Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the mechanisms of food intake and obesity in down syndrome is supported by behavioral and neurochemical abnormalities

ABSTRACT Obesity prevalence is higher in Down syndrome (DS) than in the general population. Beyond metabolic alterations, individuals with DS present increased impulsivity, a trait observed in obese people and in compulsive eaters that may affect their control of food intake. In this Thesis, we used a trisomic DS mouse model (Ts65Dn) to understand the behavioral component of obesity in DS and explore some possible underlying mechanisms. Our meal pattern analysis revealed longer and slowly meals in Ts65Dn mice, leading to reduced eating rate, which may be associated to the mandible hypoplasia described in both human and mice. When exposed to obesogenic environments, Ts65Dn mice showed higher preference for energy-dense food, gained more weight in specific conditions and scored higher in compulsivity and inflexibility tests than WT mice, as measured by binge eating during limited access and persistence of consumption of quine adulteration of energy-dense food. High performance liquid chromatography revealed reduced levels of dopamine in prefrontal cortex in Ts65Dn mice. This could lead to higher reward sensitivity that in turn would facilitate overeating as a compensatory response to restore optimal dopamine levels. Feeding behavior is also regulated by hormones and other circulating signals. We detected higher plasma leptin and glucose levels along with reduced insulin levels in Ts65Dn mice. Upon a glucose challenge, Ts65Dn mice showed reduced glucose-stimulated insulin response both in vivo and in vitro, suggesting a deficient insulin secretion or the reduced pancreatic mass. Indeed, we detected that Ts65Dn mice had altered plasma profile for some markers of inflammation and oxidative damage, in agreement with the high prevalence of autoimmune diseases and diabetes in DS people. We also explored the involvement of the serine/threonine kinase DYRK1A, a candidate DS gene, in obesity and feeding behavior. Dyrk1A overexpression was sufficient to recapitulate some behavioral aspects associated to overeating in DS, but with a distinct profile. We conclude that increased obesity prevalence in DS is explained by both metabolic and behavioral alterations, in part driven by a hypodopaminergic status, and that Dyrk1A overexpression is only involved in specific DS obesity phenotypes.
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks

Bayesian neural networks attempt to combine the strong predictive performance of neural networks with formal quantification of uncertainty associated with the predictive output in the Bayesian framework. However, it remains unclear how to endow the parameters of the network with a prior distribution that is meaningful when lifted into the output space of the network. A possible solution is proposed that enables the user to posit an appropriate covariance function for the task at hand. Our approach constructs a prior distribution for the parameters of the network, called a ridgelet prior, that approximates the posited covariance structure in the output space of the network. The approach is rooted in the ridgelet transform and we establish both finite-sample-size error bounds and the consistency of the approximation of the covariance function in a limit where the number of hidden units is increased. Our experimental assessment is limited to a proof-of-concept, where we demonstrate that the ridgelet prior can out-perform an unstructured prior on regression problems for which an informative covariance function can be a priori provided.
Mixed Dimer Model in Type D Cluster Algebras

We give a combinatorial model for F-polynomials and g-vectors for type D cluster algebras where the associated quiver is acyclic using dimers and double dimers. In particular, we give a graph theoretic recipe that describes which monomials appear in such F-polynomials, as well as a graph theoretic way to determine the coefficients of each of these monomials. In addition, we give a weighting on our mixed dimer model that gives the associated g-vector. To prove this formula, we utilize a combinatorial formula due to Thao Tran and provide explicit bijections between her combinatorial model and our own.
On a marching level‐set method for extended discontinuous Galerkin methods for incompressible two‐phase flows: Application to two‐dimensional settings

In this work a solver for two‐dimensional, instationary two‐phase flows on the basis of the extended discontinuous Galerkin (extended DG/XDG) method is presented. The XDG method adapts the approximation space conformal to the position of the interface. This allows a subcell accurate representation of the incompressible Navier‐Stokes equations in their sharp interface formulation. The interface is described as the zero set of a signed‐distance level‐set function and discretized by a standard DG method. For the interface, resp. level‐set, evolution an extension velocity field is used and a two‐staged algorithm is presented for its construction on a narrow‐band. On the cut‐cells a monolithic elliptic extension velocity method is adapted and a fast‐marching procedure on the neighboring cells. The spatial discretization is based on a symmetric interior penalty method and for the temporal discretization a moving interface approach is adapted. A cell agglomeration technique is utilized for handling small cut‐cells and topology changes during the interface motion. The method is validated against a wide range of typical two‐phase surface tension driven flow phenomena in a 2D setting including capillary waves, an oscillating droplet and the rising bubble benchmark.
Mean Shrinkage Estimation for High-Dimensional Diagonal Natural Exponential Families.

Shrinkage estimators have been studied widely in statistics and have profound impact in many applications. In this paper, we study simultaneous estimation of the mean parameters of random observations from a diagonal multivariate natural exponential family. More broadly, we study distributions for which the diagonal entries of the covariance matrix are certain quadratic functions of the mean parameter. We propose two classes of semi-parametric shrinkage estimators for the mean vector and construct unbiased estimators of the corresponding risk. Further, we establish the asymptotic consistency and convergence rates for these shrinkage estimators under squared error loss as both $n$, the sample size, and $p$, the dimension, tend to infinity. Finally, we consider the diagonal multivariate natural exponential families, which have been classified as consisting of the normal, Poisson, gamma, multinomial, negative multinomial, and hybrid classes of distributions. We deduce consistency of our estimators in the case of the normal, gamma, and negative multinomial distributions if $p n^{-1/3}\log^{4/3}{n} \rightarrow 0$ as $n,p \rightarrow \infty$, and for Poisson and multinomial distributions if $pn^{-1/2} \rightarrow 0$ as $n,p \rightarrow \infty$.
Error estimates of residual minimization using neural networks for linear PDEs

We propose an abstract framework for analyzing the convergence of least-squares methods based on residual minimization when feasible solutions are neural networks. With the norm relations and compactness arguments, we derive error estimates for both continuous and discrete formulations of residual minimization in strong and weak forms. The formulations cover recently developed physics-informed neural networks based on strong and variational formulations.
Hubble Space Telescope Observations of [O III] Emission in Nearby QSO2s: Physical Properties of the Ionised Outflows

We use Hubble Space Telescope (HST)/ Space Telescope Imaging Spectrograph (STIS) long-slit G430M and G750M spectra to analyse the extended [O III] λ5007 emission in a sample of twelve nearby (z >0.12) luminous (Lbol > 1.6 × 1045 erg s−1) QSO2s. The purpose of the study is to determine the properties of the mass outflows of ionised gas and their role in AGN feedback. We measure fluxes and velocities as functions of radial distances. Using Cloudy models and ionising luminosities derived from [O III] λ5007, we are able to estimate the densities for the emission-line gas. From these results, we derive masses of [O III]-emitting gas, mass outflow rates, kinetic energies, kinetic luminosities, momenta and momentum flow rates as a function of radial distance for each of the targets. For the sample, masses are several times 103M⊙ − 107M⊙ and peak outflow rates are 9.3 × 10−3M⊙ yr−1 to 10.3 M⊙ yr−1. The peak kinetic luminosities are 3.4 × 10−8 to 4.9 × 10−4 of the bolometric luminosity, which does not approach the 5.0 × 10−3 - 5.0 × 10−2 range required by some models for efficient feedback. For Mrk 34, which has the largest kinetic luminosity of our sample, in order to produce efficient feedback there would have to be 10 times more [O III]-emitting gas than we detected at its position of maximum kinetic luminosity. Three targets show extended [O III] emission, but compact outflow regions. This may be due to different mass profiles or different evolutionary histories.
Design and Fabrication of Elastic Geodesic Grid Structures

Elastic geodesic grids (EGG) are lightweight structures that can be easily deployed to approximate designer provided free-form surfaces. In the initial configuration the grids are perfectly flat, during deployment, though, curvature is induced to the structure, as grid elements bend and twist. Their layout is found geometrically, it is based on networks of geodesic curves on free-form design-surfaces. Generating a layout with this approach encodes an elasto-kinematic mechanism to the grid that creates the curved shape during deployment. In the final state the grid can be fixed to supports and serve for all kinds of purposes like free-form sub-structures, paneling, sun and rain protectors, pavilions, etc. However, so far these structures have only been investigated using small-scale desktop models. We investigate the scalability of such structures, presenting a medium sized model. It was designed by an architecture student without expert knowledge on elastic structures or differential geometry, just using the elastic geodesic grids design-pipeline. We further present a fabrication-process for EGG-models. They can be built quickly and with a small budget.
PRIMAL$_2$: Pathfinding Via Reinforcement and Imitation Multi-Agent Learning - Lifelong

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In particular, this work addresses lifelong MAPF (LMAPF) – an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one – in dense and highly structured environments, typical of real-world warehouse operations. Effectively solving LMAPF in such environments requires expensive coordination between agents as well as frequent replanning abilities, a daunting task for existing coupled and decoupled approaches alike. With the purpose of achieving considerable agent coordination without any compromise on reactivity and scalability, we introduce PRIMAL<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>, a distributed reinforcement learning framework for LMAPF where agents learn fully decentralized policies to reactively plan paths online in a partially observable world. We extend our previous work, which was effective in low-density sparsely occupied worlds, to highly structured and constrained worlds by identifying behaviors and conventions which improve implicit agent coordination, and enable their learning through the construction of a novel local agent observation and various training aids. We present extensive results of PRIMAL<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> in both MAPF and LMAPF environments and compare its performance to state-of-the-art planners in terms of makespan and throughput. We show that PRIMAL<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula> significantly surpasses our previous work and performs comparably to these baselines, while allowing real-time re-planning and scaling up to 2048 agents.
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?

Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of 'better inductive bias'. However, this has not been made mathematically rigorous, and the hurdle is that the fully connected net can always simulate the convolutional net (for a fixed task). Thus the training algorithm plays a role. The current work describes a natural task on which a provable sample complexity gap can be shown, for standard training algorithms. We construct a single natural distribution on $\mathbb{R}^d\times\{\pm 1\}$ on which any orthogonal-invariant algorithm (i.e. fully-connected networks trained with most gradient-based methods from gaussian initialization) requires $\Omega(d^2)$ samples to generalize while $O(1)$ samples suffice for convolutional architectures. Furthermore, we demonstrate a single target function, learning which on all possible distributions leads to an $O(1)$ vs $\Omega(d^2/\varepsilon)$ gap. The proof relies on the fact that SGD on fully-connected network is orthogonal equivariant. Similar results are achieved for $\ell_2$ regression and adaptive training algorithms, e.g. Adam and AdaGrad, which are only permutation equivariant.
Learning Monocular Dense Depth from Events

Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous ”events” instead of intensity frames. Compared to conventional image sensors, they offer significant advantages: high temporal resolution, high dynamic range, no motion blur, and much lower bandwidth. Recently, learning-based approaches have been applied to event-based data, thus unlocking their potential and making significant progress in a variety of tasks, such as monocular depth prediction. Most existing approaches use standard feed-forward architectures to generate network predictions, which do not leverage the temporal consistency presents in the event stream. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods. In particular, our method generates dense depth predictions using a monocular setup, which has not been shown previously. We pretrain our model using a new dataset containing events and depth maps recorded in the CARLA simulator. We test our method on the Multi Vehicle Stereo Event Camera Dataset (MVSEC). Quantitative experiments show up to 50% improvement in average depth error with respect to previous event-based methods. Code and dataset are available at: http://rpg.ifi. uzh.ch/e2depth
Survey of Bivalve Molluscs for Bonamia spp. and Other Parasitic Pathogens in Florida East Coast Lagoons

ABSTRACT A 2007 study of Suminoe (Crassostrea ariakensis) and eastern (Crassostrea virginica) oysters exposed to Indian River Lagoon (IRL), Florida waters revealed the presence of Bonamia spp. in exposed oysters. In 2010, a survey of oysters and other bivalves (n = 721, 18 species) was conducted in the southern IRL (10 sites) and in the more southerly Lake Worth Lagoon (LWL, 3 sites). Polymerase chain reaction (PCR) was used to assess the prevalence of three oyster parasites, Bonamia spp. , Perkinsus marinus, and Haplosporidium nelsoni, in collected samples. Histology, fluorescent in situ hybridization (FISH) and standard H&E staining, was used to confirm PCR-positive Bonamia results. Bonamia spp. was detected in eight species (Brachidontes exustus, Crassostrea virginica, Geukensia demissa, Ischadium recurvum, Isognomon alatus, Isognomon bicolor, Mercenaria campechiensis, and Ostrea stentina). The prevalence was 17.3% in the IRL and 9.9% in LWL with 66% of PCR-positive samples subsequently identified as either Bonamia exitiosa or Bonamia perspora. The presence of Bonamia spp. was confirmed by FISH in C. virginica, O. stentina, I. alatus, I. bicolor, and G. demissa, although only C. virginica and O. stentina showed evidence of infection. The presence of P. marinus was detected in eight species (Atrina rigida, B. exustus, C. virginica, G. demissa, I. recurvum, I. alatus, I. bicolor, and O. stentina). The prevalence was 12.4% in the IRL and 7.7% in LWL. The presence of MSX was not detected in any of the collected bivalves. This study confirms the presence of Bonamia spp. and P. marinus in Florida east coast waters and widens the described geographic range for Bonamia spp. , and provides further evidence that MSX is unlikely to be found in Florida waters
Investigation of prevalence of thermotolerant Campylobacter spp. in livestock feces

Abstract The purpose of this study was to determine the prevalence of thermotolerant Campylobacter spp. and antimicrobial resistance profiles isolated from in livestock feces in Isfahan, Iran. A total of 400 fecal of livestock samples were collected, and the presence of Campylobacter species was studied by culture and polymerase chain reaction-based assays and antimicrobial susceptibility test. A total of 28 Campylobacter isolates including 22 Campylobacter jejuni and 6 Campylobacter coli were recovered from feces of livestock. The prevalence rates of Campylobacter spp. were observed in this study in sheep (10%), goat (8%), cattle (5.3%), and camel (4%). The highest prevalence of Campylobacter spp. was found in the summer (10%) and the lowest was in winter (4%). Among the isolates from livestock, both C. jejuni and C. coli from fecal samples had the highest frequency of tetracycline (75.1%) and ciprofloxacin (57.1%) resistance. The results of this study showed a high prevalence of Campylobacter spp. in livestock feces in Isfahan, Iran. The presence of Campylobacter in livestock feces can contaminate the environmental and human food chain. Therefore, detection of Campylobacter spp. in livestock-originated samples is important to identify possible sources of infection and to have that a better understanding of the epidemiology of infection virulence genes of isolates is considered essential.
The role of serologic testing for Zika virus infection

&NA; Direct detection of Zika virus (ZIKV) nucleic acid with nucleic acid tests is definitive laboratory evidence of infection. Initial studies suggest that the ZIKV viremic period is limited, leading the Centers for Disease Control and Prevention to recommend that nucleic acid tests be utilized only up to 14 days postsymptom onset in serum and urine. The host immune response to flaviviruses, including to ZIKV, primarily targets the viral envelope (E) protein, which has three main domains (ED I–III). Recent studies suggest that neutralizing antibodies against the EDIII domain are relatively specific and potentially able to discriminate between ZIKV and dengue virus, which share 29% amino acid homology at that region. Comparatively, neutralizing antibodies to EDI and EDII, with amino acid homologies of 35 and 51% between these two viruses, respectively, are highly cross-reactive and likely responsible for the nonspecific reactions observed in the respective serologic assays. The current article describes the humoral immune response to ZIKV, reviews serologic assays for detection of antibodies to ZIKV, discusses performance characteristics of commercially available tests, and summarizes the strengths and limitations of currently available serologic methods. Assays examined in the review include the Centers for Disease Control and Prevention Zika IgM antibody capture ELISA, InBios ZIKV Detect IgM Capture ELISA, DiaSorin, LIAISON XL Zika Capture IgM Assay, Euroimmun anti-ZIKV IgM ELISA, and plaque reduction neutralization tests. Despite the availability of multiple diagnostic tests, there remains a need for modified and novel testing strategies, including the potential use of multiple enzyme immunoassays either in parallel or sequentially and IgG in addition to IgM determination.
Carry-over effects of larval microclimate on the transmission potential of a mosquito-borne pathogen

Climate shapes the transmission of mosquito-borne pathogens through impacts on both the vector and the pathogen. In addition to direct effects of the environment, carry-over effects from previous life history stages can influence mosquito traits relevant to disease transmission. While this has been explored in a laboratory setting, the net effect of temperature-mediated carry-over effects due to relevant environmental variation in the larval stage is ambiguous. Here, we use data collected from a semi-field experiment investigating dengue dynamics in Aedes albopictus across a natural environmental gradient to parameterize a dengue transmission model. We reared Ae. albopictus across three different land classes characterized by their proportion of impervious surface. Emerged females were offered a dengue infectious bloodmeal, kept at a constant 27 °C, and assayed for infection, dissemination, and infectiousness 21 days post infection. Incorporating carry-over effects of larval environment on measures of vector competence resulted in lower predicted dengue transmission potential across land class and season, however a strong positive relationship with larval environmental temperature remained. Given the significant impact of carry-over effects, future mechanistic models of disease transmission should include both direct and carry-over effects of environmental temperature.
Identification des cibles primaires des ARN non codant de Staphylococcus aureus et de leurs réseaux de régulation : mise au point des approches MAPS et Grad-seq

S. aureus est une bacterie pathogene opportuniste de l’homme qui pose un grave probleme de sante publique. Le pouvoir pathogene de S. aureus est confere par un tres grand nombre de facteurs de virulence, dont l’expression est finement regulee a de multiples niveaux. Les effecteurs de cette regulation sont a la fois des proteines et des ARN non codants (ARNnc) aussi appeles ARN regulateurs. Je me suis concentre au cours de ma these sur la classe majoritaire qui sont les ARNnc qui regulent la traduction d’ARNm. Ils sont impliques dans de complexes reseaux de regulation qui permettent de controler la physiologie de la cellule ainsi que sa virulence. Pour elargir nos connaissances de ces reseaux, j’ai developpe deux approches methodologiques, appelees MAPS et Grad-seq, que j’ai appliquees in vivo chez S. aureus en utilisant RsaA et RsaC comme modeles. L’application du MAPS a permis d’identifier de nouvelles cibles directes pour RsaA et des cibles potentielles pour RsaC. L’approche Grad-Seq est un outil puissant mais demande encore des ajustements. J’ai egalement pu determiner un role probable pour l’ARNnc RsaC dans la regulation de l’homeostasie oxydo-reductive de S. aureus, en lien avec la resistance au stress oxydatif et avec la persistance lors de l’internalisation par les osteoblastes.