Methods

Recombinant peptide production was pioneered in the 1970s for the generation of therapeutic peptides, with notable examples including insulin and somatostatin. These early methods required the use of cyanogen bromide (BrCN) for cleavage of the native peptide sequence from a fusion protein. Since that time, while numerous BrCN-dependent peptide methods continue to be reported, the accessibility and cost of site-specific proteases have improved dramatically. These developments have enabled alternative approaches to recombinant peptide generation that obviate the need for BrCN, an environmentally destructive toxin. We recently created an immobilized SUMO protease that can replace BrCN usage in recombinant peptide production workflows by releasing native peptides expressed as part of a SUMO-peptide fusion protein. We have used this approach to generate P113 peptide, the minimal active fragment of the antifungal peptide Histatin 5. In this report, we describe the creation and characterization of this immobilized SUMO protease and its application in the production of experimentally viable quantities of active P113 peptide.

The design of the classical fluorescence microscope has undergone few changes since the 1970s-1980s, when Ploemopak modules with filter cubes became widespread. Most of these changes have been in the replacement of mercury and xenon lamps with LED illuminators in the 2010s. However, this does not mean that this stable design cannot be improved upon. New method: The implementation of a vibrating optical fiber, positioned using a micromanipulator and connected to any suitable type of laser, enables a full spectrum of fluorescence research. This work presents an advanced version of the Ellis concept, in which light is delivered directly onto the sample, rather than into the filter cube (technical novelty).To confirm the functionality of the microscope, vibrational slices of mouse brain stained with three fluorescent markers (B3-PPC, DiI and DiD) covering most of the visible spectrum were examined. The fiber-optic illumination system eliminates the need for bulky and obsolete high-voltage plasma arc lamp units without compromising image quality (confirmed by the USAF 1951 test and SDNR assessment on fluorescent beads). Furthermore, the optical fiber mounted on manipulators is convenient and easy to integrate, for example, into stereomicroscopes for scanning large brain tissue samples.

Digital PCR (dPCR) is a powerful technology for absolute quantification of nucleic acids, valued for its accuracy, sensitivity, and repeatability. Yet, the commercialization of different instruments with proprietary software has introduced challenges to data analysis, interoperability, and comparability. Therefore, we present the Digital PCR Data Essentials Standard (DDES) - a lightweight, human- and machine-readable, and cross-platform data standard developed in collaboration with the dPCR community. The standard consists of three file types designed to enable both manual inspection and automated analysis: (i) a main file summarizing experiment and reaction-level (meta-)data; (ii) an assay file describing targets and detection chemistry, and (iii) intensity files capturing partition-level raw fluorescence data per reaction. DDES supports a wide range of current dPCR applications, including singleplex and multiplex assays, endpoint and real-time readouts, and will be curated to implement future dPCR developments. By harmonizing the data structure, DDES lays out the foundation for FAIR dPCR data practices and supports improved software compatibility, collaborative and reproducible research, and future dPCR data repositories.

Genetically encoded biosensors are GFP-based tools that can visualize the dynamics and spatial features of cellular processes. The design of a genetically encoded biosensor dictates the method that is used to measure the response. Common read-outs use some sort of fluorescence intensity measurement, which is subject to both technical and biological perturbations, including sample drift, excitation power fluctuations, changes in sample size/volume, or a change in expression level. Yet, the fluorescence lifetime of a fluorophore is not affected by the aforementioned perturbations. Therefore, biosensors that respond with a large lifetime change offer a more robust method of detecting cellular processes. Here, we report on protocols for calcium imaging using fluorescence lifetime imaging microscopy (FLIM) to measure the response of a genetically encoded lifetime biosensor. The protocols include details on biosensor production and purification, calibration of purified biosensor with FLIM, introduction of the plasmid in HeLa and endothelial cells, and timelapse analysis of FLIM data. In this chapter we use the green fluorescent biosensor G-Ca-FLITS as an example but the protocols can be generally applied to biosensors with lifetime contrast. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/717680v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@179c1cborg.highwire.dtl.DTLVardef@a20aacorg.highwire.dtl.DTLVardef@6ab811org.highwire.dtl.DTLVardef@5a9fcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Powder X-ray diffraction is highly sensitive to sample-delivery conditions, particularly when measurements are performed on platforms originally designed for single-crystal diffraction. In this study, we developed a modified Terasaki-plate-based sample-delivery method for PXRD using a laboratory single-crystal diffractometer implemented with the XtalCheck-S plate-reader module at Turkish Light Source. The method was evaluated against standard loop/pin-based loading and a grease-based Terasaki setup using [4-(2-methoxyphenyl)piperazin-1-yl]methyl}-1,3,4-oxadiazol-2-thiol as a model analyte. While the loop-based method allowed initial PXRD measurements, it provided limited sample volume and insufficient particle statistics. The grease-based plate setup enabled multi-well data collection at a time, but yielded weaker, more diffuse patterns due to increased background noise. Inversely, modification of the Terasaki wells with Kapton tape enabled secure low-volume powder loading, improved diffraction clarity, and supported batch-mode data collection. Comparative search-match and profile-fitting analyses showed that all three loading strategies sampled the same crystalline material, whereas the Kapton-based setup presented the most reliable diffraction profile, with the lowest profile residual (Rp = 9.6%). These findings indicate that this novel sample-delivery method, rather than instrument hardware, can largely improve PXRD performance on an existing in-situ crystallography platform. The Kapton-Terasaki method provides a simple, cost-effective, and practical pipeline for high-throughput PXRD analysis of small powder samples under laboratory conditions.

Spatial multi-omics methods allow researchers to study complex biological systems by integrating multiple molecular layers while preserving their spatial organization. However, integrating spatial transcriptomics and mass spectrometry imaging (MSI) remains challenging due to the differences between the two modalities, including sampling geometry, spatial resolution, signal scaling, and measurement principles. For example, 10x Genomics Visium captures transcriptomic data on a discrete hexagonal grid of spots; however, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) produces dense Cartesian pixel maps of molecular distributions. These differences make exact spatial co-registration difficult and limit the effectiveness of single-metric similarity approaches. Here, we introduce a Multi-Objective Scoring (MOS) framework designed to detect cross-modal spatial similarity without requiring exact pixel-to-pixel alignment. The MOS framework integrates multiple complementary spatial descriptors into a unified similarity score, including coordinate-based metrics (value correlation, importance-based Intersection-over-Union (IoU), and importance-map correlation) and descriptor-based metrics that capture higher-order spatial organization, such as spatial histograms, radial profiles, quadrant statistics, and Morans I spatial autocorrelation. These metrics are combined through a weighted ensemble model. This model calculates the weights using synthetic spatial datasets that simulate realistic tissue geometry, sampling differences, and spatial distortions. The framework was applied to a spatial multi-omics dataset from murine brain tissue, integrating spatial transcriptomics with MALDI-MSI lipidomics across young and aged control and Alzheimers disease (AD) models. Synthetic data validation results demonstrated strong pattern-matching performance (96.14% accuracy), and application to experimental data identified several MSI analyte features whose spatial distributions closely matched transcriptomic patterns. In particular, strong and reproducible associations were observed between myelin-related genes (Mbp and Plp1) and multiple analyte features enriched in white matter regions. Overall, whether applied conceptually or directly, the MOS framework provides a strong strategy for cross-modal spatial integration and offers a scalable tool for discovering spatial relationships across diverse multi-omics datasets and facilitating hypothesis generation.

The translation of mRNA into protein is tightly regulated by both cellular trans-factors and cis-regulatory elements encoded within transcripts. Although transcript fate can be measured by transcript abundance or translation efficiency, separating the contribution of each individual cis-element within a single transcript is an ongoing challenge. Current massively parallel reporter assay (MPRAs) approaches enable systematic interrogation of cis-regulatory elements that control transcript stability, but translation-focused MPRAs remain technically limited and often inaccessible. Here we present Nascent Peptide Translating Ribosome Affinity Purification (NaP-TRAP), a reporter-based approach that simultaneously measures translation and mRNA abundance. Unlike previous methods, NaP-TRAP captures translation directly through the immunoprecipitation of epitope-tagged nascent peptide chains, providing instantaneous, frame-specific readouts without specialized instrumentation. The method is highly scalable from single reporters to complex libraries, and adaptable across in vivo and in vitro systems. NaP-TRAP is versatile, allowing assessment of cis-regulatory impact of elements distributed throughout the mRNA, from cap-to-tail. This protocol covers experimental design, reporter construction, sample processing, and computational analysis for both low- and high-throughput applications. Bench work can be completed in 4- 5 days, with qPCR-based readouts requiring only basic Excel skills for data processing. Sequencing-based readouts require skills in command-line tools and Python scripting and add an additional 2-3 days. NaP-TRAP thus offers an accessible, robust, and quantitative platform to decode the regulatory logic of mRNA translation and stability in diverse biological contexts. Basic Protocol 1Design, assembly, and synthesis of NaP-TRAP reporter libraries. Support Protocol 1Design, assembly, and synthesis of NaP-TRAP individual reporters and spike-ins. Basic Protocol 2NaP-TRAP delivery by micro-injection in zebrafish embryos. Alternate Protocol 1NaP-TRAP delivery by transfection in cultured mammalian cells. Basic Protocol 3NaP-TRAP pulldown and RNA extraction. Basic Protocol 4Preparation of NaP-TRAP cDNA Sequencing Libraries. Alternate Protocol 2NaP-TRAP-qPCR module for low-cost validation. Basic Protocol 5Computational analysis of NaP-TRAP MPRA data.

Accurate identification of drug target proteins remain major challenges in proteomics-based target discovery, particularly for low-abundance nuclear proteins that are difficult to detect because of the complexity of whole-cell lysates. Here, we developed a detergent-free nuclear-cytoplasmic fractionation strategy compatible with peptide-centric local stability analysis (PELSA), which markedly improves detection of nuclear drug targets. Using K562 cells, we demonstrated that mild detergent-free fractionation enables high-fidelity nuclear-cytoplasmic separation with minimal cross-contamination. When coupled with PELSA, this workflow significantly increases the number of detected nuclear targets relative to whole-cell analysis. Benchmarking with well-characterized nuclear drugs, including the histone deacetylase inhibitor panobinostat and the RNA polymerase II inhibitor -amanitin, our results showed improved identification of canonical nuclear targets. Broad profiling of staurosporine target further revealed expanded kinase target coverage by combining the results of nuclear and cytoplasmic fraction, with the CLK family kinases detected exclusively in the nuclear fractions. Additionally, we showed that PELSA can also be performed on intact nucleus level. Collectively, these findings establish detergent-free nuclear-cytoplasmic fractionation-PELSA as a robust and scalable strategy for spatially resolved drug target identification, improving sensitivity for nuclear and low-abundance proteins.

Quantitative knowledge of nanoparticle properties is desirable in a large number of scientific and technological applications, but measurements with a high degree of precision usually prove to be challenging. Among a range of available methodologies, optical techniques with single particle sensitivity are especially interesting because they can reveal intrinsic hetero-geneities in a fast non-invasive manner. Recently, we presented interferometric nanoparticle tracking analysis (iNTA) as a highly sensitive label-free technique that is capable of determining the size, concentration and index of refraction of different subpopulations in a suspension mixture. Here, we enhance this method with biochemical specificity through multicolor fluorescence detection at the single-molecule sensitivity limit. We benchmark the performance of the combined technique, which we name iNTA-F, by distinguishing populations of fluorescent and non-fluorescent nanoparticles of different material, size, and fluorescence intensity, with an emphasis on the characterization of lipid vesicles and biological extracellular vesicles (EVs).

Huntingtons disease (HD) is a progressive neurodegenerative disease caused by a mutation in the exon 1 of the huntingtin (HTT) gene, which leads to an extended polyglutamine (polyQ) tract in the mutant protein. As a result, mutant huntingtin (mHTT) exon 1 fragments aggregate in cells, which disrupts proper neuronal function and eventually induces cell death. The selective reduction of these toxic mHTT fragments without disturbing the wild-type full-length HTT function would be a potential therapeutic strategy to treat and prevent HD. Intracellular antibodies (intrabodies) have emerged as an attractive strategy to specifically target disease-related proteins, with VHH intrabodies being of high interest as they are much smaller than single-chain variable fragments (scFv). Here, we describe the identification and development of VHH 1 as a lead candidate intrabody targeting the first 17 amino acids of the mHTT protein, using a humanized VHH page-display library to screen against mHTT(Q46) exon 1 to identify potential binders. Next, we further optimized VHH 1 into VHH 1a to improve cytoplasmic solubility. Using immortalized mouse striatal cells that express inducible untagged mHTT exon 1 fragments, we investigated the effects of the intrabody on soluble and insoluble mHTT species via microscopy and biochemical assays. We showed that the VHH 1a intrabody reduces the levels of insoluble mHTT species, thereby effectively interrupting the aggregation process. This study highlights the potential for VHH intrabodies to specifically target mHTT fragments, enabling therapeutic strategies to delay and prevent HD pathology. HighlightsO_LIThree binders were down-selected from a phage-display library to bind HTT N17 C_LIO_LIVHH 1a intrabody is the most efficient at reducing mutant HTT exon 1 aggregation C_LIO_LIVHH 1a acts on soluble HTT exon 1 oligomers to block the transition to inclusion body C_LI

Epitranscriptomics has recently gained significant momentum due to technological advances and translational applications, however, studies on bacterial RNA modifications remain limited. Bacterial RNA remains notoriously prone to degradation and methodologies to investigate the epitranscriptome are challenging. Prior research has shown RNA modifications modulate antimicrobial resistance, virulence and pathogenicity. This research employed CRISPR interference to knock down five known Escherichia coli rRNA modification genes (rlmF, rlmJ, rluD, rsmF and rsmG) in three E. coli strains. These isolates underwent growth curves, proteome analysis and native RNA sequencing CRISPRi adequately silenced the majority of RNA modification genes in E. coli (>80% reduction). Significant growth delays were associated with rlmF, rsmF and rsmG repression. Unique protein pathways corresponding with RNA modification loss were found for rlmJ (TreB, XylF), rluD (CysH, HycB, PutP, TrpB), rsmF (EvgA) and rsmG (OppC). Known rRNA modification sites for rluD ({Psi}) and rsmG (m7G) were detected from analysis of nanopore electrical signal, however, only a weak signal was apparent for m6A (rlmF, rlmJ) and m5C (rsmF) modifications. The inhibition of rRNA modifications resulted in mRNA modification changes including for genes ompC, cspC, dbhA, dbhB and secY. Our work provides an approach for unravelling the epitranscriptome of E. coli and gain insight into its functional role.

BackgroundRNA editing is a post-transcriptional modification that alters the sequence of an RNA transcript. Two types of RNA editing were found in mammals, involving the enzymatic deamination of either adenosine to inosine (A-to-I) or cytidine to uridine (C-to-U) nucleotides in RNA. A-to-I, which is the most common form of RNA editing, is mediated by the ADAR (adenosine deaminases acting on RNA) family of enzymes, ADAR1, ADAR2, and ADAR3. The editing event alters the hydrogen bond pairing of nucleobases, and the editing site will be recorded as guanosine rather than the original adenosine. Indeed, RNA editing deregulation has been linked to several nervous and neurodegenerative diseases. In this project work is done on Alzheimers disease (AD) and the samples are from anterior cingulate cortex of human brain tissue. AD is the main dementia in the world and a neurodegenerative condition prevalent in the elderly. MethodologyA total of 20 raw RNA-sequencing data samples containing 10 controls and 10 Alzheimers disease (AD) cases were collected from NCBI using SRA Toolkit. Quality assessment was performed using FastQC and processed using Trimmomatic. Alignment was done using STAR RNA-seq aligner. RNA editing detection was performed using REDItools, detected sites were subsequently annotated against the REDIportal database. The resulting control-specific and disease-specific novel editing sites were merged into a single dataset containing exclusively novel, group-specific A-to-I editing events. This merged dataset was subsequently used for downstream feature extraction and machine learning analysis. Probability-based filtering was done to extract high-confidence disease associated sites and their gene list was used for computational level biological validation, pathway and functional enrichment analysis as well as overlap with known AD loci. ResultsRandom Forest showed the highest accuracy score (0.804) and ROC-AUC score (0.854). Most important features that differentiated control and diseased novel sites in random forest were coverage ([~]0.35), editing level ([~]0.33) and GC content ([~]0.15). The AEI mean values is higher in both male and female diseased cases ([~]0.48-0.50) but less in male and female control cases ([~]0.14-0.21). The mean values of ADAR1_CPM higher in control cases (123.65-143.30) and is less in diseased cases (88.35-97.93), ADAR2_CPM is almost equal in all cases ([~]3.7-4.7) and ADAR3_CPM is very less in all the cases ([~]0-0.02). Most candidate editing site were present in exon ([~]62-67 %) CDS regions ([~]17-21%) and relatively smaller fraction of gene ([~]15-16 %). Editing alterations preferentially affect molecular systems governing synaptic structure, neurotransmission, and central nervous system integrity. In the main set -of the 2576 high-confidence genes identified, 33 overlapped with AD GWAS loci. In the core set -of the 1367 high-confidence genes identified, 11 overlapped with AD GWAS loci. ConclusionFeature like coverage, editing level and GC content contributed most. Alu sites are negligible as compared to non-alu sites but the AEI mean values are higher in diseased cases than in control cases. The mean values of ADAR1_CPM are higher than ADAR2_CPM and ADAR3_CPM.Sex does not play a major factor. High-confidence disease-associated RNA editing sites are strongly biased toward transcript-centric regions, particularly exons, with a notable subset affecting coding sequences. Importantly, enrichment of neurodegeneration-associated pathways and cognition-related human phenotypes further supports the disease relevance of these gene networks. RNA editing events in Alzheimers cortex may represent a regulatory mechanism largely independent of inherited genetic susceptibility loci.

Neural rosettes are hallmarks of the neural progenitor cell stage that is a necessary pre-condition for manufacturing central nervous system lineages. Characterization of early changes during differentiation through positional arrangement and metabolic shifts that occur in a multi-day protocol would facilitate cell culture quality monitoring and optimization of batch culture yield. We describe an analytical framework for identifying neural rosettes from confocal microscopy within a colony of differentiating stem cells and translating co-registered, cell-resolved MALDI imaging data into interpretable readouts that are compatible with cell manufacturing needs. Rather than evaluating hundreds of ion images sequentially, the pipeline converts each region of interest into a single-cell feature matrix and summarizes whole-spectrum variation using PCA, graph-based Leiden clustering, and UMAP visualization. The resultant metabolic neighborhoods provide quantification of molecular heterogeneity within colonies and - when mapped back to x-y space - form coherent spatial domains. Together, these outputs create a practical bridge between multimodal MALDI capabilities and process-relevant interpretation: neighborhoods can be compared across conditions, ranked markers can be prioritized as putative critical quality attributes, and spatial organization can be quantified without manual, feature-by-feature screening.

A complex of the 3'-truncated tRNAArg that lacks 9 nt and tRNase ZL works as a GCCC-recognizing RNA cutter. It recognizes an RNA substrate via four Watson-Crick-Franklin base-pairings with the 3'-truncated tRNAArg. Human SLFN11 and SLFN13 can generate 3'-truncated tRNALeu that lacks 10 nt and 3'-truncated tRNASer that lacks 11 nt, respectively, from their corresponding mature tRNAs. Here, we investigated if these 3'-truncated tRNAs together with tRNase ZL work as sequence-specific RNA cutters. We examined five RNA targets for cleavage by recombinant human tRNase ZL in the presence of the 3'-truncated tRNALeu or tRNASer. We demonstrated that the 3'-truncated tRNALeu and tRNASer together with tRNase ZL indeed work as [~]6-base-recognizing and 7-base-recognizing RNA cutters, respectively.

Tissue-clearing techniques have transformed optical imaging of fixed specimens, yet their application to living systems remains limited by toxicity and removal of key tissue components. We recently demonstrated that absorbing molecules such as tartrazine can reversibly render live mouse skin transparent. Subsequently, it was reported that isotonic protein solutions can achieve ex vivo and in vivo cellular clearing. However, discrepancies remain regarding the optimal refractive index (RI) for live-cell clearing and the impact of elevated osmolality on cell viability. Here, using cultured mammalian cells, we systematically examine the dependence of optical contrast on medium RI and the effects of hyperosmolality. We find that, contrary to the recent report of an optimal RI of 1.36[~]1.37 for suspended cells, densely-packed adherent cells exhibit a monotonic decrease in phase contrast up to an RI of 1.41 with tartrazine. Moreover, even under highly hyperosmotic conditions ([~]1200 mOsm/kg), cultured cells exhibit minimal deformation and negligible loss of viability for up to 30 min in the clearing solution. These results demonstrate that tartrazine enables effective live-cell clearing at RI up to 1.41 while preserving viability under elevated osmolality, and motivate future studies to define optimal conditions for in vivo optical clearing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=44 SRC="FIGDIR/small/717314v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1c45280org.highwire.dtl.DTLVardef@483a5org.highwire.dtl.DTLVardef@5ed60forg.highwire.dtl.DTLVardef@377714_HPS_FORMAT_FIGEXP M_FIG C_FIG

Enhancing targeted delivery of biomedicines improves efficacy and can reduce off-target effects by lowering the effective dose, but achieving targeting is challenging. Extracellular vesicles (EVs) are promising biological nanovesicles which can be targeted by displaying binding proteins and are being developed as therapeutics. Currently, discovering EV targeting constructs is limited by low throughput and resource-intensive EV production and isolation. To accelerate discovery, we developed a screening pipeline to identify EV targeting constructs without requiring EV production. This approach is premised on the hypothesis that cell-cell interactions may predict some cell-EV interactions. Our cell binding assay (CELLISA) quantifies binding of a cell surface-displayed targeting protein to its cognate receptor on a target cell, employing a microscopy-based analysis pipeline. After validating the premise using existing T cell-targeting reagents, we develop CELLISA for either adherent or suspension EV producer cells. Finally, we use CELLISA to evaluate new binders and validate that hits mediate targeting and/or delivery of genetic cargo to natural killer cells and T cells. CELLISA increased throughput > 6-fold and decreased time by 40% compared to standard EV screens, and it identified a T-cell binder conferring efficient gene delivery. CELLISA is easily adaptable to other laboratories and can accelerate EV research.

Chronic obstructive pulmonary disease (COPD), a chronic lung disease, involves complex metabolic disturbances that remain poorly characterized using non-invasive matrices. The metabolic alterations associated with cigarette smoke (CS), one of the major drivers of disease progression in COPD patients, have not been explored in detail. This study primarily aimed to investigate the metabolic signatures in COPD patients categorized into smoker (n=15), ex-smoker (n=11), and non-smoker (n=3) subgroups. Utilizing saliva as a noninvasive sample, we identified 26 metabolites with differential expression in smokers and 31 in ex-smokers. However, no such significant alteration was observed in the non-smokers subgroup. The multivariate analysis distinctly separated the COPD subgroups from healthy controls. Additionally, pathway enrichment analysis revealed perturbations in key metabolic pathways, including unsaturated fatty acid biosynthesis, arginine biosynthesis, the tricarboxylic acid (TCA) cycle, and pyruvate metabolism. Moreover, univariate Random forest analysis identified four metabolites (cyclopentanone, tetradecane 4-methyl, acetophenone, and scyllo-inositol) as potential biomarkers distinguishing COPD subgroups from healthy controls. This study offers novel molecular insights into the association of smoking with disease progression and provides a mechanistic understanding of COPD in different subgroups for better management of the disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/717654v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@11db4org.highwire.dtl.DTLVardef@1451fb5org.highwire.dtl.DTLVardef@124b62aorg.highwire.dtl.DTLVardef@133872a_HPS_FORMAT_FIGEXP M_FIG C_FIG

The emergence of antimicrobial resistance has been rapid, necessitating the development of alternative therapeutic approaches beyond traditional antibiotics. In this proof-of-concept study, we examined the antibacterial activity of citrate-stabilized, colloidally aggregated silver nanoparticles (AgNPs) against Escherichia coli by combining physicochemical characterization with experimental antibacterial testing The synthesis of silver nanoparticles was done through a modified thermal citrate reduction protocol, and UV-visible spectroscopy, dynamic light scattering (DLS), and zeta potential were used to characterize the nanoparticles. Spectroscopy analysis showed a clear surface plasmon resonance peak at 310-320 nm, indicating the formation of nanoparticles. DLS measurements showed that the dominant hydrodynamic diameter was around 250-270 nm, which is indicative of controlled colloidal aggregation, and near-neutral values of zeta potential indicated steric stabilization of the nanoparticle clusters. Agar tests demonstrated a clear zone of inhibition, and broth cultures showed a lower turbidity and slower bacterial growth with AgNPs. The above findings suggest that nanoparticles that are colloidally aggregated maintain a significant antimicrobial activity even though the surface area is lower than that of monodispersed systems. Mechanistically, the observed antibacterial effect can be explained by a multi-modal effect through direct membrane disruption, localized release of silver ions, and the induction of oxidative stress pathways in bacterial cells. The aggregated form could also help to increase the nanoparticle cell interactions through the provision of multivalent contact points of nanoparticles, and thus the antibacterial efficacy. Controlled colloidal aggregation of AgNPs is a promising approach to the development of effective and possibly more stable antimicrobial agents. These results indicate the possibilities of aggregated nanoparticle systems in fighting drug-resistant pathogens and a basis on future studies of its clinical use.

In previous studies by the author on binocular vision with the asymmetric eye (AE), which models a healthy human eye with misaligned optical components, the results were primarily presented in the Rodrigues vector (RV) framework and supported by simulations and 3D visualizations in GeoGebras dynamic geometry environment. In this paper, the novel geometric kinematics of the human eye, i.e., the eye with misaligned optics, and simplified assumptions about eye rotations (the eyes translational movements are disregarded) are developed within the framework of rigid-body rotations. Despite the eyes misaligned optical components (all eyes axes differ), the geometric formulation, which can only be approximated, yields excellent accuracy as demonstrated by simulations. The originality of the analysis lies in a precise geometric decomposition of the eyes posture changes into torsion-free (geodesic) and torsional (non-geodesic) rotations. This decomposition is extended to the corresponding decomposition of the angular velocity. A novel derivation of the eyes angular velocity from the RV formulation of the eye kinematics is proposed.

SHORT ABSTRACTA key barrier to developing effective drugs for disorders of the central nervous system (CNS) is understanding their impact on neural circuits. This protocol demonstrates how physics-based neural simulations can be used to interpret electrophysiological biomarkers of neurotherapeutics, providing a mechanistically grounded approach to the development of neurotherapeutics. LONG ABSTRACTElectroencephalography (EEG) and electrophysiology methods provide millisecond resolution biomarkers for central nervous system disorders and are used to assess treatment-related effects. However, lack of understanding about the neural mechanisms generating such biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that connects localized EEG biomarkers to their multi-scale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test possible neural mechanisms of neurotherapy-induced EEG biomarkers by optimizing parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multi-scale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow up empirical studies. An example is provided which shows how to examine the generating mechanisms of the early event-related potential (ERP) components of an auditory evoked response (P1, N1 and P2) and to assess changes following neural circuit modification due to neurotherapeutic administration. This protocol demonstration enables scientists to design simulation experiments to develop testable predictions on how EEG biomarkers reflect neural circuit mechanisms of example therapeutics. A similar protocol can be applied to study disease mechanisms or other therapies.