Postdoctoral Scholars Association

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer characterized by N-Myc (MYCN) amplification. Due to N-Myc’s disordered domains and lack of druggable sites, targeting its proteostasis offers a novel therapeutic angle. Here, we investigated the molecular chaperones involved in regulating N-Myc protein turnover. Mass spectrometry analysis following N-Myc pulldown identified several HSP70 family proteins as prominent interactors. Functional assays revealed that HSP70 interacts with N-Myc and recruits the E3 ligase STUB1 to facilitate ubiquitination at lysines 416 and 419, targeting N-Myc for K11-linked polyubiquitin-mediated proteasomal degradation. This interaction was dependent on a conserved degron motif, SELILKR, within the N-Myc protein. siRNA-mediated knockdown of N-Myc significantly reduced cell proliferation in NEPC models, underscoring its oncogenic importance. Our findings reveal a novel HSP70-STUB1-N-Myc regulatory axis and suggest a potential vulnerability in NEPC that could be exploited therapeutically through proteostasis-targeting strategies.

The growing water scarcity in California has heightened interest in investigating agricultural irrigation water conservation strategies. This study examines the diurnal crop water stress index (CWSI) using micrometeorological data from the Tree Crop Remote Evapotranspiration eXperiment (T-REX) over four almond orchards in California. Diurnal variation of air-canopy temperature difference (T_c−T_a) is used to estimate the upper and lower baseline of the theoretical CWSI. This theoretical framework relies on the surface energy balance equation. This approach assumes that the aerodynamic resistance (r_a) is driven by turbulent transport of heat, water vapor, and momentum and is modeled using the Monin-Obukhov Similarity theory, incorporating atmospheric heat and momentum transfer. As expected, we found an overall higher T_c than T_a over the entire year, and the temperature difference between the two ranges within ±10°C. The CWSI during the daytime lies in its usual range (0–1) with a mean of ~0.6 (moderately stressed), and we further noted a significant control of actual evapotranspiration (ET) on its temporal variability in addition to net ecosystem productivity. The preliminary study results opened pathways for investigating surface energy fluxes on almond stress dynamics at multi-temporal and -spatial scales.

Children in low- and middle-income countries (LMICs) face increased risks of undernutrition and adverse home environments, contributing to social-emotional difficulties. Using a randomized controlled trial in Ghana, we examined the direct and interactive effects of prenatal lipid-based nutrient supplementation (LNS) and home environment factors on children’s social-emotional development at age 4-6 years (N = 962). Path analysis models showed that LNS was associated with fewer total difficulties and peer relationship problems, while high-quality home environments—including more responsive caregiving, fewer punitive discipline practices, and greater access to learning materials—predicted better social-emotional outcomes. Multi-group comparisons revealed that LNS moderated associations between home environment factors and children’s total difficulties (χ²(5)=13.746, p=.017) and hyperactivity/inattention (χ²(5)=16.176, p=.006). LNS was linked to fewer difficulties and less hyperactivity/inattention in safer physical environments, whereas the physical environment was not predictive for the control group. Children in the control group had higher hyperactivity/inattention when academic stimulation was lower, an association not observed in the LNS group. These findings suggest that early-life nutritional supplementation differentially interacts with distinct home environment factors, influencing Ghanaian children’s social-emotional development. Integrated interventions targeting both early-life nutritional supplementation and supportive home environments may promote children’s social-emotional development in LMICs.

Controlling the release of bioactive compounds in foods is essential to preserve their health benefits. Food 3D printing offers a promising approach to precisely localize bioactive compounds within matrices and achieve specific release profiles. This study presents a computational model predicting the in vitro digestion release of curcumin—a representative bioactive compound—encapsulated in yeast microcarriers and patterned into cubes via 3D printing. This approach enables rapid screening of different configurations to design foods with targeted release profiles. Curcumin release was modeled as a mass transfer process coupled with reaction kinetics during intestinal digestion. Two reaction-diffusion equations described bile salt transport into the cube and diffusion of the curcumin–bile salt complex out of it. First-order kinetics were used to describe interactions of bile salts with curcumin and other yeast components. The system was solved using finite element methods, with curcumin concentration as the output. To validate the model, a bioink composed of curcumin-loaded yeast and pectin was 3D-printed into cubes with either core-shell or uniform curcumin distribution. Experimental results showed strong agreement with the model for both configurations. This model can simulate bioactive release under varying geometries, concentrations, and spatial distributions, offering a powerful tool for designing functional foods with controlled health effects.

As urban areas continue to expand globally, measuring urban microclimates is becoming increasingly critical. Climate change is projected to intensify the frequency and intensity of extreme weather events, such as heatwaves, raising concerns among scientists and policymakers due to their significant impacts on human health. Children are particularly vulnerable to these climate-related risks, making schoolyards essential spaces for both social interactions, learning and physical activity. Moreover, for children in underserved communities, schoolyards often provide the only access to green spaces and urban nature. As a result, there is growing interest in improving these environments to enhance their social and ecological benefits. California, with its hot summers, is experiencing more frequent and intense heat waves, amplifying the urgency of addressing urban heat mitigation strategies. Our study aims to measure the influence of different surface materials and tree canopy cover on heat exposure and human thermal comfort. By deploying different climatic sensors in selected campuses in three school districts, we will measure a range of microclimatic variables related to ‘heat exposomes’. The outcomes of our project will provide robust insights and a compelling database that will hopefully inform policies and design interventions to create cooler, more climate-resilient outdoor spaces for children.

While low-level features, e.g., color, vary continuously, semantic information, e.g., objects, often appears categorical. In past psychophysical experiments¹ we collected similarity judgments between stimuli, from five stimulus domains varying in their semantic content, and analyzed them using a variant of multidimensional scaling. The stimulus domains included 1) animal names, 2) images of the animals, 3) lightly-texturized images, 4) heavily-texturized images and 5) textured discs. Stimulus domains where object identity was evident (1-3) were represented in a common way². To study the neural correlates of these representations, we ran an fMRI study (n=7). Over four fMRI sessions, participants viewed each stimulus at least 7 times, while performing a one-back memory task. The data were acquired on a 3T Siemens scanner (multiband acquisition, TR=0.8s). Data were preprocessed using fmriprep. Voxel-wise responses to the stimulus domains were obtained using GLM and compared in early and late visual areas. Responses to image, image-like, texture-like domains (2, 3, 4) were most similar, and different from responses to words (1). Whereas psychophysical experiments showed that less semantic and more semantic domains were represented differently, the fMRI experiment showed a different progression in representations: the three picture domains were represented most similarly, despite not sharing semantic content.

The rise in plant-based consumption and the resulting accumulation of byproducts have created a strong demand for sustainable valorization strategies. To address this need, the food industry must adopt advanced analytical technologies that optimize extraction methods to preserve the bioactive compounds and their health benefits of plant-based materials. In this context, plant-based byproducts should be rigorously evaluated for their content of bioactive compounds, particularly prebiotic glycans such as oligosaccharides and polysaccharides. Recent advances in mass spectrometry have enabled molecular-level insights into carbohydrate structures. This presentation features two case studies examining carbohydrate composition and abundance in Chardonnay and pomegranate byproducts, using integrated “omics” approaches with a focus on mass spectrometry. It also discusses the strengths and limitations of current extraction and derivatization methods used prior to analysis. Chardonnay and pomegranate pomace contain a fascinating diversity of carbohydrates, including monosaccharides, oligosaccharides, and polysaccharides, accounting for around 20-40% of total weight. A total of 178 and 80 oligosaccharides (including isomers) built from 14 monosaccharide units were characterized in Chardonnay and pomegranate pomace. The discovery of these carbohydrates' presence in side streams can promote their valorization as natural sweeteners, prebiotics, and ingredients for functional food/beverage/skincare products.

Last year, summer drought cost the state over a billion dollars in crop losses. Understanding how plant can cope with this stress is a challenge, but various species in nature are already adapted and have their own mechanisms for combating drought. Native to the American west coast, Mimulus guttatus can survive in Mexican desert, on serpentine rocks, or snow-covered peaks. This suggests that different populations have evolved various resistance mechanisms. Using 29 different populations harvested across California and Oregon, we plan to carry out a genome-wide association study. We want to compare the SNPs present in these populations with their root system architecture phenotype, an analyze of differentially expressed genes and a histological analysis focusing on cell wall compounds such as lignin and suberin. We hope to find new QTLs involved in drought resistance and even shed light new defense mechanisms that could be exported to crops in the future.

Digital subtraction angiography (DSA) is an interventional radiology used for visualization of blood vessels for disease diagnosis and prognosis. DSA has been the gold standard for the identification of vascular abnormalities in patients. The blood vessels are extracted by subtracting the angiographic images (obtained after contrast injection) with the mask (prior to contrast injection). The interpretation of angiography is challenging due to motion artifacts, imaging noise, and vessel overlapping in the DSA images. Anatomic localization is a vital step in interpretation in DSA sequences to minimize misdiagnosis. Deep learning has been used for segmentation, registration, and labelling of blood vessels but limited works have been proposed for localization of vascular anatomies in DSA images. In this study, we propose a deep learning model for the classification of anatomical localization in both first and second vascular structures in DSA sequences obtained during routine clinical practice. The model’s performance on unseen data will be assessed both qualitatively and quantitatively. Hence, by using the proposed methodology, automatic and accurate interpretation of angiography is feasible leading to faster diagnosis, decision-making, and further reduce the amount of time and effort required for report generation in clinical practice.