PROGRAMMABLE NEURAL PROCESSING FRAMEWORK FOR IMPLANTABLE WIRELESS BRAIN-COMPUTER INTERFACES

Brain-computer interfaces (BCIs) are able to translate cerebral cortex neural activity into control signals for computer cursors or prosthetic limbs. Such neural prosthetics offer tremendous potential for improving the quality of life for disabled individuals. Despite the success of laboratory-based neural prosthetic systems, there is a long way to go before it makes a clinically viable device. The major obstacles include lack of portability due to large physical footprint and performance-power inefficiency of current BCI platforms. Thus, there are growing interests in integrating more BCI's components into a tiny implantable unit, which can minimize the surgical risk and maximize the usability. To date, real-time neural prosthetic systems in laboratory require a wired connection penetrating the skull to a bulky external power/processing unit. For the wireless implantable BCI devices, only the data acquisition and spike detection stages are fully integrated. The rest digital post-processing can only be performed on one chosen channel via custom ASICs, whose lack of flexibility and long development cycle are likely to slow down the ongoing clinical research.This thesis proposes and tests the feasibility of performing on-chip, real-time spike sorting/neural decoding on a programmable wireless sensor network (WSN) node, which is chosen as a compact, low-power platform representative of a future implantable chip. The final accuracy is comparable to state-of-the-art open-loop neural decoder. A detailed power/performance trade-off analysis is presented. Our experimental results show that: 1)direct on-chip neural decoding without spike sorting can achieve 30Hz updating rate, with power density lower than 62mW/cm2; 2)the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel. For the option of having spike sorting in order to keep all neural information, we propose a new neural processing workflow that incorporates a light-weight neuron selection method to the training process to reduce the number of channels required for processing. Experimental results show that the proposed method not only narrows the gap between the system requirement and current hardware technology, but also increase the accuracy of the neural decoder by 3%-22%, due to elimination of noisy channels

Empirical Likelihood Quantile Regression for Right-Censored Data

Quantile estimation of time-to-event data plays a key role in many medical applications, especially conditional on covariates of interest. In such settings, bias due to model misspecification is an important concern. As such, Empirical Likelihood (EL) is a particularly attractive estimation approach, making minimal parametric modeling assumptions without unduly compromising statistical efficiency. However, observed survival times are typically subject to right-censoring, in which case most EL approaches cannot be applied directly. In this thesis, we revisit a widely-applicable Expectation-Maximization (EM) algorithm for right-censored EL. As the covariate-free EL function becomes discontinuous in the conditional setting, we propose a continuity correction for which the computational properties of EM are retained. Several approaches to obtaining confidence intervals are explored. We provide an implementation of our method and related algorithms in the R package flexEL. The source code is written in C++ for high computational performance, and a straightforward interface allows users to fit arbitrary EL models with little programming effort

Coordinated Control of VSC Based Multi-Terminal DC (VSC-MTDC) Power Grid

Voltage source converter based multi-terminal DC (VSC-MTDC) system has raised great interest in academia and power industry. The maturing VSC technology has made such system possible for future medium and high voltage applications. Inspired by the success of DC based power distribution on electric ships, a number of VSC-MTDC systems have been proposed in literature for power grid innovation. However, there are still major technology obstacles to overcome before a VSC-MTDC grid come to utilization. Compared to the maturing technology on device level, research is still needed on the system and operation level. High dynamics and controllability of the VSC brings both opportunity and risks. Controllers must be carefully designed on grid level to fulfill multiple control objectives and coordinate local converter actions. This work provides a comprehensive solution for MTDC system from modeling to control design. The procedure and tool sets are designed to be applied to various system setups and control schemes, so that it can be applied to multiple MTDC applications. First, thorough study on the VSC-MTDC system is conducted through analytical modeling and simulation. A systematic modeling method for general VSC-MTDC system is proposed. It contains a two-stage procedure that is generalizable to arbitrary system setup and configuration. A small signal state space representation which includes local and network dynamics can be obtained. A novel reconfigurable controller concept is then proposed to address multiple control strategies and communication constraints in system level. Design of such controller is formulated into a standard LMI optimization problem so it can be efficiently solved even for large scale system. Using the proposed control design method, different control schemes can be easily explored through unified methodology and procedure. We demonstrated that existing control schemes for MTDC power balancing can be covered by this control structure. The proposed modeling and control design method is applied to four-terminal HVDC systems of multiple grid applications. Different control topologies and operation modes are evaluated and compared. Practical aspects such as LMI parameter tuning guideline and specifications for different applications are discussed

Supervised Learning and Model Analysis with Compositional Data

The compositionality and sparsity of high-throughput sequencing data poses a challenge for regression and classification. However, in microbiome research in particular, conditional modeling is an essential tool to investigate relationships between phenotypes and the microbiome. Existing techniques are often inadequate: they either rely on extensions of the linear log-contrast model (which adjusts for compositionality, but is often unable to capture useful signals), or they are based on black-box machine learning methods (which may capture useful signals, but ignore compositionality in downstream analyses). We propose KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data. It is tailored to sparse compositional data and is able to incorporate prior knowledge, such as phylogenetic structure. KernelBiome captures complex signals, including in the zero-structure, while automatically adapting model complexity. We demonstrate on par or improved predictive performance compared with state-of-the-art machine learning methods. Additionally, our framework provides two key advantages: (i) We propose two novel quantities to interpret contributions of individual components and prove that they consistently estimate average perturbation effects of the conditional mean, extending the interpretability of linear log-contrast models to nonparametric models. (ii) We show that the connection between kernels and distances aids interpretability and provides a data-driven embedding that can augment further analysis. Finally, we apply the KernelBiome framework to two public microbiome studies and illustrate the proposed model analysis. KernelBiome is available as an open-source Python package at https://github.com/shimenghuang/KernelBiome

Level of Orthographic Knowledge Helps to Reveal Automatic Predictions in Visual Word Processing

The brain generates predictions about visual word forms to support efficient reading. The “interactive account” suggests that the predictions in visual word processing can be strategic or automatic (non-strategic). Strategic predictions are frequently demonstrated in studies that manipulated task demands, however, few studies have investigated automatic predictions. Orthographic knowledge varies greatly among individuals and it offers a unique opportunity in revealing automatic predictions. The present study grouped the participants by level of orthographic knowledge and recorded EEGs in a non-linguistic color matching task. The visual word-selective N170 response was much stronger to pseudo than to real characters in participants with low orthographic knowledge, but not in those with high orthographic knowledge. Previous work on predictive coding has demonstrated that N170 is a good index for prediction errors, i.e., the mismatches between predictions and visual inputs. The present findings provide unambiguous evidence that automatic predictions modulate the early stage of visual word processing

Differences in the Gut Microbiota Establishment and Metabolome Characteristics Between Low- and Normal-Birth-Weight Piglets During Early-Life

Low-birth-weight (LBW) piglets are at a high-risk for postnatal growth failure, mortality, and metabolic disorders later in life. Early-life microbial exposure is a potentially effective intervention strategy for modulating the health and metabolism of the host. Yet, it has not been well elucidated whether the gut microbiota development in LBW piglets is different from their normal littermates and its possible association with metabolite profiles. In the current study, 16S rRNA gene sequencing and metabolomics was used to investigate differences in the fecal microbiota and metabolites between LBW and normal piglets during early-life, including day 3 (D3), 7 (D7), 14 (D14), 21 (D21, before weaning), and 35 (D35, after birth). Compared to their normal littermates, LBW piglets harbored low proportions of Faecalibacterium on D3, Flavonifractor on D7, Lactobacillus, Streptococcus, and Prevotella on D21, as well as Howardella on D21 and D35. However, the abundance of Campylobacter on D7 and D21, Prevotella on D14 and D35, Oscillibacter and Moryella on D14 and D21, and Bacteroides on D21 was significantly higher in LBW piglets when compared with normal piglets. The results of the metabolomics analysis suggested that LBW significantly affected fecal metabolites involved in fatty acid metabolism (e.g., linoleic acid, α-linolenic acid, and arachidonic acid), amino acid metabolism (e.g., valine, phenylalanine, and glutamic acid), as well as bile acid biosynthesis (e.g., glycocholic acid, 25-hydroxycholesterol, and chenodeoxycholic acid). Spearman correlation analysis revealed a significant negative association between Campylobacter and N1-acetylspermine on D7, Moryella and linoleic acid on D14, Prevotella and chenodeoxycholic acid on D21, and Howardella and phenylalanine on D35, respectively. Collectively, LBW piglets have a different gut bacterial community structure when compared with normal-birth-weight (NBW) piglets during early-life, especially from 7 to 21 days of age. Also, a distinctive metabolic status in LBW piglets might be partly associated with the altered intestinal microbiota. These findings may further elucidate the factors potentially associated with the impaired growth and development of LBW piglets and facilitate the development of nutritional interventions

Combined Toxicity Evaluation of Ochratoxin A and Aflatoxin B1 on Kidney and Liver Injury, Immune Inflammation, and Gut Microbiota Alteration Through Pair-Feeding Pullet Model

Ochratoxin A (OTA) and aflatoxin B1 (AFB1) are often co-contaminated, but their synergistic toxicity in poultry is limitedly described. Furthermore, the traditional ad libitum feeding model may fail to distinguish the specific impact of mycotoxins on the biomarkers and the indirect effect of mildew on the palatability of feed. A pair-feeding model was introduced to investigate the specific effect and the indirect effect of the combined toxicity of OTA and AFB1, which were independent and dependent on feed intake, respectively. A total of 180 one-day-old pullets were randomly divided into 3 groups with 6 replicates, and each replicate contained 10 chicks. The control group (Group A) and the pair-feeding group (Group B) received the basal diet without mycotoxin contamination. Group C was administrated with OTA- and AFB1-contaminated feed (101.41 μg/kg of OTA + 20.10 μg/kg of AFB1). The scale of feeding in Group B matched with the feed intake of Group C. The trial lasted 42 days. Compared with the control group, co-contamination of OTA and AFB1 in feed could adversely affect the growth performance (average daily feed intake (ADFI), body weight (BW), average daily weight gain (ADG), feed conversion ratio (FCR), and shank length (SL)), decrease the relative weight of the spleen (p < 0.01), and increase the relative weight of the kidney (p < 0.01). Moreover, the reduction of feed intake could also adversely affect the growth performance (BW, ADG, and SL), but not as severely as mycotoxins do. Apart from that, OTA and AFB1 also activated the antioxidative and inflammation reactions of chicks, increasing the level of catalase (CAT), reactive oxygen species (ROS), and interleukin-8 (IL-8) while decreasing the level of IL-10 (p < 0.01), which was weakly influenced by the feed intake reduction. In addition, OTA and AFB1 induced histopathological changes and apoptosis in the kidney and liver as well as stimulated the growth of pernicious bacteria to cause toxic effects. There were no histopathological changes and apoptosis in the kidney and liver of the pair-feeding group. The combined toxicity of OTA and AFB1 had more severe effects on pullets than merely reducing feed supply. However, the proper reduction of the feed intake could improve pullets’ physical health by enriching the bacteria Lactobacillus, Phascolarctobacterium, Bacteroides, Parabacteroides, and Barnesiella

JIB-04 has broad-spectrum antiviral activity and inhibits SARS-CoV-2 replication and coronavirus pathogenesis

Pathogenic coronaviruses are a major threat to global public health. Here, using a recombinant reporter virus-based compound screening approach, we identified small-molecule inhibitors that potently block the replication of severe acute respiratory syndrome virus 2 (SARS-CoV-2). Among them, JIB-04 inhibited SARS-CoV-2 replication in Vero E6 cells with a 50% effective concentration of 695 nM, with a specificity index of greater than 1,000. JIB-04 showe