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Global food shortage demands significant progress in crop production. Greenhouses offer a solution for higher crop production by providing a controllable environment. Excess levels of humidity, however, encourage pests and diseases, drastically reducing crop yields. Traditional humidity control methods for greenhouses are expensive and energy-intensive. In addition to this, nonbiodegradable plastic covers cause massive white pollution. To tackle these concerns, we present smart glazing and sensors for greenhouse humidity regulation through both passive and active paths. We created biodegradable humidity-sensitive films by blending poly(ethylene glycol) (PEG) with cellulose acetate (CA). PEG/CA covers can automatically open for air circulation at high humidity, successfully demonstrating repeatable greenhouse humidity regulation to as low as 60% relative humidity. PEG/CA-based humidity sensors can actively accelerate air circulation and humidity reduction with repeated cycles at an even higher efficiency. Overall, our research introduces a low-cost, all-in-one, sustainable, and environmentally conscious solution for addressing the greenhouse humidity control challenges. Approximately, this solution can potentially achieve annual energy savings of up to 56.6 GWh for the U.S. if fully applied. 

In the past three years, the COVID-19 pandemic has caused serious health, environmental, societal, and economic challenges globally. Sterilization is one of the most efficient methods to mitigate the spread of infectious viruses like SARS-CoV-2. However, extreme sterilization practices can cause serious health and environmental problems worldwide. Heat, ultraviolet C (UVC), and chemical disinfectants require high energy consumption and can cause health concerns, environmental pollution, and chemical overuse. In this paper, we evaluated the efficiency of corona discharge (CD) as an environmentally and energy-friendly sterilization method on different surfaces for sterilization. It was confirmed that CD is an efficient sterilization process for most surfaces and personal protective equipment (PPE). CD allows for a reduction in disinfectant use, addresses the PPE shortage problem, and reduces plastic pollution and biowaste. The air sterilization effect of CD creates a promising option to reduce airborne pathogens. To address the safety concerns of CD, the heat, UVC, and ozone emissions of CD were confirmed to be within a safe range. A wireless, affordable CD robot was developed with the capability to scan along pre-designed paths while having the capability to avoid static and moving obstacles. Automated CD devices and robots can be favorable sterilization solutions for the future as a contactless, more environmentally friendly, efficient, affordable, and portable solution. 

Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKIs) have proved effective in treating CML, but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell–cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimericBCR-ABL1transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that, under our assumed similarities between normal and leukemic cells, a key predictor of refractory response to TKI treatment is an increased maximum probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes. 

The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.