Journal of Visualized Experiments

Roseoloviruses, notably human herpesviruses 6A and 6B (HHV-6A and HHV-6B), are neurotropic viruses implicated as agents in neurological disorders, including: epilepsy, multiple sclerosis, and chronic fatigue syndrome. However, the effects of roseolovirus infection on neuronal signaling and network activity are not characterized. This is, in part, due to the complexities of monitoring electrical activity in individual neurons and across neuronal connections during viral infection. This protocol describes a human induced pluripotent stem cell (iPSC)-derived neuronal culture system that employs multi-electrode array (MEA) recordings to study neurophysiological changes during viral infection. Two culture platforms are described: (a) NGN2-induced forebrain neuronal cultures; and, (b) progenitor cell-derived neuron-astrocyte mixed cultures. Cell composition in cultures is validated by immunofluorescence staining using neuronal, glial, and viral markers. Functional activity is assessed using extracellular recordings detected via the MEA2100 system. Mean firing rate, inter-spike interval, single-electrode bursting, and network burst activity are characterized between roseolovirus-infected versus uninfected (control) states. Pharmacological treatment with bicuculline, gabazine, and nicotine in conjunction with immunofluorescence is used to confirm functional responsiveness of defined neuronal neurotransmitter chemotypes. Here, we show that HHV-6A infection changes neuronal firing compared to uninfected controls. The methods/workflows described provide a strategy to study how virus infection alters neuronal excitability and may be adapted to compare the effects of different viruses on nerve cell function, infection time courses, different multiplicities of infection (MOI), and therapeutic interventions.

Black participants remain underrepresented in electroencephalography (EEG) research, partly because current EEG devices work poorly with dense, coily Type 4 Afro-textured hair. Research-grade EEG typically wires electrodes on a cap, and therefore the electrodes often fail to reach the scalp through dense, Afro-textured hair. Protocols that ask Black participants to unbraid or restyle protective hairstyles add burdens that discourage enrollment in research studies. To address this, we designed and tested 3D-printed attachments that fit onto an EEG electrode and anchored it directly in the hair, using the hairstyle itself as the mounting structure. We fabricated three design configurations for natural Afro-textured hair, cornrows/braids, and twists/microlocs. Seventeen healthy adults with Type 4a-4c hair used electrode attachments that matched their hairstyle. During a single visit, EEG was recorded with 16 conventional cap electrodes defined by the international 10-20 system and with 16 electrodes using novel electrode attachments anchored directly to their hair. Participants completed resting and walking trials with each system. Electrode-to-scalp distance and gel volume were measured during setup. Electrode disconnection, impedance, signal quality (i.e. peak alpha power and aperiodic (1/f) component), and comfort level were measured throughout the data collection. Compared with the conventional cap, the novel attachments reduced electrode-to-scalp distance, gel use, and impedance. Twenty percent of the conventional cap electrodes failed to connect to the scalp compared with 0.1% of novel electrodes. Impedance rose across trials for the conventional cap, whereas the novel attachments showed a smaller increase and maintained lower impedance throughout. Signal quality was significantly better using the novel attachments, while comfort level did not differ between systems. In conclusion, hair-anchored electrode attachment may present a feasible alternative to conventional cap-based EEG for people with Type 4 hair, yielding more usable channels and better signal quality, and supporting more inclusive EEG acquisition.

Background: Transcardial perfusion fixation is widely used to preserve rodent brains for histological and ultrastructural analysis, but protocols can vary in the use of pre-fixation washout, fixative formulation, and osmotic additives. Relatively few studies have directly compared how the chemical composition of the perfusate affects tissue preservation. Methods: Male Sprague-Dawley rats underwent transcardial perfusion using a series of aldehyde-based fixative formulations that varied in fixative composition, use of a phosphate buffered saline washout, and the addition of mannitol or polyethylene glycol 35 kDa (PEG35). Perfusion outcomes were assessed using gross brain morphology, semi-quantitative grading of vascular blood clearance, quantitative detection of residual erythrocytes in whole-slide histological images, and light microscopic measures of cellular visualization and morphology. Perfusate osmolality was measured, and selected specimens were examined by electron microscopy. Results: Substantial vascular blood clearance was frequently achieved with fixative-only perfusion, indicating that a pre-fixation washout was not required to achieve high levels of blood clearance under the conditions tested. Routine light microscopy-based measures of cellular visibility and morphology were broadly similar across treatment groups. Adding mannitol and PEG35 to the fixative solution both led to concentration-dependent gross tissue shrinkage, particularly with PEG35, without correspondingly large or consistent changes on light microscopy. A substantial degree of case-to-case variation in perfusion quality was also observed among animals undergoing nominally similar procedures. Conclusions: Substantial variation in perfusate composition produced relatively modest differences in the light microscopy outcomes examined, despite pronounced effects of osmotic additives on gross brain morphology. There were some cases where perfusion with fixative alone led to near-complete blood vessel clearance, and the use of a preceding phosphate buffered saline washout was not associated with higher vascular clearance scores. The degree of case-to-case variability observed within treatment conditions suggests that procedural factors can play an important role on perfusion quality in addition to perfusate composition.

An increasing number of researchers, educators, and at-home growers are turning to hydroponics as an alternative method of food production, diversifying and strengthening the food supply chain. This has led to a substantial increase in the amount of academic work involving hydroponic production of plants for a variety of purposes, including but not limited to a more controlled and customizable environment than conventional production methods, utilizing hydroponics as a method of wastewater treatment, and investigating the vertical production of plants on a much smaller footprint than traditional commercial-scale production. Because so many unique stakeholders are interested in hydroponics for a variety of reasons, academic studies use a wide variety of hydroponic systems with varying levels of technology and automation, making comparisons across studies and systems challenging. A few institutions have sophisticated plant growth and research facilities, like those used by commercial companies, but these systems are often prohibitively expensive, costing upwards of $10,000 USD for a very limited amount of growing space for experimental trials. To enable more researchers, educators, and citizen scientists to conduct experiments and easily compare their results, this study details three pilot-scale hydroponic production systems (Kratky, NFT, and DWC), utilizing entirely off-the-shelf components, for under $5,000 USD each, that are capable of maintaining the desired consistent temperature and relative humidity levels, and have space for 216, 108 (6 irrigation loops), and 64 (16 irrigation bins) plants, respectively. The material lists and prices are provided along with assembly instructions and general guidelines for each hydroponic system. The testing and optimization process of environmental conditions within each growth system is also detailed, with suggested customizations for potential experimental setups. The authors developed these systems during their Masters and Doctoral degree programs, enabling improved hydroponic research at the University of Illinois Urbana-Champaign. They hope that other aspiring hydroponic researchers can benefit from this work in the development and operation of their hydroponic systems, increasing research efficiency and collaboration opportunities in this field of study.

Many studies using mouse models of human brain disorders rely on extended recordings of brain electrical activity through electroencephalogram, or EEG. The features that researchers measure in these recordings, such as seizures and epileptiform activity, are deeply influenced by whether the animal is awake, in non-rapid eye movement (NREM) sleep, or in rapid eye movement (REM) sleep. Because total sleep duration varies naturally across animals, rigorous experimental comparisons require classifying the sleep state of every few seconds of a recording. Manual annotation is highly labor-intensive, often requiring hours per subject. While automated scoring tools accelerate this process, they are typically trained exclusively on healthy cohorts and frequently fail when applied to disease models that exhibit atypical or fragmented sleep architecture. Somnus is an open-source Python package designed to automate rodent sleep staging using EEG, muscle activity (EMG), and optional video-tracking data. It includes a robust base model trained on diverse recordings from six independent laboratories, enabling out-of-the-box classification across various equipment setups without requiring prior manual labeling. A central feature of Somnus is its capacity for rapid adaptation to pathological sleep structure. The software provides a graphical interface that directs researchers to the specific epochs where the base model is least certain. Users can manually correct these targeted epochs in a built-in viewer by comparing the model's classification to the raw EEG signal and video, and then use their corrections to fine-tune the model. This workflow empowers researchers to generate accurate custom scorers for complex disease models or novel treatments with just a few minutes of manual correction, bypassing the need to hand-score entire experimental cohorts or develop their own models from scratch.

Electroencephalography (EEG) preprocessing determines signal quality and interpretation, particularly for transcranial magnetic stimulation combined with EEG (TMS-EEG), where recordings are affected by large and complex stimulation-related artifacts. Several preprocessing approaches have been developed, but substantial variability remains in how pipelines are implemented, documented, and evaluated across recordings, limiting reproducibility and comparability across studies. We developed NESTApp, a MATLAB-based graphical interface built on EEGLAB, to provide an accessible and reproducible framework for implementing and evaluating TMS-EEG preprocessing pipelines. Rather than prescribing a specific pipeline, NESTApp allows users to construct and consistently apply customisable workflows incorporating established EEGLAB and TMS-EEG processing tools, while automatically documenting processing decisions and parameters. Integrated quality-control procedures track data removal and flag recordings based on user-defined criteria, facilitating systematic evaluation across participants. The application also supports group-level visualisation and export of processed TMS-EEG measures for subsequent analyses. Applied to 29 recordings from 16 participants, NESTApp processed 27 and identified those requiring further inspection. By integrating pipeline implementation, documentation, quality control, and group-level evaluation within a single interface, NESTApp provides a transparent framework for TMS-EEG preprocessing that may improve reproducibility, facilitate consistent processing across cohorts, and lower technical barriers to the implementation of established preprocessing approaches.

Suspension mass cytometry can measure 50 or more markers per cell and is not impacted by cellular autofluorescence, making it an appealing platform for comprehensive phenotyping of single cells in tissue with high innate fluorescence, such as brain. However, this organ is challenging to reduce to a single cell suspension: it is a deeply interconnected tissue rich in extracellular material and contains a wide variety of cell types including neurons, their supporting glia, and hematopoietic cells from peripheral circulation, any or all of which may have competing requirements for survival through the dissociation process. This study compares approaches for producing single-cell preparations from white and gray matter enriched regions of adult mouse brain, testing simple mechanical dissociation, Accutase, and three gentle enzymes plus DNase: papain, collagenase II, and collagenase plus dispase, all with or without Percoll gradient centrifugation to separate intact cells from debris. Live cells were counted by hemocytometer and Trypan blue exclusion to assess yield and compare across enzyme conditions. Mass cytometry was used to assess cellular composition after representative dissociation, debris removal, and viable cryopreservation. Key proteins measured included beta III-tubulin for neurons, glial fibrillary acidic protein (GFAP) for astrocytes, oligodendrocyte transcription factor (OLIG2) for oligodendrocytes, CD31 for endothelial cells, CD24 for ependymal cells, purinergic receptor P2Y12 (P2RY12) for microglia, and CD45 for leukocytes. Mechanical disaggregation paired with enzymatic dissociation by papain, Collagenase/Disapse, or Collagenase II plus DNase produced approximately 400,000 live cells per 100 grams of tissue. Cell yield was not significantly different between conditions and yields were similar between white and grey matter. Addition of Percoll gradient centrifugation substantially reduced the number of viable cells recovered (average decrease of 88%) and resulted in viable cell numbers that were insufficient for robust analysis of less common cell subsets. In addition to neurons, mass cytometry identified six major populations of non-neuronal cells: oligodendrocytes, astrocytes, endothelial cells, ependymal cells, microglia, and leukocytes. The relative abundance of these six populations was generally unchanged by cryopreservation in both grey matter (r = 0.95), and white matter (r = 0.90), though loss of oligodendrocytes did occur in both tissues. Proliferation, as measured by Ki67 positivity, was non-existent; consistent with this, levels of phosphorylated STAT1 (p-STAT1) and p-STAT3 proteins were low in all cell populations and unchanged by cryopreservation. A modest increase in p-S6(S235/236) after cryopreservation was consistently observed only in microglia. Together, these results establish standardized procedures for generating viable single cell suspensions for cytometry that preserve the cellular diversity of the adult murine brain with minimal perturbation of intracellular signaling cascades.

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.

The Muse 2 EEG headset also records forehead photoplethysmography (PPG). We tested whether its pulse intervals can replace Polar H10 RR intervals for ultra-short heart-rate variability in 28 seated recordings (60 s eyes open, then 60 s eyes closed), using fixed processing, no exclusions and eleven sensitivity analyses. Mean heart rate agreed almost perfectly (ICC [≥] 0.999; bias -0.22 and 0.00 beats min-1). SDNN agreement was good to excellent (ICC 0.86 and 0.91; bias +8.6 and +8.2 ms). RMSSD agreement was moderate (ICC 0.60): Muse exceeded Polar in at least 27 of 28 sessions (bias +19.2 and +18.5 ms; about 1.9-fold), and the ratio bias fell as RMSSD rose. Interval differences (SD 23 ms) implied about 16 ms of independent timing error per pulse, which reproduced the RMSSD bias; without ECG, sensor jitter could not be separated from variation in pulse transit time. Tracking of the eyes-closed change in ln RMSSD was not demonstrated ({rho} = 0.35, 95% CI -0.07 to 0.67; detectable {rho} [≥] 0.51). Muse 2 PPG can supply mean heart rate and, with a stated bias, SDNN; its ultra-short RMSSD is not interchangeable with Polar RMSSD and should be reported as pulse-rate variability.

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.

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

Detection of postoperative pain in mice is challenging, and there is a need for unbiased, automated methods. Home-cage monitoring systems are promising, but their ability to detect mild pain remains unclear. We evaluated whether home-cage activity measures can be used to detect pain-related changes in a plantar incision model of postoperative pain. First, we confirmed the presence of local hypersensitivity using von Frey filaments on 48 C57BL/6JRj mice (24 males, 24 females). Subsequently, we pair-housed 64 mice (32 males, 32 females) and allocated them to receive either a 4 mm plantar incision on the hind paw under anaesthesia, or anaesthesia alone. Baseline locomotion and voluntary wheel-running activity were recorded over five days, followed by four days of postprocedural monitoring in the home cages. Both procedure groups (incision and anaesthesia-only) showed reduced locomotion and running wheel activity relative to baseline. However, activity measures did not differ between incised and anaesthesia-only mice, and we detected no sex-related effects. Under these conditions, home cage activity measures did not reliably discriminate pain-related behavioural changes in the model, despite the model showing local mechanical hypersensitivity in the von Frey test. Together, these findings indicate that, given the mild and localised nature of the plantar incision mouse model, commonly used home-cage metrics cannot yet reliably detect subtle pain-related behavioural changes.

In the murine osteochondral unit, morphology of chondrocyte lacunae (CCL) within calcified articular cartilage and osteocyte lacunae (OCL) within the underlying subchondral bone reflects local biomechanical conditions that are altered in disease, making reliable discrimination of both lacunar populations important for studying osteochondral physiology and pathology. Histology is generally considered the gold standard for identifying CCL and OCL. However, it is inherently limited to two-dimensional assessment and a relatively small tissue section, restricting comprehensive characterisation of three-dimensional lacunar structure. Synchrotron-radiation micro-computed tomography (SR-microCT) enables volumetric quantification of lacunae; where classification is typically based on the position of each lacuna relative to the cement line separating calcified cartilage from bone, requiring manual delineation of this convoluted interface. Such approaches are labour-intensive, operator-dependent, and rely on access to specialised synchrotron infrastructure that is not widely available. To address these limitations, we present a fully automated, annotation-free pipeline for classifying OCL and CCL in the mouse osteochondral unit based on desktop micro-computed tomography (microCT), independent of cement line identification. The proposed unsupervised workflow requires neither tissue separation nor labelled training data, enabling objective and scalable discrimination of lacunar populations directly from volumetric image data. Seven morphometric and positional features per lacuna were extracted from SR-microCT and desktop microCT datasets and analysed using Uniform Manifold Approximation and Projection (UMAP). Validation of the pipeline outcome on SR-microCT against manual classification across specimens yielded very-high agreement, with Gwet's AC1, the Matthews correlation coefficient, and balanced accuracy each approaching 0.99, in both healthy and post-traumatic osteoarthritis specimens. Such a workflow can be readily adapted for the investigation of osteochondral interface in various other species.

ECG waveform data, which is increasingly required for artificial intelligence analyses, is often unavailable in raw data formats. Digitization can approximate original raw data from ECG images, but digitizers reconstruct ECG waveforms only approximately. Exact waveform reconstruction is needed to guarantee that any errors, even if small, do not unexpectedly propagate into downstream analyses. Portable document format (PDF) files containing vector graphics store waveforms derived directly from raw data. This improves reconstruction accuracy, but existing PDF reconstruction methods remain approximate because raw data values are rounded during PDF encoding/generation. Despite rounding, however, there is a 1:1 mapping between raw data and PDF-encoded values which can be reversed to recover the original raw data with no error. We developed open-source software in MATLAB and Python (http://github.com/BIVectors/pdf2ECG) for bit-exact waveform reconstruction of General Electric MUSE ECG PDFs based on double quantization of ECG waveform data. The software supports multiple PDF layouts and voltage gains, automatically flags waveform clipping under which exact reconstruction is not possible, and certifies if parameter values used for waveform reconstruction are consistent with parameter values used during PDF encoding. Accuracy was assessed by comparing PDF reconstructed waveforms to raw XML waveforms, and reconstruction parameters were perturbed to determine the accuracy of waveform certification. 1,632 PDF-XML pairs were assessed in 5 layouts, each at 3 gains. After software-identified waveform clipping was excluded, the software reconstructed 100% of ECG waveforms with zero error compared to ground-truth XML. Certification tests were extremely sensitive to minor perturbation in encoding parameters, and if passed, confirmed bit-exact waveform reconstruction without need for ground-truth comparisons. Although this method does not replace the need for general ECG image digitization, for MUSE generated PDFs this method is preferable to rasterization followed by image-based digitization.

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

Understanding how neural populations support natural behaviour requires knowing what a population does while an animal performs a specific, self-initiated action. For responses evoked by an experimenter-delivered stimulus, the signal can be aligned by the trigger itself, and no behavioural scoring is needed to define the event. Complex behaviours such as parental care and social interaction provide no such trigger. They are defined from the video record, usually offline and post hoc, and are conventionally scored in one program and exported to another for analysis. In this study we developed EthoTrace, a single self-contained HTML file that runs in a desktop browser without installation, server or network access and carries a session from behaviour and calcium signal through to the statistical comparison. Behaviour is annotated on a timeline shared with the {Delta}F/F trace, the signal is corrected and scaled under a stated baseline convention, single bouts stay inspectable in their behavioural context, and repeated bouts are aligned without filling absent data. We demonstrate the complete workflow on a parenting behaviour experiment, in which behaviour was scored and the photometry signal analysed in a single session folder. We expect EthoTrace to facilitate integrative analysis in neuroscience, where behavioural observation and fiber photometry signals are interpreted together.

Stereotaxic intracranial microinjection in mice is a critical step in neuroscience workflows, enabled by robust hardware design, precise operation, and neuroanatomical expertise. Manual injections are prone to error and stereotax-mountable commercial automated injectors are extremely expensive. Further, the knowledge needed for performing accurate intracranial injections and surgeries is heterogeneous and disparate. Here we demonstrate the design and characterization of the UD Neuroinjector software-hardware-knowledge architecture, which serves to both reduce the cost of performing precisely controlled stereotaxic microinjections and provide a web platform for planning accurate targeting of specific mouse brain regions. We designed the UD Neuroinjector using low-cost hardware and 3D-printed parts, with a total build-cost of $300 --- less than 0.1x the cost of a commercial injector. Arduino-based firmware enables programmable flow rates, automated injection and extraction, and manual joystick control. Capillary-fluid measurements show stable, highly linear displacement (R2 > 0.93) at commonly used injection rates. In a head-to-head comparison with a commercial injector, in vivo DAPI injections into the mouse thalamus showed comparable spread and DAPI+ cell counts. For effective experimental design of intracranial injections, the web-based components of our architecture provide AI-enabled assistance and atlas-guided navigation. We used a hybrid retrieval-augmented generation (RAG) approach to ground AI-assisted protocol guidance for mouse intracranial surgeries using a corpus of 3,738 papers. Using the Common Coordinate Framework version 3 (CCFv3), we designed an interactive tool for identifying target coordinates. Together, the UD Neuroinjector architecture integrates and enables efficient knowledge discovery, experimental design, planning, and implementation of mouse intracranial surgeries.

Antifreeze proteins and peptides adhere to ice crystals and inhibit their growth, resulting in freezing point depression. AFP6 is an abundant antifreeze peptide in the blood serum of winter flounder (Pseudopleuronectes americanus). This 37-residue peptide forms an amphiphilic -helix with a hydrophobic ice-binding surface. In this study, prediction of the peptide structure suggested the formation of an antiparallel dimer burying the hydrophobic surfaces of both subunits. The oligomerisation of AFP6 was therefore examined in aqueous solutions using size-exclusion chromatography and chemical crosslinking along with ice-binding evaluation. Crosslinking of non-amidated AFP6, followed by denaturing electrophoresis, revealed bands consistent with monomers and dimers, whereas results for the amidated form and a recombinant mutant suggested additional larger forms. Bands consistent with the presence of monomers and dimers were also predominant following non-amidated AFP6 crosslinking under a variety of solution conditions, with smaller proportions of peptide assembled up to tetrameric sizes; however, the larger oligomers appeared to be absent from AFP6 treated with sodium bicarbonate. Isolated oligomers of non-amidated AFP6 produced ice crystal morphologies and thermal hysteresis comparable to those for the unfractionated peptide suggesting similar interaction with ice. Oligomerisation of AFP6 may allow greater solubility of this amphiphilic -helical peptide in aqueous solutions.

Background: Articulated laparoscopic instruments require a reliable mechanical connection between the handle and shaft to maintain the intended instrument configuration during surgical manipulation. The handle shaft locking interface should resist unintended relative displacement under external loading. However, the appropriateness of a specific slip resistance threshold must be evaluated in relation to the instrument design and intended conditions of use. Objective: To evaluate the mechanical performance associated with an 8 N slip resistance specification for the locked handle shaft interface of an articulated laparoscopic instrument using a controlled bench model. Methods: A bench based mechanical evaluation method was designed using the instrument under test, mechanical fixtures, a calibrated force measurement device, and displacement measurement equipment. After engagement of the handle shaft locking mechanism, external forces were applied to evaluate resistance to relative displacement. Testing included axial loading, loading in relevant transverse directions, and repeated locking unlocking cycles. The primary outcome was the force corresponding to the onset of relative slippage at the locked interface. Secondary outcomes included displacement under load, complete locking failure, and changes in mechanical performance after repeated operation. Acceptance criteria should be established before confirmatory testing. Results: All 30 instruments from three production batches met the 8 N axial requirement (30/30). The axial slip onset force was 12.40 N . Under a sustained load of 8 N for 60 s, mean relative displacement increased from 0.06 mm to 0.10 mm, and mean residual displacement was 0.02 mm. After 1,000 cycles, the axial slip-onset force was 12.00 N . Conclusions: This study provides a bench-testing framework for evaluating an 8 N slip resistance specification for the handle shaft locking interface of an articulated laparoscopic instrument. Whether this specification provides adequate mechanical stability can be determined only by comparing the measured performance with engineering acceptance criteria established in advance on the basis of the instrument's intended use and foreseeable loading conditions.