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PSIV-B-20 Genome-Wide Association Analyses Revealed Potential Molecular Mechanisms Affecting Milk Fever in Dairy Cattle

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Journal of Animal science 2022年第SUPPLEMENT_3期100卷 307–307页

作者： Lunn, Morgan A Gillette, Sydney Campos, Fernando Peterson, Daniel Sprayberry, Kim Sargolzaei, Mehdi McArthur, Gary Anderson, Paul La Frano, Michael Pokharel, Siroj Abo-Ismail, Mohammed Animal Science Department California Polytechnic State University Select Sires Swinging Udders Veterinary Service LLC Department of Computer Science and Software Engineering Cal Poly State University Food Science and Nutrition Department Cal Poly State University

Milk fever, or hypocalcemia, has a significant economic impact on the dairy industry and increases the risk of other costly disorders including ketosis and displaced abomasum. The objective of this study was to identify genetic variants, genes and molecular mechanisms causing genetic variation in the development of milk fever. A genomic based mixed linear animal model was fitted to include single nucleotide polymorphisms (SNPs), breed, and pen as fixed effects and implemented in GCTA software. A total of 436 Jersey and Holstein cows with varying milk fever history were genotyped for high density SNP chip (782,139 SNP). A total of 401 animals with 617,527 SNPs passed the quality control and were included in the association analyses. Two SNPs located on chromosome 1 were found significant at 5% genome-wide false discovery rate. The 100 most significant SNPs were mapped to corresponding genes using the Ensemble database. A total of 24 genes were found to overlap with the significant SNPs and analyzed using the in-silico functional analyses implemented in DAVID software. The genomic heritability estimated in this population was 0.11. The study identified potential biological pathways associated with milk fever including regulation of Ras and GTPase signal transduction, mammary gland development and ossification. The identified genomic regions and genes will be validated in a larger population. These results could enable us to develop genomic testing tools based on the identified genes to select for cows with low incidence of milk fever and provide insights on which animals within the herd will likely require dietary or management adjustments to prevent and treat milk fever.

关键词： milk fever genome-wide association analyses dairy cattle

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Towards Adaptive Ontology Visualization - Predicting User Success from Behavioral Data

Towards Adaptive Ontology Visualization - Predicting User Su...

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作者： Fu, Bo Steichen, Ben Zhang, Wenlu Computer Engineering and Computer Science California State University Long Beach Long BeachCA90840 United States Computer Science California State Polytechnic University Pomona PomonaCA91768 United States

Ontology visualization plays an important role in human data interaction by offering clarity and insight for complex structured datasets. Recent usability studies of ontology visualization techniques have added to our understanding of desired features when assisting users in the interactive process. However, user behavioral data such as eye gaze and event logs have largely been used as indirect evidence to explain why a user may have carried out certain tasks in a controlled environment, as opposed to direct input that informs the underlying visualization system. Although findings from usability studies have contributed to the refinement of ontology visualizations as a whole, the visualization techniques themselves remain a one-size-fits-all approach, where all users are presented with the same visualizations and interactive features. By contrast, this paper investigates the feasibility of using behavioral data, such as user gaze and event logs, as real-time indicators of how appropriate or effective a given visualization may be for a specific user at a moment in time, which in turn may be used to inform the adaptation of the visualization to the user on the fly. To this end, we apply established predictive modeling techniques in Machine Learning to predict user success using gaze data and event logs. We present a detailed analysis from a controlled experiment and demonstrate such predictions are not only feasible, but can also be significantly better than a baseline classifier during visualization usage. These predictions can then be used to drive the adaptations of visual systems in providing ad hoc visualizations on a per user basis, which in turn may increase individual user success and performance. Furthermore, we demonstrate the prediction performance using several different feature sets, and report on the results generated from several notable classifiers, where a decision tree-based learning model using a boosting algorithm produced the best overall results.

关键词： Eye tracking

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TRUST: Triangle counting reloaded on GPUs

arXiv

引用

arXiv 2021年

作者： Pandey, Santosh Wang, Zhibin Zhong, Sheng Tian, Chen Zheng, Bolong Li, Xiaoye Li, Lingda Hoisie, Adolfy Ding, Caiwen Li, Dong Liu, Hang Electrical and Computer Engineering Stevens Institute of Technology United States State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu210008 China Huazhong University of Science and Technology China Computational Research Division Lawrence Berkeley National Laboratory United States Brookhaven National Laboratory United States Department of Computer Science & Engineering University of Connecticut United States Department of Electrical Engineering and Computer Science University of California Merced United States

Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric design reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hashing-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST which performs triangle counting with the hash operation and vertex-centric mechanism at the core. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting. © 2021, CC BY.

关键词： Program processors

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Position: Topological Deep Learning is the New Frontier for Relational Learning

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Birdal, Tolga Bronstein, Michael Carlsson, Gunnar Curry, Justin Gao, Yue Hajij, Mustafa Kwitt, Roland Liò, Pietro Di Lorenzo, Paolo Maroulas, Vasileios Miolane, Nina Nasrin, Farzana Ramamurthy, Karthikeyan Natesan Rieck, Bastian Scardapane, Simone Schaub, Michael T. Veličković, Petar Wang, Bei Wang, Yusu Wei, Guo-Wei Zamzmi, Ghada Department of Mathematics The University of Manchester Manchester United Kingdom Department of Computing Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics Stanford University Stanford United States BlueLightAI Inc United States University at Albany New York United States School of Software Tsinghua University Beijing China University of San Francisco San Francisco United States Department of Artificial Intelligence and Human Interfaces University of Salzburg Austria Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Information Engineering Electronics and Telecommunications Sapienza University of Rome Rome Italy Department of Mathematics University of Tennessee Knoxville United States Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara United States Department of Mathematics University of Hawai‘i at Mānoa HI United States IBM Corporation New York United States Helmholtz Munich Munich Germany Technical University of Munich Munich Germany RWTH Aachen University Aachen Germany Google Deep-Mind United Kingdom School of Computing University of Utah Utah United States Computer Science and Engineering Department University of California San Diego San Diego United States Department of Mathematics Michigan State University East LansingMI United States University of South Florida Florida United States

Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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Impact of windows, natural materials, and diverse representations in built environments on psychological and physiological well-being: A between-subjects experiment in immersive virtual environments

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Building and Environment 2025年 280卷

作者： Altaf, Basma Tavakoli, Arash Ruth, Parker Green, Andrea Xu, Jiaxuan Jain, Sneha Chiu, Ethan Bencharit, Lucy Zhang Murnane, Elizabeth L Landay, James A Billington, Sarah L. Department of Civil & Environmental Engineering Stanford University United States Department of Computer Science Stanford University United States SPARQ Department of Psychology Stanford University United States Department of Psychology and Child Development California Polytechnic State University United States School of Engineering Dartmouth College United States

Extensive research and building frameworks advocate for wellness in design and emphasize the connection between the built environment and occupant well-being. However, the extent to which specific design features influence well-being metrics remains under investigation. While in-person laboratory experiments have yielded valuable insights, they require considerable resources. Immersive virtual environments (IVEs) serve as a tool that offers a cost-effective and efficient method for conducting research with controlled manipulations of the variables of interest, particularly when compared to physical modeling. This paper presents an enhanced IVE framework that integrates human and environmental sensors as well as in-VR questionnaires to objectively and subjectively measure occupant well-being in indoor settings. We used this framework in a study (N = 237) to examine how changes in built features, materials (natural versus artificial), windows (presence versus absence), and representations (diverse versus non-diverse), influence well-being outcomes including belonging, stress, creativity, and environmental concern. We hypothesized that exposure to windows, natural materials, and diverse representations would lead to increased belonging, reduced stress, enhanced creativity, greater environmental concern, and a higher likelihood of engaging in environmentally supportive charitable actions, compared to exposure to artificial environments and non-diverse representations. Our findings confirm significant impacts of these built features on three of the four well-being metrics examined. Specifically, the presence of windows significantly increased feelings of belonging, natural materials reduced stress, and diverse representations led to significantly greater environmental attitudes. These results emphasize the effectiveness of IVEs in studying the effects of the built environment on psychological and physiological well-being. © 2025

关键词： Virtualization

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VSEPR Theory, An Interactive and Immersive Virtual Reality

VSEPR Theory, An Interactive and Immersive Virtual Reality

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Immersive Learning Research Network (iLRN), Conference of the

作者： Danica Fujiwara Kevin Kellar Irene Humer Krzysztof Pietroszek Christian Eckhardt Software Development California Polytechnic State University San Luis Obispo California USA Computer Science California Polytechnic State University San Luis Obispo California USA Ideas Lab American University of Washington D.C Washington D.C USA

ISBN: (数字)9781734899504

ISBN: (纸本)9781728152264

This work explores the role of immersive learning in regards to predicting molecular structures and orbital hybridization through an Oculus Rift HMD. Specifically, this research utilizes an interactive virtual environment to help teach the concept of the Valence Shell Electron Pair Repulsion Theory. We developed an interactive and immersive simulation in which participants constructed virtual molecules. Following the simulation, participants were asked to identify shapes of similar molecules to measure their understanding of the theory as compared to a control group. Finally, participants answered the Game Experience Questionnaire to assess the level of Immersion. The results show that our immersive virtual reality (IVR) experience improves comprehension of three dimensional structures in chemistry.

关键词： Solid modeling Virtual reality Orbits Shape Visualization Geometry Chemistry

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Integrated optical phased arrays: LiDAR, communications, augmented reality, and beyond

Integrated optical phased arrays: LiDAR, communications, aug...

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Photonic Networks and Devices, NETWORKS 2020

作者： Notaros, Jelena Notaros, Milica Raval, Manan Poulton, Christopher V. Byrd, Matthew J. Li, Nanxi Su, Zhan Magden, Emir Salih Timurdogan, Erman Dyer, Thomas Baiocco, Christopher Kim, Taehwan Bhargava, Pavan Stojanovic, Vladimir Watts, Michael R. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Science Harvard University CambridgeMA02138 United States Colleges of Nanoscale Science and Engineering State University of New York Polytechnic Institute AlbanyNY12203 United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA94720 United States

Recent integrated optical phased array architectures, results, and applications will be reviewed, including beam steering for LiDAR and communications, near-field optical manipulation, and holographic displays for aug... 详细信息

ISBN: (纸本)9781943580798

关键词： Augmented reality

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Robust event classification using imperfect real-world PMU Data

arXiv

引用

arXiv 2021年

作者： Liu, Yunchuan Yang, Lei Ghasemkhani, Amir Livani, Hanif Centeno, Virgilio A. Chen, Pin-Yu Zhang, Junshan Department of Computer Science and Engineering University of Nevada Reno RenoNV89557 United States Computer Science and Engineering California State University San Bernardino San BernardinoCA92407 United States Department of Electrical and Biomedical Engineering University of Nevada Reno RenoNV89557 United States Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University BlacksburgVA24061 United States IBM Thomas J. Watson Research Center Yorktown HeightsNY10598 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

—This paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data);in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs;and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data. © 2021, CC BY.

关键词： Phasor measurement units

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Issue tracking systems: What developers want and use 13

Issue tracking systems: What developers want and use

引用

13th International Conference on software Technologies, ICSOFT 2018

作者： Falessi, Davide Hernandez, Freddy Khosmood, Foaad Dept. of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States GoDaddy Inc. San FranciscoCA United States

ISBN: (纸本)9789897583209

An Issue Tracking System (ITS) allows a developer to keep track of, prioritize, and assign multitudes of bugs, feature requests, and other development tasks such as testing. Despite ITSs play a significant role in day-to-day developers' activities, no previous study investigated what developers want and use in an ITS. The aim of this paper is twofold. First, we provide a feature matrix that maps six of the most used ITS to features, and second, we measure the developers' level of use and perceived importance of each feature. This knowledge has multiple benefits such as supporting the decision of the ITS to use and revealing promising areas of research and development. Specifically, quality improvement effort should target improving functionality in use, and development effort should target supporting functionalities needed. In this paper, we define and extract ten core ITS features and asked more than a hundred developers to rate their importance and use. Our results show that Advanced Search and Flexible Notifications are the most important features. Moreover, results show that no feature has been used by more than 90% of the respondents. Another interesting finding is that 27% of respondents rate Workflow Automation as a useful or required feature, despite having never used it themselves;this suggests the need to better training, exposure or of availability of ITS features. In conclusion, our results pave the way to significant research and development effort on ITS. Copyright © 2018 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved

关键词： Program debugging

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Thermal Detection for Free Flight

引用

Journal of Physics: Conference Series 2021年第1期1828卷

作者： Jake Tallman David Yu Maria Pantoja Department of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo Ca. USA

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, or simply to conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of meters of elevation gain. Estimating thermal locations is not an easy task, pilots look for different indicators like color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the conditions for thermals by estimating solar heating of ground (cloud cover and time year/date) and also the lapse rate and dew point of air. In this paper, we present a Machine Learning based solution to forecast thermals. Since pilots in general record many of their flights locally and sometimes upload them to databases, we use the flight data uploaded to determine where the pilot encountered thermals and together with other information (weather and satellite images corresponding to the location and time of the flight) train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information (obtained from Google Earth Engine). Results show that our model is able to converge on the training and validation set with a loss bellow 1%.

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