Biomass concentration is among the most important metrics in microalgal cultivation to track growth and inform operation. Sensors commonly used for biomass concentration measurements that involve direct interaction with the culture medium often face limitations such as biofouling in long-term applications. In this study, we successfully integrated noncontact spectroradiometric sensors to perform real-time, automatic dilution control in outdoor raceway ponds. By utilizing the sensor inputs, we demonstrated the successful implementation of two cultivation strategies in outdoor raceway ponds. For turbidostat operation, spectroradiometric sensors enabled automatic dilution control so that the biomass concentration could be consistently maintained near a predetermined set point. For the Optimized Illuminance strategy, light distribution was estimated in real time and used to minimize the dark zone through automatic dilution of the culture. In both cases, the dilution rate dynamically responded to varying weather, leading to substantial increases in biomass productivity—51% for turbidostat and 84% for Optimized Illuminance—compared with that of the semi-continuously diluted culture. Although challenges such as noise from low solar angles and a foamy culture surface affected sensor accuracy, the results demonstrate the potential of spectroradiometric sensing technologies to enhance algal productivity, reduce operational costs, and improve scalability for outdoor cultivation systems.

Autonomous, high-frequency monitoring of outdoor algal ponds is needed to quantify biomass productivity and detect culture decline in environments prone to contamination, grazers, and variable operating conditions. We report successes and lessons learned in translating a laboratory spectroradiometric monitoring approach to a multi-year autonomous field deployment at the Arizona Center for Algae Technology and Innovation (AzCATI). The system measures spectrally resolved pond reflectance by ratioing upwelling radiance from each raceway to simultaneous downwelling sky irradiance using fiber-coupled spectrometers. A physics-based reflectance model (ASHARP) is fit to each spectrum pair to estimate optical parameters, including a biomass-proxy coefficient (C-a) which enables near-real-time tracking of biomass accumulation and culture state at 2-5 min intervals. From May 2022 through September 2025 the platform operated continuously while scaling from two to six raceway ponds. Several strains of algae were monitored successfully, including the high productivity Tetraselmis striata and Picochlorum celeri. Transitioning data acquisition from a Windows laptop to a Raspberry Pi improved uptime from 57% (2022) to similar to 89% (2024-2025) and enabled routine real-time analysis. Further, we converted relative biomass estimates to absolute ash-free dry weight (AFDW) using experimentally-derived calibrations, providing field-relevant biomass predictions with conservative confidence bounds. These results demonstrate the feasibility of long-term, autonomous optical monitoring for well-mixed open-raceway algal cultivation and provide practical guidance for reliable field operation and scaling.

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Sudden pond crashes remain a major barrier to reliable algal biomass production, and the biological processes driving these events are not well understood. Here we report the first documented crash of Picochlorum celeri, a species otherwise recognized for exceptional resilience in outdoor cultivation. By tracking temporal microbiome dynamics during this biomass collapse in raceway ponds, we identified distinct community trends before, during, and after the crash. Metagenome-assembled genome (MAG)-level analysis revealed clear clustering by biomass, indicating that microbial assemblages remained stable over days to weeks and shifted prior to collapse. Specific taxa showed contrasting associations with Picochlorum biomass, with Psychromarinibacter positively correlated and an Azotimanducaceae-affiliated pseudomonad negatively correlated. These results indicate that microbial community change was detectable prior to measurable biomass decline. Complementing these biological signatures, high-frequency spectroradiometric reflectance measurements detected physiological deviations 24–36 h before biomass loss, including suppressed diel growth and reduced accuracy of reflectance-based biomass estimates during early crash stages. These findings link microbial dynamics to operational outcomes and highlight opportunities for biotic countermeasures, including bacteriophages, protective bacteriomes, and small-molecule inhibitors. Together, this work demonstrates that even resilient hosts such as P. celeri are vulnerable to microbial disruption and establishes a path from early detection to targeted intervention strategies, while outlining a conceptual framework for integrating microbiome and optical indicators into dynamic reliability models that enhance the scalability of algal cultivation.

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Remote assessment of physiological parameters has enabled patient diagnostics without the need for a medical professional to become exposed to potential communicable diseases. In particular, early detection of oxygen saturation, abnormal body temperature, heart rate, and/or blood pressure could affect treatment protocols. The modeling effort in this work uses an adding-doubling radiative transfer model of a seven-layer human skin structure to describe absorption and reflection of incident light within each layer. The model was validated using both abiotic and biotic systems to understand light interactions associated with surfaces consisting of complex topography as well as multiple illumination sources. Using literature-based property values for human skin thickness, absorption, and scattering, an average deviation of 7.7% between model prediction and experimental reflectivity was observed in the wavelength range of 500-1000 nm.

For workplaces which cannot operate as telework or remotely, there is a critical need for routine occupational SARS-CoV-2 diagnostic testing. Although diagnostic tests including the CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel (CDC Diagnostic Panel) (EUA200001) were made available early in the pandemic, resource scarcity and high demand for reagents and equipment necessitated priority of symptomatic patients. There is a clearly defined need for flexible testing methodologies and strategies with rapid turnaround of results for (1) symptomatic, (2) asymptomatic with high-risk exposures and (3) asymptomatic populations without preexisting conditions for routine screening to address the needs of an on-site work force. We developed a distinct SARS-CoV-2 diagnostic assay based on the original CDC Diagnostic Panel (EUA200001), yet, with minimum overlap for currently employed reagents to eliminate direct competition for limited resources. As the pandemic progressed with testing loads increasing, we modified the assay to include 5-sample pooling and amplicon target multiplexing. Analytical sensitivity of the pooled and multiplexed assays was rigorously tested with contrived positive samples in realistic patient backgrounds. Assay performance was determined with clinical samples previously assessed with an FDA authorized assay. Throughout the pandemic we successfully tested symptomatic, known contact and travelers within our occupational population with a ~ 24–48-h turnaround time to limit the spread of COVID-19 in the workplace. Our singleplex assay had a detection limit of 31.25 copies per reaction. The three-color multiplexed assay maintained similar sensitivity to the singleplex assay, while tripling the throughput. The pooling assay further increased the throughput to five-fold the singleplex assay, albeit with a subtle loss of sensitivity. We subsequently developed a hybrid ‘multiplex-pooled’ strategy to testing to address the need for both rapid analysis of samples from personnel at high risk of COVID infection and routine screening. Herein, our SARS-CoV-2 assays specifically address the needs of occupational healthcare for both rapid analysis of personnel at high-risk of infection and routine screening that is essential for controlling COVID-19 disease transmission. In addition to SARS-CoV-2 and COVID-19, this work demonstrates successful flexible assays developments and deployments with implications for emerging highly transmissible diseases and future pandemics.

Expanded industrial globalization has resulted in the release of high concentrations of heavy metals into environmental water sources and soils. Phytoremediation may help to remove these heavy metals from contaminated soils. Tall fescue (Festuca arundinacea Shreb.) exhibits phytoremediation potential due to its endurance and high stress tolerances. Here, we report photochemical and structural responses in tall fescue to acute and chronic doses of heavy metals, copper (Cu) and hexavalent chromium (Cr(VI)). Visual signs of stress and decreased photosynthetic yield measurements were detected for both the acute and chronic exposures. To gain insight into stress responses at the cellular level, structural and pigment changes in tall fescue in response to Cu and Cr(VI) stress were assessed with brightfield and confocal fluorescence imaging. While brightfield images showed qualitative changes in plant tissue structure, the quantification of changes were not statistically significant due to high variability between leaf blades. Fluorescence imaging confirmed decreasing total chlorophyll content in tall fescue cross-sections in response to Cr(VI) and Cu exposure. To spectrally separate the closely related chlorophyll pigments (Chl-a, Chl-b, and Chl in photosystem I) and visualize their relative localizations within the plant tissue, hyperspectral confocal fluorescence microscopy was conducted with multivariate curve resolution (MCR) analysis of the data. These results determined that Chl-a and Chl-b were more significantly reduced than Chl associated with photosystem I. Additionally, a new spectral component was identified. A broad autofluorescence (AF) feature appeared in the late stress response of both acute and chronically exposed tall fescue and was localized in globular bodies. While the identity of the broad AF feature remains to be identified, we hypothesize that it may be associated with degraded chlorophyll components in autophagic bodies. If confirmed, this would indicate that autophagy is a stress response to heavy metal exposure in tall fescue.

Expanded industrial globalization has resulted in the release of high concentrations of heavy metal ions into environmental water sources and soils. Phytoremediation has attracted increasing attention because it can safely remove environmental contaminates via plant uptake, accumulation, and harvesting. Tall fescue (Festuca arundinaceaShreb.) is the predominant temperate perennial grass in the United States and serves as soil erosion prevention and livestock feed, but also exhibits phytoremediation potential due to its endurance and high stress tolerances. Here, we discovered unique degradation bioindicators of photochemical and structural responses in tall fescue to heavy metals copper and chromium (VI) exposure. To quantify structural and pigment changes in tall fescue in response to copper and hexavalent chromium stresses, we performed brightfield and confocal fluorescence imaging. Fluorescence images were collected over a 9-day period, which confirmed decreasing total chlorophyll content in tall fescue cross-sections in response to Cr(VI) and Cu exposure. Brightfield fluorescence microscopy images presented multiple structural features of tall fescue: rib cross-section, vascular fiber bunder, adaxial fiber bundle, abaxial fiber bundle, metaxylem element 1, and metaxylem element 2. Observations from structural analysis indicated that the copper and chromium exposed plants became compromised by their respective stressor after 4 days of exposure resulting in varying plant survival responses. On day 9 copper and chromium treated plants displayed near-complete collapse of the plant vascular systems. To spectrally separate the closely related chlorophyll pigments (Chl-a, Chl-b, and Chl in Photosystem I) and visualize their relative localizations within the plant tissue, hyperspectral confocal fluorescence microscopy was also

Vampirovibrio chlorellavorus is a predatory and parasitic bacterium that thoroughly overtakes strains of Chlorella sorokiniana through attachment to the cell wall. Prior work has shown that many freshwater strains of C . sorokiniana become readily infected with this bacterium. However, saltwater strains of C . sorokiniana have not yet been tested for susceptibility to infection of V . chlorellavorus . The purpose of this study was to investigate the ability of V . chlorellavorus to infect two marine strains of C . sorokiniana : DOE 1116 and DOE 1044. These results are compared to C . sorokiniana DOE 1412 grown in both freshwater and saltwater environments. Laboratory-scale culture replicates of C . sorokiniana DOE 1412, 1116, and 1044 in different freshwater and saltwater media were infected with V . chlorellavorus and compared to uninfected cultures grown under the same conditions. Optical density, pulse amplitude modulation (PAM) fluorometry, and light microscopy measurements were performed to assess culture health over a 2-week period. Light and temperature remained constant throughout the course of the experiment. Microscopy results displayed clear infection of all strains of infected replicates. Further evidence for infection was provided by lower growth rates in infected cultures versus control cultures as measured by absorbance at 750 nm. Additionally, lower growth rate was observed overall for uninfected cultures of C . sorokiniana DOE 1412 in saltwater medium. PAM fluorimetry showed slightly lower values for the maximum photosynthetic efficiency in infected cultures but the results were not statistically different than the controls. C . sorokiniana DOE 1412 is known to be susceptible to V . chlorellavorus infection in freshwater medium, BG11. Using this model system for V . chlorellavorus infection, our results show clear evidence of V . chlorellavorus infection in the two marine strains, C . sorokiniana 1116 and 1044.

Over the last several years, immense technological advances have been made in imaging methods to gain a better understanding of structure and chemical composition of cells, tissue and other microscopic biological samples. Nevertheless, a need still exists for an imaging strategy where chemical and structural information can be gained in the native state without any staining and complicated sample preparation. Raman microscopy offers one such non-invasive imaging technique resolving molecular spectral fingerprints with spatial resolution as good as fluorescence microscopy. We present here a hyperspectral light-sheet Raman microscope, combining the high spatial resolution and chemical specificity of Raman microscopy with the fast data collection and 3D volume imaging capability of light-sheet microscopy. The thin sheet of laser excitation at the sample is attained by producing a Bessel beam from a 785 nm continuous wave laser and scanning the thin pencil laser beam over the sample area. Raman scattering detection is done through an orthogonally placed high-NA objective, de-scanned using a piezo scanner and detected by a sCMOS camera, after chromatic dispersion from a spectrometer. The hyperspectral data cube detection scheme employed here provides simultaneous spectral identification and spatial localization of the multiple chemical components in the sample. This microscope with its high temporal resolution will be ideal for following the rapidly localizing 13C isotope distribution in plants and capturing the dynamics of fluxes from primary metabolism into secondary metabolic pathways.