehicle shape parameters with the average values of the head injury indexes and the relative head-vehicle impact speeds

  Fig. A1 Contact stiffness of the Sedan

  Relation of the motorcycle speed with motorcyclists' average values of head injury indexes

Table 1

 1 Simulation matrix of motorcycle-vehicle impact scenarios

	Level 1	Level 2	Level 3	Level 4

Table 2

 2 Description of identified kinematic categories

	Kinematic gestures

Fig. 7 Breakdown of kinematic categories

Table 3

 3 P-values of variables in the regression models

		H	L	α	θ	H•L	H•α	H•θ	L•α	L•θ	α•θ
	HIC 15	0.230	-	0.349 0.622 0.265 0.259	-	0.480	-	0.303
	Angular acceleration	0.013 0.910 0.018 0.577 0.016 0.012	-	-	-	0.006
	CSDM	0.052	-	0.178 0.693 0.040 0.023	-	0.268	-	0.017

  This study analyzed the influences of impact scenarios and vehicle front-end design on head injury risks of the motorcyclist during motorcycle-vehicle impacts through the parametric analysis. Multibody models of five vehicles were established to study the head injuries during the motorcycle-vehicle impacts. A total of 240 simulations of different vehicle types, impact angles and impact speeds of the vehicles and motorcycle were conducted in this study. The results show that the injury index values of HIC 15 , head angular acceleration and CSDM all inclined with the increase of relative impact speed at exponential functions. It is noted that the critical relative impact speed is approximately 15 m/s for HIC 15 and head angular acceleration, while for CSDM this velocity is around 10 m/s. Based on the present ISO 13232 regulation, 90° impact is found to be the most uncertain situation without obvious trend, and 45° impacts seems to be the most vulnerable situation. With regard to vehicle front end design parameters, bonnet length L and its combination with other parameter presented high influences on motorcyclist head injuries. In this case, 1-Box vehicle can be more likely to cause head injuries than other four vehicle types. It is also worth noting that the effects of vehicle shape parameters on motorcyclist and pedestrian head injuries are quite different, which can be further studied.

Acknowledgement

This work is supported by is supported by the and Open Fund of State Key Laboratory of Vehicle NVH and Safety Technology (Grant No. NVHSKL-201901), National Natural Science Foundation of China (Grant No. 51475154, 51875187), and Hunan Province Science and Technology Plan (Grant No. 2019JJ40021).


Sébastien Bridel

Stephen C Watts

Louise M Judd

Taylor Harshegyi

Virginie Passet

Carla Rodrigues

Kathryn E Holt

Sylvain Brisse
email: sylvain.brisse@pasteur.fr

Klebsiella MALDI TypeR: a web-based tool for Klebsiella identification based on MALDI-TOF mass spectrometry

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Bacteria of the genus Klebsiella are human and animal pathogens with a wide ecological distribution [START_REF] Brisse | The genus Klebsiella[END_REF]. The genus Klebsiella, a member of the family Enterobacteriaceae, encompasses a huge diversity in terms of phylogenetic lineages, genomic content, pathogenic properties, and ecological distribution. Two major subdivisions of Klebsiella are the Klebsiella pneumoniae species complex (KpSC) and the Klebsiella oxytoca species complex (KoSC) [START_REF] Wyres | Population genomics of Klebsiella pneumoniae[END_REF]. The KpSC currently includes five taxonomic species, two of which comprise two subspecies. These taxa were initially defined as seven distinct phylogroups, Kp1 to Kp7 [START_REF] Rodrigues | Description of Klebsiella africanensis sp. nov., Klebsiella variicola subsp. tropicalensis subsp. nov. and Klebsiella variicola subsp. variicola subsp. nov[END_REF].

K. pneumoniae (phylogroup Kp1

) is one of the most problematic pathogens associated with antibiotic resistance worldwide. This species is phylogenetically closely related to K. quasipneumoniae [subsp. quasipneumoniae (Kp2) and subsp. similipneumoniae (Kp4)], K. variicola [subsp. variicola (Kp3) and subsp. tropica (Kp5)], K. quasivariicola (Kp6) and K. africana (Kp7). In turn, the KoSC comprises 6 species and 7 phylogroups [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF]: K. michiganensis (Ko1), K. oxytoca (phylogroup Ko2), K. spallanzanii (Ko3), K. pasteurii (Ko4), K. grimontii (Ko6) and K. huaxiensis (Ko8). K. oxytoca is well known as a cause of opportunistic human infections and post-antibiotic hemorrhagic diarrhea [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF][START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF].

Recent literature has brought into focus the difficulties that microbiology laboratories encounter in the identification of individual members of the K. pneumoniae and K. oxytoca species complexes [START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF][START_REF] Brisse | Development of a rapid identification method for Klebsiella pneumoniae phylogenetic groups and analysis of 420 clinical isolates[END_REF][START_REF] Seki | Fatal sepsis caused by an unusual Klebsiella species that was misidentified by an automated identification system[END_REF][START_REF] Fonseca | A one-step multiplex PCR to identify Klebsiella pneumoniae, Klebsiella variicola, and Klebsiella quasipneumoniae in the clinical routine[END_REF][START_REF] Long | Whole-Genome Sequencing of Human Clinical Klebsiella pneumoniae Isolates Reveals Misidentification and Misunderstandings of Klebsiella pneumoniae, Klebsiella variicola, and Klebsiella quasipneumoniae[END_REF]. Indeed, Klebsiella species are genetically similar and have few biochemical discriminatory biomarkers. As a consequence, the clinical significance of individual Klebsiella species and their potential epidemiological specificities are still not well defined. While Matrix Assisted Laser Desorption Ionization -Time of Flight (MALDI-TOF) mass spectrometry can identify Klebsiella species through peak discrimination [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF][START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF][START_REF] Dinkelacker | Typing and Species Identification of Clinical Klebsiella Isolates by Fourier Transform Infrared Spectroscopy and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry[END_REF], the current software implementation is limited to identifying K. pneumoniae, K. variicola and K. oxytoca [START_REF] Long | Whole-Genome Sequencing of Human Clinical Klebsiella pneumoniae Isolates Reveals Misidentification and Misunderstandings of Klebsiella pneumoniae, Klebsiella variicola, and Klebsiella quasipneumoniae[END_REF][START_REF] Dinkelacker | Typing and Species Identification of Clinical Klebsiella Isolates by Fourier Transform Infrared Spectroscopy and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry[END_REF][START_REF] Berry | An Investigation into Laboratory Misidentification of a Bloodstream Klebsiella variicola Infection[END_REF]. Here we present Klebsiella MALDI TypeR, a tool that leverages novel discriminant biomarkers in MALDI-TOF spectra to expand the number of identifiable Klebsiella species from MALDI-TOF data and improve identification accuracy, using a previously-published method [START_REF] Bridel | Genetic diversity and population structure of Tenacibaculum maritimum, a serious bacterial pathogen of marine fish: from genome comparisons to high throughput MALDI-TOF typing[END_REF].

Material and methods

Bacterial strains and reference identification by genomic sequencing. A total of 341 strains were included. A first set consisted of 78 clinical isolates (50 belonging to the Klebsiella pneumoniae species complex, 28 to the Klebsiella oxytoca species complex), selected to be representative of the phylogenetic diversity of these taxa. These isolates have been used in previous studies [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF][START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF]. Three and 24 spectra per isolate were recorded for the KpSC and for the KoSC, respectively, as described previously [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF][START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF]. A second set of 262 isolates belonging to the KpSC was collected in Australia from humans and animals. Isolate genomes were sequenced using Illumina paired-end technology and assembled using SPAdes.

Species identification was performed with Kleborate [13,[START_REF] Lam | Genomic surveillance framework and global population structure for Klebsiella pneumoniae[END_REF], which compares each genome to a curated set of species references using Mash distances; these species calls were used as reference in subsequent analyses.

Preparation of bacterial samples and MALDI-TOF MS analysis. An overnight

culture on Luria-Bertani agar (37°C, 18 h) was used to prepare the samples with the ethanol/formic acid extraction procedure following the manufacturer recommendations (Bruker Daltonics, Bremen, Germany). Samples (1 μL) were spotted onto an MBT Biotarget 96 target plate, air dried and overlaid with 1 μL of a saturated α-cyano-4-hydroxycinnamic acid matrix solution in 50% of acetonitrile and 2.5% of trifluoroacetic acid. External calibration of the mass spectra was performed using Bruker Bacterial Test Standard. For the assessment of the direct colony picking protocol, a single bacterial colony was picked with a sterile toothpick and directly deposited onto a 96-spot polished steel target and processed as the sample spot previously mentioned. In addition, the same two protocols were followed using blood agar medium (Columbia agar plus 5% sheep blood). A MALDI Biotyper Microflex LT controlled by Compass flexControl software (version 3.4; Bruker Daltonics, Billerica, Massachusetts, USA) was used to generate mass spectra for all isolates. Mass spectra were acquired using automatic mode and default settings (2000 to 20 0000 Da; linear positive mode; 240 laser shots).

Identification of phylogroups based on MALDI-TOF data in Klebsiella MALDI

TypeR. The KpSC and the KoSC encompass species and subspecies that are defined as phylogroups. As such, we aimed to distinguish them on the basis of MALDI-TOF data, using information from the spectra, defined as peak shifts [START_REF] Sauget | Can MALDI-TOF Mass Spectrometry Reasonably Type Bacteria?[END_REF]. Identification of phylogroups is therefore based on a small selection of biomarkers for which one (or more) protein weight modifications have been previously reported [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF][START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF]. In fact, each phylogroup could be seen as comprising MALDI-Types, as described by Bridel et al. 2020 [START_REF] Bridel | Genetic diversity and population structure of Tenacibaculum maritimum, a serious bacterial pathogen of marine fish: from genome comparisons to high throughput MALDI-TOF typing[END_REF]: a specific combination of protein m/z values of these biomarkers. Klebsiella MALDI TypeR was run on a 4 CPUs 16-GB RAM CentOS Linux 7 (Core) virtual machine.

Results and Discussion

Development of Klebsiella MALDI TypeR and its web interface. Klebsiella MALDI TypeR was developed as a web application with R (version 3.4.0) and R Shiny (version 1.4). A user can upload either a single spectrum or multiple spectra (a maximum number of spectra is not defined, but we recommend working with batches of up to 100 spectra per analysis). The uploaded spectra must be in zip archive format. The zip archive must contain the raw data from any spectrometer. This includes the tree structure, which must sample spectra overlaid on the reference spectra (1 to 3 spectra for each phylogroup).

Predefined windows (one for each biomarker) allow the user to directly investigate specific spectral regions. This feature could also enable the detection of new isoforms or biomarkers.

It also contains a customizable option that allows the user to select any region of the spectra.

The Klebsiella MALDI TypeR tool was made available at https://maldityper.pasteur.fr . A single isolate analysis required approximately 10 seconds to complete and the analysis of 10 isolates takes about 35 seconds. The tool does not have a limit to the number of isolates that can be analyzed simultaneously, and it performed well with batches of 100 isolates that were uploaded at once.

Evaluation of the identification tool.

We first tested fifty KpSC isolates grown overnight on lysogeny broth (LB) agar (37°C, 18h) and subjected to ethanol/formic acid extraction following the manufacturer recommendations (Bruker Daltonics, Bremen, Germany). For each strain, a total of 3 spectra from the same spot were acquired (Bruker Microflex LT mass spectrometer, Paris). This dataset yielded correct assignments to species complex for 50/50 (100%) isolates, 47 (94%) of which had an identification value >50% (Tables S2 andS3). The same samples prepared using different experimental conditions (two media: LB and Columbia agar plus 5% sheep blood; each with both extraction and direct application in the MALDI-TOF target) also yielded correct complex assignments for all isolates, albeit with lower confidence (Table S2). Regarding phylogroup assignment, using LB-extraction 90% of samples were correctly identified at phylogroup level (Table S3), while the other sample preparation methods yielded better results overall (Table S4). To validate robustness of the tool to instrument and user variation, we tested an additional dataset comprising 248 KpSC isolates prepared using the LB-extraction method (176 isolates with a single spectrum, 26 in duplicate and 68 in triplicate); these spectra were generated on a different instrument (Bruker MALDI Biotyper microflex LT-SH) in a different laboratory (Melbourne, Australia). 244/248 isolates (98%) were assigned to the correct species complex, and 219/244 (90%) were correctly identified at the phylogroup level (84% to 100% for each phylogroup, see Table S5).

The KoSC assignments were tested using 28 isolates prepared using LB-extraction and triplicate spectra generated on the Bruker Microflex LT mass spectrometer instrument (Paris).

All isolates were correctly assigned to complex level and most (89%) were assigned to the correct MALDI-Type (Tables S6 andS7). However, discrimination of MALDI-Type MT1 (n=16, 57%) into phylogroups Ko1, Ko4 and Ko6 was poor (Table S8) because the three additional biomarkers intended to discriminate between these phylogroups were captured only rarely.

Conclusions

We developed the Klebsiella MALDI TypeR web tool for identification of Klebsiella species based on MALDI-TOF MS. Our tool allows the fast analysis of single or multiple spectra, including their visualization. We show that MALDI-TOF MS leads to accurate identification for Klebsiella phylogroups of the KpSC, the major Klebsiella species complex found in clinical samples. Klebsiella MALDI TypeR therefore represents an important resource for the identification of the KpSC members, which are currently difficult to identify in routine microbiology practice. For the KoSC, we found promising data, but further

Acknowledgements

We acknowledge the help of Stevenn Volant and Amine Ghozlane from the Bioinformatics and Biostatistics HUB of Institut Pasteur for help with R Shiny deployment of the web tool on the web server.

Funding

This work was financially supported by the MedVetKlebs project, a component of European Joint Programme One Health EJP, which has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 773830.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

include spectra in the expected format (txt, csv, fid, ciphergen, mzXML, mzML, imzML, analyze, cdf or msdsee MALDIquant Foreign documentation for further details). This application has been tested with spectra (raw data folders) from Bruker spectrometer only but could theoretically handle any proper archive containing one of the previously mentioned file formats. Supplementary Figure 1 describes each step to reach one raw data folder. Each archive may correspond either to a single raw spectrum or several raw spectra of the same isolate (technical replicates). For better results, we recommend using at least 3 technical replicates for each isolate.

The spectra can be visually inspected after upload (Figure 1). A panel shows each uploaded spectrum for quick visual inspection (e.g., intensity checking) and a table highlights the presence (if any) of empty spectra.

For identification, the tool was designed with a 2-step process. The first step (Figure 2, panel B1) identifies isolates at the species complex level using two sets of biomarkers, each set corresponding to reference biomarkers for (i) the KpSC (29 biomarkers, Table S1) or (ii) the KoSC (57 biomarkers, Table S1). This step can be performed, based on user choice, with one of two alternative peak detection algorithms. The first one is based on the R package MALDIquant [START_REF] Gibb | MALDIquant: a versatile R package for the analysis of mass spectrometry data[END_REF] and is set by default, as it runs faster. The second one uses the MassSpecWavelet package [START_REF] Du | Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching[END_REF] and generally detects more significant peaks, but is slower. Species complex identification is based on the ratio of common peaks between the sample peak list and a reference peak list, to the number of peaks in the corresponding reference (this ratio is called "Identification Value"). The input sample is assigned to the species complex with the highest Identification Value.

The second step consists in the identification of the specific phylogroup (corresponding to a species or subspecies) within the defined species complex (Figure 2, panel B2). The raw spectrum is processed again by the Multi Peak Shift Typing (MPST) method [START_REF] Bridel | Genetic diversity and population structure of Tenacibaculum maritimum, a serious bacterial pathogen of marine fish: from genome comparisons to high throughput MALDI-TOF typing[END_REF], but this time using the set of biomarkers corresponding to the assigned species complex, which allows recalibration of m/z values. The biomarkers used for the two MPST schemes (one for each species complex, see Tables 1 to 4) are based on previous work [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF][START_REF] Rodrigues | Identification of Klebsiella pneumoniae, Klebsiella quasipneumoniae, Klebsiella variicola and Related Phylogroups by MALDI-TOF Mass Spectrometry[END_REF].

Briefly, an MPST scheme is analogous to a multi-locus sequence typing (MLST) scheme where an allele type (AT) corresponds to an isoform (IF) for one protein (each IF is defined by a specific massor a m/z value on the spectrum) and a MALDI-Type (MT) corresponds to a sequence type (ST). Each MT was observed within a single phylogroup. The tool attempts to define the IF for each biomarker, and then to attribute the spectrum to the phylogroup corresponding to the relevant MT (Figure 2, panel B3). The only exception to this process was introduced for the discrimination of K. oxytoca phylogroups Ko1, Ko4 and Ko6 (K. oxytoca, K. pasteurii and K. grimontii, respectively). These three species form a phylogenetic branch of closely related species within the K. oxytoca species complex [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF], and their spectra are very similar such that they share the same MT. In order to distinguish among these three phylogroups, we added 3 previously described biomarkers [START_REF] Merla | Description of Klebsiella spallanzanii sp. nov. and of Klebsiella pasteurii sp. nov[END_REF], for which the tool checks for presence/absence within the spectra (i.e., these 3 biomarkers do not correspond to peak shifts). If any of the above biomarkers (peak shifts or presence/absence) is not found, automatic identification is not considered possible, and the result is reported to the user as NT (not typable). In this case, manual analysis or data re-acquisition are needed.

To help resolve these cases and allow users to further explore the data, we included a feature enabling to interactively explore the spectra. The plot window (Figure 3) shows the validation based on a larger dataset would be required. The current biomarker approach does not appear to be suitable for accurate discrimination of Ko1, Ko4 and Ko6. In the future, the website may be updated to integrate additional methods for identification and typing, such as supervised learning.

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Competing interests

The authors declare that they have no competing interests. All authors contributed to and approved the final version of the manuscript.