Data Science Commons^™

Systematic Investigation Of The Homology Sequences Around The Human Fusion Gene Breakpoints In Pan-Cancer - Bioinformatics Study For A Potential Link To Mmej, Pora Kim, Himansu Kumar, Chengyuan Yang, Ruihan Luo, Jiajia Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

Microhomology-mediated end joining (MMEJ), an error-prone DNA damage repair mechanism, frequently leads to chromosomal rearrangements due to its ability to engage in promiscuous end joining of genomic instability and also leads to increasing mutational load at the sequences flanking the breakpoints (BPs). In this study, we systematically investigated the homology sequences around the genomic breakpoint area of human fusion genes, which were formed by the chromosomal rearrangements initiated by DNA double-strand breakage. Since the RNA-seq data is the typical data set to check the fusion genes, for the known exon junction fusion breakpoints identified from RNA-seq data, we have to …

Machine Learning And Causality For Interpretable And Automated Decision Making, Maria Lentini

Theses and Dissertations

This abstract explores two key areas in decision science: automated and interpretable decision making. In the first part, we address challenges related to sparse user interaction data and high item turnover rates in recommender systems. We introduce a novel algorithm called Multi-View Interactive Collaborative Filtering (MV-ICTR) that integrates user-item ratings and contextual information, improving performance, particularly for cold-start scenarios. In the second part, we focus on Student Prescription Trees (SPTs), which are interpretable decision trees. These trees use a black box "teacher" model to predict counterfactuals based on observed covariates. We experiment with a Bayesian hierarchical binomial regression model as …

Reu-Deim Classification Of Hispanic Voters In Hispanic Groups Using Name And Zip Code Data In Palm Beach, Florida, Kamila Soto-Ortiz

Beyond: Undergraduate Research Journal

When it comes to registering to vote, Hispanic voters can only register as “Hispanic” in the “Race/Ethnicity” category, causing difficulties when analyzing voting trends amongst the Hispanic community. Upon the recent idea that not all Hispanic Groups vote the same, the goal is to create a model that can possibly identify a voter’s Hispanic Group with the information provided on the public Florida voter file. This is accomplished using name and zip code data for all voters in Palm Beach, Florida. This paper will explore the model implemented, its findings and limitations. Palm Beach, Florida, is met with low confidence …

Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley

African Conference on Information Systems and Technology

Developing sustainable solutions is critical for adoption of digital solutions. As the high number of learners dropping out of school continues to increase, it is critical to find innovative ways of predicting and preventing high drop out. Current literature has documented a number of factors that influence learner drop out. Innovative ideas, techniques and activities have been undertaken to motivate learners to stay at school. It is unfortunate that most of the initiatives have not helped to avoid drop out of learners. The study is based on a mixed approached that was used targeting female learns from Oliver Tambo District …

Mettl14 Is A Chromatin Regulator Independent Of Its Rna N6-Methyladenosine Methyltransferase Activity, Xiaoyang Dou, Lulu Huang, Yu Xiao, Chang Liu, Yini Li, Xinning Zhang, Lishan Yu, Ran Zhao, Lei Yang, Chuan Chen, Xianbin Yu, Boyang Gao, Meijie Qi, Yawei Gao, Bin Shen, Shuying Sun, Chuan He, Jun Liu

Faculty, Staff and Student Publications

METTL3 and METTL14 are two components that form the core heterodimer of the main RNA m6A methyltransferase complex (MTC) that installs m6A. Surprisingly, depletion of METTL3 or METTL14 displayed distinct effects on stemness maintenance of mouse embryonic stem cell (mESC). While comparable global hypo-methylation in RNA m6A was observed in Mettl3 or Mettl14 knockout mESCs, respectively. Mettl14 knockout led to a globally decreased nascent RNA synthesis, whereas Mettl3 depletion resulted in transcription upregulation, suggesting that METTL14 might possess an m6A-independent role in gene regulation. We found that METTL14 colocalizes with the repressive H3K27me3 modification. Mechanistically, METTL14, but not METTL3, binds …

A Low-Complexity Algorithm To Determine Spacecraft Trajectories, Sirani Perera

Math Department Colloquium Series

The growing traffic within the Cislunar region has created a need for computationally effective methods to obtain the trajectories of spacecraft in the Cislunar region. By developing algorithms with low time and arithmetic complexities, we can effectively address these needs.

In this talk, we will present a mathematical model that uses interpolation and boundary conditions to obtain trajectories for satellites based on the principles of three-body dynamics. Following the model, we propose a low- complexity algorithm to generate satellite trajectories. Once the algorithm is proposed, we will apply it to the relevant periodic orbits in the Cislunar region. Finally, we …

Pathogen-Driven Crispr Screens Identify Trex1as A Regulator Of Dna Self-Sensing During Influenza Virus Infection, Cason R King, Yiping Liu, Katherine A Amato, Grace A Schaack, Clayton Mickelson, Autumn E Sanders, Tony Hu, Srishti Gupta, Ryan A Langlois, Judith A Smith, Andrew Mehle

Faculty, Staff and Student Publications

Host:pathogen interactions dictate the outcome of infection, yet the limitations of current approaches leave large regions of this interface unexplored. Here, we develop a novel fitness-based screen that queries factors important during the middle to late stages of infection. This is achieved by engineering influenza virus to direct the screen by programming dCas9 to modulate host gene expression. Our genome-wide screen for pro-viral factors identifies the cytoplasmic DNA exonuclease TREX1. TREX1 degrades cytoplasmic DNA to prevent inappropriate innate immune activation by self-DNA. We reveal that this same process aids influenza virus replication. Infection triggers release of mitochondrial DNA into the …

Collagene Enables Privacy-Aware Federated And Collaborative Genomic Data Analysis, Wentao Li, Miran Kim, Kai Zhang, Han Chen, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 …

An Electronic Origin Of Charge Order In Infinite-Layer Nickelates, Hanghui Chen, Yi-Feng Yang, Guang-Ming Zhang, Hongquan Liu

Faculty, Staff and Student Publications

A charge order (CO) with a wavevector [Formula: see text] is observed in infinite-layer nickelates. Here we use first-principles calculations to demonstrate a charge-transfer-driven CO mechanism in infinite-layer nickelates, which leads to a characteristic Ni1+-Ni2+-Ni1+ stripe state. For every three Ni atoms, due to the presence of near-Fermi-level conduction bands, Hubbard interaction on Ni-d orbitals transfers electrons on one Ni atom to conduction bands and leaves electrons on the other two Ni atoms to become more localized. We further derive a low-energy effective model to elucidate that the CO state arises from a delicate competition between Hubbard interaction on Ni-d …

Gamerfit-Asd Beta Test: Adapting An Evidence-Based Exergaming And Telehealth Coaching Intervention For Autistic Youth, Daniel P. Hatfield, Aviva Must, Winston Kennedy, Amanda E. Staiano, James Slavet, Rachael A. Sabelli, Carol Curtin, Linda G. Bandini, Phillip Nauta, Christopher Stuetzle, April Bowling

Computer and Data Science Faculty Publications

Background: Health disparities faced by autistic youth are exacerbated by inadequate physical activity (PA) and sleep, whereas healthy PA and sleep may improve mood and function. Adaptive Game Squad (AGS) is an evidence-based telehealth coaching and exergaming intervention to improve PA and sleep for adolescents with diverse neurodevelopmental and psychiatric conditions. This study aimed to adapt AGS for autistic youth ages 10–15 years; beta-test the modified intervention for feasibility, accessibility, and engagement; and further refine the intervention for a larger planned demonstration pilot.

Methods: Interdisciplinary experts adapted AGS to create GamerFit-ASD, a 12-week intervention that included a progressive exergame schedule, …

Uncertainties In Retrieval Of Remote Sensing Reflectance From Ocean Color Satellite Observations, Eder I. Herrera Estrella

Dissertations, Theses, and Capstone Projects

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance (������) from satellite imagery at different scales and over different time periods. ������ spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. ������ is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of ������ from the total radiance at TOA involves the application of atmospheric correction …

Human-Ai Complex Task Planning, Sepideh Nikookar

Dissertations

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. …

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …

On Explainability Of Neural Networks, Cem Benar

Dissertations

It is widely reported that deep neural networks outperform most competitors for a range of applications. The state-of-the-art neural networks have built-in inductive bias of architectural choices, regularizations, optimizer types, and initialization methods. Using inductive bias is intuitive to enhance the model approximation. Deep neural networks are mostly dense and heavily overparameterized. They tend to be biased towards low-rank solutions to reduce complexity and improve generalization performance, known as implicit regularization. The implicit regularization as observed in specific architectures and various real-world data sets suggests to overparameterize neural networks judiciously and learn compressed representations (lower rank approximation) with improved performance. …

Crabr-Net: A Contextual Relational Attention-Based Recognition Network For Remote Sensing Scene Objective, Ningbo Guo, Mingyong Jiang, Lijing Gao, Yizhuo Tang, Jinwei Han, Xiangning Chen

Faculty, Staff and Student Publications

Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the "summation" way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a …

Syllabus For Computational Physics (Phys 39907), Mark D. Shattuck

Open Educational Resources

Syllabus for City College of New York Computational Physics course.

On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference, Gui Huang, Ru Tao

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study reviews the newest legislation and policies of Regional Comprehensive Economic Partnership (RCEP) participating countries on cross-border data flow, and then categorized them according to the ban on data transfer, local storage of data, permission-based regulation, and standards-based regulation. By referring to the indexes in the ASEAN Digital Integration Index, the subject and object factors of digital productivity in RCEP parities are sorted out, as well as the status quo of digital economy. Through the introduction of data value chain theory, the decisive impact of digital productivity factors on the policy formulation of cross-border data flow is expounded; by …

Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data localization is a focal point of global data governance and its impact on global data governance is no longer confined to a single country. Over the years, India has followed a unique policy framework in terms of cross-border data flows and data localization, and its insistence on data sovereignty reflects its position in the international arena. This study uses the Indian data localization paradigm as a research base to discuss the common phenomenon of disconnect between policy motivations and practical effects of data localization, and as an entry point to introduce the latest Indian research findings in this area. …

Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the era of multi-source heterogeneous big data, big data presents new features such as cross, diversity and variability. The applications of big data in a wider range of fields have new requirements for data fusion. Under this background, the connotation of data fusion is enriched and expanded. The generalized data fusion includes the fusion of data resources, the fusion of model methods, and the fusion of decision-makers' knowledge and experience. This study analyzes the characteristics of multi-source heterogeneous data fusion at three different fusion levels: data level, information level and decision level, and discusses challenges for data fusion in …

Verifying Empirical Predictive Modeling Of Societal Vulnerability To Hazardous Events: A Monte Carlo Experimental Approach, Yi Victor Wang, Seung Hee Kim, Menas C. Kafatos

Institute for ECHO Articles and Research

With the emergence of large amounts of historical records on adverse impacts of hazardous events, empirical predictive modeling has been revived as a foundational paradigm for quantifying disaster vulnerability of societal systems. This paradigm models societal vulnerability to hazardous events as a vulnerability curve indicating an expected loss rate of a societal system with respect to a possible spectrum of intensity measure (IM) of an event. Although the empirical predictive models (EPMs) of societal vulnerability are calibrated on historical data, they should not be experimentally tested with data derived from field experiments on any societal system. Alternatively, in this paper, …