The Application Of Small Molecule Inhibitors Of Ngr1 And Lpa1 Towards The Goal Of Neuroregeneration In The Central Nervous System

Disorders affecting the CNS are significantly disabling and often carry a poor prognosis of functional recovery. Pharmacotherapies that promote functional improvement via neuroregeneration have proven to be an elusive goal. Factors intrinsic to the neuronal microenvironment, particularly myelin-associated proteins such as Nogo-A, MAG, and OMgp, have been shown to be important in inhibiting such regeneration through neuronal NgR1. Additionally, LPA signaling through LPA1 has also shown to be important in inhibiting neuroregeneration through mechanisms that are currently being researched. It has been previously shown that application of a NgR1 decoy receptor (AA-NgR(310)ecto-Fc) increases sprouting below the site of the lesion in rats with spinal cord contusion injuries. Likewise, application of this same decoy receptor effectively disinhibited functional recovery as exemplified by the increase in percentage of weight-bearing rats treated with the decoy receptor. Noting the more ideal synthetic properties of a small molecule pharmaceutical, here we attempt to use a small molecule inhibitor of NgR1 to induce the in vitro regeneration of axons following scrape injury as well as in an in vivo model of mice with SCI. Additionally, noting the importance of LPA1 as shown through previous studies, we also attempt to utilize a small molecule inhibitor of LPA1 to promote axonal regeneration. Our results show that inhibition of NgR1 with the small molecule inhibitor YU-NR-008 did not significantly improve axonal regeneration in vitro. Application of the NgR1 inhibitor YU-NR-008 alone showed a trend toward improved axonal regeneration, albeit insignificant (mean signal intensity for YU-NR-008 treated animals at 1.243 ± 0.128 vs. control 1.00 ± 0.00, p = 0.0787). Co-treatment with YU-NR-008 and Nogo-22 did not rescue Nogo-22-mediated inhibition of axonal regeneration (Nogo-22 0.771 ± 0.051 vs. Nogo-22 with YU-NR-008 at 0.801 ± 0.073). Additionally, functional recovery as measured by the Basso Mouse Scale (BMS) was not improved with the administration of YU-NR-008 following SCI for 2 or 4 weeks (D32 BMS scores were 4.643 ± 0.713 (SEM) for control vs. 3.550 ± 0.669 for animals treated with YU-NR-008 for 4 weeks). Likewise, administration of the LPA1 antagonist AM095 did not improve functional recovery following SCI (mean BMS at 54 days for AM095-treated animals was 3.182 ± 0.532 vs. 5.033 ± 0.448 for vehicle-treated animals). We conclude that the tested doses of YU-NR-008 and AM095 were ineffective in promoting recovery in a rodent model spinal cord injury. Additional studies will be needed to determine whether axonal growth was stimulated by these doses, or if drug doses failed to achieve the cellular target effect following spinal cord injury

Automatic Recognition of Facial Displays of Unfelt Emotions

Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datas

Automatic Recognition of Facial Displays of Unfelt Emotions

Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase

p300/CBP-associated factor selectively regulates the extinction of conditioned fear

It is well established that the activity of chromatin-modifying enzymes is crucial for regulating gene expression associated with hippocampal-dependent memories. However, very little is known about how these epigenetic mechanisms influence the formation of cortically dependent memory, particularly when there is competition between opposing memory traces, such as that which occurs during the acquisition and extinction of conditioned fear. Here we demonstrate, in C57BL/6mice, that the activityofp300/CBP-associated factor (PCAF) within the infralimbic prefrontal cortex is required for long-term potentiation and is necessary for the formation of memory associated with fear extinction, but not for fear acquisition. Further, systemic administration of the PCAF activator SPV106 enhances memory for fear extinction and prevents fear renewal. The selective influence of PCAF on fear extinction is mediated, in part, by a transient recruitment of the repressive transcription factor ATF4tothe promoter of the immediate early genezif268, which competitively inhibits its expression. Thus, within the context of fear extinction, PCAF functions as a transcriptional coactivator, which may facilitate the formation of memory for fear extinction by interfering with reconsolidation of the original memory trace

Exploring Neuromodulatory Systems for Dynamic Learning

In a continual learning system, the network has to dynamically learn new tasks from few samples throughout its lifetime. It is observed that neuromodulation acts as a key factor in continual and dynamic learning in the central nervous system. In this work, the neuromodulatory plasticity is embedded with dynamic learning architectures. The network has an inbuilt modulatory unit that regulates learning depending on the context and the internal state of the system, thus rendering the networks with the ability to self modify their weights. In one of the proposed architectures, ModNet, a modulatory layer is introduced in a random projection framework. This layer modulates the weights of the output layer neurons in tandem with hebbian learning. Moreover, to explore modulatory mechanisms in conjunction with backpropagation in deeper networks, a modulatory trace learning rule is introduced. The proposed learning rule, uses a time dependent trace to automatically modify the synaptic connections as a function of ongoing states and activations. The trace itself is updated via simple plasticity rules thus reducing the demand on resources. A digital architecture is proposed for ModNet, with on-device learning and resource sharing, to facilitate the efficacy of dynamic learning on the edge. The proposed modulatory learning architecture and learning rules demonstrate the ability to learn from few samples, train quickly, and perform one shot image classification in a computationally efficient manner. The ModNet architecture achieves an accuracy of ∼91% for image classification on the MNIST dataset while training for just 2 epochs. The deeper network with modulatory trace achieves an average accuracy of 98.8%±1.16 on the omniglot dataset for five-way one-shot image classification task. In general, incorporating neuromodulation in deep neural networks shows promise for energy and resource efficient lifelong learning systems

In vitro and In vivo Characterization of the Infection Efficiency of a Modified Retrovirus Envelope Glycoprotein for Targeting Gene Transduction

Attributes of both the viral glycoprotein and its cellular receptor play key roles in determining the outcome of infection. This body of work endeavors to illustrate how these two components influence the efficiency of virus infection in in vitro and in vivo systems with an emphasis on characterizing the transduction capacity of a novel entry-targeting glycoprotein. In previous work, the Moloney murine leukemia virus (MLV) envelope glycoprotein (Env) was modified to generate the Sst-RBS glycoprotein. This glycoprotein was created by replacing the wild type (WT) receptor binding site (RBS), located on the surface subunit (SU) of the Env, with the somatostatin peptide hormone sequence SST-14. The modifications resulted in abrogating transduction via the natural MLV receptor and redirecting transduction to a family of five somatostatin receptors (SSTR). I demonstrate that structural characteristics of the Sst-RBS glycoprotein and the intracellular fate of the SSTR receptor influence the infection efficiency of pseudotyped MLV and human immunodeficiency virus type 1 (HIV-1) based lentiviral (LV) particles. Infection and western blot assays indicate that Sst-RBS retains the structural requirements for mediating levels of transduction that are comparable to WT and approach within 5-fold that of transduction mediated by vesicular stomatitis virus (VSV) G protein when each envelope protein is pseudotyped on MLV particles. To address the contribution of receptor characteristics on infection efficiency, HEK 293 cell lines stably expressing comparable cell surface levels of SSTR-2, SSTR-3 and SSTR-5; which have natural differences in intracellular trafficking; were generated. Infection assays revealed that distinctive SSTR subtype-specific destinations correlated with observable differences in the level of Sst-RBS MLV and LV transduction. Taken together the results of virus binding, internalization kinetics, pH-neutralizing agents, protease inhibitor and penetration assays support that SSTR-5 allows a greater level of transduction because viruses internalized by this subtype are exposed to more permissive intracellular compartments. Specifically, SSTR-5-associated virions are directed to compartments that are more favorable to cytosolic penetration of viral cores than the compartment(s) to which virions bound to subtypes 2 and 3 are directed; possibly due to a more beneficial complement of host cell proteases. These data suggested that receptor characteristics such as the intracellular fate of internalized virus-receptor complexes exert a strong influence on the efficiency of infection. Surprisingly, even though the difference in the in vitro transduction capacity of Sst-RBS and VSV G pseudotyped LV particles was greater than that of the MLV pseudotypes, the difference did not translate to a reduction in in vivo transduction capacity. A pilot study examining the feasibility of in vivo transduction demonstrated proof of principle and identified regions of the murine brain with endogenous surface expression of SSTRs that were as efficiently transduced by Sst-RBS LV as by VSV G LV

Diagnostic and adaptive redundant robotic planning and control

Neural networks and fuzzy logic are combined into a hierarchical structure capable of planning, diagnosis, and control for a redundant, nonlinear robotic system in a real world scenario. Throughout this work levels of this overall approach are demonstrated for a redundant robot and hand combination as it is commanded to approach, grasp, and successfully manipulate objects for a wheelchair-bound user in a crowded, unpredictable environment. Four levels of hierarchy are developed and demonstrated, from the lowest level upward: diagnostic individual motor control, optimal redundant joint allocation for trajectory planning, grasp planning with tip and slip control, and high level task planning for multiple arms and manipulated objects. Given the expectations of the user and of the constantly changing nature of processes, the robot hierarchy learns from its experiences in order to more efficiently execute the next related task, and allocate this knowledge to the appropriate levels of planning and control. The above approaches are then extended to automotive and space applications