Data Science Commons^™

Tissue-Specific Atlas Of Trans-Models For Gene Regulation Elucidates Complex Regulation Patterns, Robert Dagostino, Assaf Gottlieb

Faculty, Staff and Student Publications

BACKGROUND: Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

RESULTS: We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the …

The Study On Mechanical Model Considering Optimal Self-Adaption In The Bottleneck Area, Longcheng Yang, Huajun Wang, Jun Hu, Hongyu Pan, Juan Wei, Lei You, Hao Zhang, Junxi Wang

Faculty, Staff and Student Publications

It aims to solve the problem that the evacuation state of pedestrians depicted by the traditional social force model in a crowded multiexit scenario has a relatively large difference with the actual state, especially the 'optimal path' considered by the self-driving force is the problem of shortest path, and the multiexit evacuation mode depicted by the 'herd behavior' is the local optimum problem. Through in-depth analysis of actual evacuation data of pedestrians and causes of problem, a new crowd evacuation optimization model is established in order to effectively improve the simulation accuracy of crowd evacuation in a multi-exit environment. The …

Non-Traumatic Osteonecrosis Of The Femoral Head Induced By Steroid And Alcohol Exposure Is Associated With Intestinal Flora Alterations And Metabolomic Profiles, Qing-Yuan Zheng, Ye Tao, Lei Geng, Peng Ren, Ming Ni, Guo-Qiang Zhang

Faculty, Staff and Student Publications

OBJECTIVE: Osteonecrosis of the femoral head (ONFH) is a severe disease that primarily affects the middle-aged population, imposing a significant economic and social burden. Recent research has linked the progression of non-traumatic osteonecrosis of the femoral head (NONFH) to the composition of the gut microbiota. Steroids and alcohol are considered major contributing factors. However, the relationship between NONFH caused by two etiologies and the microbiota remains unclear. In this study, we examined the gut microbiota and fecal metabolic phenotypes of two groups of patients, and analyzed potential differences in the pathogenic mechanisms from both the microbial and metabolic perspectives.

METHODS: …

Genomic Population Structure Of Great Hammerhead Sharks (Sphyrna Mokarran) Across The Indo-Pacific, Naomi L. Brunjes, Samuel M. Williams, Alexis L. Levengood, Matt K. Broadhurst, Vincent Raoult, Alastair V. Harry, Matias Braccini, Madeline E. Green, Julia L. Y. Spaet, Michael J. Travers, Bonnie J. Holmes

Fisheries Research Articles

Context

Currently, little information exists describing the population structure of great hammerhead sharks (Sphyrna mokarran) in Australian waters. Aims

This study used single nucleotide polymorphisms to investigate fine-scale population structure in S. mokarran across the Indo-Pacific. Methods

DNA was extracted from 235 individuals across six Australian locations and a Red Sea outgroup. Population parameters were calculated and visualised to test structuring across locations. Key results

No fine-scale population structuring was observed for S. mokarran across the Indo-Pacific. However, population structuring occurred for all Australian locations when compared to the Red Sea outgroup. Conclusions

Findings suggest a single stock …

Nanocavity-Mediated Purcell Enhancement Of Er In Tio2 Thin Films Grown Via Atomic Layer Deposition, Cheng Ji, Michael T Solomon, Gregory D Grant, Koichi Tanaka, Muchuan Hua, Jianguo Wen, Sagar Kumar Seth, Connor P Horn, Ignas Masiulionis, Manish Kumar Singh, Sean E Sullivan, F Joseph Heremans, David D Awschalom, Supratik Guha, Alan M Dibos

Faculty, Staff and Student Publications

The use of trivalent erbium (Er3+), typically embedded as an atomic defect in the solid-state, has widespread adoption as a dopant in telecommunication devices and shows promise as a spin-based quantum memory for quantum communication. In particular, its natural telecom C-band optical transition and spin-photon interface make it an ideal candidate for integration into existing optical fiber networks without the need for quantum frequency conversion. However, successful scaling requires a host material with few intrinsic nuclear spins, compatibility with semiconductor foundry processes, and straightforward integration with silicon photonics. Here, we present Er-doped titanium dioxide (TiO2) thin film growth on silicon …

The Social And Economic Dimensions Of One Of The World’S Longest-Operating Shark Fisheries, Matias Braccini, Maddison Watt, Clinton Syers, Nick Blay, Matthew Navarro, Michael Burton

Fisheries Research Articles

Context

Social and economic information is limited for coastal commercial and recreational fisheries, particularly shark fisheries, which are perceived as unsustainable and as targeting sharks for fins.

Aims

To characterise the social and economic dimensions of one of the world’s few long-standing sustainable shark fisheries.

Methods

We reviewed historic data and surveyed stakeholders to understand the economic and social dimensions of the shark fishery currently operating in Western Australia.

Key results

Since the fishery’s historic peak, there has been a substantial reduction in the number of operating vessels and ports due to management intervention. For the vessels that have remained, …

Leveraging Explainable Artificial Intelligence To Optimize Clinical Decision Support, Siru Liu, Allison B Mccoy, Josh F Peterson, Thomas A Lasko, Dean F Sittig, Scott D Nelson, Jennifer Andrews, Lorraine Patterson, Cheryl M Cobb, David Mulherin, Colleen T Morton, Adam Wright

Faculty, Staff and Student Publications

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

METHODS: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. …

Transcriptional Signature Of Durable Effector T Cells Elicited By A Replication Defective Hcmv Vaccine, Xiaohua Ye, David J H Shih, Zhiqiang Ku, Junping Hong, Diane F Barrett, Richard E Rupp, Ningyan Zhang, Tong-Ming Fu, W Jim Zheng, Zhiqiang An

Faculty, Staff and Student Publications

Human cytomegalovirus (HCMV) is a leading infectious cause of birth defects and the most common opportunistic infection that causes life-threatening diseases post-transplantation; however, an effective vaccine remains elusive. V160 is a live-attenuated replication defective HCMV vaccine that showed a 42.4% efficacy against primary HCMV infection among seronegative women in a phase 2b clinical trial. Here, we integrated the multicolor flow cytometry, longitudinal T cell receptor (TCR) sequencing, and single-cell RNA/TCR sequencing approaches to characterize the magnitude, phenotype, and functional quality of human T cell responses to V160. We demonstrated that V160 de novo induces IE-1 and pp65 specific durable polyfunctional …

Learning From Data In Dentistry: Summary Of The Third Annual Openwide Conference, Elsbeth Kalenderian, Kawtar Zouaidi, Jan Yeager, Janelle Urata, Alfa Yansane, Bunmi Tokede, D Brad Rindal, Heiko Spallek, Joel White, Muhammad Walji

Faculty, Staff and Student Publications

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) …

Evaluating And Improving The Usability Of A Mhealth Platform To Assess Postoperative Dental Pain, Ana M Ibarra-Noriega, Alfa Yansane, Joanna Mullins, Kristen Simmons, Nicholas Skourtes, David Holmes, Joel White, Elsbeth Kalenderian, Muhammad F Walji

Faculty, Staff and Student Publications

OBJECTIVES: The use of interactive mobile health (mHealth) applications to monitor patient-reported postoperative pain outcomes is an emerging area in dentistry that requires further exploration. This study aimed to evaluate and improve the usability of an existing mHealth application.

MATERIALS AND METHODS: The usability of the application was assessed iteratively using a 3-phase approach, including a rapid cognitive walkthrough (Phase I), lab-based usability testing (Phase II), and

RESULTS: The rapid cognitive walkthrough identified 23 potential issues that could negatively impact user experience, with the majority classified as system issues. The lab-based usability testing yielded 141 usability issues.; 43% encountered by …

Unveiling Gene Interactions In Alzheimer's Disease By Integrating Genetic And Epigenetic Data With A Network-Based Approach, Keith L Sanders, Astrid M Manuel, Andi Liu, Boyan Leng, Xiangning Chen, Zhongming Zhao

Faculty, Staff and Student Publications

Alzheimer’s Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD’s pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted …

Digital Health Technologies For High-Risk Pregnancy Management: Three Case Studies Using Digilego Framework, Sahiti Myneni, Alexandra Zingg, Tavleen Singh, Angela Ross, Amy Franklin, Deevakar Rogith, Jerrie Refuerzo

Faculty, Staff and Student Publications

OBJECTIVE: High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health.

METHODS: We describe three studies that leverage core methods from

RESULTS: Scalable social computing models using deep learning classifiers …

Identification Of Novel F2-Isoprostane Metabolites By Specific Udp-Glucuronosyltransferases, Ginger L Milne, Marina S Nogueira, Benlian Gao, Stephanie C Sanchez, Warda Amin, Sarah Thomas, Camille Oger, Jean-Marie Galano, Harvey J Murff, Gong Yang, Thierry Durand

Faculty, Staff and Student Publications

UDP-glucuronosyltransferases (UGTs) catalyze the conjugation of glucuronic acid with endogenous and exogenous lipophilic small molecules to facilitate their inactivation and excretion from the body. This represents approximately 35 % of all phase II metabolic transformations. Fatty acids and their oxidized eicosanoid derivatives can be metabolized by UGTs. F2-isoprostanes (F2-IsoPs) are eicosanoids formed from the free radical oxidation of arachidonic acid. These molecules are potent vasoconstrictors and are widely used as biomarkers of endogenous oxidative damage. An increasing body of evidence demonstrates the efficacy of measuring the β-oxidation metabolites of F2-IsoPs rather than the unmetabolized F2-IsoPs to quantify oxidative damage in …

Social And Behavior Factors Of Alzheimer's Disease And Related Dementias: A National Study In The Us, David Ciciora, Elizabeth Vásquez, Edward Valachovic, Lifang Hou, Yinan Zheng, Hua Xu, Xiaoqian Jiang, Kun Huang, Kelley Pettee Gabriel, Hong-Wen Deng, Mary P Gallant, Kai Zhang

Faculty, Staff and Student Publications

Introduction: Considerable research has linked many risk factors to Alzheimer's Disease and Related Dementias (ADRD). Without a clear etiology of ADRD, it is advantageous to rank the known risk factors by their importance and determine if disparities exist. Statistical-based ranking can provide insight into which risk factors should be further evaluated.

Methods: This observational, population-based study assessed 50 county-level measures and estimates related to ADRD in 3,155 counties in the U.S. using data from 2010 to 2021. Statistical analysis was performed in 2022-2023. The machine learning method, eXtreme Gradient Boosting, was utilized to rank the importance of these variables by …

High Dietary Folic Acid Supplementation Reduced The Composition Of Fatty Acids And Amino Acids In Fortified Eggs, Ao-Chuan Yu, Yu-Han Deng, Cheng Long, Xi-Hui Sheng, Xiang-Guo Wang, Long-Fei Xiao, Xue-Ze Lv, Xiang-Ning Chen, Li Chen, Xiao-Long Qi

Faculty, Staff and Student Publications

AIMS: The study aimed to evaluate the effects of dietary folic acid (FA) on the production performance of laying hens, egg quality, and the nutritional differences between eggs fortified with FA and ordinary eggs.

METHODS: A total of 288 26-week-old Hy-Line Brown laying hens (initial body weights 1.65 ± 0.10 kg) with a similar weight and genetic background were used. A completely randomized design divided the birds into a control group and three treatment groups. Each group consisted of six replicates, with twelve chickens per replicate. Initially, all birds were fed a basal diet for 1 week. Subsequently, they were …

Deep Learning Can Be Used To Classify And Segment Plant Cell Types In Xylem Tissue, Reem Al Dabagh, Benjamin Shin, Sean Wu, Fabien Scalzo, Helen Holmlund, Jessica Lee, Chris Ghim, Samuel Fitzgerald, Marinna Grijalva

Seaver College Research And Scholarly Achievement Symposium

Studies of plant anatomical traits are essential for understanding plant physiological adaptations to stressful environments. For example, shrubs in the chaparral ecosystem of southern California have adapted various xylem anatomical traits that help them survive drought and freezing. Previous studies have shown that xylem conduits with a narrow diameter allows certain chaparral shrub species to survive temperatures as low as -12 C. Other studies have shown that increased cell wall thickness of fibers surrounding xylem vessels improves resistance to water stress-induced embolism formation. Historically, these studies on xylem anatomical traits have relied on hand measurements of cells in light micrographs, …

A Self-Supervised Learning Approach For Registration Agnostic Imaging Models With 3d Brain Cta, Yingjun Dong, Samiksha Pachade, Xiaomin Liang, Sunil A Sheth, Luca Giancardo

Faculty, Staff and Student Publications

Deep learning-based neuroimaging pipelines for acute stroke typically rely on image registration, which not only increases computation but also introduces a point of failure. In this paper, we propose a general-purpose contrastive self-supervised learning method that converts a convolutional deep neural network designed for registered images to work on a different input domain, i.e., with unregistered images. This is accomplished by using a self-supervised strategy that does not rely on labels, where the original model acts as a teacher and a new network as a student. Large vessel occlusion (LVO) detection experiments using computed tomographic angiography (CTA) data from 402 …

Label-Aware Distance Mitigates Temporal And Spatial Variability For Clustering And Visualization Of Single-Cell Gene Expression Data, Shaoheng Liang, Jinzhuang Dou, Ramiz Iqbal, Ken Chen

Faculty, Staff and Student Publications

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our …

An Exposome Atlas Of Serum Reveals The Risk Of Chronic Diseases In The Chinese Population, Lei You, Jing Kou, Mengdie Wang, Guoqin Ji, Xiang Li, Chang Su, Fujian Zheng, Mingye Zhang, Yuting Wang, Tiantian Chen, Ting Li, Lina Zhou, Xianzhe Shi, Chunxia Zhao, Xinyu Liu, Surong Mei, Guowang Xu

Faculty, Staff and Student Publications

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, …

The Acceptance And Use Of Digital Technologies For Self-Reporting Medication Safety Events After Care Transitions To Home In Patients With Cancer: Survey Study, Yun Jiang, Misun Hwang, Youmin Cho, Christopher R Friese, Sarah T Hawley, Milisa Manojlovich, John C Krauss, Yang Gong

Faculty, Staff and Student Publications

BACKGROUND: Actively engaging patients with cancer and their families in monitoring and reporting medication safety events during care transitions is indispensable for achieving optimal patient safety outcomes. However, existing patient self-reporting systems often cannot address patients' various experiences and concerns regarding medication safety over time. In addition, these systems are usually not designed for patients' just-in-time reporting. There is a significant knowledge gap in understanding the nature, scope, and causes of medication safety events after patients' transition back home because of a lack of patient engagement in self-monitoring and reporting of safety events. The challenges for patients with cancer in …

Geospatial Analysis Of Agricultural Potential In The United States, Diana Febrita

Graduate Industrial Research Symposium

Traditionally, the agriculture sector is responsible for providing food and crop products. However, the role of agriculture has expanded beyond its traditional function. It is the main sector that contributes to the provision of food, income, employment, environmental protection, and local economic development. Reflecting on the roles of agriculture, understanding the potential of agriculture in the United States is crucial to discovering the prospects and challenges. This study will briefly discuss the agricultural potential in the United States based on the five assets, including natural capital, financial capital, human capital, physical capital, and social capital. To identify the states with …

Online Class-Incremental Learning For Real-World Food Image Classification, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu

Graduate Industrial Research Symposium

Food image classification is essential for monitoring health and tracking dietary in image-based dietary assessment methods. However, conventional systems often rely on static datasets with fixed classes and uniform distribution. In contrast, real-world food consumption patterns, shaped by cultural, economic, and personal influences, involve dynamic and evolving data. Thus, it requires the classification system to cope with continuously evolving data. Online Class Incremental Learning (OCIL) addresses the challenge of learning continuously from a single-pass data stream while adapting to the new knowledge and reducing catastrophic forgetting. Experience Replay (ER) based OCIL methods store a small portion of previous data and …

Modelling The "Bottom-Up" Development Pattern Of Tar Spot Disease In Corn, Brenden Lane, Joaquín Guillermo Ramírez-Gil, Carlos Góngora-Canul, Mariela Sofia Fernandez Campos, Andres Cruz-Sancan, Fidel E. Jiménez-Beitia, Alex G. Acosta-Guatemal, Wily Sic, C. D. Cruz

Graduate Industrial Research Symposium

In 2015, the corn-infecting pathogen Phyllachora maydis (causal agent of tar spot disease) was reported for the first time in the United States. The disease has since spread across the US, causing major yield losses. In 2021 alone, 5.88 million metric tons (231.3 million bushels) of US corn yield were lost to this disease, costing an estimated US$1.25 billion. Though fungicides can protect against these agroeconomic losses, application timing can be difficult to optimize because our understanding of tar spot dynamics is still evolving. The current view is that tar spot typically develops bottom-up through a repeating infection cycle. Because …

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

Deep Learning Model For Personalized Prediction Of Positive Mrsa Culture Using Time-Series Electronic Health Records, Masayuki Nigo, Laila Rasmy, Bingyu Mao, Bijun Sai Kannadath, Ziqian Xie, Degui Zhi

Faculty, Staff and Student Publications

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events from Memorial Hermann Hospital System, Houston, Texas are used for model development. PyTorch_EHR outperforms logistic regression (LR) and light gradient boost machine (LGBM) models in accuracy (AUROC

Spec: A Soft Prompt-Based Calibration On Performance Variability Of Large Language Model In Clinical Notes Summarization, Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also …

Multiple Control Of Azoquinoline Based Molecular Photoswitches, Youming Lv, Hebo Ye, Lei You

Faculty, Staff and Student Publications

Multi-addressable molecular switches with high sophistication are creating intensive interest, but are challenging to control. Herein, we incorporated ring-chain dynamic covalent sites into azoquinoline scaffolds for the construction of multi-responsive and multi-state switching systems. The manipulation of ring-chain equilibrium by acid/base and dynamic covalent reactions with primary/secondary amines allowed the regulation of

Hillside Agricultural Machinery And Agricultural Intelligence Driven By New Technologies, Hong Qiao, Yanfeng Lyu, Enhao Zheng

Bulletin of Chinese Academy of Sciences (Chinese Version)

Hilly and mountainous areas play a crucial role in China’s agricultural production. However, the low level of comprehensive mechanization for crop cultivation, planting, and harvesting in these regions severely hampers the modernization of agriculture. The complex terrain and diverse cropping patterns in hilly areas necessitate the development of specialized agricultural machinery and robots tailored to these unique landscapes. To advance this process, the Chinese government has introduced a series of policies in recent years to support the research and development of agricultural machinery for hilly regions, providing strong backing for the advancement of agricultural technology in these areas. Against this …

The Pathogenicity Of Vancomycin-Resistant Enterococcus Faecalis To Colon Cancer Cells, Li Zhang, Mingxia Deng, Jing Liu, Jiajie Zhang, Fangyu Wang, Wei Yu

Faculty, Staff and Student Publications

BACKGROUND: The aim of this study was to investigate the pathogenicity of vancomycin-resistant Enterococcus faecalis (VREs) to human colon cells in vitro.

METHODS: Three E. faecalis isolates (2 VREs and E. faecalis ATCC 29212) were cocultured with NCM460, HT-29 and HCT116 cells. Changes in cell morphology and bacterial adhesion were assessed at different time points. Interleukin-8 (IL-8) and vascular endothelial growth factor A (VEGFA) expression were measured via RT-qPCR and enzyme-linked immunosorbent assay (ELISA), respectively. Cell migration and human umbilical vein endothelial cells (HUVECs) tube formation assays were used for angiogenesis studies. The activity of PI3K/AKT/mTOR signaling pathway was measured …

The Fuxi Farm: Practice Exploration And Reflection On Integrated Innovation Of Smart Agriculture Technology, Yucheng Zhang, Xiaobo Zhang, Shuqin Gao, Congcong Zheng, Jingyao Zhang, Ya Wen, Lujun Li, Zhuo Wang, Tie Li, Honglong Zhao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Smart agriculture plays a pivotal role in enhancing food security by optimizing resource use and improving crop yields through the application of AI technologies. The establishment of smart farms is critical to rapidly integrating AI into agricultural production, offering platforms for both the deployment and testing of AI solutions and equipment. This paper examines the development of smart farms, with a focus on practices from developed countries, and assesses the current status of smart farming in China. We then focus on exploring the content, pathways, and practices for constructing a smart agricultural production system in China through the establishment of …