Diversity of Receptor Tyrosine Kinase Signaling Mechanisms

Receptor tyrosine kinases (RTKs) are a family of 58 transmembrane proteins in humans that play crucial roles in many biological processes and diseases. Different RTKs utilize subtly (but importantly) distinct molecular mechanisms for transmembrane signaling, and understanding these differences is crucial for devising new ways to intervene pharmacologically when aberrant RTK signaling causes cancer and other diseases. In this thesis, I focus on three RTK families: the ALK/LTK family, the Wnt-binding RTKs, and the EGF receptor – where I concentrate on efforts to understand its C-terminal regulatory region. My studies of ALK, for anaplastic lymphoma kinase, were motivated by the fact that this RTK sub-group has a unique domain architecture in its extracellular region. Little is known about the mechanisms of ligand binding to – and activation of – ALK, and the nature of its ligand(s) is(are) still not completely clear. Using biochemical, biophysical and structural biology approaches, I characterized the low-resolution structure of the ALK extracellular region. I further identified the binding mode of ALK binding to heparin, a recently discovered modulatory ligand for ALK. Based on a low-resolution structural analysis of ALK/heparin complex, I propose a model for ligand-induced ALK dimerization and activation. Ryk is one of the five RTKs that are now known to be Wnt receptors. In this thesis, I studied the Drosophila homolog of Ryk, Derailed (Drl), and its binding to ligand DWnt5. We were able to express and purify milligrams of active DWnt5 – thus overcoming a major obstacle in this field. We further characterized Drl/DWnt5 interactions. Using hydrogen/deuterium exchange approaches, I identified the DWnt5-binding interface on Drl. My efforts to understand the molecular mechanisms of Drl/DWnt5 binding using experimental and computational approaches suggest that DWnt5 may interact with Drl through a binding mode that differs from Wnt binding to other receptors. Across the RTK family, many receptors contain a long carboxy-terminal tail (C-tail) that harbors autophosphorylation sites for docking of downstream signaling molecules. This region is generally considered to be intrinsically disordered. I studied the dynamics of the EGFR C-tail, and showed that it is highly unstructured – but contains some somewhat ‘structured’ regions. I also showed that phosphorylation of the EGFR C-tail promotes receptor dimerization. Using hydrogen exchange, I identified possible C-tail docking sites on the kinase domain that may be responsible for this effect. I also studied binding of downstream SH2 domain-containing molecules to the EGFR C-tail, with results that indicate that not all features of SH2 domain binding to the C-tail can be recapitulated by simple phosphopeptides; binding of SH2 domains to the C-tail exhibits binding affinities and stoichiometries that are not captured by simple peptide-level studies. Moreover, my binding competition assays suggest that there may be cooperativity in binding of multiple SH2 domains to a single phosphorylated C-tail

Trace elements accumulation in the Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) - A threat to the endangered freshwater cetacean

As a freshwater cetacean with a population of only approximately 1000 individuals, the Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) is threatened by water pollution. However, studies of contaminants accumulated in the Yangtze finless porpoise remain limited. In this study, concentrations of 11 trace elements in different tissues sampled from 38 Yangtze finless porpoise individuals were determined. The elements V, Ni, Zn, and Pb were mostly accumulated in the epidermis, Cr, Mn, Cu, Se, and Hg were mostly accumulated in the liver, while As and Cd were mostly accumulated in the blubber and kidney, respectively. The results show that trace elements concentrations in the epidermis do not reliably indicate concentrations in internal tissues of the Yangtze finless porpoises. Positive correlations between different trace elements concentrations in tissues with the highest concentrations suggested the similar mechanism of metabolism or uptake pathway of those elements. Concentrations of As, Se, Cd, Hg, and Pb in the tissues with the highest concentrations were significantly positively correlated with the body length. Furthermore, significantly higher trace elements concentrations were measured in the reproductive organs of females (ovaries) than males (testis). However, no significant difference of trace elements concentrations between habitats was found. In consideration of higher Hg and Cd level in Yangtze finless porpoises compared to other small cetaceans, the potential risk of Hg (in particular) and Cd toxicity to Yangtze finless porpoises needs further attention. (C) 2019 Elsevier B.V. All rights reserved.</p

Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion

Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whether the user prefers them or not, based on his/her historical interaction sequence. Although effective, we reveal two inherent limitations:(1) it may differ from human behavior in that a user could imagine an oracle item in mind and select potential items matching the oracle; and (2) the classification is limited in the candidate pool with noisy or easy supervision from negative samples, which dilutes the preference signals towards the oracle item. Yet, generating the oracle item from the historical interaction sequence is mostly unexplored. To bridge the gap, we reshape sequential recommendation as a learning-to-generate paradigm, which is achieved via a guided diffusion model, termed DreamRec.Specifically, for a sequence of historical items, it applies a Transformer encoder to create guidance representations. Noising target items explores the underlying distribution of item space; then, with the guidance of historical interactions, the denoising process generates an oracle item to recover the positive item, so as to cast off negative sampling and depict the true preference of the user directly. We evaluate the effectiveness of DreamRec through extensive experiments and comparisons with existing methods. Codes and data are open-sourced at https://github.com/YangZhengyi98/DreamRec

Magnetic field-modulated exciton generation in organic semiconductors: an intermolecular quantum correlation effect

Magnetoelectroluminescence (MEL) of organic semiconductor has been experimentally tuned by adopting blended emitting layer consisting of both hole and electron transporting materials. A theoretical model considering intermolecular quantum correlation is proposed to demonstrate two fundamental issues: (1) two mechanisms, spin scattering and spin mixing, dominate the two different steps respectively in the process of the magnetic field modulated generation of exciton; (2) the hopping rate of carriers determines the intensity of MEL. Calculation successfully predicts the increase of singlet excitons in low field with little change of triplet exciton population.Comment: 16 pages, 4 figure

Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation

Reinforcement learning (RL) has been widely applied in recommendation systems due to its potential in optimizing the long-term engagement of users. From the perspective of RL, recommendation can be formulated as a Markov decision process (MDP), where recommendation system (agent) can interact with users (environment) and acquire feedback (reward signals).However, it is impractical to conduct online interactions with the concern on user experience and implementation complexity, and we can only train RL recommenders with offline datasets containing limited reward signals and state transitions. Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders.Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender. To address these challenges, we propose a novel RL recommender named model-enhanced contrastive reinforcement learning (MCRL). On the one hand, we learn a value function to estimate the long-term engagement of users, together with a conservative value learning mechanism to alleviate the overestimation problem.On the other hand, we construct some positive and negative state-action pairs to model the reward function and state transition function with contrastive learning to exploit the internal structure information of MDP. Experiments demonstrate that the proposed method significantly outperforms existing offline RL and self-supervised RL methods with different representative backbone networks on two real-world datasets.Comment: 11 pages, 7 figure

Detecting Attacks in CyberManufacturing Systems: Additive Manufacturing Example

CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms - (i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection - have been adopted in the research and shown to be effective in detecting such defects

Large Language Model Can Interpret Latent Space of Sequential Recommender

Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a common approach is to model item sequences using discrete IDs, learning representations that encode sequential behaviors and reflect user preferences. Inspired by recent success in empowering large language models (LLMs) to understand and reason over diverse modality data (e.g., image, audio, 3D points), a compelling research question arises: ``Can LLMs understand and work with hidden representations from ID-based sequential recommenders?''.To answer this, we propose a simple framework, RecInterpreter, which examines the capacity of open-source LLMs to decipher the representation space of sequential recommenders. Specifically, with the multimodal pairs (\ie representations of interaction sequence and text narrations), RecInterpreter first uses a lightweight adapter to map the representations into the token embedding space of the LLM. Subsequently, it constructs a sequence-recovery prompt that encourages the LLM to generate textual descriptions for items within the interaction sequence. Taking a step further, we propose a sequence-residual prompt instead, which guides the LLM in identifying the residual item by contrasting the representations before and after integrating this residual into the existing sequence. Empirical results showcase that our RecInterpreter enhances the exemplar LLM, LLaMA, to understand hidden representations from ID-based sequential recommenders, especially when guided by our sequence-residual prompts. Furthermore, RecInterpreter enables LLaMA to instantiate the oracle items generated by generative recommenders like DreamRec, concreting the item a user would ideally like to interact with next. Codes are available at https://github.com/YangZhengyi98/RecInterpreter

Effects of oil-in-water based nanolubricant containing TiO2 nanoparticles in hot rolling of 304 stainless steel

Energy saving and improvement of product quality are of crucial importance in hot rolling of 304 stainless steel. In this paper, oil-in-water (O/W) based nanolubricants containing TiO2 nanoparticles were developed to reduce the rolling force and improve the surface quality of rolled 304 stainless steel product. Practical hot rolling tests with and without application of lubricant were conducted to systematically investigate the effects of the developed O/W based nanolubricants on the rolling force, surface roughness, oxide scale thickness and tribological behaviour. The obtained results indicate that the nanoparticles can enter the deform zone with oil droplets to take a lubrication effect. The optimal lubrication effect can be achieved when the O/W (1% oil mass fraction) based nanolubricant with a TiO2 mass fraction of 1.5% was applied. The novel nanolubricant has a great potential to be applied in the hot steel rolling, to realise the cost-effective and environmental-friendly manufacturing process

Collaborative interactions of heterogenous ribonucleoproteins contribute to transcriptional regulation of sterol metabolism in mice.

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are a group of functionally versatile proteins that play critical roles in the biogenesis, cellular localization and transport of RNA. Here, we outline a role for hnRNPs in gene regulatory circuits controlling sterol homeostasis. Specifically, we find that tissue-selective loss of the conserved hnRNP RALY enriches for metabolic pathways. Liver-specific deletion of RALY alters hepatic lipid content and serum cholesterol level. In vivo interrogation of chromatin architecture and genome-wide RALY-binding pattern reveal insights into its cooperative interactions and mode of action in regulating cholesterogenesis. Interestingly, we find that RALY binds the promoter region of the master metabolic regulator Srebp2 and show that it directly interacts with coactivator Nuclear Transcription Factor Y (NFY) to influence cholesterogenic gene expression. Our work offers insights into mechanisms orchestrating selective promoter activation in metabolic control and a model by which hnRNPs can impact health and disease states