Tissue and Process Specific microRNA–mRNA Co-Expression in Mammalian Development and Malignancy

An association between enrichment and depletion of microRNA (miRNA) binding sites, 3′ UTR length, and mRNA expression has been demonstrated in various developing tissues and tissues from different mature organs; but functional, context-dependent miRNA regulations have yet to be elucidated. Towards that goal, we examined miRNA–mRNA interactions by measuring miRNA and mRNA in the same tissue during development and also in malignant conditions. We identified significant miRNA-mediated biological process categories in developing mouse cerebellum and lung using non-targeted mRNA expression as the negative control. Although miRNAs in general suppress target mRNA messages, many predicted miRNA targets demonstrate a significantly higher level of co-expression than non-target genes in developing cerebellum. This phenomenon is tissue specific since it is not observed in developing lungs. Comparison of mouse cerebellar development and medulloblastoma demonstrates a shared miRNA–mRNA co-expression program for brain-specific neurologic processes such as synaptic transmission and exocytosis, in which miRNA target expression increases with the accumulation of multiple miRNAs in developing cerebellum and decreases with the loss of these miRNAs in brain tumors. These findings demonstrate the context-dependence of miRNA–mRNA co-expression

Algorithms for design and interrogation of functionally graded material solids

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.Includes bibliographical references (leaves 109-112).A Functionally Gradient Material (FGM) part is a 3D solid object that has varied local material composition that is defined by a specifically designed function. Recently, research has been performed at MIT in order to exploit the potential of creating FGM parts using a modern fabrication process, 3D Printing, that has the capability of controlling composition to the length scale of 100 [mu]m. As part of the project of design automation of FGM parts, this thesis focuses on the issue of the development of efficient algorithms for design and composition interrogation. Starting with a finite element based 3D model, the design tool based on the distance function from the surface of the part and the design tool allowing the user to design within a .STL file require enhanced efficiency and so does the interrogation of the part. The approach for improving efficiency includes preprocessing the model with bucket sorting, digital distance transform of the buckets and an efficient point classification algorithm. Based on this approach, an efficient algorithm for distance function computation is developed for the design of FGM through distance to the surface of the part or distance to a .STL surface boundary. Also an efficient algorithm for composition evaluation at a point, along a ray or on a plane is developed. The theoretical time complexities of the developed algorithms are analyzed and experimental numerical results are provided.by Hongye Liu.S.M

Feature-based design of solids with local composition control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2004.Includes bibliographical references (leaves 126-134).This thesis presents a parametric and feature-based methodology for the design of solids with local composition control (LCC). A suite of composition design features are conceptualized and implemented. The designer can use them singly or in combination, to specify the composition of complex components. Each material composition design feature relates directly to the geometry of the design, often relying on user interaction to specify critical aspects of the geometry. This approach allows the designer to simultaneously edit geometry and composition by varying parameters until a satisfactory result is attained. The identified LCC features are those based on volume, transition, pattern, and (user-defined) surface features. The material composition functions include functions parametrized with respect to distance or distances to user-defined geometric features; and functions that use Laplace's equation to blend smoothly various boundary conditions including values and gradients of the material composition on the boundaries. The Euclidean digital distance transform and the boundary element method are adapted to the efficient computation of composition functions. Theoretical and experimental complexity, accuracy and convergence analyses are presented. The developed model is a multi-level and graph-based representation, thereby allowing for controls on the model validity and efficiency in model management. The representations underlying the composition design features are analytic in nature and therefore concise. Evaluation for visualization and fabrication is performed only at the resolutions required for these purposes, thereby reducing the computational burden.by Hongye Liu.Ph.D

Optimization-Based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework

We present a hierarchical framework based on graph search and model predictive control (MPC) for electric autonomous vehicle (EAV) parking maneuvers in a tight environment. At high-level, only static obstacles are considered, and the scenario-based hybrid A* (SHA*), which is faster than the traditional hybrid A*, is designed to provide an initial guess (also known as a global path) for the parking task. To extract the velocity and acceleration profile from an initial guess, an optimal control problem (OCP) is built. At the low level, an NMPC-based strategy is used to avoid dynamic obstacles (also known as local planning). The efficacy of SHA* is evaluated through 148 different simulation schemes and the proposed hierarchical parking framework is demonstrated through a real-time parallel parking simulation

Transcriptome profiling reveals the crucial biological pathways involved in cold response in Moso bamboo (Phyllostachys edulis).

Most bamboo species including Moso bamboo (Phyllostachys edulis) are tropical or subtropical plants that greatly contribute to human well-being. Low temperature is one of the main environmental factors restricting bamboo growth and geographic distribution. Our knowledge of the molecular changes during bamboo adaption to cold stress remains limited. Here, we provided a general overview of the cold-responsive transcriptional profiles in Moso bamboo by systematically analyzing its transcriptomic response under cold stress. Our results showed that low temperature induced strong morphological and biochemical alternations in Moso bamboo. To examine the global gene expression changes in response to cold, 12 libraries (non-treated, cold-treated 0.5, 1 and 24 h at -2 °C) were sequenced using an Illumina sequencing platform. Only a few differentially expressed genes (DEGs) were identified at early stage, while a large number of DEGs were identified at late stage in this study, suggesting that the majority of cold response genes in bamboo are late-responsive genes. A total of 222 transcription factors from 24 different families were differentially expressed during 24-h cold treatment, and the expressions of several well-known C-repeat/dehydration responsive element-binding factor negative regulators were significantly upregulated in response to cold, indicating the existence of special cold response networks. Our data also revealed that the expression of genes related to cell wall and the biosynthesis of fatty acids were altered in response to cold stress, indicating their potential roles in the acquisition of bamboo cold tolerance. In summary, our studies showed that both plant kingdom-conserved and species-specific cold response pathways exist in Moso bamboo, which lays the foundation for studying the regulatory mechanisms underlying bamboo cold stress response and provides useful gene resources for the construction of cold-tolerant bamboo through genetic engineering in the future

Deep Domain Adversarial Adaptation for Photon-efficient Imaging

Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel. However, the existing computational methods for photon-efficient imaging are pre-tuned on a restricted scenario or trained on simulated datasets. When applied to realistic scenarios whose signal-to-background ratios (SBR) and other hardware-specific properties differ from those of the original task, the model performance often significantly deteriorates. In this paper, we present a domain adversarial adaptation design to alleviate this domain shift problem by exploiting unlabeled real-world data, with significant resource savings. This method demonstrates superior performance on simulated and real-world experiments using our home-built up-conversion single-photon imaging system, which provides an efficient approach to bypass the lack of ground-truth depth information in implementing computational imaging algorithms for realistic applications

Novel flexible heteroarotinoid, SL-1-39, inhibits HER2-positive breast cancer cell proliferation by promoting lysosomal degradation of HER2.

SL-1-39 [1-(4-chloro-3-methylphenyl)-3-(4-nitrophenyl)thiourea] is a new flexible heteroarotinoid (Flex-Het) analog derived from the parental compound, SHetA2, previously shown to inhibit cell growth across multiple cancer types. The current study aims to determine growth inhibitory effects of SL-1-39 across the different subtypes of breast cancer cells and delineate its molecular mechanism. Our results demonstrate that while SL-1-39 blocks cell proliferation of all breast cancer subtypes tested, it has the highest efficacy against HER2+ breast cancer cells. Molecular analyses suggest that SL-1-39 prevents S phase progression of HER2+ breast cancer cells (SKBR3 and MDA-MB-453), which is consistent with reduced expression of key cell-cycle regulators at both the protein and transcriptional levels. SL-1-39 treatment also decreases the protein levels of HER2 and pHER2 as well as its downstream effectors, pMAPK and pAKT. Reduction of HER2 and pHER2 at the protein level is attributed to increased lysosomal degradation of total HER2 levels. This is the first study to show that a flexible heteroarotinoid analog modulates the HER2 signaling pathway through lysosomal degradation, and thus further warrants the development of SL-1-39 as a therapeutic option for HER2+ breast cancer

Meta-augmented Prompt Tuning for Better Few-shot Learning

Prompt tuning is a parameter-efficient method, which freezes all PLM parameters and only prepends some additional tunable tokens called soft prompts to the input text. However, soft prompts heavily rely on a better initialization and may easily result in overfitting under few-shot settings, which causes prompt-tuning performing much worse than fine-tuning. To address the above issues, this paper proposes a novel Self-sUpervised Meta-prompt learning framework with MEtagradient Regularization for few shot generalization (SUMMER). We leverage self-supervised meta-learning to better initialize soft prompts and curriculum-based task augmentation is further proposed to enrich the meta-task distribution. Besides, a novel meta-gradient regularization method is integrated into the meta-prompt learning framework, which meta-learns to transform the raw gradient during few-shot learning into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUMMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability