Nkd1 functions downstream of Axin2 to attenuate Wnt signaling.

Wnt signaling is a crucial developmental pathway involved in early development as well as stem-cell maintenance in adults and its misregulation leads to numerous diseases. Thus, understanding the regulation of this pathway becomes vitally important. Axin2 and Nkd1 are widely utilized negative feedback regulators in Wnt signaling where Axin2 functions to destabilize cytoplasmic ß-catenin, and Nkd1 functions to inhibit the nuclear localization of ß-catenin. Here, we set out to further understand how Axin2 and Nkd1 regulate Wnt signaling by creating axin2gh1/gh1, nkd1gh2/gh2 single mutants and axin2gh1/gh1;nkd1gh2/gh2 double mutant zebrafish using sgRNA/Cas9. All three Wnt regulator mutants were viable and had impaired heart looping, neuromast migration defects, and behavior abnormalities in common, but there were no signs of synergy in the axin2gh1/gh1;nkd1gh2/gh2 double mutants. Further, Wnt target gene expression by qRT-PCR and RNA-seq, and protein expression by mass spectrometry demonstrated that the double axin2gh1/gh1;nkd1gh2/gh2 mutant resembled the nkd1gh2/gh2 phenotype demonstrating that Nkd1 functions downstream of Axin2. In support of this, the data further demonstrates that Axin2 uniquely alters the properties of ß-catenin-dependent transcription having novel readouts of Wnt activity compared with nkd1gh2/gh2 or the axin2gh1/gh1;nkd1gh2/gh2 double mutant. We also investigated the sensitivity of the Wnt regulator mutants to exacerbated Wnt signaling, where the single mutants displayed characteristic heightened Wnt sensitivity, resulting in an eyeless phenotype. Surprisingly, this phenotype was rescued in the double mutant, where we speculate that cross-talk between Wnt/ß-catenin and Wnt/Planar Cell Polarity pathways could lead to altered Wnt signaling in some scenarios. Collectively, the data emphasizes both the commonality and the complexity in the feedback regulation of Wnt signaling.

Influence of repulsion on entropy scaling and density scaling of monatomic fluids.

Entropy scaling is applied to the shear viscosity, self-diffusion coefficient, and thermal conductivity of simple monatomic fluids. An extensive molecular dynamics simulation series is performed to obtain these transport properties and the residual entropy of three potential model classes with variable repulsive exponents: n, 6 Mie (n = 9, 12, 15, and 18), Buckingham's exponential-six (α = 12, 14, 18, and 30), and Tang-Toennies (αT = 4.051, 4.275, and 4.600). A wide range of liquid and supercritical gas- and liquid-like states is covered with a total of 1120 state points. Comparisons to equations of state, literature data, and transport property correlations are made. Although the absolute transport property values within a given potential model class may strongly depend on the repulsive exponent, it is found that the repulsive steepness plays a negligible role when entropy scaling is applied. Hence, the plus-scaled transport properties of n, 6 Mie, exponential-six, and Tang-Toennies fluids lie basically on one master curve, which closely corresponds with entropy scaling correlations for the Lennard-Jones fluid. This trend is confirmed by literature data of n, 6 Mie, and exponential-six fluids. Furthermore, entropy scaling holds for state points where the Pearson correlation coefficient R is well below 0.9. The condition R > 0.9 for strongly correlating liquids is thus not necessary for the successful application of entropy scaling, pointing out that isomorph theory may be a part of a more general framework that is behind the success of entropy scaling. Density scaling reveals a strong influence of the repulsive exponent on this particular approach.

Prevalence of SARS-CoV-2 Infection among Children and Adults in 15 US Communities, 2021

During January-August 2021, the Community Prevalence of SARS-CoV-2 Study used time/location sampling to recruit a cross-sectional, population-based cohort to estimate SARS-CoV-2 seroprevalence and nasal swab sample PCR positivity across 15 US communities. Survey-weighted estimates of SARS-CoV-2 infection and vaccine willingness among participants at each site were compared within demographic groups by using linear regression models with inverse variance weighting. Among 22,284 persons >2 months of age and older, median prevalence of infection (prior, active, or both) was 12.9% across sites and similar across age groups. Within each site, average prevalence of infection was 3 percentage points higher for Black than White persons and average vaccine willingness was 10 percentage points lower for Black than White persons and 7 percentage points lower for Black persons than for persons in other racial groups. The higher prevalence of SARS-CoV-2 infection among groups with lower vaccine willingness highlights the disparate effect of COVID-19 and its complications.

Pharmacologic characterization of atogepant: A potent and selective calcitonin gene-related peptide receptor antagonist.

BACKGROUND: The trigeminal sensory neuropeptide calcitonin gene-related peptide (CGRP) is identified as an essential element in migraine pathogenesis. METHODS: In vitro and in vivo studies evaluated pharmacologic properties of the CGRP receptor antagonist atogepant. Radioligand binding using 125I-CGRP and cyclic adenosine monophosphate (cAMP) accumulation assays were conducted in human embryonic kidney 293 cells to assess affinity, functional potency and selectivity. Atogepant in vivo potency was assessed in the rat nitroglycerine model of facial allodynia and primate capsaicin-induced dermal vasodilation (CIDV) pharmacodynamic model. Cerebrospinal fluid/brain penetration and behavioral effects of chronic dosing and upon withdrawal were evaluated in rats. RESULTS: Atogepant exhibited high human CGRP receptor-binding affinity and potently inhibited human α-CGRP-stimulated cAMP responses. Atogepant exhibited significant affinity for the amylin1 receptor but lacked appreciable affinities for adrenomedullin, calcitonin and other known neurotransmitter receptor targets. Atogepant dose-dependently inhibited facial allodynia in the rat nitroglycerine model and produced significant CIDV inhibition in primates. Brain penetration and behavioral/physical signs during chronic dosing and abrupt withdrawal were minimal in rats. CONCLUSIONS: Atogepant is a competitive antagonist with high affinity, potency and selectivity for the human CGRP receptor. Atogepant demonstrated a potent, concentration-dependent exposure/efficacy relationship between atogepant plasma concentrations and inhibition of CGRP-dependent effects.

Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer.

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis-linear discriminant analysis (PCA-LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA-LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.

Entropy scaling of viscosity for molecular models of molten salts.

Entropy scaling relates dynamic and thermodynamic properties by reducing the viscosity to a function of only the residual entropy. Molecular simulations are used to investigate the entropy scaling of the viscosity of three models of sodium chloride and five monovalent salts. Even though the correlation between the potential energy and the virial is weak, entropy scaling applies at liquid densities for all models and salts investigated. At lower densities, entropy scaling breaks down due to the formation of ion pairs and chains. Entropy scaling can be used to develop more extendable correlations for the dynamic properties of molten salts.

An Analysis of the Critical Region of Multiparameter Equations of State.

In this work, two classes of defects with multiparameter equations of state are investigated. In the first, it is shown that the critical point provided by equation of state developers often does not exactly meet the criticality conditions based on the first two density derivatives of the pressure being zero at the critical point. Based on the more accurate locations of the critical points given in the first part, the scaling of the densities along the binodal and spinodal in the critical region are investigated, and we find that the vast majority of equations have reasonable behavior but a few do not.

Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.

Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length2/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.

The NIST REFPROP Database for Highly Accurate Properties of Industrially Important Fluids.

The NIST REFPROP software program is a powerful tool for calculating thermophysical properties of industrially important fluids, and this manuscript describes the models implemented in, and features of, this software. REFPROP implements the most accurate models available for selected pure fluids and their mixtures that are valid over the entire fluid range including gas, liquid, and supercritical states, with the goal of uncertainties approaching the level of the underlying experimental data. The equations of state for thermodynamic properties are primarily of the Helmholtz energy form; a variety of models are implemented for the transport properties. We document the models for the 147 fluids included in the current version. A graphical user interface generates tables and provides extensive plotting capabilities. Properties can also be accessed through third-party apps or user-written code via the core property subroutines compiled into a shared library. REFPROP disseminates international standards in both the natural gas and refrigeration industries, as well as standards for water/steam.

Connecting entropy scaling and density scaling.

It is shown that the residual entropy (entropy minus that of the ideal gas at the same temperature and density) is mostly synonymous with the independent variable of density scaling, identifying a direct link between these two approaches. The residual entropy and the effective hardness of interaction (itself a derivative at constant residual entropy) are studied for the Lennard-Jones monomer and dimer as well as a range of rigid molecular models for carbon dioxide. It is observed that the density scaling exponent appears to be related to the two-body interactions in the dilute-gas limit.

Gut-derived metabolites influence neurodevelopmental gene expression and Wnt signaling events in a germ-free zebrafish model.

BACKGROUND: Small molecule metabolites produced by the microbiome are known to be neuroactive and are capable of directly impacting the brain and central nervous system, yet there is little data on the contribution of these metabolites to the earliest stages of neural development and neural gene expression. Here, we explore the impact of deriving zebrafish embryos in the absence of microbes on early neural development as well as investigate whether any potential changes can be rescued with treatment of metabolites derived from the zebrafish gut microbiota. RESULTS: Overall, we did not observe any gross morphological changes between treatments but did observe a significant decrease in neural gene expression in embryos raised germ-free, which was rescued with the addition of zebrafish metabolites. Specifically, we identified 354 genes significantly downregulated in germ-free embryos compared to conventionally raised embryos via RNA-Seq analysis. Of these, 42 were rescued with a single treatment of zebrafish gut-derived metabolites to germ-free embryos. Gene ontology analysis revealed that these genes are involved in prominent neurodevelopmental pathways including transcriptional regulation and Wnt signaling. Consistent with the ontology analysis, we found alterations in the development of Wnt dependent events which was rescued in the germ-free embryos treated with metabolites. CONCLUSIONS: These findings demonstrate that gut-derived metabolites are in part responsible for regulating critical signaling pathways in the brain, especially during neural development. Video abstract.

Unraveling the Chemosensing Mechanism by the 7-(Diethylamino)coumarin-hemicyanine Hybrid: A Ratiometric Fluorescent Probe for Hydrogen Peroxide.

The hemicyanine hybrid containing the 7-(diethylamino)coumarin (ACou) donor attached to the cationic indolenium (Ind) acceptor through a vinyl linkage (ACou-Ind) represents a classic ratiometric fluorescent probe for detecting nucleophilic analytes, such as cyanide and reactive sulfur species (RSS), through addition reactions that disrupt dye conjugation to turn off red internal charge transfer (ICT) fluorescence and turn on blue coumarin emission. The chemosensing mechanism for RSS detection by ACou-Ind suggested in the literature has now been revised. Our studies demonstrate that thiolates react with ACou-Ind through conjugate addition to afford C4-SR adducts that lack coumarin fluorescence due to photoinduced electron transfer quenching by the electron-rich enamine intermediate. Thus, ACou-Ind serves as a turn-off probe through loss of red ICT fluorescence upon RSS addition. The literature also suggests that blue coumarin emission of thiolate adducts is enhanced in the presence of reactive oxygen species (ROS) due to ROS-mediated cellular changes. Our studies predict that such a scenario is unlikely and that thiolate adducts undergo oxidative deconjugation in the presence of H2O2, the pervasive ROS. Under basic conditions, H2O2 also reacts directly with ACou-Ind to generate intense coumarin fluorescence through an epoxidation process. The relevance of our chemosensing mechanism for ACou-Ind was assessed within live zebrafish, and implications for the utility of ACou-Ind for unraveling the interplay between RSS and ROS are discussed.

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature.

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants.

A total of 2160 thermal conductivity data points, measured using a transient hot-wire instrument, are reported for binary mixtures of R-134a, R-1234yf, and R-1234ze(E) refrigerants from 200 to 340 K to pressures of 12 MPa for mixtures containing R-1234yf and to 50 MPa for R-134a/1234ze(E) mixtures. Data are reported at compositions of approximately (0.33/0.67) mole fraction and (0.67/0.33) mole fraction for each binary mixture investigated. The estimated relative expanded uncertainty of the thermal conductivity measurements is less than 2%. The data are used to refit binary interaction parameters for the Extended Corresponding States (ECS) model implemented in REFPROP (version 10.0). Additionally, the data in this study are used to assess the performance of a generalized entropy scaling model for refrigerants. Finally, the strengths and weaknesses of the ECS and entropy scaling models are compared.

Bridging the gap between non-canonical and canonical Wnt signaling through Vangl2.

Non-canonical Wnt signaling (encompassing Wnt/PCP and WntCa2+) has a dual identity in the literature. One stream of research investigates its role in antagonizing canonical Wnt/ß-catenin signaling in cancer, typically through Ca2+, while the other stream investigates its effect on polarity in development, typically through Vangl2. Rarely do these topics intersect or overlap. What has become clear is that Wnt5a can mobilize intracellular calcium stores to inhibit Wnt/ß-catenin in cancer cells but there is no evidence that Vangl2 is involved in this process. Conversely, Wnt5a can independently activate Vangl2 to affect polarity and migration but the role of calcium in this process is also limited. Further, Vangl2 has also been implicated in inhibiting Wnt/ß-catenin signaling in development. The consensus is that a cell can differentiate between canonical and non-canonical Wnt signaling when presented with a choice, always choosing non-canonical at the expense of canonical Wnt signaling. However, these are rare events in vivo. Given the shared resources between non-canonical and canonical Wnt signaling it is perplexing that there is not more in vivo evidence for cross talk between these two pathways. In this review we discuss the intersection of non-canonical Wnt, with a focus on Wnt/PCP, and Wnt/ß-catenin signaling in an attempt to shed some light on pathways that rarely meet at a crossroads in vivo.

Dynamic Crossover in Fluids: From Hard Spheres to Molecules.

We propose a simple and generic definition of a demarcation reconciling structural and dynamic frameworks when combined with the entropy scaling framework. This crossover line between gas- and liquid-like behaviors is defined as the curve for which an individual property, the contribution to viscosity due to molecules' translation, is exactly equal to a collective property, the contribution to viscosity due to molecular interactions. Such a definition is shown to be consistent with the one based on the minima of the kinematic viscosity. For the hard sphere, this is shown to be an exact solution. For Lennard-Jones spheres and dimers and for some simple real fluids, this relation holds very well. This crossover line passes nearby the critical point, and for all studied fluids, it is well captured by the critical excess entropy curve for atomic fluids, emphasizing the link between transport properties and local structure.

Extreme rainfall events and cooling of sea turtle clutches: Implications in the face of climate warming.

Understanding how climate change impacts species and ecosystems is integral to conservation. When studying impacts of climate change, warming temperatures are a research focus, with much less attention given to extreme weather events and their impacts. Here, we show how localized, extreme rainfall events can have a major impact on a species that is endangered in many parts of its range. We report incubation temperatures from the world's largest green sea turtle rookery, during a breeding season when two extreme rainfall events occurred. Rainfall caused nest temperatures to drop suddenly and the maximum drop in temperature for each rain-induced cooling averaged 3.6°C (n = 79 nests, min = 1.0°C, max = 7.4°C). Since green sea turtles have temperature-dependent sex determination, with low incubation temperatures producing males, such major rainfall events may have a masculinization effect on primary sex ratios. Therefore, in some cases, extreme rainfall events may provide a "get-out-of-jail-free card" to avoid complete feminization of turtle populations as climate warming continues.

Discovery of an Anion-Dependent Farnesyltransferase Inhibitor from a Phenotypic Screen.

By employing a phenotypic screen, a set of compounds, exemplified by 1, were identified which potentiate the ability of histone deacetylase inhibitor vorinostat to reverse HIV latency. Proteome enrichment followed by quantitative mass spectrometric analysis employing a modified analogue of 1 as affinity bait identified farnesyl transferase (FTase) as the primary interacting protein in cell lysates. This ligand-FTase binding interaction was confirmed via X-ray crystallography and temperature dependent fluorescence studies, despite 1 lacking structural and binding similarity to known FTase inhibitors. Although multiple lines of evidence established the binding interaction, these ligands exhibited minimal inhibitory activity in a cell-free biochemical FTase inhibition assay. Subsequent modification of the biochemical assay by increasing anion concentration demonstrated FTase inhibitory activity in this novel class. We propose 1 binds together with the anion in the active site to inhibit farnesyl transferase. Implications for phenotypic screening deconvolution and HIV reactivation are discussed.