Biomechanics and Neural Control of Movement: CMI\u27s Effects on Downstream Motor Processing and Gait in Forwards and Backwards Walking

Analyzing the effects of cognitive motor interferences (CMI) on walking is usually done in patients with neurological comorbidity or during forward walking (FW). However, there are few studies that examine gait differences between FW and backward walking (BW) under the presence of CMI when speed is kept constant on a treadmill. In this study we examined how CMI would disrupt sensory feedback and affect the descending motor pathway. We hypothesized that subjects that walked backwards and were given a cognitive task would show the greatest differences in gait due to a lack of visual input and the presence of CMI. A three-dimensional motion capture system was used to acquire the movement of the leg and calculate gait characteristics (stride length, stance phase, swing phase). Across the entire population, direction had a significant effect on all gait characteristics, but the presence of CMI did not have a significant effect on any of them. Additionally, there was no significant interaction between the two variables. Specifically, the overall stride was shorter, stance was shorter and swing was longer during backward conditions. However, within subject variability demonstrates that each subject utilizes different strategies to compensate for both the lack of sensory feedback and presence of CMI. Results of this study contradict findings from previous work that direction had no effect on stance and swing phase of walking and suggests that backward walking does change more gait characteristics. This implies that sensory feedback has a large impact on modulating motor output, and these effects may be amplified in those with movement-based neurological disorders like Parkinson’s Disease

Predicting Whole Forest Structure, Primary Productivity, and Biomass Density From Maximum Tree Size and Resource Limitations

In the face of uncertain biological response to climate change and the many critiques concerning model complexity it is increasingly important to develop predictive mechanistic frameworks that capture the dominant features of ecological communities and their dependencies on environmental factors. This is particularly important for critical global processes such as biomass changes, carbon export, and biogenic climate feedback. Past efforts have successfully understood a broad spectrum of plant and community traits across a range of biological diversity and body size, including tree size distributions and maximum tree height, from mechanical, hydrodynamic, and resource constraints. Recently it was shown that global scaling relationships for net primary productivity are correlated with local meteorology and the overall biomass density within a forest. Along with previous efforts, this highlights the connection between widely observed allometric relationships and predictive ecology. An emerging goal of ecological theory is to gain maximum predictive power with the least number of parameters. Here we show that the explicit dependence of such critical quantities can be systematically predicted knowing just the size of the largest tree. This is supported by data showing that forests converge to our predictions as they mature. Since maximum tree size can be calculated from local meteorology this provides a general framework for predicting the generic structure of forests from local environmental parameters thereby addressing a range of critical Earth-system questions.Comment: 26 pages, 4 figures, 1 Tabl

Optoelectronic Neural Implant Sensors for Cerebral Blood Volume Monitoring

Nearly 50 million people are afflicted with epilepsy, worldwide. These patients suffer from unprovoked seizures, where neurons in the cerebral cortex under go uncontrolled, hypersynchronous firing of neurons. 30\% of patients with epilepsy do not respond to drug treatments. For these patients, surgical treatment involving the removal or disconnection of brain matter is one of the only alternatives. Such surgical treatments often rely on long-term monitoring of neuronal activity in the brain using subdurally implanted surface electrodes to locate the epileptic focus, but these clinical methods for mapping neuronal activity suffer from low spatial resolutions and poor noise, which can limit the success of surgical treatments where an error of even 1 mm can be critical. The work described here involves the development of an implantable system for performing optical recordings of intrinsic signal (ORIS) on the surface of the brain. By taking advantage of the unique absorption spectrum of hemoglobin, cerebral blood volume (CBV) can be measured via reflectivity changes in the brain at at specific wavelengths of light. Due to the metabolic demands of the brain, the exaggerated neuronal activity and spiking associated with epileptic seizures can be detected indirectly through changes in CBV. While high resolution ORIS measurements have been recorded using externally mounted CCD sensors, this work presents some of the first developments in producing a fully implantable ORIS sensor. Progress in the development of an implantable ORIS sensor described here includes: an implantable organic light emitting diode (OLED) and organic photodetector (OPD) integrated on a highly flexible parylene-c substrate, an implantable sensor using a microLED array embedded on a flexible polyimide substrate, and the application of quantum dots to microLEDs for optical down-conversion. Successful in vivo detection of seizures is achieved with high signal-to-noise using these methods. Additionally, spatial localization of seizure activity is performed using the microLED array. These developments represent crucial first steps in the development of a full 2D neuronal mapping system using implantable ORIS devices

Interaction of prion protein with divalent metals: possible role in neuroprotection and neurodegeneration in prion disease model

Transmissible Spongiform Encephalopathy (TSE) is a set of diseases caused by a change in conformation of the cellular prion protein. This alteration in structural formation of the prion protein results in fatal neurodegenerative conditions characterized by protein aggregation and massive neuronal death. The pathogenic mechanisms of infectious prion protein and the cellular function of normal prion protein are poorly understood. Prion protein contains metal binding sites, and various divalent metals can effectively bind to cellular prion protein. Therefore, the overarching hypothesis of this work was that metal exposure can significantly alter the function of prion protein, which might contribute to the pathogenesis of prion disease. Various cell culture models of prion disease were used to test the hypothesis. First, we examined the effect of manganese toxicity and cell death in modified prion protein (3F4) expressing mouse neuronal cell (PrPC) and prion knockout mouse neuronal cell (PrPKO) lines. PrPKO-cells were more susceptible to manganese toxicity than PrPC-cells. PrPKO-cells contained lower basal levels of both copper and manganese, and acute exposure to manganese resulted in higher susceptibility to manganese toxicity in PrPKO-cells. Rapid internalization of manganese inside the cells was observed, as well as generation of reactive oxygen species (ROS) and mitochondrial dysfunction. Treatment with manganese also initiated the apoptotic cascade in both cell lines with sequential activation of caspase-9 and -3, followed by cellular apoptosis. In particular, PrP KO-cells treated with manganese retained a higher intracellular manganese content and higher ROS generation as compared to PrPC-cells. This translated into faster onset of mitochondrial dysfunction and higher caspase activity, ensued by increased cell death in PrPKO-cells. Thus, our data indicate that the expression of prion protein was crucial in mediating internalization of manganese and protecting the cells against the apoptotic cascade induced by manganese. Interestingly, further examination of the manganese and prion protein interaction showed increased prion protein expression in PrPC-cells in a time-dependent manner. We subsequently analyzed the cellular mechanism of manganese-induced increases in prion protein levels. Our data indicate that the increase was neither due to increased translational activity nor to decreases in the cellular degradative system, including the ubiquitin proteasomal system and the lysosomal system, but to decreased protein turnover. However, the cation cadmium significantly inhibited the cellular ubiquitin proteasomal system, leading to formation of oligomers and ubiquitinated prion proteins. Further investigation revealed the decreased protein turnover rate of prion proteins induced by manganese treatment was due to increased protein stability, as measured by limited proteolysis. Additional experiments with copper also revealed similar findings, suggesting that the divalent metal and prion protein interaction could significantly modulate the protein function. Translating cell culture findings to mouse brain slice cultures showed manganese can also increase prion protein expression in the slice cultures. To examine whether the increased prion protein expression in mouse brain slice cultures results in altered susceptibility to conversion to infectious prion protein (PrPSc), samples corresponding to the greatest increase in prion protein expression were collected and subjected to protein misfolding cyclic amplification. The results indicate that manganese treated mouse brain slice cultures were not more susceptible to conversion to PrPSc by PMCA. The findings suggest that the increased PrP C levels are not indicative of higher susceptibility to PrP Sc conversion under our experimental conditions.;Taken together, the interaction of prion protein with manganese can be construed as a complex interaction. However, it is clear that prion protein protects against oxidative stress induced by transition metals, thereby ameliorating the neurotoxic effect of metals. Decreased susceptibility to proteolytic digestion following manganese treatment suggests increased protein stability of prion protein, but does not promote biochemical behavior similar PrPSc under our experimental conditions. Collectively, our data suggest that cellular prion protein is a key metal binding antioxidant protein, and its stability can be altered by chronic metal exposure. This functional interaction of metals with prion protein may play a role in the pathogenesis of prion diseases

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Growth control of oxygen stoichiometry in homoepitaxial SrTiO3 films by pulsed laser epitaxy in high vacuum

In many transition metal oxides (TMOs), oxygen stoichiometry is one of the most critical parameters that plays a key role in determining the structural, physical, optical, and electrochemical properties of the material. However, controlling the growth to obtain high quality single crystal films having the right oxygen stoichiometry, especially in a high vacuum environment, has been viewed as a challenge. In this work, we show that through proper control of the plume kinetic energy, stoichiometric crystalline films can be synthesized without generating oxygen defects, even in high vacuum. We use a model homoepitaxial system of SrTiO3 (STO) thin films on single crystal STO substrates. Physical property measurements indicate that oxygen vacancy generation in high vacuum is strongly influenced by the energetics of the laser plume, and it can be controlled by proper laser beam delivery. Therefore, our finding not only provides essential insight into oxygen stoichiometry control in high vacuum for understanding the fundamental properties of STO-based thin films and heterostructures, but expands the utility of pulsed laser epitaxy of other materials as well

E-Learning Design for Microsoft Access & ASP.NET

The problem definition is that the current video tutorial series is outdated and lacks interactive features that aid with E-Learning. The goal of the project is to create a platform that incorporates features such as structure, interaction, assessments, and competency tests. The project revolved around integrating four softwares together in order to create an effective E-Learning platform. The four softwares were Adobe Captivate, Camtasia, Microsoft Access and Microsoft Visual Studios. Adobe Captive is used to store the tutorial video and assign quizzes. Camtasia is used to record the tutorial videos. Microsoft Access is used to store all the questions given at each tutorial video as well as store the results the student attains. Microsoft Visual Studio is used to create a webform that integrates everything together. There were some key features that were wanted in the prototype. The first feature is an assessment test at the end of each tutorial. The second feature is that the student must pass the assessment test before moving to the next video. The third feature is that if the student did not pass assessment quiz, the student must rewatch the same tutorial before re attempting assessment quiz. The fourth feature is that the questions at the end of the tutorial video will be randomize and drawn from the Microsoft Access database. The fifth feature is that the results of the quiz for each student are recorded in the database. The objective of the project is to find a way to incorporate all these features. The end product of the project is a prototype for E-Learning with the features. Future students will benefit from this prototype. Incorporating these features has been proven to aid student learning. The project is not intended to create an aesthetically pleasing E-Learning platform but a effective one. The overall project is successful in that discoveries in integrating softwares and implementing features to advance E-Learning were made

Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201

Enhanced quantum coherence in exchange coupled spins via singlet-triplet transitions

Manipulation of spin states at the single-atom scale underlies spin-based quantum information processing and spintronic devices. Such applications require protection of the spin states against quantum decoherence due to interactions with the environment. While a single spin is easily disrupted, a coupled-spin system can resist decoherence by employing a subspace of states that is immune to magnetic field fluctuations. Here, we engineered the magnetic interactions between the electron spins of two spin-1/2 atoms to create a clock transition and thus enhance their spin coherence. To construct and electrically access the desired spin structures, we use atom manipulation combined with electron spin resonance (ESR) in a scanning tunneling microscope (STM). We show that a two-level system composed of a singlet state and a triplet state is insensitive to local and global magnetic field noise, resulting in much longer spin coherence times compared with individual atoms. Moreover, the spin decoherence resulting from the interaction with tunneling electrons is markedly reduced by a homodyne readout of ESR. These results demonstrate that atomically-precise spin structures can be designed and assembled to yield enhanced quantum coherence