Wisconsin Institutes for Discovery

The serotonergic psychedelic psilocybin induces neural plasticity and profoundly alters consciousness. The benzodiazepine midazolam blunts neural plasticity and induces conscious sedation and amnesia at low doses. In our recent open label pilot study, we administered oral psilocybin (25 mg) along with intravenous midazolam at doses allowing a full psychedelic experience while blunting memory for the experience. We previously reported preliminary results from high density scalp electroencephalography (EEG) recorded during the dosing session. Here, we examined changes in EEG band power, normalized Lempel Ziv complexity (LZCn), and spectral exponent. We used linear mixed effects models that incorporated time and the subjective effects of midazolam and psilocybin, measured with the Observer's Assessment of Arousal and Sedation (OAA/S) and selected items from the Altered States of Consciousness (ASC) questionnaire, respectively. At 15-30 min, when midazolam but not psilocybin was at its targeted effect site concentration, we observed increased beta power and decreased spectral exponent. As the subjective effects of psilocybin commenced and over the next six hours, we observed increased LZCn and spectral exponent and decreased broadband power, with the most prominent power reductions in the delta, theta, and alpha frequency bands. OAA/S improved model fits for alpha power while ASC improved model fits for LZCn and spectral exponent. While recognizing limitations inherent to the small sample size and variability in dosages of midazolam, these data are further evidence that the effects of psilocybin are maintained in the presence of midazolam, supporting its utility in mechanistic studies of psilocybin's therapeutic activity.

A new continuous, room temperature synthesis process incorporating an in-line dynamic mixer was used to produce Zr-doped NMC811 (LiNi 0.8 Mn 0.094 Co 0.1 Zr 0.006 O 2 ; Zr-NMC811) precursors at mass production rates in the range 0.14 to 0.56 kg·h −1 . These precursors were then mixed with lithium hydroxide prior to heat-treatment at 800 °C for 12 h to give the corresponding Zr-NMC811 cathode material. The products were investigated using a range of analytical techniques including powder X-ray diffraction which confirmed a single phase layered α-NaFeO₂-type structure with rhombohedral R 3 ¯ m symmetry was obtained in all cases. The Zr-doped NMC811 samples underwent electrochemical investigation in half cells vs Li metal with the best sample displaying a discharge capacity of 227 mAh·g −1 at 0.05C (and 201 mAh·g −1 at 0.1C), with a capacity retention of 92.4% over 100 cycles at 1C, performing significantly better than the equivalent undoped NMC811 material that was made using a near identical method. The use of a scalable, room temperature and atmospheric pressure continuous precipitator offered a robust and flexible alternative to traditional batch stirred tank reactors and all solid-state routes, not least in terms of simplicity of process and scalability. • ACTIM co-precipitation enabled efficient, water-based synthesis of Zr-doped NMC811. • 0.6 at.% Zr-NMC811 showed 201 mAhg −1 and 92.4% retention after 100 cycles at 1C. • Adding 2.5 wt% MWCNTs in 80:10:10 ink improved high-rate performance significantly.

Ecological interactions between bacteria and nematodes in many environments provide a basis for the prediction that diverse bacteria produce anti-nematode compounds. The discovery of microbial secondary metabolites with broad-spectrum nematostatic or nematicidal properties can be hastened by drug screening approaches that include several nematode species and phenotypes. We cultured a collection of 22 soil-derived bacterial isolates that carry in their genomes putative pathways for production of unknown secondary metabolites. Isolates were cultured in various media to enhance natural product diversity and yield, and we evaluated culture filtrates for activity against two evolutionarily distinct nematode species: Clade V free-living nematode Caenorhabditis elegans and Clade III mammalian parasitic nematodes in the genus Brugia. Partitioned extracts from Pseudomonas sp. strain TE4607 stunted C. elegans development and caused motility defects in both blood-circulating larval and adult stages of Brugia. The primary active compound was identified as labradorin 1, an indole with known antibacterial and anticancer properties that had not been previously described as affecting nematodes. Notably, filtrates of Pseudomonas sp. TE4607 cultures attracted free-living nematodes in sensory assays, adding to evidence that certain Pseudomonas species modulate the behavior of free-living nematodes. These findings underscore the need to further explore the link between nematode sensory responses and whole-organism effects of microbial metabolites, with potential applications in anthelmintic discovery.

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data. Our software is available as an R package at https://github.com/YunyiShen/CAR-LASSO.

One-size-fits-all opioid treatment for chronic pain may undermine treatment effectiveness and cause adverse effects. We explore personalized nonpharmacological and noninvasive chronic pain treatment as a promising approach to minimize these risks and optimize outcomes for each patient. Our paper introduces the definition of personalized treatment, emphasizes the significance of intervention personalization, and outlines strategies for inter- and intra-patient personalization in chronic pain management. We also discuss empirical studies on personalized chronic pain treatment. Research has shown a variety of strategies for personalizing nonpharmacological and noninvasive chronic pain treatment. These strategies have demonstrated the potential to improve effectiveness. However, many critical scientific questions remain unanswered. To fully harness the potential of personalized nonpharmacological and noninvasive chronic pain treatment, future research should conduct robust clinical trials and meta-analyses comparing personalized and non-personalized chronic pain interventions, examine diverse personalization approaches, incorporate ecological momentary assessment into outcomes assessment, leverage digital technologies, engage patients in treatment design, and assess factors influencing the implementation of personalized chronic pain treatment.

Background. Ischemia and reperfusion (IR) injury is a major complication in solid organ transplantation, necessitating new therapeutic strategies to suppress IR injury and thereby reduce early allograft dysfunction and delayed graft function. In kidney transplantation, IR injury is associated with disruption of mitochondrial homeostasis, including depletion of nicotinamide adenine dinucleotide (NAD + ) leading to diminished ATP levels, which cells need for survival. Here, we describe a novel approach to attenuate IR injury in a mouse kidney model using a NAD + -loaded nanoparticle, capable of direct intracellular delivery of NAD + . Methods. C57BL/6NCrl mice underwent a laparotomy and occlusion of the left renal pedicle for 30 min to induce IR injury. Right nephrectomy was performed during the injury incubation. Mice received the test agents by 2 different techniques: direct kidney injection via the renal artery and systemically via intravenous (IV) injection into the vena cava. Mice received a single dose of the NAD + -loaded nanoparticle at 3 different concentrations. Blood and kidney tissue were collected for analysis 24 h after IR injury. Measurement of blood creatinine levels and histological analysis was performed to assess the protective effectiveness of the NAD + -loaded nanoparticle. Results. A single dose of NAD + -loaded nanoparticles resulted in significantly reduced creatinine levels in both the direct kidney-injected animals ( P < 0.0001) and the IV-injected animals ( P < 0.01) compared with empty nanoparticle, free NAD + , or saline controls. IR-induced renal tubular injury scores were markedly reduced for both IV delivery ( P < 0.01) and direct injection ( P < 0.05) compared with control treatments. Conclusions. These results justify further development of this novel NAD + -loaded nanoparticle in the realm of organ transplantation to ameliorate IR injury and potentially increase usage of expanded criteria organs and reduce instances of delayed graft function.

Abstract Starships are a recently discovered superfamily of extremely large mobile genetic element (MGE)s in fungi that encode diverse gene sequences, many of unknown function. Starships are widespread throughout filamentous Ascomycetes (Pezizomycotina), but relatively little is known about their fine-grained distributions at lower taxonomic levels. As more and more Starships are discovered, it is increasingly important to more effectively catalog their gene contents and taxonomic distributions to better understand their contributions to fungal evolution. To address this, we developed starbase , a web server and comparative toolkit for exploring Starship diversity and hypothesis generation. The starbase database is constructed from Starships identified from existing studies, as well as an exhaustive de novo survey of Starship sequences within a set of 19 863 publicly available fungal genome assemblies. This database consists of 5 493 Starships , their associated nucleotide sequences, captain gene protein sequences, cargo gene annotations, and other metadata pertaining to the annotation and analysis of Starships in fungal genomes. As a resource, starbase provides new avenues for studying structural variation in fungal genomes. starbase provides several key features for the research community: a centralized repository of curated Starship annotations, a standardized accessioning system enabling consistent referencing of elements across studies, tools for searching existing sequences and classifying novel Starships based on established classification schemes, and a submission portal encouraging community contributions. As Starship identification becomes a routine component of fungal genome annotation, starbase provides a framework for organizing this growing body of data and facilitating comparative analyses across the expanding landscape of fungal genomic diversity.

The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation and collect data. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them impossible to run. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models.

Abstract Microbial networks offer critical insights into community structure, ecological interactions and host–microbe dynamics. However, constructing reliable microbiome networks remains challenging due to variability among existing inference methods, limited overlap between inferred networks and the absence of a gold standard (a universally accepted reference for benchmarking) for validation. We developed CMiNet , an R package and interactive Shiny App (https://cminet.wid.wisc.edu ) that enables consensus microbiome network construction by integrating up to 10 widely used inference algorithms. CMiNet supports both correlation‐based and conditional dependence‐based methods and provides users with flexible options to construct individual or consensus networks across different approaches. CMiNet integrates results from multiple inference methods through a voting strategy that retains edges supported by a user‐defined number of methods. To assess robustness, we complement this with a bootstrap analysis that quantifies edge stability under resampling. By jointly reporting method support and bootstrap confidence, CMiNet provides a reproducible framework that explicitly communicates both agreement across methods and stability under perturbation. We applied CMiNet to gut and soil microbiome datasets, constructing consensus networks that retained edges supported by multiple methods and confirmed by bootstrap reproducibility values. To identify disease‐associated taxa, we developed an integrative strategy that compared results across machine learning, differential abundance and network‐based approaches, ensuring that selected taxa were consistently recovered across methods. In the soil dataset, this analysis highlighted key taxa such as Ktedonobacteria, Acidobacteriae, Vicinamibacteria, MB‐A2‐108, Ignavibacteria and Anaerolineae , all of which were confirmed by multiple independent strategies.