About

Chen Zhao, Ph.D., is an Assistant Professor affiliated with the ZhaoLab at the University of Florida. He completed his Bachelor of Science degree at the University of Science and Technology of China, followed by a Ph.D. from Yale University. After earning his doctorate, he pursued postdoctoral research at The Rockefeller University. The information provided does not include specific details about his research focus or key contributions.

Research topics

• Mathematics
• Computer Science
• Data Mining
• Machine Learning
• Organic chemistry
• Inorganic chemistry
• Agronomy
• Systems engineering
• Engineering
• Photochemistry
• Environmental science
• Agricultural engineering
• Chemistry
• Ecology
• Biology
• Materials science
• Statistics

Selected publications

• Tea tree recognition based on multi-source satellite data across Southeast China

  Frontiers in Plant Science · 2026-05-14

  articleOpen accessSenior author

  The tea plant (Camellia Sinensis), as the world’s most popular non-alcoholic beverage, underscores the importance of precise and timely spatial data for industry insights and sustainability. Yet, the accurate delineation of spectrally similar vegetation types, notably tea trees, continues to elude conventional methods. This study innovatively integrates Sentinel-1 radar data with Sentinel-2 imagery to effectively overcome optical observation limitations imposed by Yunnan’s cloudy climate, particularly during non-growing seasons (e.g., February). By systematically analyzing annual phenological dynamics, we quantitatively identified April as the optimal temporal window for discriminating tea trees from spectrally similar vegetation, such as rubber and natural forests. Furthermore, optimizing the model through a feature selection process to eliminate redundant features significantly enhanced the overall classification accuracy from 87.1% to 89.1%. This study could assistant monitoring the crop dynamics and timely respond to cultivation activity.

  PublisherOA PDFDOI

• Modeling tumor relapse using proliferation tracing and ablation transgenic mouse

  npj Breast Cancer · 2025-07-17 · 2 citations

  articleOpen access1st authorCorresponding

  Tumor relapse remains a significant obstacle to successful therapy. Preclinical animal models that accurately reflect tumor relapse in patients are urgently needed. Here, we employed a dual recombinase-mediated genetic system to genetically trace and ablate proliferating cells in a polyomavirus middle T antigen (PyMT)-induced spontaneous murine breast cancer model. This system enabled the acute ablation of cells that had undergone proliferation within a defined time window, resulting in a drastic tumor shrinkage, followed by a gradual tumor relapse due to the presence of residual low-cycling cells. We then applied single-cell RNA sequencing (scRNA-seq) to unbiasedly compare the tumor ecosystems of the primary and relapsed PyMT tumors. Compared with the primary tumors, the relapsed tumors exhibited a higher proportion of cancer stem cells and pro-tumor γδ T cells, as well as co-expression of Spp1 and Vegfa in multiple myeloid cell populations - features that predict poor therapeutic response and unfavorable outcomes in human breast cancer patients. Collectively, this proliferation tracing and ablation model emulates chemotherapies that preferentially eliminate proliferating cancer cells, serving as a robust tool and a valuable resource for testing novel therapeutic strategies in relapsed tumors.

  PublisherOA PDFDOI

• Real‐time emulation of leafy vegetables yield and quality response to 2 °C warming in northeast China

  Journal of the Science of Food and Agriculture · 2025-06-16 · 1 citations

  articleOpen accessCorresponding

  BACKGROUND: Vegetables provide essential parts of healthy, balanced diets in our daily life. Climatic warming is challenging the global vegetable productions, but realistic real-time evidences, especially in temperate regions, are still lacking. In this study, we developed two large, customized, fully artificial climate-controlled chambers capable of replicating the complexity of natural environmental fluctuations. We simulated two temperature treatments - the observed real-time daily average temperature over the past 32 years in northeast China, and an arbitrary increase of +2 °C for each day - on two leafy vegetables, pak choi and lettuce. RESULTS: The results show that warming shortened the growing-season length by 1-2 days for both vegetables. But on growth, the two vegetables differed greatly. The canopy development of lettuce was accelerated, with an increase in leaf area index, efficiency of photosynthesis, and final yield (+35.2%). Pak choi had only 6.8% of yield increase. Furthermore, we observed no significant change in the overall quality level of the two vegetables, although individual components varied. CONCLUSION: The real-time evidence imply the warming benefit to vegetable production in relatively cool conditions and future positive adaptations. © 2025 Society of Chemical Industry.

  PublisherOA PDFDOI

• A facile entry toward multi-substituted chiral cyclic nitrones and N-heterocycles via Pd-catalyzed enantioselective cyclization coupling of alkenyl oxime

  Chinese Chemical Letters · 2025-10-24

  article
  PublisherDOI

• Winter Dormant Wheat Will Benefit From Mean Temperature Increase of 2°C When Well‐Watered and Fertilized in the Main Producing Regions of China

  Global Change Biology · 2025-07-01 · 8 citations

  articleCorresponding

  Rising temperatures are projected to lead to a decline in global wheat production. However, this global trend belies the regional nuances of this impact, such as observed local yield increases in some field experiments in the winter wheat-growing region of China. This study combines detailed data from eight field warming experiments and outputs of simulation by an ensemble of three point-based crop models and an ensemble of 10 global gridded crop models to scrutinize the influence of warming on winter wheat yield in the main producing regions of China (MPC). Observed data were obtained from published reports of field experiments, where winter wheat was grown with sufficient water and nitrogen under free-air-temperature increase (FATI) by 2°C. Growth and physiology of winter wheat in the field experiments were simulated by three point-based crop models to validate the effects of warming on wheat growth and yield as simulated by grid-based crop models. Results of field observations and grid simulations both indicate notable increases in average grain yield (observed +13%, simulated +8%) and aboveground biomass (observed +15%, simulated +7%) under 2°C warming across the MPC. The winter dormancy and pre-anthesis duration were shorter with warmer temperature, with the effect that the grain filling period between anthesis to maturity was extended by 6 days. The shorter phenology affected wheat photosynthesis because less solar radiation was available (-6%) over the growth period. However, the leaf area index started to develop earlier and reached a higher maximum than un-warmed control, so the cumulative solar radiation for photosynthesis intercepted by warmed wheat was higher (+9%), as well as the radiation use efficiency (+1%). These findings suggest that well-irrigated and well-fertilized winter dormant wheat is likely to experience yield gains with local warming of up to 2°C, bolstering confidence in future adaptation of wheat production in China.

  PublisherDOI

• Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction

  ArXiv.org · 2025-03-02

  preprintOpen access

  Self-reflection for Large Language Models (LLMs) has gained significant attention. Existing approaches involve models iterating and improving their previous responses based on LLMs' internal reflection ability or external feedback. However, recent research has raised doubts about whether intrinsic self-correction without external feedback may even degrade performance. Based on our empirical evidence, we find that current static reflection methods may lead to redundant, drift, and stubborn issues. To mitigate this, we introduce Instruct-of-Reflection (IoRT), a novel and general reflection framework that leverages dynamic-meta instruction to enhance the iterative reflection capability of LLMs. Specifically, we propose the instructor driven by the meta-thoughts and self-consistency classifier, generates various instructions, including refresh, stop, and select, to guide the next reflection iteration. Our experiments demonstrate that IoRT achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability.

  PublisherOA PDFDOI

• Contribution of LGD source carbon to the carbon emission of eutrophic steppe lakes in northern cold and arid regions

  Ecological Indicators · 2025-06-07

  articleOpen access

  We present a conceptual model for the biogeochemical cycle of carbon in lakes of eutrophic grasslands in northern cold and arid regions containing LGD-derived carbon. The main source of groundwater carbon in steppe lakes is cow and sheep feces, which infiltrates into aquifers, undergoes decomposition, transformation (such as CH 4 oxidation, denitrification, etc.) and accumulation, flows along groundwater, and finally discharge into lakes. LGD is therefore an important source of various forms of carbon (DOC, DIC, CO 2 , CH 4 ) in lakes. We estimate the contribution of LGD to lake GHG (CH 4 : 12.3%, CO 2 : 14.7%) through greenhouse gas flux quantifcation model and 222 Rn isotope mass balance model. • Lake groundwater discharge (LGD) brings a large amount of carbon-containing substances into the lake. • Groundwater and lake water have homology. • A binding model of 222 Rn isotope and greenhouse gas flux was established to estimate the contribution of LGD. • LGD contributes significantly to lake greenhouse gas emissions. Lake groundwater discharge (LGD) represents a critical but underappreciated source of greenhouse gases in lacustrine systems, though its governing mechanisms and quantitative significance are not yet clearly defined. This investigation focuses on Hulun Lake, a representative eutrophic lake in China’s cold-arid northern regions, through integrated analysis of stable isotope signatures (δ 13 C-CO 2 , δ 13 C-CH 4 , δ 13 C-DIC). We employed 222 Rn mass balance modeling and greenhouse gas flux quantification to assess LGD’s contribution to CO 2 and CH 4 emissions. Results demonstrate elevated carbon component concentrations in groundwater relative to surface waters (DOC: groundwater 1755.50 μmol/L vs. lake water 1480.00 μmol/L vs. river water 933.25 μmol/L; DIC: groundwater 5089.00 μmol/L vs. lake water 7311.00 μmol/L vs. river water 3345.75 μmol/L). Groundwater exhibits higher mean dissolved greenhouse gas concentrations compared to lake water (CO 2 : 2.300 vs. 0.458 μmol/L; CH 4 : 0.029 vs. 0.027 μmol/L). Isotopic evidence confirms hydrological connectivity between groundwater and lake systems. Quantitative analysis reveals LGD accounts for 14.7 % of lacustrine CO 2 emissions and 12.3 % of CH 4 emissions. These findings establish LGD as a critical regulator of carbon biogeochemical cycling in lake ecosystems, necessitating its systematic integration into regional carbon budget assessments. The demonstrated significance of LGD in carbon dynamics underscores its essential consideration in regional and global carbon cycle models.

  PublisherDOI

• Climate-crop models to support opportunity crop adaptation in Africa

  Nature Communications · 2025-12-17 · 2 citations

  articleOpen access

  In an era marked by climatic uncertainties and burgeoning food security challenges, climate-crop models emerge as important tools for guiding investments in climate-resilient agriculture. Here, we construct climate-crop model applications for 24 crops spanning the African food basket, including cereals, legumes, oilseeds, roots/tubers, and vegetables, in support of the Vision for Adapted Crops & Soils (VACS) program. Notably, we expand the modeling framework for 19 “opportunity” crops that have hitherto been underrepresented or overlooked but hold potential to bolster agricultural resilience across the continent. We calibrate and parameterize our process-based models using field observations and comprehensive literature reviews. We evaluate the model outputs spatially and temporally against national crop statistics across Africa, showing acceptable reproduction of yields and response to reported extreme droughts and heat waves. This work offers a scalable framework for climate-crop modeling and impact assessments related to food security in Africa and beyond. Climate-crop models are important tools for guiding investments and exploring adaptation strategies in climate-resilient agriculture. Here, the authors expand climate-crop model applications for 19 African opportunity crops, including cereals, legumes, oilseeds, roots/tubers, and vegetables.

  PublisherOA PDFDOI

• Potential expansion of wheat planting areas driven by climate warming offsets yield losses and enhances global production

  One Earth · 2025-10-28 · 4 citations

  article
  PublisherDOI

• Radiation use efficiency of maize under high-density optimal growth conditions in Jilin Province, China

  Journal of Integrative Agriculture · 2025-09-17

  articleOpen access

  1. During the entire growth period, maize radiation use efficiency (RUE) averaged 5.71 g MJ -1 APAR (absorbed photosynthetically active radiation) under high-density optimal growth conditions. 2. Within the vegetative and reproductive growth periods, maize RUE averaged 6.85 and 5.64 g MJ -1 APAR, respectively. 3. RUE, 5.07-5.85 g MJ -1 APAR, should be used to derive the optimal potential yield in maize simulation model. The study's framework and highlights To evaluate the impact of climate change on maize production, it is critical to accurately measure the radiation use efficiency (RUE) for maize. In this study, we focused on three maize cultivars in Jilin Province, China: Zhengdan 958 (ZD958), Xianyu 335 (XY335), and Liangyu 99 (LY99). Under the optimal growing conditions for high density (9 plants m -2 ), we investigated the maize RUE during the vegetative and reproductive phases, and the entire growth period. The results showed that the canopy light interception for maize peaked during anthesis. After anthesis, maize plant biomass continued to accumulate. Based on the absorbed photosynthetically active radiation (APAR), we calculated maize RUE. During the entire growth period, maize RUE averaged 5.71 g MJ -1 APAR among the three cultivars, with a high-to-low order of ZD958 (5.85 g MJ -1 APAR)>XY335 (5.64 g MJ -1 APAR)>LY99 (5.07 g MJ -1 APAR). Within the vegetative and reproductive growth periods, maize RUE averaged 6.85 and 5.64 g MJ -1 APAR, respectively. When utilizing maize models, such as APSIM, that depend on radiation use efficiency (RUE) to predict aboveground biomass accumulation, we observed that the current RUE value of 3.6 g MJ -1 APAR is considerably lower than the measured value obtained under high-density optimal growing conditions. Consequently, to derive the optimal potential yield for maize in such planting conditions, we recommend adjusting the RUE to a range of 5.07-5.85 g MJ -1 APAR.

  PublisherDOI

Frequent coauthors

• Senthold Asseng

  Technical University of Munich

  26 shared

• Kurt Christian Kersebaum

  26 shared

• Peter J. Thorburn

  Commonwealth Scientific and Industrial Research Organisation

  23 shared

• Liujun Xiao

  Nanjing Agricultural University

  23 shared

• Zvi Hochman

  Commonwealth Scientific and Industrial Research Organisation

  22 shared

• Gerrit Hoogenboom

  University of Florida

  21 shared

• Shilong Piao

  20 shared

• Benjamin Dumont

  18 shared

Labs

• ZhaoLabPI

Education

• PhD

  Peking University

  2017

Awards & honors

• Jane Coffin Childs Fellowship (2018)
• Jane Coffin Childs Foundation
• Gruber Science Fellowship (2013)
• Gruber Science Foundation/Yale University
• Muo Ruo Guo Fellowship (2012)