Miracle-dz/UniFAR-FTUs huggingface dataset:
Class Groups of Number Fields: Numerical Heuristics

Extending previous work of H. W. Lenstra, Jr. and the first author, we give quantitative conjectures for the statistical behavior of class groups and class numbers for every type of field of degree less than or equal to four (given the signature and the Galois group of the Galois closure). The theoretical justifications for these conjectures will appear elsewhere, but the agreement with the existing tables is quite good. 1. Introduction and Notations. In (3), H. W. Lenstra, Jr., and the first author developed a method for conjecturing quantitative results on class groups of quadratic fields and cyclic extensions of prime degree. In a forthcoming paper (4) we shall show that this technique can be extended to a much wider class of number fields, and also to relative extensions. The aim of the present paper is to rapidly make available the numerical conjec- tures obtained, for people not really interested in our heuristic reasoning or not wanting to wait for (4) to appear. Hence, apart from a total lack of justifications for the conjectures that we present, this paper is essentially self-contained. The plan is as follows. In the rest of this section we present the notations used in the sequel. Some of them being nonstandard (and in general differing from the notations of (3)), we urge the reader to read the notations carefully before applying the conjectures. In the next section we present templates for the subsequent conjectures, and then the conjectures themselves, illustrated by numerical examples, first for their own sake, and second as a double check for the reader to understand the templates. These conjectures are given for all types of fields of degree less than or equal to four. In the final section we comment on the consistency of the conjectures with existing tables (which is quite good). Combinatorial Notations: * If X is a set, {XI denotes its cardinality.
DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allelespecific methylation in 48 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that time- and tissue-specific patterns of development are more strongly correlated with methylation entropy than with mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.
Inflammatory Synovial Fibroblast Culture in 3D Systems: A Comparative Transcriptomic and Functional Study

Inflammation is essential for responding to infections and subsequent tissue healing. However, chronic unresolved inflammation can become a serious health problem, as exemplified in the joints during Rheumatoid Arthritis (RA). Why does inflammation persist in RA? The answer could lie with synovial fibroblasts, non-haemopoietic cells that can adopt a pathogenic phenotype that fuels disease progression for years. Critically, targeting local fibroblasts could stop joint inflammation without suppressing systemic immunity. Nevertheless, basic research findings have not been translated to new drugs, perhaps because non-physiological data can be inadvertently generated in 2D cultures. Thus, developing better in vitro platforms is an urgent need in biomedical research. In this work, we sought to understand how distinct 3D environments affect fibroblast-mediated inflammation. Arthritic synovial fibroblasts were expanded and cultured in 2D, 3D rigid scaffolds and engineered hydrogels. The results reveal that SFs are plastic and adopt inflammatory or remission-like phenotypes in response to their surroundings. This work identifies new directions to develop better models for drug testing, and even signposts candidate mechanisms by which to rewire destructive SFs.
Topology optimization of compliant circular path mechanisms based on an aggregated linear system and singular value decomposition

This paper proposes a topology optimization method for the design of compliant circular path mechanisms, or compliant mechanisms having a set of output displacement vectors with a constant norm, which is induced by a given set of input forces. To perform the optimization, a simple linear system composed of an input force vector, an output displacement vector and a matrix connecting them is constructed in the context of a discretized linear elasticity problem using FEM. By adding two constraints: 1, the dimensions of the input and the output vectors are equal; 2, the Euclidean norms of all local input force vectors are constant; from the singular value decomposition of the matrix connecting the input force vector and the output displacement vector, the optimization problem, which specifies and equalizes the norms of all output vectors, is formulated. It is a minimization problem of the weighted summation of the condition number of the matrix and the least square error of the second singular value and the specified value. This methodology is implemented as a topology optimization problem using the solid isotropic material with penalization method, sensitivity analysis and method of moving asymptotes. The numerical examples illustrate mechanically reasonable compliant circular path mechanisms and other mechanisms having multiple outputs with a constant norm. Copyright © 2011 John Wiley & Sons, Ltd.
Identification and Elimination of Antifungal Tolerance in Candida auris

Antimicrobial resistance is a global health crisis to which pathogenic fungi make a substantial contribution. The human fungal pathogen C. auris is of particular concern due to its rapid spread across the world and its evolution of multidrug resistance. Fluconazole failure in C. auris has been recently attributed to antifungal “tolerance”. Tolerance is a phenomenon whereby a slow growing subpopulation of tolerant cells, which are genetically identical to susceptible cells, emerges during drug treatment. We use microbroth dilution and disk diffusion assays together with image analysis to investigate antifungal tolerance in C. auris to all three classes of antifungal drugs used to treat invasive candidiasis. We find that 1) C. auris is tolerant to several common fungistatic and fungicidal drugs, which in some cases can be visually detected after 24 hours, as well as after 48 hours, of antifungal drug exposure; 2) the tolerant phenotype reverts to the susceptible phenotype in C. auris; and 3) combining azole, polyene, and echinocandin antifungal drugs with the adjuvant chloroquine reduces or eliminates tolerance and resistance in patient-derived C. auris isolates. These results suggest that tolerance contributes to treatment failure in C. auris infections for a broad range of antifungal drugs and that antifungal adjuvants may improve treatment outcomes for patients infected with antifungal-tolerant or antifungal-resistant fungal pathogens.
Deep learning-enabled design of synthetic orthologs of a signaling protein

Evolution-based deep generative models represent an exciting direction in understanding and designing proteins. An open question is whether such models can represent the constraints underlying specialized functions that are necessary for organismal fitness in specific biological contexts. Here, we examine the ability of three different models to produce synthetic versions of SH3 domains that can support function in a yeast stress signaling pathway. Using a select-seq assay, we show that one form of a variational autoencoder (VAE) recapitulates the functional characteristics of natural SH3 domains and classifies fungal SH3 homologs hierarchically by function and phylogeny. Locality in the latent space of the model predicts and extends the function of natural orthologs and exposes amino acid constraints distributed near and far from the SH3 ligand-binding site. The ability of deep generative models to specify orthologous function in vivo opens new avenues for probing and engineering protein function in specific cellular environments.
Upper limb rehabilitation after stroke: constraint versus intensive training. A longitudinal case-control study correlating motor performance with fMRI data

Background The reproducible beneficial effect of constraint-induced movement therapy (CIMT) in hemiparetic stroke patients makes it a good model to study brain plasticity during rehabilitation procedures. Objective Assess the functional brain reorganization induced by each of the two components of CIMT: (i) non-affected upper-limb constraint and (ii) intensive training of the paretic arm. Methods Brain activity of a right hemiparetic chronic stroke patient and of 10 healthy controls was recorded with a functional magnetic resonance imaging (fMRI) during a finger opposition task. For the patient, a total of 8 assessments were performed, before and after each component of CIMT. At each time point, brain activity during movement was compared with rest. Patient’s results were first compared to the control group and then correlated to motor performance across sessions. Results Constraint-therapy-related improvement was correlated with a decrease of cerebral activity in sensory-motor regions of both the affected and the non-affected hemispheres. Intensive-therapy-related improvement was correlated with the recruitment of pre-motor cortices and cerebellum in both hemispheres. Conclusions Two different patterns of brain activity underlie the effects of intensive training and constraint which could account for the respective effect of each component of the therapy.
A high-level programming language for generative protein design

Combining a basic set of building blocks into more complex forms is a universal design principle. Most protein designs have proceeded from a manual bottom-up approach using parts created by nature, but top-down design of proteins is fundamentally hard due to biological complexity. We demonstrate how the modularity and programmability long sought for protein design can be realized through generative artificial intelligence. Advanced protein language models demonstrate emergent learning of atomic resolution structure and protein design principles. We leverage these developments to enable the programmable design of de novo protein sequences and structures of high complexity. First, we describe a high-level programming language based on modular building blocks that allows a designer to easily compose a set of desired properties. We then develop an energy-based generative model, built on atomic resolution structure prediction with a language model, that realizes all-atom structure designs that have the programmed properties. Designing a diverse set of specifications, including constraints on atomic coordinates, secondary structure, symmetry, and multimerization, demonstrates the generality and controllability of the approach. Enumerating constraints at increasing levels of hierarchical complexity shows that the approach can access a combinatorially large design space.
Dorsal raphe stimulation relays a reward signal to the ventral tegmental area via GluN2C NMDA receptors

Background Glutamate relays a reward signal from the dorsal raphe (DR) to the ventral tegmental area (VTA). However, the role of the different subtypes of N-methyl-D-aspartate (NMDA) receptors is complex and not clearly understood. Therefore, we measured NMDA receptors subunits expression in limbic brain areas. In addition, we studied the effects of VTA down-regulation of GluN2C NMDA receptor on the reward signal that arises from DR electrical stimulation. Methods Using qPCR, we identified the relative composition of the different Grin2a-d subunits of the NMDA receptors in several brain areas. Then, we used fluorescent in situ hybridization (FISH) to evaluate the colocalization of Grin2c and tyrosine hydroxylase (TH) mRNA in VTA neurons. To assess the role of GluN2C in brain stimulation reward; we downregulated this receptor using small interfering RNA (siRNA) in rats self-stimulating for electrical pulses delivered to the DR. To delineate further the specific role of GluN2C in relaying the reward signal, we pharmacologically altered the function of VTA NMDA receptors by bilaterally microinjecting the NMDA receptor antagonist PPPA. Results We identified GluN2C as the most abundant subunit of the NMDA receptor expressed in the VTA. FISH revealed that about 50% of TH-positive neurons colocalize with Grin2c transcript. siRNA manipulation produced a selective down-regulation of the GluN2C protein subunit and a significant reduction in brain stimulation reward. Interestingly, PPPA enhanced brain stimulation reward, but only in rats that received the nonactive RNA sequence. Conclusion The present results suggest that VTA glutamate neurotransmission relays a reward signal initiated by DR stimulation by acting on GluN2C NMDA receptors. Highlights With the use of qpCR we identified the NMDA receptor composition in different brain areas Using Double-fluorescent in situ hybridization we demonstrated that TH+ cells contain the NMDA Glun2C subunit Using deep brain stimulation of the dorsal raphe and small interfering RNA we demonstrate that the reward signal is carried to VTA neurons through activation of Glun2C NMDA receptors.
Optical modulators and beam steering based on electrically tunable plasmonic material

Abstract. Surface plasmon based photonic (or plasmonic) circuits merge electronics and photonics at the nanoscale, creating the ability to combine the superior technical advantages of photonics and electronics on the same chip. Recent work has demonstrated their remarkable applications in subwavelength optics, data storage and transmission, light harvesting and generation, and microscopy, as well as bioapplications. Plasmonics has become one of the most intensive research subjects in recent years, and much effort has been made to develop novel and efficient waveguiding structures and plasmonic materials. We will first review some major progress in subwavelength plasmonic waveguides and plasmonic materials. Then, focusing on the applications of a class of promising alternative plasmonic materials, transparent conducting oxides, we will introduce some of our up-to-date study, especially on electro-absorption modulators and beam steering.
Large-eddy simulation of passive scalar dispersion in an urban-like canopy

Abstract Results from large-eddy simulations of short-range dispersion of a passive scalar from a point source release in an urban-like canopy are presented. The computational domain is that of a variable height array of buildings immersed in a pressure-driven, turbulent flow with a roughness Reynolds number ${\mathit{Re}}_{\tau } = 433$ . A comparative study of several cases shows the changes in plume behaviour for different mean flow directions and source locations. The analysis of the results focuses on utilizing the high-fidelity datasets to examine the three-dimensional flow field and scalar plume structure. The detailed solution of the flow and scalar fields within the canopy allows for a direct assessment of the impact of local features of the building array geometry. The staggered, skewed and aligned arrangements of the buildings with respect to the oncoming flow were shown to affect plume development. Additional post-processing quantified this development through parameters fundamental to reduced-order Gaussian dispersion models. The parameters include measures of concentration decay with distance from the source as well as plume trajectory and spread. The horizontal plume trajectory and width were found to be more sensitive to source location variations, and hence local geometric features, than vertical plume parameters.
A numerical study of seafloor scattering

The processes which result in backscattered acoustic energy from the seafloor are of primary interest for interpreting sonar signals measured in the deep ocean basins. This complex problem can be approached from a realistic perspective with the employment of modern high‐resolution seafloor bathymetry measurements and viscoelastic wave simulation techniques capable of modeling highly heterogeneous anelastic media. A numerical study of low‐frequency (100–200 Hz) backscattering processes from rough seafloor models is presented and several scattering hypotheses are proposed. In particular the effects that low‐Q sediments have on the backscattered field from a rough seafloor and the effects that perturbations in the different material properties have on the backscattered energy are investigated. The principal conclusions reached are that anelastic sediments on the seafloor can have a significant impact on the backscattered field from low grazing angle scattering experiments, and that small perturbations in the.
Anuário do Instituto de Geociências

Springs play an important role in maintaining the hydrological cycle, thus spring management is essential for improving the population’s quality of life. In this sense, this research aimed to analyze the conservation status of three springs in the watersheds of Mineirinho and Paraíso streams located in the city of São Carlos/SP, Brazil, in order to understand the possible causes of their conditions. We applied the Integrated Assessment and Monitoring Protocol for Springs of Water Streams (PANÁgua), whose structure is summarized in three stages: field assessment, bibliographic research, and analysis using Geographic Information Systems (GIS). We identified that the three springs in both studied watersheds are in a poor conservation status. There is evidence that most impacts present in all three springs are related to intense anthropic actions that resulted in a lack of natural vegetation, the presence of strong erosion processes, irregular dumping of sewage, and pluvial water runoff. Furthermore, the disposal of various solid residues in the Permanent Preservation Areas (PPA) of the studied springs was also observed. The results show the need to prioritize environmental management actions in urbanized watersheds, as well as expand research on the status of watersheds in Brazil.
Micro-displacement by Means of Optoelectronic Nonlinear Joint Transform Correlation of Speckle Pattern

Two objective speckle patterns are obtained—one before and one after the object has been moved—and are placed in the input plane of an optoelectronic nonlinear joint transform correlator.Nonlinear transformation of the joint power spectrum permits a sharper correlation peak and a high signal-to-noise ratio. The autocorrelation peak coordinates of the first pattern are set as a reference for measuring displacements of the cross-correlation peak, and also, the calibration of the measurement system is performed.It is well known that speckle can be regarded as a random spatial carrier in which information on the shape and position of the diffusing surface is encoded. The correlation of speckle patterns is highly appropriate for measuring displacements of tens of micrometers.1,2 In this work, an alternative technique based on the correlation between two speckle patterns by means of a digital bipolar joint transform correlator3 is presented. Initially the technique is described and then experimental results are shown.
Multilevel Factor Analysis by Model Segregation

Measures of classroom environments have become central to policy efforts that assess school and teacher quality. This has sparked a wide interest in using multilevel factor analysis to test measurement hypotheses about classroom-level variables. One approach partitions the total covariance matrix and tests models separately on the between-classroom and within-classroom levels. This article shows that when using this approach, robust test statistics, including rescaled and residual-based test statistics provide better inferences about the classroom-level measurement structure than the widely used likelihood ratio test statistic even when the number of classrooms is large, and there is no excess kurtosis in the observed variables. This article then presents an empirical example and a simulation study to demonstrate how item intraclass correlations and within-group sample sizes influence test statistic performance. The results have implications for the study of classroom environments.
Performance Comparison of Recombinant Baculovirus and Rabies Virus-like Particles production Using Two Culture Platforms

This work aimed to assess, following upstream optimization in Schott flasks, the scalability from this culture platform to a stirred-tank bioreactor in order to yield rabies-recombinant baculovirus, bearing genes of G (BVG) and M (BVM) proteins, and to obtain rabies virus-like particles (VLP) from them, using Sf9 insect cells as a host. Equivalent assays in Schott flasks and a bioreactor were performed to compare both systems and a multivariate statistical approach was also carried out to maximize VLP production as a function of BVG and BVM’s multiplicity of infection (MOI) and harvest time (HT). Viable cell density, cell viability, virus titer, BVG and BVM quantification by dot-blot, and BVG quantification by Enzyme-Linked Immunosorbent Assay (ELISA) were monitored throughout the assays. Furthermore, transmission electron microscopy was used to characterize rabies VLP. The optimal combination for maximum VLP expression was BVG and BVM MOI of 2.3 pfu/cell and 5.1 pfu/cell, respectively, and 108 h of harvest time. The current study confirmed that the utilization of Schott flasks and a benchtop bioreactor under the conditions applied herein are equivalent regarding the cell death kinetics corresponding to the recombinant baculovirus infection process in Sf9 cells. According to the results, the hydrodynamic and chemical differences in both systems seem to greatly affect the virus and VLP integrity after release.
Based on Canny Operator with Regional Growth Combined Research on Accurate Image Segmentation Algorithm

In this paper, first we introduce the digital image algorithm of Canny edge detection, and then simulation results of Canny edge detection are given particularly. Test results show that the improved algorithm improves the accuracy of edge detection effectively. Secondly, the algorithm of the region growth, which algorithm is divided into two parts: one is growing region; the other is merging region, is realized. Finally, based on above analysis, we find out that a continuous boundary is not obtained by edge detection, and the method of regional extraction may produce transition segmentation. In response to these deficiencies, we present a region growth algorithm of edge detection.
Search for point-contact giant magnetoresistance in Co/Cu multilayers

We report the first measurements of the point-contact magnetoresistance (MR) of magnetic multilayers. These measurements were intended to see if the additional local resistance associated with a small contact area between a sharpened tip and a magnetic multilayer shows a giant magnetoresistance (GMR) such as that seen in bulk multilayers. Point-contact measurements on two independent multilayers of Co/Cu revealed MRs dominated simply by that of the square resistance of the multilayer, with little or no contribution from the contact area itself. Further studies are still needed to determine if this result means that a small contact has little or no local GMR, or if the needle point-contact technique used destroyed the multilayer around the contact, thereby suppressing the GMR.