First Search for Dark-Trident Processes Using the MicroBooNE Detector.

We present a first search for dark-trident scattering in a neutrino beam using a dataset corresponding to 7.2×10^{20} protons on target taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the main injector produce π^{0} and η mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A^{'}. A convolutional neural network is trained to identify interactions of the DM particles in the liquid-argon time projection chamber (LArTPC) exploiting its imagelike reconstruction capability. In the absence of a DM signal, we provide limits at the 90% confidence level on the squared kinematic mixing parameter ϵ^{2} as a function of the dark-photon mass in the range 10≤M_{A^{'}}≤400 MeV. The limits cover previously unconstrained parameter space for the production of fermion or scalar DM particles χ for two benchmark models with mass ratios M_{χ}/M_{A^{'}}=0.6 and 2 and for dark fine-structure constants 0.1≤α_{D}≤1.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data.

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Patients', physiotherapists' and other stakeholders' experiences and perceptions about supported home physiotherapy for people with musculoskeletal conditions: a qualitative study.

OBJECTIVES: Exercise, support and advice are the key treatment strategies of musculoskeletal problems. The aims of this study were to determine patients', physiotherapists', and other stakeholders' perspectives about supported home physiotherapy for the management of musculoskeletal problems and to identify the barriers and facilitators to rolling out this model of physiotherapy service delivery. METHODS: This study was conducted as part of a process evaluation run alongside a large trial designed to determine whether supported home physiotherapy is as good or better than a course of in-person physiotherapy. Forty interviews were conducted with 20 trial participants, 15 physiotherapists, and 5 other stakeholders. The interviews were semi-structured and based on interview guides. Each interview was transcribed and a three-tiered coding tree was developed. RESULTS: Six key themes were identified. Supported home physiotherapy (i) is convenient for some patients, (ii) does not always align with patients' and therapists' expectations about treatment (iii) is suitable for some but not all, (iv) can reduce personal connection and accountability, (v) has implications for physiotherapists' workloads, and (vi) has barriers and facilitators to future implementation. CONCLUSIONS: Findings suggest that patients are far more accepting of supported home physiotherapy than physiotherapists assume. This model of service delivery could be rolled out to improve access to physiotherapy and to provide a convenient and effective way of delivering physiotherapy to some patients with musculoskeletal conditions if our trial results indicate that supported home physiotherapy is as good or better than in-person physiotherapy. CLINICAL TRIAL REGISTRY NUMBER: ACTRN12619000065190 CONTRIBUTIONS OF THIS PAPER.

Unique challenges required reassessment and alterations to critical reagents to rescue a neutralizing antibody assay.

Aim: To redevelop a neutralizing antibody (NAb) assay to be much more drug tolerant, have a large dynamic range and have high inhibition when using high levels of positive control (PC). Materials & methods: Early assay data suggested that typical biotin labeling of the capture reagent (Drug 1, produced in a human cell line) was blocking it from binding with the PC or the detection target, and that the detection target was out competing the PC. Methodical biotin labeling experiments were performed at several challenge ratios and an Fc linker was added to the detection target. Results & conclusion: A larger dynamic range, high inhibition and higher drug tolerance were achieved by adding an acid dissociation step to the assay, performing atypical biotin labeling of Drug 1 and switching to a detection target that contained an Fc linker to increase steric hinderance and decrease its binding affinity to Drug 1.

Many of the drugs available today are produced by a living organism and these are called biologics. Biologics are larger than chemical drugs and the human body can detect them as foreign and create antibodies against them. This is called immunogenicity. When the antibodies created against the biologic blocks the drug's ability to work correctly, they are called neutralizing antibodies (NAbs). Testing for NAbs is one of the requirements of regulatory agencies for biologics. Here we describe challenges encountered developing an assay to test for NAbs against a biologic.

Systematic engineering for production of anti-aging sunscreen compound in Pseudomonas putida.

Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production.

Regulating nanoscale directional heat transfer with Janus nanoparticles.

Janus nanoparticles (JNPs) with heterogeneous compositions or interfacial properties can exhibit directional heating upon external excitation with optical or magnetic energy. This directional heating may be harnessed for new nanotechnology and biomedical applications. However, it remains unclear how the JNP properties (size, interface) and laser excitation method (pulsed vs. continuous) regulate the directional heating. Here, we developed a numerical framework to analyze the asymmetric thermal transport in JNP heating under photothermal stimulation. We found that JNP-induced temperature contrast, defined as the ratio of temperature increase on the opposite sides in the surrounding medium, is highest for smaller JNPs and when a low thermal resistance coating covers a minor fraction of JNP surface. Notably, we discovered up to 20-fold enhancement of the temperature contrast based on thermal confinement under pulsed heating compared with continuous heating. This work brings new insights to maximize the asymmetric thermal responses for JNP heating.

Amiodarone-Induced Epididymitis: A Case Report and Review of the Literature.

Amiodarone is commonly used to prevent and treat life-threatening cardiac arrhythmias. However, it is also known to have an extensive side effect profile. A rare adverse effect of amiodarone is epididymitis. Epididymitis is inflammation of the epididymis that causes moderate pain in the posterior scrotum. The patient, in this case, developed left scrotal pain seven months after starting amiodarone and presented with symptoms consistent with epididymitis. The patient's work-up included urinalysis with culture, treatment with antibiotics, and testicular ultrasound before being diagnosed with amiodarone-induced epididymitis. This diagnosis led to the discontinuation of amiodarone, which resulted in the complete resolution of the patient's symptoms within two weeks. This case report is intended to increase awareness of epididymitis as a possible adverse effect of amiodarone and to stress the importance of considering this when there are no apparent anatomical or infectious causes of epididymitis.

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning.

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

Optimization of electroporation method and promoter evaluation for type-1 methanotroph, Methylotuvimicrobium alcaliphilum.

Methanotrophic bacteria are promising hosts for methane bioconversion to biochemicals or bioproducts. However, due to limitations associated with long genetic manipulation timelines and, lack of choice in genetic tools required for strain engineering, methanotrophs are currently not employed for bioconversion technologies. In this study, a rapid and reproducible electroporation protocol is developed for type 1 methanotroph, Methylotuvimicrobium alcaliphilum using common laboratory solutions, analyzing optimal electroshock voltages and post-shock cell recovery time. Successful reproducibility of the developed method was achieved when different replicative plasmids were assessed on lab adapted vs. wild-type M. alcaliphilum strains (DASS vs. DSM19304). Overall, a ∼ 3-fold decrease in time is reported with use of electroporation protocol developed here, compared to conjugation, which is the traditionally employed approach. Additionally, an inducible (3-methyl benzoate) and a constitutive (sucrose phosphate synthase) promoter is characterized for their strength in driving gene expression.

Mimicking opioid analgesia in cortical pain circuits.

The anterior cingulate cortex plays a pivotal role in the cognitive and affective aspects of pain perception. Both endogenous and exogenous opioid signaling within the cingulate mitigate cortical nociception, reducing pain unpleasantness. However, the specific functional and molecular identities of cells mediating opioid analgesia in the cingulate remain elusive. Given the complexity of pain as a sensory and emotional experience, and the richness of ethological pain-related behaviors, we developed a standardized, deep-learning platform for deconstructing the behavior dynamics associated with the affective component of pain in mice-LUPE (Light aUtomated Pain Evaluator). LUPE removes human bias in behavior quantification and accelerated analysis from weeks to hours, which we leveraged to discover that morphine altered attentional and motivational pain behaviors akin to affective analgesia in humans. Through activity-dependent genetics and single-nuclei RNA sequencing, we identified specific ensembles of nociceptive cingulate neuron-types expressing mu-opioid receptors. Tuning receptor expression in these cells bidirectionally modulated morphine analgesia. Moreover, we employed a synthetic opioid receptor promoter-driven approach for cell-type specific optical and chemical genetic viral therapies to mimic morphine's pain-relieving effects in the cingulate, without reinforcement. This approach offers a novel strategy for precision pain management by targeting a key nociceptive cortical circuit with on-demand, non-addictive, and effective analgesia.

Study of the Effects of Remote Heavy Group Vibrations on the Temperature Dependence of Hydride Kinetic Isotope Effects of the NADH/NAD+ Model Reactions.

It has recently been observed that the temperature(T)-dependence of KIEs in H-tunneling reactions, characterized by isotopic activation energy difference (ΔEa = EaD - EaH), is correlated to the rigidity of the tunneling ready states (TRSs) in enzymes. A more rigid system with narrowly distributed H-donor-acceptor distances (DADs) at the TRSs gives rise to a weaker T-dependence of KIEs (i.e., a smaller ΔEa). Theoreticians have attempted to develop new H-tunneling models to explain this, but none has been universally accepted. In order to further understand the observations in enzymes and provide useful data to build new theoretical models, we have studied the electronic and solvent effects on ΔEa's for the hydride-tunneling reactions of NADH/NAD+ analogues. We found that a tighter charge-transfer (CT) complex system gives rises to a smaller ΔEa, consistent with the enzyme observations. In this paper, we use the remote heavy group (R) vibrational effects to mediate the system rigidity to study the rigidity-ΔEa relationship. The specific hypothesis is that slower vibrations of a heavier remote group would broaden the DAD distributions and increase the ΔEa value. Four NADH/NAD+ systems were studied in acetonitrile but most of such heavy group vibrations do not appear to significantly increase the ΔEa. The remote heavy group vibrations in these systems may have not affected the CT complexation rigidity to a degree that can significantly increase the DADs, and further, the ΔEa values.

Adoption of an Enhanced Recovery After Surgery Protocol Increases Cost of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy and Does not Improve Outcomes.

BACKGROUND: Enhanced recovery after surgery (ERAS) protocols have been shown to reduce length of stay (LOS) and complications. The impact of ERAS protocols on the cost of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) has not been studied. PATIENTS AND METHODS: We performed a retrospective cohort analysis of patients undergoing CRS-HIPEC from 2016-2022 at a single quaternary center. Propensity score matching was used to create pre-and post-ERAS cohorts. Cost, overall and serious complications, and intensive care unit (ICU) length of stay (LOS) between the two cohorts were compared using the Mann-Whitney U-test for continuous variables and χ2 test for categorical variables. RESULTS: Our final matched cohort consisted of 100 patients, with 50 patients in both the pre- and post-ERAS groups. After adjusting for patient complexity and inflation, the median total cost [$75,932 ($67,166-102,645) versus $92,992 ($80,720-116,710), p = 0.02] and operating room cost [$26,817 ($23,378-33,121) versus $34,434 ($28,085-$41,379), p < 0.001] were significantly higher in the post-ERAS cohort. Overall morbidity (n = 22, 44% versus n = 17, 34%, p = 0.40) and ICU length of stay [2 days (IQR 1-3) versus 2 days (IQR 1-4), p = 0.70] were similar between the two cohorts. A total cost increase of $22,393 [SE $13,047, 95% CI (-$3178 to $47,965), p = 0.086] was estimated after implementation of ERAS, with operating room cost significantly contributing to this increase [$8419, SE $1628, 95% CI ($5228-11,609), p < 0.001]. CONCLUSIONS: CRS-HIPEC ERAS protocols were associated with higher total costs due to increased operating room costs at a single institution. There was no significant difference in ICU LOS and complications after the implementation of the ERAS protocol.

Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents.

Carbon dioxide (CO2) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO2 capture. Chemically reactive DESs, which form chemical bonds with the CO2, are superior to nonreactive, physically based DESs for CO2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO2 in chemically reactive DESs. As training data, we collected 214 data points for the CO2 solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.

Prevalence of H. pylori and Gastric Intestinal Metaplasia in BRCA1 and BRCA2 Carriers.

BRCA1 and BRCA2 carriers may be at increased risk for gastric cancer; however, the mechanisms of gastric carcinogenesis remain poorly understood. We sought to determine the prevalence of gastric cancer risk factors Helicobacter pylori (H. pylori) infection and gastric intestinal metaplasia (GIM) among BRCA1/2 carriers to gain insight into the pathogenesis of gastric cancer in this population. A total of 100 unselected BRCA1/2 carriers who underwent endoscopic ultrasound from March 2022 to March 2023 underwent concomitant upper endoscopy with nontargeted gastric antrum and body biopsies. The study population (70% women; mean age 60.1 years) included 66% BRCA2 carriers. H. pylori was detected in one (1%) individual, 7 (7%) had GIM, 2 (2%) had autoimmune atrophic gastritis, and no gastric cancers were diagnosed. Among BRCA1/2 carriers, H. pylori prevalence was low and GIM prevalence was similar to that in the general population; however, identification of H. pylori or GIM may help inform future gastric cancer risk management strategies in BRCA1/2 carriers. Prevention Relevance: Evaluating the burden of H. pylori infection and GIM among BRCA1/2 carriers is warranted to better understand the mechanisms of gastric carcinogenesis and to help inform risk management strategies for gastric cancer among this at-risk population.

Disruption of epithelium integrity by inflammation-associated fibroblasts through prostaglandin signaling.

Inflammation-associated fibroblasts (IAFs) are associated with progression and drug resistance of chronic inflammatory diseases such as inflammatory bowel disease (IBD), but their direct impact on epithelial cells is unknown. Here, we developed an in vitro model whereby human colon fibroblasts are induced by specific cytokines and recapitulate key features of IAFs in vivo. When cocultured with patient-derived colon organoids (colonoids), IAFs induced rapid colonoid expansion and barrier disruption due to swelling and rupture of individual epithelial cells. Colonoids cocultured with IAFs also show increased DNA damage, mitotic errors, and proliferation arrest. These IAF-induced epithelial defects are mediated by a paracrine pathway involving prostaglandin E2 and its receptor EP4, leading to protein kinase A -dependent activation of the cystic fibrosis transmembrane conductance regulator. EP4-specific chemical inhibitors effectively prevented IAF-induced colonoid swelling and restored normal proliferation and genome stability. These findings reveal a mechanism by which IAFs could promote and perpetuate IBD and suggest a therapeutic avenue to mitigate inflammation-associated epithelial injury.

Essential and virulence-related protein interactions of pathogens revealed through deep learning.

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

Perspective on Lignin Conversion Strategies That Enable Next Generation Biorefineries.

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.

First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE.

We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232). We measure a flux-integrated cross section for neutrino-induced η production on argon of 3.22±0.84(stat)±0.86(syst) 10^{-41} cm^{2}/nucleon. By demonstrating the successful reconstruction of the two photons resulting from η production, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments.