Exploring dual-lipopolysaccharide exposure on schizophrenia-like behavior and the kynurenine pathway in rodent models

Immune activation contributes to the pathophysiology of schizophrenia. The kynurenine pathway, serving as the primary route of tryptophan catabolism, has been posited as a potential link between immune activation and the manifestation of symptoms associated with schizophrenia. We previously demonstrated that the administration of dual-lipopolysaccharide (LPS) treatments induces the kynurenine pathway and elicits behavioral changes reminiscent of schizophrenia symptoms in mice. The dual-LPS treatment model may serve as a valuable tool for studying the effects of immune activation on the progression of schizophrenia. It is worth mentioning that rats and mice, which are frequently employed as laboratory animals in preclinical research, exhibit species-specific disparities in the kynurenine pathway metabolism. The present thesis aims to further characterize the behavioral effects induced by dual-LPS treatment in both mice and rats and evaluate the feasibility of the dual-LPS treatment model as a schizophrenia-like animal model. Moreover, we aim to investigate the changes in the kynurenine pathway in rats following dual-LPS treatment and explore possible species differences by comparing kynurenine pathway metabolism under physiological conditions and after dual-LPS treatment in both rats and mice. Thus, mice and rats administered dual-LPS treatment were studied for behavioral abnormalities associated with schizophrenia and biochemical changes in the kynurenine pathway metabolism in both the brain and periphery. Dual-LPS treatment induces behavioral changes associated with symptoms resembling schizophrenia in both rats and mice. In both species, dual-LPS treatment resulted in reduced spontaneous locomotion, heightened locomotion responsiveness to D-amphetamine, and the elicitation of anxiety-like behavior. In mice, dual-LPS treatment induced deficits in associative learning, while in rats, it was shown to impair recognition memory. Under basal physiological conditions, mice and rats exhibited distinct kynurenine pathway metabolite profiles in the brain, plasma, and liver. Rats consistently displayed higher KYNA/3HK and QUIN/PIC ratios across all brain regions, indicating a predominance of the neuroprotective branch in rats compared to mice under normal physiological conditions. In both mice and rats, dual-LPS treatment induced the kynurenine pathway, leading to elevated brain KYNA levels. Following dual-LPS treatment, species-specific changes were observed in brain, plasma, and liver tissues, with the most significant difference being the robust induction of QUIN in the mouse brain but not in the rat brain following dual-LPS treatment. Additionally, brain region-specific responses were observed in both species. Our radiochemical detection data confirmed that dual-LPS treatment alters the local kynurenine pathway metabolism in the brain, augmenting the de novo production of brain KYNA and increasing the KYNA/3-HK ratio in the striatum. The findings presented in this thesis further validate the utility of the dual-LPS model as a promising animal model of schizophrenia. Additionally, the results presented in the thesis comprehensively elucidate species-specific differences in kynurenine pathway metabolism under both physiological conditions and following dual-LPS treatment. These insights bear significant relevance for the selection of appropriate animal models when examining the kynurenine pathway, aiding in animal model selection for related research

HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts

In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our work is motivated by an important observation that it is, in many cases, possible to achieve temporally robust predictive performance via hyperparameter optimization. Based on this observation, we leverage the `worst-case-oriented' philosophy from the robust optimization literature to help find such robust hyperparameter configurations. HyperTime imposes a lexicographic priority order on average validation loss and worst-case validation loss over chronological validation sets. We perform a theoretical analysis on the upper bound of the expected test loss, which reveals the unique advantages of our approach. We also demonstrate the strong empirical performance of the proposed method on multiple machine learning tasks with temporal distribution shifts.Comment: 19 pages, 7 figure

Active targeting of chemotherapy to disseminated tumors using nanoparticle-carrying T cells

Tumor cells disseminate into compartments that are poorly accessible from circulation, which necessitates high doses of systemic chemotherapy. However, the effectiveness of many drugs, such as the potent topoisomerase I poison SN-38, is hampered by poor pharmacokinetics. To deliver SN-38 to lymphoma tumors in vivo, we took advantage of the fact that healthy lymphocytes can be programmed to phenocopy the biodistribution of the tumor cells. In a murine model of disseminated lymphoma, we expanded autologous polyclonal T cells ex vivo under conditions that retained homing receptors mirroring lymphoma cells, and functionalized these T cells to carry SN-38–loaded nanocapsules on their surfaces. Nanocapsule-functionalized T cells were resistant to SN-38 but mediated efficient killing of lymphoma cells in vitro. Upon adoptive transfer into tumor-bearing mice, these T cells served as active vectors to deliver the chemotherapeutic into tumor-bearing lymphoid organs. Cell-mediated delivery concentrated SN-38 in lymph nodes at levels 90-fold greater than free drug systemically administered at 10-fold higher doses. The live T cell delivery approach reduced tumor burden significantly after 2 weeks of treatment and enhanced survival under conditions where free SN-38 and SN-38–loaded nanocapsules alone were ineffective. These results suggest that tissue-homing lymphocytes can serve as specific targeting agents to deliver nanoparticles into sites difficult to access from the circulation, and thus improve the therapeutic index of chemotherapeutic drugs with unfavorable pharmacokinetics.United States. Department of Defense (W81XWH-10-1-0290)National Institutes of Health (U.S.) (CA140476 and CA172164)National Cancer Institute (U.S.) (David H. Koch Institute for Integrative Cancer Research at MIT. Support (Core) Grant P30-CA14051

Diffusion-Augmented Depth Prediction with Sparse Annotations

Depth estimation aims to predict dense depth maps. In autonomous driving scenes, sparsity of annotations makes the task challenging. Supervised models produce concave objects due to insufficient structural information. They overfit to valid pixels and fail to restore spatial structures. Self-supervised methods are proposed for the problem. Their robustness is limited by pose estimation, leading to erroneous results in natural scenes. In this paper, we propose a supervised framework termed Diffusion-Augmented Depth Prediction (DADP). We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner. An object-guided integrality loss is also proposed to further enhance regional structure integrality by fetching objective information. We evaluate DADP on three driving benchmarks and achieve significant improvements in depth structures and robustness. Our work provides a new perspective on depth estimation with sparse annotations in autonomous driving scenes.Comment: Accepted by ACM MM'202

Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning

Recent advancements in pre-trained large-scale language-image models have ushered in a new era of visual comprehension, offering a significant leap forward. These breakthroughs have proven particularly instrumental in addressing long-standing challenges that were previously daunting. Leveraging these innovative techniques, this paper tackles two well-known issues within the realm of visual analytics: (1) the efficient exploration of large-scale image datasets and identification of potential data biases within them; (2) the evaluation of image captions and steering of their generation process. On the one hand, by visually examining the captions automatically generated from language-image models for an image dataset, we gain deeper insights into the semantic underpinnings of the visual contents, unearthing data biases that may be entrenched within the dataset. On the other hand, by depicting the association between visual contents and textual captions, we expose the weaknesses of pre-trained language-image models in their captioning capability and propose an interactive interface to steer caption generation. The two parts have been coalesced into a coordinated visual analytics system, fostering mutual enrichment of visual and textual elements. We validate the effectiveness of the system with domain practitioners through concrete case studies with large-scale image datasets

A cross-species alignment tool (CAT)

<p>Abstract</p> <p>Background</p> <p>The main two sorts of automatic gene annotation frameworks are <it>ab initio </it>and alignment-based, the latter splitting into two sub-groups. The first group is used for intra-species alignments, among which are successful ones with high specificity and speed. The other group contains more sensitive methods which are usually applied in aligning inter-species sequences.</p> <p>Results</p> <p>Here we present a new algorithm called <it>CAT </it>(for Cross-species Alignment Tool). It is designed to align mRNA sequences to mammalian-sized genomes. <it>CAT </it>is implemented using C scripts and is freely available on the web at <url>http://xat.sourceforge.net/</url>.</p> <p>Conclusions</p> <p>Examined from different angles, <it>CAT </it>outperforms other extant alignment tools. Tested against all available mouse-human and zebrafish-human orthologs, we demonstrate that <it>CAT </it>combines the specificity and speed of the best intra-species algorithms, like <it>BLAT </it>and <it>sim4</it>, with the sensitivity of the best inter-species tools, like <it>GeneWise</it>.</p

How Does Attention Work in Vision Transformers? A Visual Analytics Attempt

Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a sequence. Multi-head self-attentions are then applied to the sequence to learn the attention between patches. Despite many successful interpretations of transformers on sequential data, little effort has been devoted to the interpretation of ViTs, and many questions remain unanswered. For example, among the numerous attention heads, which one is more important? How strong are individual patches attending to their spatial neighbors in different heads? What attention patterns have individual heads learned? In this work, we answer these questions through a visual analytics approach. Specifically, we first identify what heads are more important in ViTs by introducing multiple pruning-based metrics. Then, we profile the spatial distribution of attention strengths between patches inside individual heads, as well as the trend of attention strengths across attention layers. Third, using an autoencoder-based learning solution, we summarize all possible attention patterns that individual heads could learn. Examining the attention strengths and patterns of the important heads, we answer why they are important. Through concrete case studies with experienced deep learning experts on multiple ViTs, we validate the effectiveness of our solution that deepens the understanding of ViTs from head importance, head attention strength, and head attention pattern.Comment: Accepted by PacificVis 2023 and selected to be published in TVC