HardwareX

Three-dimensional (3D) printing is increasingly used in cell biology to prototype custom cell culture devices, microscopy chambers, and other experimental hardware, but practical guidance on sterilization and material compatibility remains limited. Here, we develop and validate a low-temperature paraformaldehyde vapor sterilization method for fused filament fabrication (FFF) components that avoids heat-induced deformation. Using bacterial challenge assays with Escherichia coli and Geobacillus stearothermophilus, we confirm effective sterilization of 3D printed components. We also assess the effects of common filaments and adhesives on HL-60 human cell growth to identify materials suitable for cell culture workflows. Most untreated plastics were well tolerated over 48 hours, whereas some formaldehyde-treated materials required post-treatment with ammonia vapor to restore compatibility. Together, these results provide a practical framework for sterilizing and deploying 3D-printed materials in cell culture and biological research. Multidisciplinary Abstract3D printers allow laboratories to quickly make custom experimental equipment, but many printed materials cannot be sterilized using standard methods. We developed a low-temperature sterilization procedure using paraformaldehyde vapor and tested whether common printing materials and adhesives are compatible with cultured human cells. We identified several plastics, silicone-based adhesives and acrylic tapes that can be safely used after sterilization, while others require additional ammonia treatment. These results provide guidance for researchers using 3D printing to build custom laboratory and cell culture devices. Methods SummaryA low-temperature paraformaldehyde vapor sterilization method for 3D-printed FFF components was developed and validated using bacterial challenge assays. Compatibility of common printing filaments, adhesives, and tapes with cell culture workflows was assessed by exposing HL-60 cells to untreated, sterilized, and ammonia- neutralized materials and quantifying cell growth by flow cytometry.

Electrical stimulation (ES) and transepithelial/transendothelial electrical resistance (TEER) measurements are essential techniques in cell biology and tissue engineering, yet commercial devices for these applications cost between USD 2,500-9,000 and typically offer only one functionality. We present LATEER (Low-cost Arduino-based TEER and Electrical stimulation device), an open-source hardware platform that combines both ES and TEER measurement capabilities at a total cost below USD 100. The device features four independent channels, configurable pulsatile signals (amplitude up to 8.2 V, frequency 0.1-500 Hz, pulse width [&ge;]0.1 ms), and a resistance measurement range of 300 {Omega} to 1 M{Omega}, with <5% error for R {gtrsim} 4.7 k{Omega}. LATEER uses commercially available graphite pencil leads as electrodes ([~]USD 2 vs. USD 350 for commercial Ag/AgCl electrodes), which demonstrated excellent biocompatibility in cell culture. The system includes 3D-printed electrode holders compatible with standard 12-well and 24-well plates, allowing microscope visualization without electrode removal, and a Python-based graphical user interface for parameter configuration and real-time data acquisition. Because the electrodes remain fixed in the plate lid and only a single cable enters the incubator, both stimulation and resistance measurement can run continuously under standard culture conditions (37 {degrees}C, 5% CO2) without removing the plate or repositioning the electrodes, avoiding the temperature excursions and placement variability inherent to manual chopstick measurements. Validation with human pluripotent stem cell-derived cardiomyocytes demonstrated reliable frequency capture (electrical pacing) of the contracting monolayer, with a capture threshold between 250 and 400 mV/mm and controlled pacing across the 0.5-5 Hz range. TEER functionality was verified with mesenchymal stem cells, where the device resolved cell-density-dependent differences in electrical resistance in real time. All design files, firmware, and software are freely available under the CERN-OHL-S v2 license, enabling replication and customization by research laboratories worldwide. HighlightsO_LIAn open-source device combines electrical stimulation and TEER measurement under $100 C_LIO_LIGraphite electrodes offer biocompatibility at 0.6% cost of commercial alternatives C_LIO_LIFour independent channels with configurable parameters and real-time data logging. C_LIO_LIContinuous run setup in-incubator; no electrode repositioning needed C_LIO_LIValidated with stem cell-derived cardiomyocytes, achieving frequency capture (threshold 250-400 mV/mm) C_LIO_LI3D-printed holders enable microscope visualization without electrode removal C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/743263v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@98f9deorg.highwire.dtl.DTLVardef@13c73aborg.highwire.dtl.DTLVardef@1cdf099org.highwire.dtl.DTLVardef@16ed0cf_HPS_FORMAT_FIGEXP M_FIG C_FIG Specifications Table O_TBL View this table: org.highwire.dtl.DTLVardef@4ef802org.highwire.dtl.DTLVardef@7c651borg.highwire.dtl.DTLVardef@d20013org.highwire.dtl.DTLVardef@102fc89org.highwire.dtl.DTLVardef@111b927_HPS_FORMAT_FIGEXP M_TBL C_TBL

Magnetic field effects (MFEs) in biological systems are typically small and experimentally challenging to measure reproducibly across large sample populations. Existing approaches to measure such effects often rely on low-throughput microscopy or custom-built magnetic stimulation systems that provide limited control over magnetic field geometry, synchronization, or experimental automation. Here, we present an open-source magnetofluorescence imaging platform designed for bacterial plate-scale screening of MFEs in live colonies. The instrument integrates a programmable three-axis vector electromagnet, synchronized fluorescence excitation and imaging, and integrated acquisition software with per-frame metadata logging on a hardware-synchronized data acquisition card. An extensive calibration procedure enables accurate generation of arbitrary magnetic field vectors, while synchronized triggering ensures deterministic alignment between field application, illumination, and image acquisition. The system images an entire 100 mm Petri dish in a single acquisition. Typical experiments monitor hundreds of bacterial colonies simultaneously over multi-hour acquisition sequences. Control software, calibration routines, mechanical design files, and acquisition workflows are provided openly to facilitate replication. Instrument performance is demonstrated through detection of magnetic field-dependent fluorescence changes in E. coli expressing the engineered magnetosensitive fluorescent protein MagLOV2. This instrument provides a flexible and scalable platform for high-throughput magnetobiology, synthetic biology, and quantum biology experiments.

Significance: Fluorescence imaging remains largely qualitative and device-specific, limiting reproducibility and intersystem comparisons. SI-traceable radiometric characterization requires calibrated sources suitable for transferring known radiance to imaging systems. Aim: Establish and evaluate a compact, calibrated solid-state radiometric emitter target (RET) for SI-traceable radiance transfer, and assess methods for evaluating radiance invariance, imager responsivity, and extension of radiance transfer to digitally defined regions of interest (ROIs). Approach: The radiance invariance of an LED-based RET was evaluated by measuring its radiance across varying source-to-aperture distances and collection apertures. A previously described radiance-transfer formalism based on calibrated source radiance and collection geometry was implemented to determine imager responsivity (R{lambda}), which was evaluated across varying working distances and lens apertures. Grubbs and MAD outlier tests and one-way ANOVA were used to assess radiance and responsivity invariance. Finally, responsivity obtained using digitally defined ROIs was compared with matched physical apertures to assess ROI-based radiance transfer. Results: The measured RET radiance remained invariant across the tested source-to-aperture distances and collection apertures, with no significant differences observed across distance (p = 0.88) or aperture (p = 0.15). Imager responsivity remained stable across working distances for both tested lens configurations (p = 0.48 and p > 0.99). Responsivity was also invariant across lens apertures, except for deviations at f/11 that were consistent with practical aperture tolerances. Digitally defined ROIs reproduced responsivity values obtained using matched physical apertures, demonstrating that the radiance-transfer approach can be extended to image-defined regions. Conclusions: The presented methods provide an approach for evaluating compact LED-based sources for invariant-radiance emission and suitability for SI-traceable radiance transfer. The characterized RET supported stable imager responsivity across changes in imaging geometry and extension to digitally defined ROIs. These methods can be adapted to other LED-based sources, with further angular characterization and uncertainty analysis needed to establish broader applicability.

Studying algae requires specialized equipment to characterize culture growth and physiological performance and non-invasive monitoring of these cultures typically relies on optical density (OD) measurements. Currently, OD is often measured using standard spectrometers that suffer from detector saturation at high cell densities, which requires time consuming sample dilutions and lacks compatibility with flat-walled cell culture flasks like Roux bottles. Here we describe a 3D-printable device to measure optically dense algal cultures directly within these cultivation vessels. This instrument combines light attenuation and scattering, resulting in a dual-sensor system that utilizes near-infrared light (950 nm) to precisely quantify biomass without interference from changing photosynthetic pigments. Cultures that reach high cell densities can be assumed to be accurately quantified in situ using the scattered signal, preserving the integrity of the culture and eliminating the need for subsampling. This method is adaptable to the most common geometries of Roux bottles (T-25, T-75, and T-175) and could be used to monitor small-scale batch cultures without contamination risks. A cyanobacteria algal species (Limnospira platensis) and three flask sizes were analysed on light attenuation linearity and scattering detection limits at different cell concentration, demonstrating that the light attenuation module can reliably measure biomass up to an OD of 3.1 (equivalent to >1.5 g/L) without requiring dilution.

Point-of-care (POC) molecular detection has enabled rapid and accessible testing at or near the site of need, facilitating timely decision-making in clinical, field, and industrial settings. Despite these advancements, molecular detection platforms remain limited in their ability to prepare DNA from complex tissue matrices, restricting their applicability to a narrow range of sample types. To address sample preparation inefficiencies, focused ultrasound extraction (FUSE) has been developed for the rapid release of DNA from robust tissue matrices. FUSE disintegrates target tissue and releases DNA by delivering high pressure ultrasound pulses that generate a cavitation bubble cloud. In prior work, FUSE was performed using large, laboratory-based equipment. Here, a miniaturized FUSE device was designed and tested to enable POC DNA sample preparation. Design constraints were defined based on prior FUSE experiments, and based on these metrics, a 750 kHz cylindrical transducer was fabricated. The pressure output was measured, and high-speed optical imaging demonstrated that the transducer generated sustained cavitation within the defined focal region. The feasibility of DNA extraction from piscine and timber tissues was evaluated, and results show that the device can release DNA with quality suitable for PCR-based detection from both tissue types. These results demonstrate the potential of this device to rapidly extract DNA from complex samples and expand the applicability of molecular detection platforms.

Augmented reality glasses typically have poor optical efficiency (<1%), which is detrimental for high power applications, such as prosthetic vision. Maxwellian view optics are a candidate for increasing efficiency, but suffer from a very limited exit pupil and thus field of view. Many approaches to extend the eyebox inherently decrease the optical power. We present a design that uses a high-density VCSEL array and eye tracking to create an extended eyebox without sacrificing the advantages of a Maxwellian view display. Our system achieves a 20 degree field of view, a resolution 48.8 LP/mm (10 m), and an end-to-end optical efficiency of 83%

BackgroundContinuous glucose monitoring (CGM) systems are used not only for retrospective assessment of the glycemic profile but also for real-time decision-making, including automated insulin delivery. Accordingly, CGM performance characterization must capture not only the agreement of individual paired values but also the systems ability to reproduce the direction, rate, amplitude, and shape of glucose concentration change. Summary metrics, most notably MARD, cannot establish whether an observed deviation reflects an error in the formation of the test profile itself, a constant sensor offset, amplitude compression, a change in response rate, temporal misalignment, or hysteresis. ObjectiveTo adapt a programmable flow-based in vitro platform for the separate assessment of the experimentally delivered glucose profile and the dynamic response of CGM systems, and to propose a set of metrics that decomposes dynamic error into its components. MethodsGLU profiles were generated by programmable mixing of solutions at a constant total flow rate of 2 mL/min. Actual GLU concentration was independently measured with a SUPER GL2 glucose analyzer. Four static levels, three repeats of a 5.5[-&gt;]12.0[-&gt;]5.5 mmol/L profile, three repeats of a 6.0[-&gt;]3.0[-&gt;]6.0 mmol/L hypoglycemic profile, three 5.0[-&gt;]15.0[-&gt;]5.0 mmol/L profiles at different rates, one complex 4[-&gt;]18[-&gt;]3[-&gt;]12[-&gt;]5.5 mmol/L profile, and two proof-of-concept sensor experiments at 100- and 200-min transitions were investigated. Dynamic response was characterized by bias, MAE, RMSE, MARD, amplitude transfer coefficient K_A, rate transfer coefficients K_up and K_down, normalized shape RMSE, residual shift, and hysteresis loop area. ResultsAt the static levels, measured GLU exceeded the programmed value by 0.234-0.780 mmol/L. In the repeated 5.5[-&gt;]12.0[-&gt;]5.5 profiles, the ratio of actual to programmed rate was 0.978-1.083 on the rising phase and 0.987-1.157 on the falling phase, while the amplitude transfer coefficient was 0.967-1.066. In the hypoglycemic profile, minimum GLU was 2.55- 2.96 mmol/L, and time below 3.0 mmol/L was 15.2-72.6 min. The measured rates of 0.0519, 0.1045, and 0.2027 mmol/L/min preserved the intended ratio of approximately 1:2:4. In the complex profile, the programmed plateau of 18 mmol/L was not reached: mean measured GLU was 16.20 mmol/L. For CGM-A, K_A was 0.682 and 0.650, and K_up/K_down were 0.666/0.730 and 0.634/0.626; the corresponding values for CGM-B were 1.228 and 1.128, and 1.564/1.328 and 1.276/1.145. Hysteresis loop area differed 5- to 10-fold between the two sensor responses, exceeding an order of magnitude at the 100-min transition. ConclusionThe programmed concentration should be treated as a control setpoint, rather than as a reference measurement. The "programmed trajectory -- measured glucose -- CGM output" cascade first allows quantitative assessment of the agreement between the programmed and actually realized profile and only then separate characterization of sensor response. Decomposition of dynamic error into amplitude, rate, shape, and hysteresis components reveals differences that a single MARD value or correlation coefficient cannot capture.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.

Recombinase polymerase amplification (RPA) enables rapid nucleic acid testing in low-resource environments, but poorly characterized byproducts can compromise assay specificity and cause false-positive results. Here, we amplified the thirteen original CODIS core loci and Amelogenin to characterize recurrent RPA artefacts and establish conditions that reduce their formation. First, RPA products were analyzed for two reference samples by Oxford Nanopore Technologies sequencing. This revealed two distinct classes of multimeric products: primer multimers and amplicon multimers, consisting of repeated primer or amplicon sequences, respectively. Individual artefacts contained up to 281 primer copies or 22 amplicon copies, demonstrating the extensive range of these products. Next, we performed an optimization study to evaluate the effects of reaction temperature and reagent concentrations at two representative loci, D3S1358 and D5S818. Among the conditions tested, temperature had the most pronounced effect. Reducing the temperature from 42{degrees}C to 34{degrees}C increased the relative target amplicon fraction from 15% to 83% for D3S1358 and from 84% to 98% for D5S818, while maintaining or increasing absolute target concentration. Lower primer concentrations and higher T4 UvsX concentrations also reduced multimer formation, although lower primer concentrations reduced target yield and caused allelic dropout. Finally, amplification at 34{degrees}C was evaluated across all fourteen loci by sequencing. Relative to 42{degrees}C, the target read fraction increased by more than 5 percentage points for 7/14 loci in one reference sample and 9/14 loci in the other, with the largest improvements at multimer-prone loci. These findings identify multimers as an important class of RPA artefacts and establish reaction temperature and T4 UvsX concentration as promising conditions to improve RPA specificity.

Imaging spectroscopy often requires expensive and complex equipment. Here we show a simple procedure for attaching a standard miniature fiber spectrometer to a conventional microscope, allowing easy integration of 2D imaging with 1D high-resolution spectral measurements. This combination provides much of the benefit of a full imaging spectrometer without the large equipment investment, and we provide instructions for modifying microscopes to this setup and the present measurements of living cells that demonstrate their performance. Using this setup, we compare the quantitative measurement of chlorophyll concentration in Physcomitrium patens moss using color imaging and spectral sampling.

Background: Point-of-care manufacturing of CAR-T cells could reduce cost and improve patient access, but few closed, automated systems support both flexible process development and GMP production. Methods and results: We describe LimCORE, a single-use consumable for cell incubation and processing, implemented in LimGROW (manual system) and LimONE (closed and automated system). In LimGROW, T cells expanded 246 +/-18 fold over 10 days at optimized seeding density, versus 89 +/-9 fold in a G-Rex control, with similar viability (>90%) and phenotype. Process parameters directly transferred to LimONE, where automated, closed buoyancy-based CD3+ selection achieved 90.3 +/-3.5% purity and 59.2 +/-17.0% recovery in under two hours. Using this workflow, we manufactured CD19 CAR-T cells in a fully closed, automated 7-day LimONE process requiring 155 minutes of operator intervention. LimONE manufactured CAR-T cells reached 42.6 +/-2.4 fold expansion (532 +/-29x10e6; cells at harvest), with 43.6 +/-6.0% transduction efficiency and viability similar to G-Rex controls. CD4+/CD8+ ratios, CD8+ differentiation subsets, and cytotoxicity were comparable between platforms, while LimONE-manufactured cells showed increased spare respiratory capacity and oxygen consumption. Across four manufacturing runs on the LimONE, no hardware or software failures occurred, and automated liquid transfers and volume concentration stayed within +/-5% accuracy. Conclusions: These data show that LimCORE supports T cell expansion, selection, and complete CD19 CAR-T manufacturing with performance similar to existing platforms while reducing operator time, across R&D and GMP scale closed-system formats.

Emerging MEMS-based audio devices generate ultrasonic acoustic output, but little is known about long-term biological effects of such exposures, which vary in frequency, intensity, duration, and coupling pathway. In this study, a novel transducer was characterized for its ultrasonic acoustic output, generating amplitude-modulated (AM) pressure pulses at a carrier frequency of approximately 200 kHz and operating frequency of approximately 100 kHz. To understand ultrasonic pressure wave transmission through the human middle and inner ear, sound pressure levels in the ear canal (PEC) and mechanical vibration velocities at the stapes (VST) and promontory (VPRM) were measured in human cadaveric temporal bones across frequencies up to 240 kHz. This represents the first such measurements at ultrasonic frequencies in human temporal bone specimens. Results demonstrated relatively consistent PEC measurements at ultrasonic frequencies across specimens, with minimal inter-specimen variation. At the operating frequency (~100 kHz), overall PEC was 82.2 dB SPL (SNR = 40.6 dB), while at the carrier frequency (~200 kHz), PEC increased to 90.6 dB SPL (SNR = 34.8 dB). VST and VPRM measurements at the operating frequency remained relatively independent of audio stimulus-drive frequency and voltage, with comparable magnitudes, suggesting that bone conduction pathways contribute significantly to inner ear ultrasonic exposure. When compared to existing safety guidelines, this device appears to meet specified criteria; however, given the limited scientific basis for these guidelines and unknown long-term effects, cautious application is recommended.

The development of a new integrated operant system was driven by two challenges in behavioral neuroscience: the high cost and technical complexity of commercial rigs, and their limited adaptability across experiments. We developed the NeuroHab, an integrated behavioral arena for high-fidelity operant conditioning and automated data collection in a single unified system. Food and water reward, conditioned-stimulus presentation, and event recording are tied together programmatically with easy-to-install open-source code to facilitate throughput and reproducibility. All behavioral events are processed by internal microcontrollers and logged with <1 ms latency (typical range 56-728 s). This precise timing is critical for integrating the system with two-photon imaging and electrophysiology, enabling real-time alignment of behavior with brain activity. The NeuroHab uses solenoid-actuated, capacitive-sensing Lickports that let an untethered mouse drink from an automated port, and delivers food via the Kravitz Lab FED3. Conditioned stimuli are presented by dedicated buzzer/LED modules. A central controller (the Core) coordinates all modules and logs event timestamps using TTL-low signaling between two microcontrollers, at a maximum recording rate of 16.67 Hz for single-pulse events. We have deployed the NeuroHab in over 50 behavior trials and over 20 sessions alongside a Mini two-photon microscope. At approximately $1,400, easily modified, and compatible with existing analysis tools, the NeuroHab lowers barriers to multimodal behavioral neuroscience. Significance StatementThe study of how neural activity gives rise to behavior depends on operant systems that are both temporally precise and affordable, yet commercial rigs are costly and difficult to adapt across experiments. We introduce the NeuroHab, an integrated, open-source operant platform that unifies reward delivery, conditioned-stimulus presentation, and event logging with sub-millisecond timing (typical latency 56-728 s). Built for approximately $1,400, the system forwards all behavioral timestamps to external acquisition hardware, enabling millisecond-scale alignment of behavior with two-photon imaging and electrophysiology. By lowering the cost and technical barriers to synchronized behavioral and neural recording, the NeuroHab makes multimodal, reproducible operant neuroscience accessible to a broad range of laboratories and adaptable to diverse experimental paradigms.

Automated home-cage systems measure individual behavior in social groups for days to months. Among these systems, the IntelliCage has become a widely used platform for longitudinal and socially embedded behavioral phenotyping. Yet the analysis layer often remains less standardized than the experiment itself: raw exports, phase definitions, exclusion rules, time alignment, and derived behavioral metrics are transformed by lab-specific scripts that are difficult to audit, compare, or reuse. We present ic-analysis, an open-source Python toolkit for standardized, scriptable, and shareable IntelliCage workflows. The toolkit separates user-defined experiment metadata and workflow scripts from a reusable analysis core with modular analysis and plotting functions, allowing users to flexibly assemble experiment-specific pipelines without editing package internals. Due to its modular design, the analysis core can be applied to a broad range of experimental paradigms, including general activity, exploratory, motivational, cognitive, and social readouts, rather than being limited to a single fixed protocol. It aligns biological phase windows across staggered cage runs and exports plots together with quantitative result tables, applied settings, and audit files that support reproducible and FAIR reporting. Here, we demonstrate this flexible design in a realistic synthetic place-learning/place-reversal experiment with two mouse groups and deliberately offset cage starts. The workflow recovered the implanted behavioral differences while preserving the required experimental-time alignment. Group A showed stronger endpoint saccharin preference (81.7 +/- 1.7% vs. 27.4 +/- 3.4%, p = 1.9e-), higher liquid uptake, faster place-learning onset (56.6 +/- 8.1 vs. 278.3 +/- 33.2 visits), and better reversal performance (64.1 +/- 1.3% vs. 25.6 +/- 1.7% rewarded correct-corner visits) compared to Group B. This demonstration shows how standardized, explicitly defined readouts can turn complex IntelliCage exports into interpretable behavioral profiles while preserving the analysis history needed for inspection and reuse. ic-analysis therefore provides both a working analysis scaffold and an extensible, community-friendly route toward IntelliCage workflows that are easier to reproduce, compare, extend, and share.

High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.

Advances in camera technology and computer vision techniques have allowed researchers to track animals in 3D in ways which previously were difficult or impossible. Many such 3D tracking tools make use of multiple cameras, but unfamiliarity with the principles and technology involved can make it difficult to employ such techniques. In this protocol, we describe Braid, open-source software for live, multi-camera 3D tracking of insects. Using background-subtraction, Braid performs detection of objects without requiring the use of physical markers affixed to the insect. Braid constructs low-latency 3D position estimates using Kalman filtering and nearest neighbor data association. We document in detail the process of tracking freely flying bees within a flight arena using Braid. This protocol includes instructions on installation, configuration of cameras, setup, calibration, and operation. Within the system described here, we demonstrate that Braid can achieve position estimates accurate to <1 millimeter (within a 0.3 cubic meter volume). These factors make Braid suitable for tracking small, fast-flying animals like insects. Braid's low latency allows live tracking, removing the necessity to collect large video files and making it suitable for integration in closed loop systems such as virtual reality. Code is available at https://github.com/strawlab/strand-braid

Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process management and cause delays in decision-making. This study develops a machine learning-enabled Raman spectroscopy framework for Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) applications in bioprocess manufacturing. Five predictive modeling techniques--Partial Least Squares (PLS) regression, Support Vector Regression (SVR), Random Forest, Extreme Gradient Boosting (XGBoost), and Neural Networks--were used to analyze Raman spectral data from an Escherichia coli fermentation dataset. The models were assessed using the coefficient of determination (R{superscript 2}), root mean square error (RMSE), and mean absolute error (MAE) to predict two crucial fermentation parameters: the concentrations of glucose and acetate. The superior performance of PLS regression for glucose prediction and the improved prediction accuracy of XGBoost for acetate concentration demonstrated the importance of selecting modeling techniques based on biological complexity. Explainable artificial intelligence using SHAP analysis was incorporated to improve model transparency by identifying Raman spectral regions contributing to predictions. The suggested architecture shows how Raman spectroscopy and machine learning can be combined to assist automated process monitoring, enhance process comprehension, and hasten the implementation of real-time quality judgments in next-generation biomanufacturing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/740653v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@6a460aorg.highwire.dtl.DTLVardef@11c68e5org.highwire.dtl.DTLVardef@2acf7aorg.highwire.dtl.DTLVardef@9b890a_HPS_FORMAT_FIGEXP M_FIG C_FIG Overall workflow of the Raman spectroscopy-based machine learning framework for PAT and RTRT implementation. Raman spectra collected from E. coli fermentation were preprocessed and analyzed using multiple machine learning algorithms for the prediction of glucose and acetate concentrations. Model performance evaluation and SHAP-based explainable AI analysis enabled the identification of important spectral features for real-time bioprocess monitoring. HighlightsO_LIDeveloped a Raman spectroscopy-based machine learning framework for real-time monitoring of critical bioprocess parameters. C_LIO_LICompared traditional chemometric modeling (PLS regression) with advanced machine learning approaches, including SVR, Random Forest, XGBoost, and neural networks. C_LIO_LIShowed that the biochemical target affects the models performance, with XGBoost improving acetate prediction and PLS offering better glucose prediction. C_LIO_LIIntegrated explainable artificial intelligence to identify Raman spectral regions contributing to bioprocess predictions. C_LIO_LIEstablished a pathway toward interpretable Raman-based Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) implementation. C_LI

The increasing world population necessitates new sustainable nutrient sources, making microalgae like Chlorella sorokiniana interesting due to its rich nutrient profile and sustainable cultivation methods. With genetic optimization tools like CRISPR/Cas9, microalgae as a nutrient source can be improved even further. However, degradation of the rigid cell wall of microalgae, and thereby developing protoplasts, is often necessary prior to transformation, but monitoring protoplast development in spherical, single-celled organisms like C. sorokiniana is challenging using bright-field microscopy. Carbotrace 480 and 630 were tested as fluorescent markers of the cell wall of a C. sorokiniana mutant for protoplast detection, and Carbotrace 480 was successfully used to distinguish protoplast from normal cells in a cell suspension. The enzymes Driselase, Glucanex, Snailase, and Saczyme were tested in different combinations to degrade the cell wall of the mutant, with Snailase as the most effective yielding ~60 % protoplasts. This study provides a quick and easy tool for monitoring protoplast development in the microalgae C. sorokiniana, the first step to improve C. sorokiniana as a sustainable nutrient source using genetic optimization tools like CRISPR/Cas9.

Paper-based colorimetric sensors offer a low-cost and accessible platform for point-of-care (POC) analysis, but enzyme activity loss during coating and drying can weaken analytical signals and require high enzyme loadings or complex immobilization procedures. Although surfactants are widely used to improve wettability in paper-based assays, their potential contribution to colorimetric performance beyond these effects remains unclear. Here, we investigated surfactant-assisted colorimetric signal enhancement in a glucose assay implemented on a 96-puddle paper plate (96-PPP) and identified Tween 20 as the most effective surfactant. Its effect on detection performance became more pronounced as glucose oxidase (GOx) loading decreased; at 0.1 mg/mL GOx, Tween 20 lowered the limit of detection (LoD) from 0.113 to 0.034 mg/mL (approximately 3.3-fold) over a working range of 0-5 mg/mL, despite no statistically significant change in the measured contact angle at this loading. Tween 20 had no appreciable effect on the reaction in solution but preserved 95% of the apparent reaction rate constant after drying, compared with 11% without it, and atomic force microscopy (AFM) revealed a more dispersed dried enzyme morphology on mica. Tween 20-containing sensors also showed slower signal decay during repeated wetting-drying cycles and thermal stress, retained 77% (vs 26%) of the response at 400 mM NaCl, and exhibited within-PPP and between-batch coefficients of variation (CVs) below 10% (vs 12.3-19.5%), while maintaining glucose selectivity over potentially interfering molecules. These results indicate that Tween 20 enhances paper-based glucose sensing beyond wettability, in part by retaining enzyme cascade activity during drying, although the contributions of the individual enzymes and the underlying mechanism remain to be established.