Local BDNF Delivery to the Injured Cervical Spinal Cord using an Engineered Hydrogel Enhances Diaphragmatic Respiratory Function.

We developed an innovative biomaterial-based approach to repair the critical neural circuitry that controls diaphragm activation by locally delivering brain-derived neurotrophic factor (BDNF) to injured cervical spinal cord. BDNF can be used to restore respiratory function via a number of potential repair mechanisms; however, widespread BDNF biodistribution resulting from delivery methods such as systemic injection or lumbar puncture can lead to inefficient drug delivery and adverse side effects. As a viable alternative, we developed a novel hydrogel-based system loaded with polysaccharide-BDNF particles self-assembled by electrostatic interactions that can be safely implanted in the intrathecal space for achieving local BDNF delivery with controlled dosing and duration. Implantation of BDNF hydrogel after C4/C5 contusion-type spinal cord injury (SCI) in female rats robustly preserved diaphragm function, as assessed b

FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resolution of 0.25 degree. However, the challenge remains to perform comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous studies have demonstrated the importance of mitigating the accumulation of forecast errors for effective long-term forecasts. Despite numerous efforts to reduce accumulation errors, including autoregressive multi-time step loss, using a single model is found to be insufficient to achieve optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts with a temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation, based on latitude-weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first ML-based weather forecasting system to accomplish this achievement

Toward data-driven solutions to interactive dynamic influence diagrams

With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains

Diversifying agent's behaviors in interactive decision models

Modeling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimize its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modeling insufficiency occurs when the agents are competitive and the subject agent cannot get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. Most of the recent research still assumes that the agents have common knowledge about their environments and a subject agent has the true behavior of other agents in its mind. Consequently, the resulting techniques are not applicable in many practical problem domains. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model before their interactions. The challenges lie in generating and measuring new behaviors of other agents. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top‐ K $K$ behaviors. We demonstrate the performance of the new techniques in two well‐studied problem domains. The top‐ K $K$ behavior selection embarks the study of unknown behaviors in multiagent decision making and inspires investigation of diversifying agents' behaviors in competitive agent interactions. This study will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship

Development and Characterization of Anti-Inflammatory Coatings for Implanted Neural Probes

Stable single-unit recordings from the nervous system using microelectrode arrays can have significant implications for the treatment of a wide variety of sensory and movement disorders. However, the long-term performance of the implanted neural electrodes is compromised by the formation of glial scar around these devices, which is a typical consequence of the inflammatory tissue reaction to implantation-induced injury in the CNS. The glial scar is inhibitory to neurons and forms a barrier between the electrode and neurons in the surrounding brain tissue. Therefore, to maintain long-term recording stability, reactive gliosis and other inflammatory processes around the electrode need to be minimized. This work has succeeded in the development of neural electrode coatings that are capable of sustained release of anti-inflammatory agents while not adversely affecting the electrical performance of the electrodes. The effects of coating methods, initial drug loadings on release kinetics were investigated to optimize the coatings. The physical properties of the coatings and the bioactivity of released anti-inflammatory agents were characterized. The effect of the coatings on the electrical property of the electrodes was tested. Two candidate anti-inflammatory agents were screened by evaluating their anti-inflammatory potency in vitro. Finally, neural electrodes coated with the anti-inflammatory coatings were implanted into rat brains to assess the anti-inflammatory potential of the coatings in vivo. This work represents a promising approach to attenuate astroglial scar around the implanted silicon neural electrodes, and may provide a promising strategy to improve the long-term recording stability of silicon neural electrodes.Ph.D.Committee Chair: Ravi V. Bellamkonda; Committee Member: Julia E. Babensee; Committee Member: Michelle C. LaPlaca; Committee Member: Robert J. McKeon; Committee Member: Todd C. McDevit

Minocycline targets multiple secondary injury mechanisms in traumatic spinal cord injury

Minocycline hydrochloride (MH), a semi-synthetic tetracycline derivative, is a clinically available antibiotic and anti-inflammatory drug that also exhibits potent neuroprotective activities. It has been shown to target multiple secondary injury mechanisms in spinal cord injury, via its anti-inflammatory, anti-oxidant, and anti-apoptotic properties. The secondary injury mechanisms that MH can potentially target include inflammation, free radicals and oxidative stress, glutamate excitotoxicity, calcium influx, mitochondrial dysfunction, ischemia, hemorrhage, and edema. This review discusses the potential mechanisms of the multifaceted actions of MH. Its anti-inflammatory and neuroprotective effects are partially achieved through conserved mechanisms such as modulation of p38 mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/Akt signaling pathways as well as inhibition of matrix metalloproteinases (MMPs). Additionally, MH can directly inhibit calcium influx through the N-methyl-D-aspartate (NMDA) receptors, mitochondrial calcium uptake, poly(ADP-ribose) polymerase-1 (PARP-1) enzymatic activity, and iron toxicity. It can also directly scavenge free radicals. Because it can target many secondary injury mechanisms, MH treatment holds great promise for reducing tissue damage and promoting functional recovery following spinal cord injury