In individual dogs, despite good quality of raw sperm, some parameters are significantly changed after thawing, which cannot be predicted. We therefore investigated whether motility parameters objectively obtained by CASA, membrane integrity (MI), cell morphology or a combination are suitable to improve the prediction of bad post-thaw quality. For this purpose 250 sperm analysis protocols from 141 healthy stud dogs, all patients introduced for sperm cryopreservation, were evaluated and a Classification and Regression Tree (CART) -analysis performed. The sperm was routinely collected, analysed, and frozen by using a modified Uppsala system. After thawing, data were routinely examined by using CASA, fluorescent microscopy for membrane integrity (MI) and Hancock’s fixation for evaluation of cell morphology. Samples were sorted by post-thaw progressive motility (P) in good (P > / = 50%, n=135) and bad freezers (P<50%, n=115). Among bad freezers, 73.9% showed in addition post-thaw total morphological abberations of >40% and/or MI <50%.

Bad freezers were significantly older than good freezers (p<0.05). Progressive motility (P), velocity curvilinear (VCL), mean coefficient (STR), and linear coefficient (LIN) were potential predictors for post-thaw sperm quality since specifity was best (85.8%) and sensitivity (75.4 %) and accuracy (80.4 %) good. For these objectively measured raw sperm parameters, cut-off values were calculated allowing prediction of bad post-thaw results with high accuracy: P = 83.1 % VCL = 161.3 µm/sec, STR = 0.83 %, and LIN = 0.48 %. Raw sperm samples with values below these cut off values will have below average post-thaw quality with a probability of 85.8%. We conclude that VCL, P, STR and LIN are potential predictors of the outcome of sperm cryopreservation, when combined.

This study set out to investigate, for the first time, the distribution and colocalization pattern of cocaine-and amphetamine-regulated transcript (CART) and one of the calcium binding-proteins: parvalbumin (PV) in the chinchilla’s hippocampus proper (HP). HP, consisting of Ammon’s horn (CA) and the dentate gyrus (DG), is an important component of the limbic system, involved in learning and memory processes. CA showed a higher immunoreactivity of CART (-IR) compared to DG. CART-IR neurons were mainly observed in the molecular layer of DG and in the pyramidal layer of CA. CART-IR fibers were present in the granular layer; in the hilus numerous mossy fibers were detected, while in the molecular layer CART-IR fibers were not found. In all CA fields (CA1-CA3), CART-IR fibers were only present in the lacuno- sum-molecular layer. Immunofluorescence with double- labeling showed that only CART-IR cells stained positive for PV, whereas in CART-IR fibers there was no PV-positive reaction. Our research supplements missing knowledge about the distribution and colocalization pattern of CART with PV in the chinchilla’s hippocampus, and also provides a better understanding of the similarities and differences among individuals of the same species and also with other mammals.

This paper presents a concept of humanoid robot motion generation using the dedicated simplified dynamic model of the robot (Extended Cart-Table model). Humanoid robot gait with equal steps length is considered. Motion pattern is obtained here with use of Preview Control method. Motion trajectories are first obtained in simulations (off-line) and then they are verified on a test-bed. Tests performed using the real robot confirmed the correctness of the method. Robot completed a set of steps without losing its balance.

To understand the contributory factors to rear-end accident severity on mountainous expressways, a total of 1039 rear-end accidents, occurring on G5 Jingkun Expressway from Hechizhai to Qipanguan in Shaanxi, China over the period of 2012 to 2017, were collected, and a non-parametric Classification and Regression Tree (CART) model was used to explore the relationship between severity outcomes and driver factors, vehicle characteristics, roadway geometry and environmental conditions. Then the random forest model was introduced to examine the accuracy of variable selection and rank their importance. The results show that driver’s risky driving behaviours, vehicle type, radius of curve, angle of deflection, type of vertical curve, time, season, and weather are significantly associated with rear-end accident severity. Speeding and driving while drunk and fatigued are more prone to result in severe consequences for such accidents and driving while fatigued is found to have the highest fatality probability, especially during the night period (18:00–24:00). The involvement of heavy trucks increases the injury probability significantly, but decreases the fatality probability. In addition, adverse weather and sharp curve with radius less than 1000mare the most risk combination of factors. These findings can help agencies more effectively establish stricter regulations, adopt technical measures and strengthen safety education to ensure driver’s driving safety on mountainous expressways for today and tomorrow.