Browsing by Author "Li, Jian"

Now showing 1 - 14 of 14
  • Thumbnail Image
    Association between Reward Sensitivity and Smoking Status in Major Depressive Disorder
    Feng, Shengchuang (Virginia Tech, 2017-05-10)
    Chronic nicotine use has been linked to increased sensitivity to nondrug rewards as well as improvement in mood among individuals with depression, and these effects have been hypothesized to be mediated through alternations in striatal dopamine activity. Similarly, chronic nicotine use is hypothesized to influence the mechanisms by which healthy and depressed individuals learn about rewards in their environment. However, the specific behavioral and neural mechanisms by which nicotine influences the learning process is poorly understood. Here, we use a probabilistic learning task, functional magnetic resonance imaging and neurocomputational analyses, to show that chronic smoking is associated with higher reward sensitivity, along with lower learning rate and striatal prediction error signal. Further, we show that these effects do not differ between individuals with and without major depressive disorder (MDD). In addition, a negative correlation between reward sensitivity and striatal prediction error signal was found among smokers, consistent with the suggestion that enhanced tonic dopamine associated with increased reward sensitivity leads to an attenuation of phasic dopamine activity necessary for updating of reward value during learning.
  • Thumbnail Image
  • Thumbnail Image
    Challege and Opportunities of Membrane Bioelctrochemical Reactors for Wastewater Treatment
    Li, Jian (Virginia Tech, 2016-04-26)
    Microbial fuel cells (MFCs) are potentially advantageous as an energy-efficient approach for wastewater treatment. Integrating membrane filtration with MFCs could be a viable option for advanced wastewater treatment with a low energy input. Such an integration is termed as membrane bioelectrochemical reactors (MBERs). Comparing to the conventional membrane bioreactors or anaerobic membrane bioreactors, MBER could be a competitive technology, due to the its advantages on energy consumption and nutrients removal. By installing the membrane in the cathodic compartment or applying granular activated carbon as fluidized bed materials, membrane fouling issue could be alleviated significantly. In order to drive MBER technology to become a more versatile platform, applying anion exchange membrane (AEM) could be an option for nutrients removal in MBERs. Wastewater can be reclaimed and reused for subsequent fermentation use after a series MFC-MBR treatment process. Such a synergistic configuration not only provide a solution for sustainable wastewater treatment, but also save water and chemical usage from other non-renewable resource. Integrating membrane process with microbial fuel cells through an external configuration provides another solution on sustainable wastewater treatment through a minimal maintenance requirement.
  • Thumbnail Image
    Computational investigation of the flow field contribution to improve electricity generation in granular activated carbon-assisted microbial fuel cells
    Zhao, Lei; Li, Jian; Battaglia, Francine; He, Zhen (Elsevier, 2016-11-30)
    Microbial fuel cells (MFCs) offer an alternative approach to treat wastewater with less energy input and direct electricity generation. To optimize MFC anodic performance, adding granular activated carbon (GAC) has been proved to be an effective way, most likely due to the enlarged electrode surface for biomass attachment and improved mixing of the flow field. The impact of a flow field on the current enhancement within a porous anode medium (e.g., GAC) has not been well understood before, and thus is investigated in this study by using mathematical modeling of the multi-order Butler-Volmer equation with computational fluid dynamics (CFD) techniques. By comparing three different CFD cases (without GAC, with GAC as a nonreactive porous medium, and with GAC as a reactive porous medium), it is demonstrated that adding GAC contributes to a uniform flow field and a total current enhancement of 17%, a factor that cannot be neglected in MFC design. However, in an actual MFC operation, this percentage could be even higher because of the microbial competition and energy loss issues within a porous medium. The results of the present study are expected to help with formulating strategies to optimize MFC with a better flow pattern design. (C) 2016 Elsevier B.V. All rights reserved.
  • Thumbnail Image
    Coupling microbial fuel cells with a membrane photobioreactor for wastewater treatment and bioenergy production
    Tse, Hei Tsun; Luo, Shuai; Li, Jian; He, Zhen (Springer, 2016-11-01)
  • Thumbnail Image
    Current-Mode Control: Modeling and its Digital Application
    Li, Jian (Virginia Tech, 2009-04-14)
    Due to unique characteristics, current-mode control architectures with different implementation approaches have been widely used in power converter design to achieve current sharing, AVP control, and light-load efficiency improvement. Therefore, an accurate model for current-mode control is indispensable to system design due to the existence of subharmonic oscillations. The fundamental difference between current-mode control and voltage-mode control is the PWM modulation. The inductor current, one of state variables, is used in the modulator in current-mode control while an external ramp is used in voltage-mode control. The dynamic nonlinearity of current-mode control results in the difficulty of obtaining the small-signal model for current-mode control in the frequency domain. There has been a long history of the current-mode control modeling. Many previous attempts have been made especially for constant-frequency peak current-mode control. However, few models are available for variable-frequency constant on-time control and V2 current-mode control. It's hard to directly extend the model of peak current-mode control to those controls. Furthermore, there is no simple way of modeling the effects of the capacitor ripple which may result in subharmonic oscillations in V2 current-mode control. In this dissertation, the primary objective to investigate a new and general modeling approach for current-mode control with different implementation methods. First, the fundamental limitation of average models for current-mode control is identified. The sideband components are generated and coupled with the fundamental component through the PWM modulator in the current loop. Moreover, the switching frequency harmonics cannot be ignored in the current loop since the current ripple is used for the PWM modulation. Available average models failed to consider the sideband effects and high frequency harmonics. Due to the complexity of the current loop, it is difficult to analyze current loop in the frequency domain. A new modeling approach for current-mode control is proposed based on the time-domain analysis. The inductor, the switches and the PWM modulator are treated as a single entity to model instead of breaking them into parts to do it. Describing function method is used. Proposed approach can be applied not only to constant-frequency modulation but also to variable-frequency modulation. The fundamental difference between different current-mode controls is elaborated based on the models obtained from the new modeling approach. Then, an equivalent circuit representation of current-mode control is presented for the sake of easy understanding. The effect of the current loop is equivalent to controlling the inductor current as a current source with certain impedance. The circuit representation provides both the simplicity of the circuit model and the accuracy of the proposed model. Next, the new modeling approach is extended to V2 current-mode control based on similar concept. The model for V2 current-mode control can accurately predict subharmonic oscillations due to the influence of the capacitor ripple. Two solutions are discussed to solve the instability issue. After that, a digital application of current-mode control is introduced. High-resolution digital pulse-width modulator (DPWM) is considered to be indispensable for minimizing the possibility of unpredicted limit-cycle oscillations, but results in high cost, especially in the application of voltage regulators for microprocessors. In order to solve this issue, a fully digital current-mode control architecture which can effectively limit the oscillation amplitude is presented, thereby greatly reducing the design challenge for digital controllers by eliminating the need for the high-resolution DPWM. The new modeling strategy is also used to model the proposed digital current-mode control to help system design. As a conclusion, a new modeling approach for current-mode control is fully investigated. Describing function method is utilized as a tool in this dissertation. Proposed approach is quite general and not limit by implementation methods. All the modeling results are verified through simulation and experiments.
  • Thumbnail Image
    Detecting epistatic interactions contributing to human gene expression using the CEPH family data
    Li, Hua; Gao, Guimin; Li, Jian; Page, Grier P.; Zhang, Kui (2007-12-18)
    It is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed method was implemented in a widely used software program SOLAR. We evaluated the power of the method by simulation studies and applied this method to the analysis of the Centre d'Etude du Polymorphisme Humain family gene expression data provided by Genetics Analysis Workshop 15 (GAW15).
  • Thumbnail Image
    Development of a dynamic mathematical model for membrane bioelectrochemical reactors with different configurations
    Li, Jian; He, Zhen (Springer, 2016-01-01)
    Membrane bioelectrochemical reactors (MBERs) integrate membrane filtration into bioelectrochemical systems for sustainable wastewater treatment and recovery of bioenergy and other resource. Mathematical models for MBERs will advance the understanding of this technology towards further development. In the present study, a mathematical model was implemented for predicting current generation, membrane fouling, and organic removal within MBERs. The relative root-mean-square error was used to examine the model fit to the experimental data. It was found that a constant to determine how fast the internal resistance responds to the change of the anodophillic microorganism concentration could have a dominant impact on current generation. Hydraulic cross-flow exhibited a minor effect on membrane fouling unless it was reduced below 0.5 m s−1. This MBER model encourages further optimization and eventually can be used to guide MBER development.
  • Thumbnail Image
  • Thumbnail Image
    Neural responses to sanction threats in two-party economic exchange
    Li, Jian; Xiao, Erte; Houser, Daniel; Montague, P. Read (NAS, 2009-08-11)
    Sanctions are used ubiquitously to enforce obedience to social norms. However, recent field studies and laboratory experiments have demonstrated that cooperation is sometimes reduced when incentives meant to promote prosocial decisions are added to the environment. Although various explanations for this effect have been suggested, the neural foundations of the effect have not been fully explored. Using a modified trust game, we found that trustees reciprocate relatively less when facing sanction threats, and that the presence of sanctions significantly reduces trustee’s brain activities involved in social reward valuation [in the ventromedial prefrontal cortex (VMPFC), lateral orbitofrontal cortex, and amygdala] while it simultaneously increases brain activities in the parietal cortex, which has been implicated in rational decision making. Moreover,wefound that neural activity in a trustee’s VMPFC area predicts her future level of cooperation under both sanction and no-sanction conditions, and that this predictive activity can be dynamically modulated by the presence of a sanction threat.
  • Thumbnail Image
    New approaches to investigating social gestures in autism spectrum disorder
    Kishida, Kenneth T.; Li, Jian; Schwind, Justin; Montague, P. Read (BMC, 2012-05-24)
    The combination of economic games and human neuroimaging presents the possibility of using economic probes to identify biomarkers for quantitative features of healthy and diseased cognition. These probes span a range of important cognitive functions, but one new use is in the domain of reciprocating social exchange with other humans - a capacity perturbed in a number of psychopathologies. We summarize the use of a reciprocating exchange game to elicit neural and behavioral signatures for subjects diagnosed with autism spectrum disorder (ASD). Furthermore, we outline early efforts to capture features of social exchange in computational models and use these to identify quantitative behavioral differences between subjects with ASD and matched controls. Lastly, we summarize a number of subsequent studies inspired by the modeling results, which suggest new neural and behavioral signatures that could be used to characterize subtle deficits in information processing during interactions with other humans.
  • Thumbnail Image
    Optimizing the performance of a membrane bio-electrochemical reactor using an anion exchange membrane for wastewater treatment
    Li, Jian; He, Zhen (The Royal Society of Chemistry, 2015-03-05)
    A membrane bioelectrochemical reactor (MBER) is a system integrating ultrafiltration membranes into microbial fuel cells (MFCs) for energy-efficient wastewater treatment. To improve nitrogen removal, an MBER based on an anion exchange membrane (AEM), the MBER-A, was investigated for treating synthetic solution or actual wastewater during a 200-day operation. The MBER-A significantly improved the removal of total nitrogen to 56.9% with the synthetic solution, compared with 7.6% achieved in the MBER containing a cation exchange membrane (MBER-C). This was mainly due to the removal of nitrate through both nitrate migration across AEM and heterotrophic denitrification in the anode. The final filtrate from MBER-A contains 11.9 mg L-1 nitrate-nitrogen, 6.0 mg L-1 nitrite-nitrogen, and less than 1 mg L-1 ammonia-nitrogen. The MBER-A achieved 91.3 +/- 6.4% of COD removal, resulting in a COD concentration of 21.6 +/- 17.8 mg L-1 in its membrane filtrate. The transmembrane pressure (TMP) remained below 10 kPa when being operated with synthetic solution. The actual wastewater (primary effluent) led to the decrease in both COD and nitrogen removal, likely due to complex composition of organic compounds and low electricity generation. The MBER-A decreased the COD concentration by 84.5 +/- 14.4% and total nitrogen concentration by 48.4 +/- 1.9%. The ammonia-nitrogen concentration remained at 0.3 mg L-1 in the final filtrate. The energy consumption by the MBER-A could be significantly decreased through reducing the strength of the anolyte recirculation rate. Those results encourage further investigation and development of the MBER technology for energy efficient removal of organic and nitrogen compounds from wastewater.
  • Thumbnail Image
    Policy Adjustment in a Dynamic Economic Game
    Li, Jian; McClure, Samuel M.; Casas, Brooks; Montague, P. Read (PLOS, 2006-12)
    Making sequential decisions to harvest rewards is a notoriously difficult problem. One difficulty is that the real world is not stationary and the reward expected from a contemplated action may depend in complex ways on the history of an animal’s choices. Previous functional neuroimaging work combined with principled models has detected brain responses that correlate with computations thought to guide simple learning and action choice. Those works generally employed instrumental conditioning tasks with fixed action-reward contingencies. For real-world learning problems, the history of reward-harvesting choices can change the likelihood of rewards collected by the same choices in the near-term future. We used functional MRI to probe brain and behavioral responses in a continuous decision-making task where reward contingency is a function of both a subject’s immediate choice and his choice history. In these more complex tasks, we demonstrated that a simple actor-critic model can account for both the subjects’ behavioral and brain responses, and identified a reward prediction error signal in ventral striatal structures active during these non-stationary decision tasks. However, a sudden introduction of new reward structures engages more complex control circuitry in the prefrontal cortex (inferior frontal gyrus and anterior insula) and is not captured by a simple actor-critic model. Taken together, these results extend our knowledge of reward-learning signals into more complex, history-dependent choice tasks. They also highlight the important interplay between striatum and prefrontal cortex as decision-makers respond to the strategic demands imposed by non-stationary reward environments more reminiscent of real-world tasks.
  • Thumbnail Image
    Self- and other-regarding reinforcement learning: Disruptions in mental disorders and oxytocin's modulating role in healthy people
    Feng, Shengchuang (Virginia Tech, 2020-06-17)
    It has been suggested that reward processing and related neural substrates are disrupted in some common mental disorders such as depression, addiction, and anxiety. An increasing number of psychiatric studies have been applying reinforcement learning (RL) models to examine these disruptions in self-regarding learning (learning about rewards delivered to the learners themselves). A review of RL alterations associated with mental disorders in extant studies will be beneficial for uncovering the mechanisms of these health problems. Although impaired social reward processing is common in some mental disorders [e.g., post-traumatic stress disorder (PTSD), social anxiety and autism], RL has not been widely used to detect the potentially disrupted social reward learning, especially for other-regarding learning (learning about rewards delivered to others). Meanwhile, it has not been clear whether some drugs, e.g., oxytocin (OT), can alter other-regarding learning, so they may serve as a therapeutic intervention when related deficits occur. In the present set of studies, we summarized common and distinct features in terms of self-regarding RL disturbances among depression, addiction and anxiety disorders based on previous findings (Paper I), tested whether behavioral and neural self- and other-regarding RL were impaired in PTSD with and without comorbid depression (Paper II), and investigated OT's behavioral and neural effects on self- and other-regarding RL in healthy males (Paper III). The results of our literature review showed that the commonalities in all three mental disorders were inflexibility and inconsistent choices, and the differences included decreased learning rates in depression, a higher weight to rewards versus punishments in addiction, and hypersensitivity to punishments in anxiety. The results of the PTSD study demonstrated impaired behavioral other-regarding learning in PTSD patients with and without depression, supposedly due to their hypervigilance to unexpected outcomes for others, as evidenced by the heightened responses in their inferior parietal lobule. The OT study detected OT's effects of attenuating behavioral other-regarding learning, as well as the neural coding of unexpected outcomes for others in the anterior cingulate cortex. These findings provide new evidence of self- and other-regarding RL alterations in mental disorders, reveal potential targets for their treatments, and bring caution for using OT as a therapeutic intervention.