Simple aneuploidy evades p53 surveillance and promotes niche factor-independent growth in human intestinal organoids.

Aneuploidy is nearly ubiquitous in tumor genomes, but the role of aneuploidy in the early stages of cancer evolution remains unclear. Here, by inducing heterogeneous aneuploidy in non-transformed human colon organoids (colonoids), we investigated how the effects of aneuploidy on cell growth and differentiation may promote malignant transformation. Previous work implicated p53 activation as a downstream response to aneuploidy induction. We found that simple aneuploidy, characterized by 1-3 gained or lost chromosomes, resulted in little or modest p53 activation and cell cycle arrest when compared with more complex aneuploid cells. Single-cell RNA sequencing analysis revealed that the degree of p53 activation was strongly correlated with karyotype complexity. Single-cell tracking showed that cells could continue to divide despite the observation of one to a few lagging chromosomes. Unexpectedly, colonoids with simple aneuploidy exhibited impaired differentiation after niche factor withdrawal. These findings demonstrate that simple aneuploid cells can escape p53 surveillance and may contribute to niche factor-independent growth of cancer-initiating colon stem cells. [Media: see text].

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.

The psychology of precarity: A critical framework.

This article presents the rationale and a new critical framework for precarity, which reflects a psychosocial concept that links structural inequities with experiences of alienation, anomie, and uncertainty. Emerging from multiple disciplines, including anthropology, cultural studies, sociology, political science, and psychology, the concept of precarity provides a conceptual scaffolding for understanding the complex causes of precarious life circumstances while also seeking to identify how people react, adapt, and resist the forces that evoke such tenuous psychosocial experiences. We present a critical conceptual framework as a nonlinear heuristic that serves to identify and organize relevant elements of precarity in a presumably infinite number of contexts and applications. The framework identifies socio-political-economic contexts, material conditions, and psychological experiences as key elements of precarity. Another essential aspect of this framework is the delineation of interrelated and nonlinear responses to precarity, which include resistance, adaptation, and resignation. We then summarize selected implications of precarity for psychological interventions, vocational and organizational psychology, and explorations and advocacy about race, gender, and other systems of inequality. Future research directions, including optimal methodologies to study precarity, conclude the article. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Sustainable production of 2,3,5,6-tetramethylpyrazine at high titer in engineered Corynebacterium glutamicum.

The industrial amino acid production workhorse, Corynebacterium glutamicum naturally produces low levels of 2,3,5,6-tetramethylpyrazine (TMP), a valuable flavor, fragrance and commodity chemical. Here we demonstrate TMP production (∼0.8 ​g L-1) in C. glutamicum type strain ATCC13032 via overexpression of acetolactate synthase and/or alpha-acetolactate decarboxylase from Lactococcus lactis in CGXII minimal medium supplemented with 40 g L-1 glucose. This engineered strain also demonstrated growth and TMP production when the minimal medium was supplemented with up to 40% (v v-1) hydrolysates derived from ionic liquid pretreated sorghum biomass. A key objective was to take the fully engineered strain developed in this study and interrogate media parameters that influence the production of TMP, a critical post strain engineering optimization. Design of experiments in a high throughput plate format identified glucose, urea and their ratio as significant components affecting TMP production. These two components were further optimized using response surface methodology. In the optimized CGXII medium, the engineered strain could produce up to 3.56 g L-1 TMP (4-fold enhancement in titers and 2-fold enhancement in yield, mol mol-1) from 80 g L-1 glucose and 11.9 g L-1 urea in shake flask batch cultivation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Is there evidence for factorial invariance of the COVID Stress Scales? an analysis of North American and cross-cultural populations.

The COVID-19 pandemic impacted the mental health of more citizens globally than any previous modern viral outbreak. In response to the psychological challenges associated with COVID-19, the COVID Stress Scales (CSS) were developed to assess the presence and severity of COVID-related distress. The initial North American validation study of the CSS identified that the scale comprised five factors: danger and contamination fears, fear of socioeconomic consequences, xenophobia, checking and reassurance seeking, and traumatic stress symptoms. The CSS have since been validated across a multitude of international populations. However, findings support a five- and six-factor model. Methodological issues make interpreting most studies supporting a five-factor model challenging. The purpose of this study was to re-evaluate the factor structure of the CSS using data from North American samples, to assess for potential factorial invariance, and compare these results to cross-cultural findings. Multiple confirmatory factor analyses (mCFA) were conducted across 28 different groups (e.g., age, ethnicity/race, sex) from two large independent North American samples from 2020 (n = 6827) and 2021 (n = 5787), assessing the fit indices of the five-, six-, and alternative-factor model of the CSS. The current results provide evidence for factorial invariance of the six-factor model of the CSS across different North American demographics and highlight potential challenges in interpreting the results of studies that have supported a five-factor model of the CSS.