The Story of a Blind Wolf

The Story of a Blind Wolf; is a 3D animated graduate thesis film, whose length is five minutes forty seconds. It was screened in the School of Animation at Rochester Institute of Technology. The film is in a realistic style and made by CG (computer graphics). The story takes place in a small puppet theater. While a piper is playing a flute for his audience, a wolf interrupted his performance. The wolf asks the piper to help him escape the hunt. The piper has sympathy for the wolf, so he helps him, lies to the hunter, and saved the wolf. They became friends, dancing and celebrating in the forest. While celebrating, the wolf sees through the tree behind the piper a dangerous snake that is going to hurt the piper. The piper doesn’t notice the snake behind him. So, when the wolf attacks the snake to save the piper’s life, the piper thinks the wolf is going to hurt him after he saved his life. The piper misunderstands the wolf and runs away. Then he met the hunter, in the hunter’s abetted, the piper using hoe beat the wolf to die. In this film, I want to propose that when confronting things, we are easily interfered by the outside world and give up the ability of thinking independently, which gives rise to unnecessary misunderstandings among people and eventually leads to tragedies

Exploring visitor meanings of place in the National Capital Parks - Central

This study uses a new approach to interpretative research based on (1) understanding the meanings visitors attach to park resources, and (2) examining the connections that visitors made after attending an on-site interpretive program. The study was conducted at the National Capital Parks in Washington, DC. This study revealed that many visitors to the Lincoln Memorial, the Vietnam Veterans Memorial, and the Korean War Veterans Memorial (i.e. the Triangle) seek something of value for themselves, including everything from connecting with the past and rededicating themselves to the ideals of the nation. The study incorporated mixed method design, including purposeful sampling for visitor interview participants, quasi-experimental pre-test/post-test design, focus group interview, and both quantitative and qualitative data analysis. During the summer of 1998, researchers conducted 89 focus group interviews and interviewed a total of 527 visitors. Study results suggest that visitors attach meanings and many of them desire quality interpretative experiences

Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between centre and surround classes. Discriminant power of features for the classification is measured as mutual information between distributions of image features and corresponding classes . As the estimated discrepancy very much depends on considered scale level, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden Markov Tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, a saliency value for each square block at each scale level is computed with discriminant power principle. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) method against the well-know information based approach AIM on its released image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396

A Comparison of Mutation and Amplification-Driven Resistance Mechanisms and Their Impacts on Tumor Recurrence

Tumor recurrence, driven by the evolution of drug resistance is a major barrier to therapeutic success in cancer. Resistance is often caused by genetic alterations such as point mutation, which refers to the modification of a single genomic base pair, or gene amplification, which refers to the duplication of a region of DNA that contains a gene. Here we investigate the dependence of tumor recurrence dynamics on these mechanisms of resistance, using stochastic multi-type branching process models. We derive tumor extinction probabilities and deterministic estimates for the tumor recurrence time, defined as the time when an initially drug sensitive tumor surpasses its original size after developing resistance. For models of amplification-driven and mutation-driven resistance, we prove law of large numbers results regarding the convergence of the stochastic recurrence times to their mean. Additionally, we prove sufficient and necessary conditions for a tumor to escape extinction under the gene amplification model, discuss behavior under biologically relevant parameters, and compare the recurrence time and tumor composition in the mutation and amplification models both analytically and using simulations. In comparing these mechanisms, we find that the ratio between recurrence times driven by amplification vs. mutation depends linearly on the number of amplification events required to acquire the same degree of resistance as a mutation event, and we find that the relative frequency of amplification and mutation events plays a key role in determining the mechanism under which recurrence is more rapid. In the amplification-driven resistance model, we also observe that increasing drug concentration leads to a stronger initial reduction in tumor burden, but that the eventual recurrent tumor population is less heterogeneous, more aggressive, and harbors higher levels of drug-resistance.Comment: 52 Pages, 5 figure

Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks

Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies have predominately focused on brain functional connectivity (i.e. Fisher z-score), which requires manual processing steps for feature extraction from fMRI images. Deep learning approaches (i.e., Convolutional Neural Networks) have achieved remarkable success on learning directly from image data, yet have not been applied on fetal fMRI for understanding fetal neurodevelopment. Here, we bridge this gap by applying a novel application of deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data. Specifically, we test a supervised CNN framework as a data-driven approach to isolate variation in fMRI signals that relate to younger v.s. older fetal age groups. Based on the learned CNN, we further perform sensitivity analysis to identify brain regions in which changes in BOLD signal are strongly associated with fetal brain age. The findings demonstrate that deep CNNs are a promising approach for identifying spontaneous functional patterns in fetal brain activity that discriminate age groups. Further, we discovered that regions that most strongly differentiate groups are largely bilateral, share similar distribution in older and younger age groups, and are areas of heightened metabolic activity in early human development.Comment: 9 page

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment