The octopus arm requires special motor control schemes because it consists almost entirely of muscles and lacks a rigid skeletal support. Here we present a 2D dynamic model of the octopus arm to explore possible strategies of movement control in this muscular hydrostat. The arm is modeled as a multisegment structure, each segment containing longitudinal and transverse muscles and maintaining a constant volume, a prominent feature of muscular hydrostats. The input to the model is the degree of activation of each of its muscles. The model includes the external forces of gravity, buoyancy, and water drag forces (experimentally estimated here). It also includes the internal forces generated by the arm muscles and the forces responsible for maintaining a constant volume. Using this dynamic model to investigate the octopus reaching movement and to explore the mechanisms of bend propagation that characterize this movement, we found the following. 1) A simple command producing a wave of muscle activation moving at a constant velocity is sufficient to replicate the natural reaching movements with similar kinematic features. 2) The biomechanical mechanism that produces the reaching movement is a stiffening wave of muscle contraction that pushes a bend forward along the arm. 3) The perpendicular drag coefficient for an octopus arm is nearly 50 times larger than the tangential drag coefficient. During a reaching movement, only a small portion of the arm is oriented perpendicular to the direction of movement, thus minimizing the drag force.

The dynamic model of the octopus arm described in the first paper of this 2-part series was used here to investigate the neural strategies used for controlling the reaching movements of the octopus arm. These are stereotypical extension movements used to reach toward an object. In the dynamic model, sending a simple propagating neural activation signal to contract all muscles along the arm produced an arm extension with kinematic properties similar to those of natural movements. Control of only 2 parameters fully specified the extension movement: the amplitude of the activation signal (leading to the generation of muscle force) and the activation traveling time (the time the activation wave takes to travel along the arm). We found that the same kinematics could be achieved by applying activation signals with different activation amplitudes all exceeding some minimal level. This suggests that the octopus arm could use minimal amplitudes of activation to generate the minimal muscle forces required for the production of the desired kinematics. Larger-amplitude signals would generate larger forces that increase the arm's stability against perturbations without changing the kinematic characteristics. The robustness of this phenomenon was demonstrated by examining activation signals with either a constant or a bell-shaped velocity profile. Our modeling suggests that the octopus arm biomechanics may allow independent control of kinematics and resistance to perturbation during arm extension movements.

Octopus arms, as well as other muscular hydrostats, are characterized by a very large number of degrees of freedom and a rich motion repertoire. Over the years, several attempts have been made to elucidate the interplay between the biomechanics of these organs and their control systems. Recent developments in electrophysiological recordings from both the arms and brains of behaving octopuses mark significant progress in this direction. The next stage is relating these recordings to the octopus arm movements, which requires an accurate and reliable method of movement description and analysis. Here we describe a semiautomatic computerized system for 3D reconstruction of an octopus arm during motion. It consists of two digital video cameras and a PC computer running custom-made software. The system overcomes the difficulty of extracting the motion of smooth, nonrigid objects in poor viewing conditions. Some of the trouble is explained by the problem of light refraction in recording underwater motion. Here we use both experiments and simulations to analyze the refraction problem and show that accurate reconstruction is possible. We have used this system successfully to reconstruct different types of octopus arm movements, such as reaching and bend initiation movements. Our system is noninvasive and does not require attaching any artificial markers to the octopus arm. It may therefore be of more general use in reconstructing other nonrigid, elongated objects in motion.

The octopus arm is a muscular hydrostat and due to its deformable and highly flexible structure it is capable of a rich repertoire of motor behaviors. Its motor control system uses planning principles and control strategies unique to muscular hydrostats. We previously reconstructed a data set of octopus arm movements from records of natural movements using a sequence of 3D curves describing the virtual backbone of arm configurations. Here we describe a novel representation of octopus arm movements in which a movement is characterized by a pair of surfaces that represent the curvature and torsion values of points along the arm as a function of time. This representation allowed us to explore whether the movements are built up of elementary kinematic units by decomposing each surface into a weighted combination of 2D Gaussian functions. The resulting Gaussian functions can be considered as motion primitives at the kinematic level of octopus arm movements. These can be used to examine underlying principles of movement generation. Here we used combination of such kinematic primitives to decompose different octopus arm movements and characterize several movement prototypes according to their composition. The representation and methodology can be applied to the movement of any organ which can be modeled by means of a continuous 3D curve.

In recent years, there is a growing demand to fortify the scientific basis of forensic methodology. During 2016, the President's Council of Advisors on Science and Technology (PCAST) published a report that states there are no appropriate empirical studies that support the foundational validity of footwear analysis to associate shoeprints with particular shoes based on specific identifying marks, which is a basic scientific demand from the field. Furthermore, meaningful databases that can support such studies do not exist. Without such databases, statistical presentation of the comparison results cannot be fulfilled either. In this study, a database of over 13,000 randomly acquired characteristics (RACs) such as scratches, nicks, tears, and holes, as they appear on shoe sole test impressions, from nearly 400 shoe soles was collected semiautomatically. The location, orientation, and the contour of each RAC were determined for all the RACs on each test impression. The statistical algorithm Statistic Evaluation of Shoeprint Accidentals (SESA) was developed to calculate a score for finding another feature similar to a particular scanned and digitized RAC in the same shape, location, and orientation as the examined one. A correlation was found between the results of SESA and the results of real casework, strengthening our belief in the ability of SESA to assist the expert in reaching a conclusion while performing casework. The score received at the end of the process serves the expert as a guiding number, allowing more objective and accurate results and conclusions.Recently, the President's Council of Advisors on Science and Technology (PCAST) (4) published a report that went much further in the scientific demands for forensic practice and specifically for the area of shoeprints. In the words of the committee:"PCAST finds there are no appropriate empirical studies to support the foundational validity of footwear analysis to associate shoeprints with particular shoes based on specific identifying marks." ([4], p. 93).The practical way of conducting pattern comparison is well demonstrated by the shoeprint examination process. Today, shoeprints revealed at crime scenes are compared manually against suspect shoes. The first step is to determine whether the class characteristics match. These include the sole pattern, size, 1 Questioned Documents Lab, DIFS, Israel Police, 1 Bar Lev Rd., Jerusalem, 91906, Israel . 2 Toolmarks and Materials Lab, DIFS, Israel Police, 1 Bar Lev Rd., Jerusalem, 91906, Israel. 3 R&D Unit, DIFS, Israel Police, 1 Bar Lev Rd.,