Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and characterization of new patient-derived xenograft (PDX) models of osteosarcoma with distinct metastatic capacities

Models to study metastatic disease in rare cancers are needed to advance preclinical therapeutics and to gain insight into disease biology, especially for highly aggressive cancers with a propensity for metastatic spread. Osteosarcoma is a rare cancer with a complex genomic landscape in which outcomes for patients with metastatic disease are poor. As osteosarcoma genomes are highly heterogeneous, a large panel of models is needed to fully elucidate key aspects of disease biology and to recapitulate clinically-relevant phenotypes. We describe the development and characterization of osteosarcoma patient-derived xenografts (PDXs) and a panel of PDX-derived cell lines. Matched patient samples, PDXs, and PDX-derived cell lines were comprehensively evaluated using whole genome sequencing and RNA sequencing. PDXs and PDX-derived cell lines largely maintained the expression profiles of the patient from which they were derived despite the emergence of whole-genome duplication (WGD) in a subset of cell lines. These cell line models were heterogeneous in their metastatic capacity and their tissue tropism as observed in both intravenous and orthotopic models. As proof-of-concept study, we used one of these models to test the preclinical effectiveness of a CDK inhibitor on the growth of metastatic tumors in an orthotopic amputation model. Single-agent dinaciclib was effective at dramatically reducing the metastatic burden in this model. Graphical Abstract
Wireless in vivo Recording of Cortical Activity by an Ion-Sensitive Field Effect Transistor

Wireless brain technologies are empowering basic neuroscience and clinical neurology by offering new platforms that minimize invasiveness and refine possibilities during electrophysiological recording and stimulation. Despite their advantages, most systems require on-board power supply and sizeable transmission circuitry, enforcing a lower bound for miniaturization. Designing new minimalistic architectures that can efficiently sense neurophysiological events will open the door to standalone microscale sensors and minimally invasive delivery of multiple sensors. Here we present a circuit for sensing ionic fluctuations in the brain by an ion-sensitive field effect transistor that detunes a single radiofrequency resonator in parallel. We establish sensitivity of the sensor by electromagnetic analysis and quantify response to ionic fluctuations in vitro. We validate this new architecture in vivo during hindpaw stimulation in rodents and verify correlation with local field potential recordings. This new approach can be implemented as an integrated circuit for wireless in situ recording of brain electrophysiology.
Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Congenital myasthenic syndromes are rare inherited neuromuscular disorders. Here, the authors attempt to explain diverse disease severity seen in 20 patients with shared CHRNE gene mutations with a multilayer network analysis that identifies individual-level impairments at the neuromuscular junction. Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.
Subtractive adaptation is a more effective and general mechanism in binocular rivalry than divisive adaptation

The activity of neurons is influenced by random fluctuations and can be strongly modulated by firing rate adaptation, especially in sensory systems. Still, there is an ongoing debate about the characteristics of neuronal noise and the mechanisms of adaptation, and even less is known about how exactly they affect perception. Noise and adaptation play central roles in binocular rivalry, a visual phenomenon where two images compete for perceptual dominance. Here, we investigated the effects of different noise processes and adaptation mechanisms on visual perception by simulating a model of binocular rivalry with Gaussian white noise, Ornstein-Uhlenbeck noise, and pink noise, in variants with divisive adaptation, subtractive adaptation, and without adaptation. By simulating the nine versions of the model for a wide range of parameter values, we find that white noise only produces rivalry when paired with subtractive adaptation and that subtractive adaptation reduces the influence of noise intensity on rivalry strength and introduces convergence of the mean percept duration, an important metric of binocular rivalry, across all noise processes. In sum, our results show that white noise is an insufficient description of background activity in the brain and that subtractive adaptation is a stronger and more general switching mechanism in binocular rivalry than divisive adaptation, with important noise-filtering properties. Author Summary Visual neurons adapt to the environment by reducing the number of spikes evoked by a constant stimulus. They are also susceptible to random spikes produced by nearby neurons. These two phenomena, adaptation and noise, are essential features of brain activity and affect how we perceive the world. Although we know a great deal about the visual system, our understanding of the properties and mechanisms of neuronal noise and adaptation is still piecemeal, and even less is known about how these microscopic processes affect macroscopic behaviors. We shed light on this question by studying a bistable visual phenomenon called binocular rivalry, where two images compete for perception and where noise and adaptation play important roles. We simulated the activity of neurons involved in binocular rivalry to test different hypotheses about the statistics of neuronal noise and the mechanisms of adaptation. Our results reveal important differences between subtractive and divisive adaptation, suggesting that subtractive adaptation is a stronger switching mechanism in binocular rivalry and an effective noise filter. Our simulations also show the fundamental distinction between noise with and without temporal correlation, supporting the correlated noise hypothesis.
The heme-responsive PrrH sRNA regulates Pseudomonas aeruginosa pyochelin gene expression

Pseudomonas aeruginosa is an opportunistic pathogen that requires iron for growth and virulence, yet this nutrient is sequestered by the innate immune system during infection. When iron is limiting, P. aeruginosa expresses the PrrF1 and PrrF2 small regulatory RNAs (sRNAs), which post-transcriptionally repress expression of non-essential iron-containing proteins thus sparing this nutrient for more critical processes. The genes for the PrrF1 and PrrF2 sRNAs are arranged in tandem on the chromosome, allowing for the transcription of a longer heme-responsive sRNA, termed PrrH. While the functions of PrrF1 and PrrF2 have been studied extensively, the role of PrrH in P. aeruginosa physiology and virulence is not well understood. In this study, we performed transcriptomic and proteomic studies to identify the PrrH regulon. In shaking cultures, the pyochelin synthesis proteins were increased in two distinct prrH mutants compared to wild type, while the mRNAs for these proteins were not affected by prrH mutation. We identified complementarity between the PrrH sRNA and sequence upstream of the pchE mRNA, suggesting potential for PrrH to directly regulate expression of genes for pyochelin synthesis. We further showed that pchE mRNA levels were increased in the prrH mutants when grown in static but not shaking conditions. Moreover, we discovered controlling for the presence of light was critical for examining the impact of PrrH on pchE expression. As such, our study reports on the first likely target of the PrrH sRNA and highlights key environmental variables that will allow for future characterization of PrrH function. Importance In the human host, iron is predominantly in the form of heme, which Pseudomonas aeruginosa can acquire as an iron source during infection. We previously showed that the iron-responsive PrrF sRNAs are critical for mediating iron homeostasis during P. aeruginosa infection; however the function of the heme-responsive PrrH sRNA remains unclear. In this study, we identified genes for pyochelin siderophore biosynthesis, which mediate uptake of inorganic iron, as a novel target of PrrH regulation. This study therefore highlights a novel relationship between heme availability and siderophore biosynthesis in P. aeruginosa.
Three-dimensional computation for flow around two circular cylinders in side-by-side arrangement by a third-order upwind finite element method

We present numerical results of flow around two circular cylinders in side-by-side arrangement by the three-dimensional computation using a third-order upwind finite element scheme. In the case of side-by-side arrangement of two circular cylinders, it has been known from experimental data that the strong interference of vortices behind the cylinders appears when the spacing between the cylinders is short. Consequently, the drag and lift forces acting on two circular cylinders in side-by-side arrangement, as well as two circular cylinders in tandem arrangement, vary according to the spacing between the cylinders. In addition, the drag forces of two kinds are obtained, when the spacing is short. In our computation, the Reynolds number, Re, is set as 1000. The obtained numerical results are qualitatively compared with experimental data.
mRNA decay can be uncoupled from deadenylation during stress response

The polyadenosine tail (pA-tail) regulates mRNA nuclear export, stability, and translatability. Based on reporter constructs, the prevailing model suggests that pA-tail removal mediated by Ccr4-NOT or PAN2/3 deadenylases is required for mRNA decapping and degradation. Here, we use direct RNA sequencing to track mRNA deadenylation and decay at steady-state and in stress conditions to show a global correlation between deadenylation and decay. Interestingly, codon optimality, previously postulated to dictate mRNA stability, only strongly affects decay of conserved and abundant transcripts, such as coding for ribosomal protein subunits. Degradation of those mRNAs is also accelerated in response to stress. Still, the in-depth analysis revealed that deadenylation is a factor that contributes to degradation but is not indispensable for decapping. We further demonstrate that deadenylation is the fastest for newly made tails depending on polyA-binding protein Pab1. Unexpectedly, decapping initiates on mRNAs of pA-tails of 20-35 adenosines presumably bound by Pab1.
Speech separation by means of stationary least-squares harmonic estimation

It is shown that stationary least-squares harmonic analysis and sinusoidal synthesis of speech can provide an efficient modeling tool for the problem of speech separation in voiced regions. For an input signal-to-signal ratio of -9 dB, the procedure yields an output signal-to-noise ratio ranging from 4.7 to 10.8 dB, which represents a gain of 13.7 to 19.8 dB. The interfering speech is not perceptible in the output, the only audible degradation being of tonal character. As in other pitch-based systems, the problem of pitch estimation is still a major limitation of the model. Experimental results confirm the improvement in speech separation over that previously reported in the literature. <<ETX>>
Mapping cortical excitability in the human dorsolateral prefrontal cortex

Objective To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and that EL-TEP amplitude is inversely related to muscle artifact. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach. Methods In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses. Results Stimulation location significantly influenced EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%. Significance Early local TMS-evoked potentials (EL-TEPs) can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols. Highlights Early local TMS-evoked potentials (EL-TEPs) varied significantly across the dlPFC as a function of TMS target. TMS targets with less muscle artifact had significantly larger EL-TEPs. Selection of a postero-medial target increased EL-TEPs by 102% compared to anterior targets.
Environmental Cell Thinning of Advanced Liquid Crystal Displays

A structured abrasive is applied to a thinly assembled liquid crystal display (LCD) cell in single-sided lapping. This effectively reduces the overall thickness and weight of the panels, thereby enhancing the aesthetics and ease of use of portable display devices. The technology enables an environmentally friendly means of mechanical thinning. Moreover, compared with conventional slurry lapping, it achieves a better surface roughness (0.24 µm), a higher cut rate (11.6–13.4 µm/min) and a lower operating pressure (45 g/cm2) for different specimens. Furthermore, it can be used to process 3.5G (610×720 mm2) glass substrates on a 1200-mm-diameter tool.
Onsager Principle and Electrorheological Fluid Dynamics

We present the formulation of a two-phase, electrical-hydrodynamic model for the description of electrorheological fluid dynamics, based on the Onsager principle of minimum energy dissipation. By considering the energetics of (induced) dipole-dipole interaction between the solid particles in terms of a field variable n(� x ), we employ the Onsager principle to derive the relevant coupled hydrodynamic equations, together with a continuity equation for n(� x ). Numerical solution of the relevant equations yields predictions that display very realistic behaviors as seen experimentally. In particular, we show that while the predicted results have features that resemble Bingham fluids, there can be important differences. For example, the yield stress obtained by extrapolating the shear rate to zero is 30-40% lower than that obtained from the maximum of the stress-strain relation. Moreover, for the conventional electrode configuration where the field is perpendicular to the shearing direction, there is very clear shearing-thinning effect that has been seen experimentally.
Comparison of force fields to study the zinc-finger containing protein NPL4, a target for Antabuse in cancer therapy

All-atom molecular dynamics (MD) simulations are a powerful approach to study the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging for MD simulations. Over one-third of known protein structures bind metal ions and have various cellular functions, such as structural stability, catalysis, and regulation. NPL4 is a zinc-binding protein and works as a cofactor for p97, and together they regulate protein homeostasis. NPL4 is also of biomedical importance and has been proposed as the target of Antabuse, a drug recently repurposed for cancer treatment. Recent experimental studies have proposed that the Antabuse metabolites, bis- (diethyldithiocarbamate)-copper (CuET) and cupric ions released from CuET, induce NPL4 misfolding and consequent aggregation. However, the molecular details of the mechanisms of interactions of Antabuse metabolites with NPL4 and the consequent structural effects are still elusive. In this context, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper or Antabuse metabolites the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We first examined different sets of non-bonded parameters, because we want to study the misfolding mechanism and cannot rule out that the zinc ion may detach from the protein structure during the process and copper replaces it in the metal binding site. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of the zinc coordination site for NPL4. Furthermore, we investigated the performance of a MD force field including bonded parameters to treat copper ions and metal-coordinating atoms in NPL4 that we obtained based on QM calculations.
StratoMod: Predicting sequencing and variant calling errors with interpretable machine learning

The structure of the genome, specifically repetitive and duplicated regions, leads to difficulty in accurately characterizing variation across a variety of sequencing technologies and variant detection methods. The Genome in a Bottle consortium generates variant benchmarks for DNA Reference Materials from seven human cell lines to reliably identify errors in comparison variant sets, as well as stratification files for error analysis in specific genomic regions. Current genome stratifications include discrete bins of homopolymer size, tandem repeat size, mappability, and segmental duplications focusing on regions subject to bias in sequencing technologies and/or variant calling methods. To move to a data-driven approach for genome stratification development and interrogation of variant calling errors, we developed a stratification model - StratoMod. StratoMod is built with an interpretable machine learning classifier, Explainable Boosting Machines (EBMs), to predict variant calling errors from features derived from genomic and sequencing data. EBMs quantify univariate effects and pairwise interactions, which facilitates understanding how each feature contributes to a prediction. Here, we describe the design, training, testing, and application of StratoMod, including demonstrating its ability to identify characteristics of sequencing reads and repetitive genome regions that are likely to generate variant calling errors. Specifically, StratoMod identified distinct associations with errors for A/T vs. G/C homopolymer lengths, and quantified sources of error for a new sequencing technology. We also demonstrated that the model could predict clinically-relevant variants that may be missed by certain methods, using DeepVariant calls from Illumina as an example. For this example, we produced a resource of difficult-to-map genes with challenging variants and large challenging INDELs. Using this model with GIAB benchmarks facilitates a deeper quantitative understanding of sources of variant calling errors from a wide variety of methods.
Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction

We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiomebased human host phenotype prediction. The prior-knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully-connected neural network based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in six out of the seven cases. More importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Our results suggested that the metabolic potential of the bacterial species contributed more than the two other sources of prior knowledge. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.
Ultrastructural identification and molecular characterization of two new parabasalid species that naturally colonize laboratory mice, Tritrichomonas musculus and Tritrichomonas casperi

Tritrichomonas muris is a flagellated protist isolated from the cecum of wild mice in the Czech Republic. This commensal protist has been shown previously to alter immune phenotypes in laboratory mice. Other trichomonads, previously referred to as Tritrichomonas musculis and Tritrichomonas rainier, also naturally colonize laboratory mice and cause immune alterations. This report formally describes two new trichomonads, Tritrichomonas musculus n. sp. , and Tritrichomonas casperi n. sp. , at the ultrastructural and molecular level. These two protists were isolated from laboratory mice, and were differentiated by their size and the structure of their undulating membrane and posterior flagellum. Analysis at the 18S rRNA and trans-ITS genetic loci supported their designation as distinct species, related to T. muris. To further assess the true extent of parabasalid diversity infecting laboratory mice, 135 mice were screened at the NIH using pan-parabasalid primers that amplify the trans-ITS region. Forty-four percent of mice were positive for parabasalids, encompassing a total of 8 distinct sequence types. Tritrichomonas casperi and Trichomitus-like protists were dominant. T. musculus and T. rainier were also detected, but T. muris was not. Our work establishes a previously underappreciated diversity of commensal trichomonad protists that naturally colonize the enteric cavity of laboratory mice.
Ordering ordered parameters

A stepwise multiple comparison method similar to the Newman-Keuls method is proposed for the case where it is known a priori that the parameters are ordered. It is shown how the significance levels at the various steps can be chosen to keep the probability of at least one false rejection less than a given upper bound. The method can be used for both continuous and discrete distributions since it is essentially based upon tests ordering any two of the parameters.