Scholarly Works, Biological Systems Engineering

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A Comparative Analysis of Transfected and Integrated Auxin Reporter Systems Reveals Sensitivity Advantages in Protoplast Transient Expression Assays
Taylor, Joseph S.; Villaseñor, Eric A.; Rashkovsky, James; Simson, Jaime; Wright, R. Clay; Bargmann, Bastiaan O. R. (2025-02-28)
Reporter-gene activation studies using transient transformation of protoplasts are a powerful tool for the investigation of transcriptional regulation in plants. Here, we perform a comparative analysis of reporter-gene activation sensitivity using an integrated versus a co-transfected reporter-gene construct in Arabidopsis seedling mesophyll protoplasts. The DR5 synthetic auxin-responsive promoter was used to assay the response to auxin treatment and over-expression of activator Auxin Response Factors. We show that sensitivity, as measured by the fold-change in fluorescent-protein reporter-gene expression, is significantly increased by using a co-transfected reporter-gene construct.
Automated Flow-Cytometric Readout of Reporter-Gene Activation in Transiently Transformed Protoplasts
Chisholm, Samuel; Taylor, Joseph S.; Wright, R. Clay; Bargmann, Bastiaan O. R. (Springer Nature, 2026-04-01)
Reporter-gene activation studies are essential for dissecting gene regulatory mechanisms, yet traditional whole-plant assays are often low-throughput and difficult to quantify. This chapter presents a streamlined method for analyzing reporter-gene activity using transiently transformed Arabidopsis thaliana mesophyll protoplasts. We describe the use of the pBeaconRFP vector for positive-fluorescent selection, enabling the isolation of successfully transformed cells via flow cytometry and fluorescence-activated cell sorting. Additionally, we introduce the pEvTV Gateway-compatible vector for flexible reporter-gene construct delivery and an automated pipeline for reproducible flow-cytometric data analysis. These methods facilitate rapid, robust, and scalable quantification of transcriptional responses, exemplified by the activation of the DR5 auxin-responsive reporter by gain-of-function Auxin Response Factor expression. The protocols are adaptable to other tissues and species, offering a versatile platform for high-throughput functional genomics.
Beyond linearity: reimagining AI as a participant in circular bioeconomies
Muthukumar, Aarthi; Rashid, Barira; Yang, Lihong (2026-03-27)
As artificial intelligence transitions from industry-exclusive tool to public-facing technology, society faces critical decisions about its integration into socioecological systems. This paper proposes a reimagining of AI as a synthetic participant in the circular bioeconomy (CBE)—a regenerative model emphasizing cyclical flows of resources, information, and energy. Drawing on Bruno Latour’s Actor-Network Theory and Donna Haraway’s posthumanism, we reconceptualize AI as a non-living organism capable of functioning within multispecies systems, analogous to viruses that shape ecosystems without conventional life. Conventional, in that it meets the standard biological criteria for like: metabolism, reproduction, and homeostasis. AI, like viruses, does not meet this biological criteria. Current AI applications in CBE—from biowaste recycling to precision agriculture—demonstrate both transformative potential and ethical concerns. While AI enables unprecedented efficiency through advanced algorithms and embodied robotics, it risks perpetuating extractive logics that treat information as a resource to be mined rather than circulated. Critical ethical challenges emerge including algorithmic bias amplifying inequalities, epistemic opacity eroding stakeholder trust, blurred accountability for AI-driven harm, displacement of human labor, and marginalization of indigenous and local ecological knowledge. Through examples in medicine and remote sensing, we argue that AI becomes a “friend” to the Circular Bioeconomy (CBE) only when designed as circular and relational rather than linear and extractive. This requires synthetic datasets preserving privacy, multimodal architectures enabling dimensional understanding, and human-machine-ecosystem feedback loops replacing terminal outputs with ongoing accountability. Ultimately, AI’s role depends on intentional design grounded in justice and multispecies dignity—transforming it from extractive tool into participant in shared regenerative futures.
Agroclimatic Sensing, Communication, and Computational Systems-Based Methods and Technologies for Precision Irrigation Management: Current State and Prospects
Sarr, Aminata; Chandel, Abhilash K.; Diop, Lamine; Soro, Yrébégnan Moussa; Tossa, Alain K.; Hota, Smrutilipi; Manimozhian, Arunachalam (MDPI, 2026-02-23)
Agriculture uses most of the world’s fresh water. Given that the worldwide population is expanding at an alarming rate, more land cultivation is apparently in demand. As a result, much more water would be required to irrigate cultivable lands. However, fresh water is becoming scarce at a faster rate due to climate uncertainties and over-exploitation. Several controlled irrigation techniques, such as drip and sprinkler irrigation, have been introduced to safeguard water resources. However, these techniques do not readily meet crop water demands and often end up causing overapplication of water. Under these circumstances, smart precision irrigation is the best solution. Smart irrigation techniques facilitate delivery of water in an amount that is required by the crop as per site/location and temporal requirements. Several studies have been carried out in this area, and remarkable progress has been observed. These studies range from making use of in situ sophisticated sensors that are low-cost and consume minimum energy up to the use of small unmanned aerial systems (SUAS) and satellite imagery for irrigation management. This review summarizes research studies that highlight the components of developing and deploying various precision irrigation technologies, their benefits, and their limitations. Specifically, the scientific value of this study lies in outlining implications of using different sensors, parameters, and equipment, the agroclimatic models, communication technologies, artificial intelligence, and the energy sources to implement automated irrigation systems. A future scope of precision irrigation is also discussed in accordance with cost-effectiveness and sustainability. This study should also act as a referring guideline for new researchers as well as technology manufacturers who seek to design and develop a futuristic yet efficient irrigation system. Overall, this review is aimed at contributing to the understanding of automated irrigation systems for their effective deployment towards enhanced agricultural production, conserved water resources, and sustainable use of energy sources.
Multimodal Sensing Technologies for HPAI Biosurveillance in Poultry Production Systems
Ali, Md. Azahar; Ataei Kachouei, Matin; Jacobs, Leonie; Zhou, Wei (Springer, 2026-02-19)
Highly pathogenic avian influenza (HPAI), with a current focus on the emergent H5N1 clade 2.3.4.4b, remains a substantial and evolving threat to animal health, food security, and zoonotic safety. Since early 2024, novel genotypic variants within this clade have driven widespread epizootics across US poultry and dairy production systems, along with zoonotic transmission events. With the depopulation of over 168 million birds and economic losses exceeding 1.4 billion USD since 2022, recent outbreaks highlight the urgent need for complementary, decentralized, real-time biosurveillance strategies. This review outlines the molecular pathobiology and transmission kinetics of contemporary HPAI strains and evaluates diagnostic bottlenecks. Then, we explore how molecular amplification, electrochemical detection, and acoustic anomaly analysis can be combined into a single approach for in situ disease recognition. Finally, we describe how behavioral and physiological signal integration can enhance biosensor accuracy and support adaptive One Health biosurveillance systems for anticipatory and scalable field responses.
Identification and Mitigation of Inhibitory Substances Contained in High-Salinity Crude Glycerol Generated from Biodiesel Production for Polyhydroxyalkanoate Synthesis by Haloferax mediterranei
Zhang, Xueyao; Helm, Richard F.; McCoy, Emily L.; Zhao, Fujunzhu; Wang, Mingxi; Yebo Li, Stephanie Lansing; Huang, Haibo; Wang, Zhiwu (American Chemical Society, 2025-09-22)
High-salinity crude glycerol generated from biodiesel production poses significant challenges to microbial valorization due to inhibitory ingredients that severely limit microbial growth. This study identified and mitigated inhibitory substances contained in high-salinity glycerol sludge to enable its conversion to polyhydroxyalkanoates (PHAs) by the extreme halophilic archaeon Haloferax mediterranei. The long-chain fatty acids (LCFAs) were consistently identified as the primary inhibitors by liquid chromatography−mass spectrometry, Fourier transform infrared spectroscopy, and ultraviolet−visible spectroscopy. Acid precipitation at pH 2 efficiently removed these LCFAs, substantially reducing the required feedstock dilution from 23 to 3 times, improving PHA titer by 40%. Furthermore, this dilution reduction also increased the feedstock salinity utilization, achieving a 46% reduction in external salt supplementation for H. mediterranei growth. In contrast, overliming and arrested anaerobic digestion were confirmed to be ineffective in inhibitor removal. This study provides deep insights into inhibitor chemistry and presents acid precipitation as an effective pretreatment strategy for waste valorization of highsalinity crude glycerol.
Coupling physical selection with biological selection for the startup of a pilot-scale, continuous flow, aerobic granular sludge reactor without treatment interruption
An, Zhaohui; Wang, Jiefu; Zhang, Xueyao; Bott, Charles B.; Angelotti, Bob; Brooks, Matt; Wang, Zhiwu (Elsevier, 2023-06-01)
This study removes two technical constraints for transitioning full-scale activated sludge infrastructure to continuous flow, aerobic granular sludge (AGS) facilities. The first of these is the loss of treatment capacity as a result of the rapid washout of flocculent sludge inventory and in turn the potential loss of nitrification during initial AGS reactor startup. The second is the physical selector design which currently is limited to either the complex sequencing batch reactor selection or sidestream hydrocyclones. Briefly, real wastewater data collected from this study suggested that by increasing the surface overflow rate (SOR) of an upflow clarifier to 10 m h 􀀀 1, the clarifier can be taken advantage of as a physical selector to separate flocculant sludge from AGS. Redirecting the physical selector underflow and overflow sludge to the feast and famine zones of a treatment train, respectively, can create a biological selection that not only promotes AGS formation but also safeguards the effluent quality throughout the AGS reactor startup period. This study provides a novel concept for economically implementing continuous flow AGS within existing full-scale, continuous flow treatment trains.
Mechanistic understandings of the convoluted impacts of hydraulic loading rates, temperature, chlorine doses, and media sizes on the performance of biologically active filtration applied for full-scale drinking water treatment
Sun, Yewei; Khunjar, Wendell O.; Rosenfeldt, Erik J.; Selbes, Meric; Wang, Zhiwu (Elsevier, 2025-06-11)
This study filled the knowledge gap remaining in understanding the convoluted contribution of factors governing the contaminant removal and headloss development performance of Biologically Active Filtration (BAF). This new knowledge was interpreted with an existing model framework built for simulating the performance of BAF by taking into consideration the effects of hydraulic shear stress on biofilm detachment, the effects of temperature on viscosity, and “first dose phenomenon” on chlorine inhibition, which have been confirmed to be critical for accurately interpreting the full-scale headloss data. This redefined BAF model was calibrated and validated using 240 days of continuous data from four full-scale BAF systems operated in a local drinking water treatment plant operated at different media sizes and chlorine doses under fluctuating loading rates and temperatures. The model successfully predicted that BAF runtime is less sensitive to loading rate than to temperature after taking hydraulic shear stress into model consideration. Also, increasing media size from 1.0 to 1.4 mm or applying 1 mg/L chlorine dose could lead to equivalent improvements in BAF runtime and reductions in TOC removal. However, when choosing between these two strategies for real world applications, their differences in flexibility and costs should be taken into consideration.
Effects of carbon diversion to primary sludge production on thermal hydrolysis pretreatmentenhanced anaerobic digestion
Luo, Hao; Zhang, Xueyao; Nguyen, Caroline; Taylor, Malcolm; Wang, Zhiwu (The Royal Society of Chemistry, 2024)
Redirecting wastewater organic carbon to anaerobic digesters through primary sludge (PS) production from aeration tanks has been pursued as a viable means for achieving energy neutrality in water resource recovery facilities (WRRFs). A comprehensive evaluation of the approach was investigated in this study by taking into account the solid reduction, energy recovery and savings, sludge dewaterability, and recalcitrant dissolved organic nitrogen (rDON) formation when thermal hydrolysis pretreatment (THP) was used. Experimental results revealed that increasing the PS-to-waste activated sludge (WAS) ratio from 0: 1 to 1 : 1 and 3 : 1 through chemically enhanced primary treatment (CEPT) led to residual solids increase of 9% and 16%, aeration energy savings of 35% and 60%, and energy recovery increase of 67% and 120%, respectively. Very importantly, this study reported for the first time that blending PS and WAS led to excessive rDON formation during THP. Fortunately, high Al³⁺ doses used during CEPT precipitated most of the rDON along with orthophosphate removal, which also led to sludge dewaterability improvement. Thereby, a major drawback of carbon diversion is the extra biosolid production plus increased chemical use in comparison with the scenario without diversion. Since the primary responsibility of WRRFs is discharge water quality control and solid reduction, it was concluded that carbon diversion may not fit all WRRFs and should be considered on a case-by-case basis with an overall evaluation of its gains.
Pivotal role of municipal wastewater resource recovery facilities in urban agriculture: A review
Wang, Jiefu; Sun, Yuepeng; Xia, Kang; Deines, Allison; Cooper, Ross; Pallansch, Karen; Wang, Zhiwu (Wiley, 2022-05-18)
Urban agriculture provides a promising, comprehensive solution to water, energy, and food scarcity challenges resulting from the population growth, urbanization, and the accelerating effects of anthropogenic climate change. Their close access to consumers, profitable business models, and important roles in educational, social, and physical entertainment benefit both developing and developed nations. In this sense, Urban Water Resource Reclamation Facilities (WRRFs) can play a pivotal role in the sustainable implementation of urban agriculture. Reclaimed water as a recovered resource has less supply variability and in certain cases can be of higher quality than other water sources used in agriculture. Another recovered resource, namely, biosolids, as byproduct from wastewater treatment can be put to beneficial use as fertilizers, soil amendments, and construction material additives. The renewable electricity, heat, CO₂, and bioplastics produced from WRRFs can also serve as essential resources in support of urban agriculture operation with enhanced sustainability. In short, this review exhibits a holistic picture of the state of-the-art of urban agriculture in which WRRFs can potentially play a pivotal role.
Agrivoltaics Policy Frameworks in the United States: Selected Policies and Programs Through 2024
Akbari, Pardis; Hall, Ralph P.; Ignosh, John (Virginia Tech, 2026-01-30)
Agrivoltaics, also known as dual-use solar or agrisolar, is an integrated land-use approach that combines agricultural production and photovoltaic electricity generation on the same site, allowing crops to be cultivated, livestock to be grazed, or pollinator habitats to be maintained while producing renewable energy from solar panels (Department of Energy, 2022; Macknick et al., 2022). Agrivoltaics presents a potential sustainable solution to land-use competition between food and energy production (Jain, 2024). By integrating solar power generation with agriculture, agrivoltaics systems optimize land use and can increase overall land productivity by 35–73% compared to traditional single-use approaches (Dupraz et al., 2011). The systems can also improve water-use efficiency beneath photovoltaic (PV) panels, reducing evaporation and conserving soil moisture (Adeh et al., 2018). Additionally, agrivoltaics can lower solar panel temperatures by 1-2°C, improving energy efficiency and extending a system’s lifespan (Patel et al., 2019). The partial shading from panels can benefit crops sensitive to heat and sunlight stresses, potentially creating a more favorable microclimate for growth in some production systems and locations (Kussul, 2020; Marucci et al., 2018). Beyond environmental benefits, agrivoltaics may enhance the economic resilience of farms by providing an additional revenue stream from energy generation (Dinesh & Pearce, 2016). The Virginia Department of Energy commissioned this review to better understand evolving agrivoltaics practices, policies, and programs across the United States at both the federal and state levels. Its purpose is to identify emerging trends and provide an overview of current and recent efforts supporting the integration of agriculture and solar energy development. This review focuses primarily on agrivoltaics initiatives through 2024. The United States federal government has introduced several policies and programs that indirectly support the growth of agrivoltaics as part of the country’s broader clean energy transition. Key legislative actions, including the Bipartisan Infrastructure Law of 2021 and the Inflation Reduction Act of 2022, have provided significant funding to the Department of Energy (DOE) to expand clean energy infrastructure and strengthen domestic energy resilience. Although these laws do not specifically focus on agrivoltaics, they helped to create a more favorable environment for its development. Federal incentives such as the Investment Tax Credit (ITC) and the U.S. Department of Agriculture’s (USDA’s) Rural Energy for America Program (REAP) have also encouraged the use of renewable energy within agricultural settings. In addition, research and development efforts by the Department of Energy (DOE) through its Solar Energy Technologies Office, including the FARMS and InSPIRE programs, and by the USDA’s National Institute of Food and Agriculture (NIFA), have helped improve the understanding of how agrivoltaics systems perform and how they can support both energy generation and agricultural production. Across the states, there is growing momentum to promote agrivoltaics through new policies and incentives. Massachusetts continues to lead the way with its SMART program and Agricultural Solar Tariff Generation Unit (ASTGU) incentive, which provide payments and clear design guidelines to ensure that farmland remains in active agricultural use while supporting solar energy production. Other states have developed similar initiatives. For example, New Jersey’s Dual-Use Pilot Program offers incentives for projects that combine solar power with ongoing farming operations, while Colorado supports agrivoltaics through property tax exemptions, research funding, and pilot grant programs. In Virginia, the Department of Environmental Quality’s (DEQ’s) Permit-by-Rule framework now includes reduced project mitigation requirements when practices such as managed grazing and crop cultivation are incorporated when solar projects impact prime farmland. Collectively, these efforts show a growing commitment to balance farmland protection with renewable energy expansion. A closer look at these initiatives reveals several common elements are emerging that shape the direction of agrivoltaics policy in the United States. Most initiatives rely on financial incentives to make agrivoltaics projects economically viable, recognizing that dual-use systems often require higher upfront costs for design and construction. In addition, many programs include pilot and demonstration projects as a central strategy, providing opportunities to test system designs, crop performance, and management practices under real-world agricultural conditions before broader implementation. To support the effective expansion of agrivoltaics in Virginia, a harmonized policy framework and a consistent definition of the practice are necessary. Coordination among incentives, performance standards, and data-sharing mechanisms can enhance agricultural productivity and renewable energy generation goals. When properly integrated, agrivoltaics can be an effective approach toward energy production, food security, and land stewardship goals. This alignment could turn land-use conflicts into opportunities for sustainable development and resilient clean energy growth. This report summarizes various agrivoltaics initiatives across the United States. Because energy and land-use planning policies are frequently updated, the details of these initiatives are often in flux. However, this summary aims to capture the full range of efforts, even if some programs are inactive. By doing so, the compilation helps inform future work in Virginia by sharing national experiences and providing resources for further review of each approach. References: Adeh, E. H., Selker, J. S., & Higgins, C. W. (2018). Remarkable agrivoltaic influence on soil moisture, micrometeorology and water-use efficiency. PLoS ONE, 13(11), e0203256. https://doi.org/10.1371/journal.pone.0203256 Dinesh, H., & Pearce, J. M. (2016). The potential of agrivoltaic systems. Renewable and Sustainable Energy Reviews, 54, 299–308. https://doi.org/10.1016/j.rser.2015.10.024 Dupraz, C., Marrou, H., Talbot, G., Dufour, L., Nogier, A., & Ferard, Y. (2011). Combining solar photovoltaic panels and food crops for optimising land use: Towards new agrivoltaic schemes. Renewable Energy, 36(10), 2725–2732. https://doi.org/10.1016/j.renene.2011.03.005 Jain, S. (2024). Agrivoltaics: The synergy between solar panels and agricultural production. Darpan International Research Analysis, 12(3), 137–148. https://doi.org/10.36676/dira.v12.i3.61 Kussul, E., Baydyk, T., Garcia, N., Velasco Herrera, G., & Curtidor López, A. V. (2020). Combinations of solar concentrators with agricultural plants. Journal of Environmental Science and Engineering B, 9(5), 168–181. https://doi.org/10.17265/2162-5263/2020.05.002 Macknick, J., Hartmann, H., Barron-Gafford, G., Beatty, B., Burton, R., Choi, C. S., Davis, M., Davis, R., Figueroa, J., Garrett, A., Hain, L., Herbert, S., Janski, J., Kinzer, A., Knapp, A., Lehan, M., Losey, J., Marley, J., MacDonald, J., McCall, J., Nebert, L., Ravi, S., Schmidt, J., Staie, B., & Walston, L. (2022). The 5 Cs of agrivoltaic success factors in the United States: Lessons from the InSPIRE research study (NREL/ TP-6A20-83566). National Renewable Energy Laboratory. https://docs.nrel.gov/docs/fy22osti/83566.pdf (Archived at https://perma.cc/A7HS-SC8R) Marucci, A., Zambon, I., Colantoni, A., & Monarca, D. (2018). A combination of agricultural and energy purposes: Evaluation of a prototype of photovoltaic greenhouse tunnel. Renewable and Sustainable Energy Reviews, 82, 1178–1186. https://doi.org/10.1016/j.rser.2017.09.029 Patel, B., Gami, B., Baria, V., Patel, A., & Patel, P. (2019). Cogeneration of solar electricity and agriculture produce by photovoltaic and photosynthesis—Dual model by Abellon, India. Journal of Solar Energy Engineering, 141(3), 031014. https://doi.org/10.1115/1.4041899 U.S. Department of Energy. (2022, December 8). Foundational Agrivoltaic Research for Megawatt Scale (FARMS) funding program. https://www.energy.gov/eere/solar/foundational-agrivoltaic-research-megawattscale-farms-funding-program (Archived at https://perma.cc/8SFL-4NVM)
Extreme Weather Events and Risk Communication Challenges in Central Appalachia: A Qualitative Inquiry
Khan, Azmat; Chadwick, Amy E.; Kruse-Daniels, Natalie; Dabelko, Geoffrey D.; Krometis, Leigh Anne H.; Shinn, Jamie E.; Lynch, Amy J.; Garner, Emily; Hession, W. Cully; Bowman, Jen (2025-03-01)
This study inventories and identifies communication challenges faced by emergency management agencies in Central Appalachia as they engage communities in preparation, response and recovery efforts for extreme weather events (EWEs). Drawing on data from nine group discussions and guided by the Social Ecological Model, the analysis discerned an array of barriers to effective risk communication, originating from cultural, organizational, interpersonal and individual dynamics. It was found that a pervasive distrust of emergency agencies and broader climate governance, articulated through the notion of ‘mining,’ undermines organizational legitimacy. Conflicting messages from emergency sources with ambiguous or overlapping roles create confusion, numb and desensitize populations, and further erode source credibility. Poor internet and cellular connectivity constrain timely information delivery and exacerbate vulnerabilities. Additionally, the region's ingrained culture of ‘riding‐it‐out’, while a valuable source of organic resilience and self‐efficacy, is seen by some emergency managers as ‘stubbornness,’ which leads to misalignment in risk communication. This study re‐contextualizes these cultural attributes as essential ‘social capital’ and offers strategies to align communication practices and resources with local identity and agency needs. Findings contribute to culturally responsive approaches to participatory risk communication.
Comparing in-home and bottled drinking water quality: regulated and emerging contaminants in rural Central Appalachia
Albi, Kate; Krometis, Leigh-Anne H.; Ling, Erin; Cohen, Alasdair; Xia, Kang; Gray, Austin D.; Dudzinski, Emerald; Ellis, Kimberly P. (IWA Publishing, 2025-09)
An increasing number of Americans rely on bottled water for household use, citing perceptions of poor in-home water quality and/or distrust of public water utilities. We analyzed in-home (n = 23), roadside spring (n = 4), and bottled drinking water (n = 36) in Central Appalachia. All samples were analyzed for regulated (bacteria, inorganic ions) and emerging (PFAS, microplastics) contaminants. Study survey results indicated the majority (83%) of participants viewed their in-home water quality as satisfactory or poor due to negative organoleptic perceptions. Coliform bacteria and sodium levels exceeding recommended levels were detected in 52% of home water samples, though detections varied by source, i.e., high sodium was more often observed in municipal water, while bacteria were more often observed in private system water. Bottled water samples did not exceed any regulations, though median microplastic concentrations were statistically higher (p = 0.001, Wilcoxon rank-sum test) than those recovered from in-home samples. PFAS compounds were detected in some in-home and bottled water samples at very low levels. While in general bottled water appears to be a safe drinking water source in these areas, the associated costs in time and money for lower-income households are considerable, and were estimated by participants as $68–400/month.
AI-Driven Livestock Biosensing for Prediction of Metabolic Diseases
Ali, Md. Azahar; Kachouei, Matin Ataei (IEEE, 2025)
We report the development of a highly sensitive 3D-printed sensor for the on-farm, early detection of subclinical hypocalcemia (SHC) in dairy cows. The printed 3D sensing structure incorporates periodic micropatterns of ion-to-electron polymer-based transducing layer that enhances sensitivity when analyzing milk samples. This novel sensor detects radiometric targets of calcium (Ca²⁺) and phosphate (PO4^2-) in milk, enabling the identification of SHC in under a minute. We apply regression models, including k Nearest Neighbors (k-NN) and Logistic Regression, to predict livestock health, evaluating performance through accuracy, area under the curve (AUC), and confusion matrices. Unlike traditional tests, this sensor provides dairy farmers with a tool to monitor the health of transition dairy cows.
Micro-Nano Hybrid Architectures for Sub-Nanogram Detection of Avian Influenza H5N1
Kachouei, Matin Ataei; Jacobs, Leonie; Ha, Dong Sam; Ali, Md. Azahar (IEEE, 2025)
The ongoing spread of the highly pathogenic avian influenza H5N1 virus has caused severe disruptions in the poultry industry, leading to economic losses and raising concerns about cross-species transmission. Recent outbreaks in mammals increase the risk of zoonotic spillover, making rapid and sensitive virus detection crucial for effective containment and management. We report here a low-cost, lithography-free biosensor incorporating graphene oxide, silver nanowires, and self-assembled monolayer as micro-nano hybrid transducers for the detection of H5N1 hemagglutinin. The sensor achieved a detection limit of 40 picograms per mL. We have also manufactured a fully 3D-printed micropillar array-based sensor and evaluated its performance as a viral sensor against traditional 2D planar electrodes. These printed sensors will be useful for on-farm poultry testing, providing a practical solution for early virus detection and control.
AI-Powered Nanosensing of Lactate in Dairy Cows
Kachouei, Matin Ataei; Chick, Shannon; Ali, Md. Azahar (IEEE, 2025)
Early detection of metabolic diseases, including lactic acidosis, is crucial for effective livestock health management. This study presents the development of a nanosensor platform using graphene nanosheets and lactate oxidase (LOx) enzyme to detect lactate and hydrogen peroxide (H2O2) concentrations within a minute. Machine learning (ML) techniques, including polynomial regression and random forest (RF) regression, were used to optimize sensor calibration. Polynomial regression (degrees 3 and 4) achieved perfect accuracy (r²=1.00), while RF regression demonstrated strong predictive performance (r²=0.857). These results underscore the lactate sensor's potential for precise, reliable detection in complex biological fluids, providing an advantage over traditional methods in dairy cattle health monitoring.
Nanosensing of Hepatitis E Virus in Swine Using Graphene
Chick, Shannon; Ataei Kachouei, Matin; Knowlton, Katharine; Meng, Xiang-Jin; Ali, Md. Azahar (IEEE, 2025-07-15)
Sensing of the hepatitis E virus is crucial for effective porcine health management and prevention of spread to humans. This study presents the development of a nanosensor using graphene nanosheets to detect hepatitis E antigen within a minute. The graphene layer not only increases the loading of antibodies specific to the hepatitis E virus but also enhances sensitivity and selectivity. This sensor is sensitive to 10 fM of hepatitis E antigen. This nanosensor holds significant potential for the rapid and early detection and monitoring of hepatitis E, thereby contributing to enhanced public health outcomes and the safety of pork products.
3D-Printed Wearable Biosensors for Livestock Health Monitoring
Ali, Md. Azahar; Howell, Brittany R.; Zhang, Liqing (IEEE, 2025-07)
Livestock health monitoring stands as a linchpin in ensuring both the welfare of animals and the optimization of productivity. As we navigate toward meeting current and future food crises, the role of biosensors in this context cannot be overstated. Such biosensors serve as indispensable tools, offering real-time insights into the health status of livestock, thereby enabling early detection of diseases and prompt intervention. In addressing the challenges and potential of biosensors for livestock sensing, it is clear that while biosensors have seen extensive use in human health monitoring, their application in livestock is crucial for ensuring animal well-being and productivity, vital in meeting global food demands. To maximize effectiveness, there is a need for advanced manufacturing to develop customized, user-friendly, and cost-effective sensors. By harnessing the synergistic potential of electrochemical biosensors and advanced manufacturing, this review discusses the challenges that currently impede the widespread adoption of wearable electrochemical biosensors, advanced manufacturing techniques, and artificial intelligence in livestock sensing. This strategic approach not only bolsters animal welfare and productivity but also fortifies agricultural resilience in the face of evolving global food demands. This review highlights recent advancements in biosensors for livestock monitoring.
The pH-Dependent Specificity of Cathepsin S and Its Implications for Inflammatory Communications and Disease
DeHority, Riley; Gil Pineda, Laura I.; Cochran, Kari; Chen, Bentley; Bratek, Daniel; Helm, Richard F.; Lemkul, Justin A.; Zhang, Chenming (American Chemical Society, 2025-09-16)
Proteases have two major roles in health and disease: making functional changes to proteins as a post-translational modification and degradation of proteins as a regulatory or waste management mechanism. The cysteine protease cathepsin S serves both of these functions. It digests antigens in the adaptive immune system and is associated with many autoimmune diseases and cancers. Here, we show that the catalytic specificity of human cathepsin S is regulated by the pH conditions of its environment and identify the structural determinants of this switch. Peptide digests show that the proteolytic specificity of cathepsin S narrows at extracellular pH. Crystal structures reveal that a lysine residue descends into the S3 pocket of the active site above pH 7, which can be explained by changes in the protein's surface charge at that pH. We discuss biological compartment transitions and disease processes associated with cathepsin S in which these pH-dependent specificity switches may be triggered.
Evaluating the Effectiveness of Machine Learning for Alzheimer’s Disease Prediction Using Applied Explainability
Huang, Chih-Hao; Batarseh, Feras A.; Ullah, Aman (MDPI, 2025-11-12)
Early and accurate diagnosis of Alzheimer’s disease (AD) is critical for patient outcomes yet presents a significant clinical challenge. This study evaluates the effectiveness of four machine learning models—Logistic Regression, Random Forest, Support Vector Machine, and a Feed-Forward Neural Network—for the five-class classification of AD stages. We systematically compare model performance under two conditions, one including cognitive assessment data and one without, to quantify the diagnostic value of these functional tests. To ensure transparency, we use SHapley Additive exPlanations (SHAPs) to interpret the model predictions. Results show that the inclusion of cognitive data is paramount for accuracy. The RF model performed best, achieving an accuracy of 84.4% with cognitive data included. Without this, performance for all models dropped significantly. SHAP analysis revealed that in the presence of cognitive data, models primarily rely on functional scores like the Clinical Dementia Rating—Sum of Boxes. In their absence, models correctly identify key biological markers, including PET (positron emission tomography) imaging of amyloid burden (FBB, AV45) and hippocampal atrophy, as the next-best predictors. This work underscores the indispensable role of cognitive assessments in AD classification and demonstrates that explainable AI can validate model behavior against clinical knowledge, fostering trust in computational diagnostic tools.

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