There is currently no established injury criterion for the spine in compression with lateral load components despite this load combination commonly contributing to spinal injuries in rollover vehicle crashes, falls and sports. This study aimed to determine an injury criterion and accompanying tolerance values for cervical spine segments in axial compression applied with varying coronal plane eccentricity. Thirty-three human cadaveric functional spinal units were subjected to axial compression at three magnitudes of lateral eccentricity of the applied force. Injury was identified by high-speed video and graded by spine surgeons. Linear regression was used to define neck injury tolerance values based on a criterion incorporating coronal plane loads accounting for specimen sex, age, size and bone density. Larger coronal plane eccentricity at injury was associated with smaller resultant coronal plane force. https://www.selleckchem.com/products/rk-24466.html The level of coronal plane eccentricity at failure appears to distinguish between the types of injuries sustained, with hard tissue structure injuries more common at low levels of eccentricity and soft tissue structure injuries more common at high levels of eccentricity. There was no relationship between axial force and lateral bending moment at injury which has been previously proposed as an injury criterion. These results provide the foundation for designing and evaluating strategies and devices for preventing severe spinal injuries.Adipose-derived mesenchymal stem cells (hADMSC) retaining proliferation and multi-differentiation potential may support the central nervous system (CNS) regeneration. Multipotency of MSC may result in both desirable and undesirable cells, post-transplantation. A better strategy to attain desired cells may be in vitro commitment of hADMSCs to uni-/bi- potent neural progenitor cells (NPCs), prior to transplantation. Derivation of stable NPCs may require a suitable niche eliciting proliferation and differentiation signals. The present study designed a biomimetic niche comprising insoluble fibrin supported adhesion matrix and exogenously added growth factors (GFs) for deriving different neural cells and established the role of Notch and Wnt signals for proliferation and differentiation of hADMSCs, respectively. The stable transformation of hADMSCs into neurospheres (NS) comprising Nestin+ve NPCs was achieved consistently. Slight modifications of niche enable differentiation of NS to NPCs; NPCs to neurons; NPCs to oligodendrocyte progenitor cells (OPCs); and OPCs to oligodendrocytes (OLG). Fibrin plays a crucial role in the conversion of hADMSC to NS and NPCs to OPCs; but, not essential for OPC to OLG maturation. Co-survival and cell-cell interaction of NPC derived neurons and OPCs promoting OLG maturation is illustrated. The designed biomimetic niche shows the potential for directing autologous ADMSCs to neural cells for applications in regenerative medicine.The role of rotational molecular motors of the ATP synthase class is integral to the metabolism of cells. Yet the function of FliI6-FliJ complex, a homolog of the F1 ATPase motor, within the flagellar export apparatus remains unclear. We use a simple two-state model adapted from studies of linear molecular motors to identify key features of this motor. The two states are the 'locked' ground state where the FliJ coiled coil filament experiences angular fluctuations in an asymmetric torsional potential, and a 'free' excited state in which FliJ undergoes rotational diffusion. Michaelis-Menten kinetics was used to treat transitions between these two states, and obtain the average angular velocity of the unloaded FliJ filament within the FliI6 stator ωmax ≈ 9.0 rps. The motor was then studied under external counter torque conditions in order to ascertain its maximal power output Pmax ≈ 42 kBT/s (or 102 kW/mol), and the stall torque Gstall ≈ 3 kBT/rad (or 0.01 nN·nm/rad). Two modes of action within the flagellar export apparatus are proposed, in which the motor performs useful work either by continuously 'grinding' through the resistive environment of the export gate, or by exerting equal and opposite stall force on it. In both cases, the resistance is provided by flagellin subunits entering the flagellar export channel prior to their unfolding. We therefore propose that the function of the FliI6-FliJ complex is to lower the energy barrier, and therefore assist in unfolding of the flagellar proteins before feeding them into the transport channel.Most of the existing recognition algorithms are proposed for closed set scenarios, where all categories are known beforehand. However, in practice, recognition is essentially an open set problem. There are categories we know called "knowns", and there are more we do not know called "unknowns". Enumerating all categories beforehand is never possible, consequently, it is infeasible to prepare sufficient training samples for those unknowns. Applying closed set recognition methods will naturally lead to unseen-category errors. To address this problem, we propose the prototype-based Open Deep Network (P-ODN) for open set recognition tasks. Specifically, we introduce prototype learning into open set recognition. Prototypes and prototype radiuses are trained jointly to guide a CNN network to derive more discriminative features. Then P-ODN detects the unknowns by applying a multi-class triplet thresholding method based on the distance metric between features and prototypes. Manual labeling the unknowns which are detected in the previous process as new categories. Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically. The weights of new predictors are initialized exquisitely by applying a distances based algorithm to transfer the learned knowledge. Consequently, this initialization method speeds up the fine-tuning process and reduce the samples needed to train new predictors. Extensive experiments show that P-ODN can effectively detect unknowns and needs only few samples with human intervention to recognize a new category. In the real world scenarios, our method achieves state-of-the-art performance on the UCF11, UCF50, UCF101 and HMDB51 datasets.