Abnormal developmental of hippocampal subfields and amygdalar subnuclei volumes in young adults with heavy cannabis use: A three-year longitudinal study

Background: Differences in the volumes of the hippocampus and amygdala have consistently been observed between young adults with heavy cannabis use relative to their non-using counterparts. However, it remains unclear whether the subfields of these functionally and structurally heterogenous regions exhibit similar patterns of change in young adults with long-term heavy cannabis use disorder (CUD). Objectives: This study aims to investigate the effects of long-term heavy cannabis use in young adults on the subregional structures of the hippocampus and amygdala, as well as their longitudinal alterations. Methods: The study sample comprised 20 young adults with heavy cannabis use and 22 matched non-cannabis using healthy volunteers. All participants completed the Cannabis Use Disorder Identification Test (CUDIT) and underwent two T1-structural magnetic resonance imaging (MRI) scans, one at baseline and another at follow-up 3 years later. The amygdala, hippocampus, and their subregions were segmented on T1-weighted anatomical MRI scans, using a previously validated procedure. Results: At baseline, young adults with heavy CUD exhibited significantly larger volumes in several hippocampal (bilateral presubiculum, subiculum, Cornu Ammonis (CA) regions CA1, CA2-CA3, and right CA4-Dentate Gyrus (DG)) and amygdala (bilateral paralaminar nuclei, right medial nucleus, and right lateral nucleus) subregions compared to healthy controls, but these differences were attenuated at follow-up. Longitudinal analysis revealed an accelerated volumetric decrease in these subregions in young adults with heavy CUD relative to controls. Particularly, compared to healthy controls, significant accelerated volume decreases were observed in the right hippocampal subfields of the parasubiculum, subiculum, and CA4-DG. In the amygdala, similar trends of accelerated volumetric decreases were observed in the left central nucleus, right paralaminar nucleus, right basal nucleus, and right accessory basal nucleus. Conclusions: The current findings suggest that long-term heavy cannabis use impacts maturational process of the amygdala and hippocampus, especially in subregions with high concentrations of cannabinoid type 1 receptors (CB1Rs) and involvement in adult neurogenesis.</p

Quantum chemistry predictions with neural networks encoding pairwise interactions

Accurate ab-initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. After a introductory chapter on quantum chemistry and artificial neural networks, in Chapter 2, we illustrate an expressive and transferable deep neural network (T-dNN) model for the predictions of electron correlation energies at the MP2 and CCSD levels of theory, trained with the large amount of pairwise descriptors and energies hidden in a small amount of molecular data. The model is data efficient and makes highly transferable predictions across alkanes of various lengths, organic molecules with non-covalent and biomolecular interactions, as well as water clusters of different sizes and morphologies. Existing machine learning models, including T-dNN, attempt to predict the energies of large molecules by training small molecules, but eventually fail to retain high accuracy as the errors increase with system size. In chapter 3, we move on to remove the intrinsic failure of predictions on the large systems, by fine-tuning the pretrained representation on small systems with only few molecular or crystal data with ResT-dNN. Our model introduces a residual connection to explicitly learn the pairwise energy corrections, and employs various low-rank retraining techniques to modestly adjust the learned network parameters. We demonstrate that with as few as only one larger molecule retraining the base model originally trained on only small molecules of (H2O)6, the MP2 correlation energy of the large liquid water (H2O)64 in a periodic supercell can be predicted at chemical accuracy. Similar performance is observed for large protonated clusters and periodic poly-glycine chains. A demonstrative application is presented to predict the energy ordering of symmetrically inequivalent sublattices for distinct hydrogen orientations in the ice XV phase. We believe that underpinning the success of the T-dNN and ResT-dNN models, is the huge amount of pairwise data decomposed from few molecular training data, in addition to the engineered electronic features. The encouraging results motivate us to generalize it into existing graph neural networks to circumvent their need for large amount of molecular training data in the future.published_or_final_versionChemistryDoctoralDoctor of Philosoph

Breaking the trade-off between capacity, stability, and selectivity for electrochemical lithium extraction via a dual-ion doping strategy

With the rapid expansion of electric vehicle markets, the efficient selective extraction of lithium from salt lakes is critical to addressing the supply-demand gap. Against this backdrop, hybrid capacitive deionization (HCDI) technology has drawn tremendous interest in lithium extraction owing to superior selectivity and low pollution. However, conventional Li+-extraction electrodes still face significant challenges in balancing electrosorption capacity, stability, and selectivity. This work proposed a dual-ion doping strategy to achieve Fe3+ and Cl− co-doped Li3V2(PO4)3 (FC-LVP), aimed at enhancing the electrochemical lithium extraction performance of LVP electrode. The 0.15FC-LVP electrode exhibited an ultra-high specific capacity of 415.5 F g−1, a maximum electrosorption capacity of 19.1 mg g−1, and an electrosorption capacity retention of 79 % after 100 cycles. Furthermore, exceptional Li+ selectivity coefficients of 610.6 and 343 are achieved in simulated salt solutions with Mg/Li and Na/Li molar ratios of 60:1 and 45:1, respectively. The electrochemical behavior, in-situ X-ray diffraction (XRD) analysis, and an evaluation of actual brine sourced from Xizang, China, collectively demonstrate the feasibility of the 0.15FC-LVP for lithium extraction. Theoretical calculations reveal that the Fe and Cl co-doping improves the structural stability and electrochemical activity of LVP by lowering the formation energy and band gap. This work presents a novel approach for designing HCDI electrodes with high stability, capacity, and selectivity in extracting lithium from salt lake

Connectivity Determination Algorithm for Complex Directed Networks

Connectivity characterizes the ability of information transmission in systems modeled by complex networks. It is essential to develop an efficient connectivity determination algorithm with low time complexity and minimal storage requirements. To fulfill this need, a connectivity determination algorithm is designed by incorporating Tarjan's algorithm to identify strongly connected components and leveraging a depth-first search idea to traverse the reachability. This algorithm can ascertain strong connectivity, unilateral connectivity, and weak connectivity of complex directed networks. Besides, the accessibility matrix of complex directed networks is computed and visualized through an interface. As this algorithm relies on only two depth-first searches to accomplish connectivity determination tasks, its computational complexity does not exceed O(n2), where n denotes the number of network nodes. Experiments carried out on some specific networks reveal that the probability of network connections decreases with the increasing number of nodes in directed injective graphs, while in Erdős–Rényi graphs, the likelihood of connections increases as the number of nodes increases. Finally, a comparative example and an application example are provided to demonstrate the effectiveness of the algorithm program.</p

Perceived stress mediates the longitudinal effect of sleep quality on internalizing symptoms

Background: Numerous studies have explored the relationship between sleep quality and internalizing symptoms (i.e., depression and anxiety), but there is uncertainty about their directional pathways. Here, we investigated the longitudinal associations between sleep quality and internalizing symptoms and tested the potential mediation effect of perceived stress. Methods: A longitudinal survey of Chinese healthcare students (N = 343) was conducted at three time points: Time 1 (baseline), Time 2 (1 week later), and Time 3 (3 weeks after Time 2). Participants completed the Sleep Quality Questionnaire (SQQ), Perceived Stress Questionnaire-30 (PSQ-30), and the Patient Health Questionnaire-4 (PHQ-4) at each time point, where each asked about participants' experiences over the past week. A higher SQQ score indicated poorer sleep quality, while higher scores on the PHQ-4 and PSQ-30 indicated more severe internalizing symptoms and perceived stress. Using autoregressive cross-lagged panel modeling (CLPM), we examined the bidirectional relationships among sleep quality, internalizing symptoms, and perceived stress. Results: CLPM revealed that baseline sleep quality negatively predicted subsequent changes in internalizing symptoms, and vice versa. While perceived stress mediated the relationship between sleep quality and internalizing symptoms at the 3-week follow-up (β = 0.017, p = .038), it did not mediate the reverse relationship between internalizing symptoms and sleep quality. Conclusions: There was a negative bidirectional relationship between sleep quality and internalizing symptoms. Furthermore, perceived stress mediated the effect of poor sleep quality on internalizing symptoms, suggesting that good-quality sleep may enhance stress resilience and alleviate symptoms of depression and anxiety, thereby improving overall wellbeing.</p

Essays on empirical industrial organization and spatial economics

The thesis explores industrial agglomeration, production networks, and place-based industrial policies, analyzing their implications for empirical industrial organization and spatial economics. This thesis improves the measurement of industrial clustering, examines how production linkages and geographical proximity influence firms' heterogeneous performance, and provides insights into the evaluation of industrial policies. Firms benefit from input-output relationships within production networks, incentivizing them to co-locate and form industrial clusters. The first chapter examines the formation of industrial clusters with input-output linkages in the context of Chinese manufacturing. This chapter documents China's industrial clustering patterns over nearly two decades using detailed firm-level data and analyzes their impact on firm performance and regional economic growth. We introduce a new measure of industrial clustering that incorporates both horizontal agglomeration (firms within the same industry) and vertical clustering (firms from upstream and downstream industries). The analysis reveals robust industrial clustering in China, with substantial variation across industries and regions. The overall spatial concentration of China's manufacturing sector has changed considerably over time, peaking in 2004, followed by a shift of industries from the eastern coast to the central part of the country. Micro-level evidence demonstrates that industrial clustering enhances firms' productivity, reduces sourcing costs, and fosters innovation, with vertical clustering playing a key role in these effects. It further enhances regional industrial dynamism and economic growth by encouraging new firm entries and accelerating sales growth among incumbent firms. Variance decomposition indicates that horizontal clustering accounts for 83% of the variation in regional industrial sales, while upstream and downstream clustering contributes the remaining 17%. Neglecting production networks and vertical clustering significantly underestimates the benefits of industrial clustering. Place-based industrial policies have gained global prominence in recent decades. The second chapter examines the impact of place-based industrial policies on industrial geography and regional economic growth in China, emphasizing the role of input-output linkages in production networks. Based on comprehensive geo-coded data on new firm registrations and Development Zone (DZ) policies in China from 1979 to 2019, we present novel evidence of both direct and indirect spillover effects, using a staggered differences-in-differences framework. Our findings reveal that most Development Zones target specific industries, fostering policy-induced clusters within these sectors. This direct effect is mainly attributed to reduced entry costs facilitated by subsidies and loans. Additionally, the DZs significantly boosted growth in the downstream and upstream sectors of the DZ-target industries by promoting new firm entry and increasing sales of incumbent firms. The effects are stronger for industries that are more closely linked to the DZ-target industries through input-output connections. The spillover effects on input-output-linked industries arise from cost reductions and demand increases facilitated by industrial clustering. Moreover, the observed effects are primarily driven by private firms. Overall, Development Zones substantially contributed to regional economic development, with spillovers to upstream and downstream industries (and their interactions with DZ-target industries) playing a crucial role.published_or_final_versionEconomicsDoctoralDoctor of Philosoph

Effects of melatonin treatment on 5-mC regulation in 5XFAD mouse model of Alzheimer's disease

published_or_final_versionBiomedical SciencesMasterMaster of Research in Medicin