Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part I: Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, long-range and extrinsic connectivity. We delineated the limits of determining connectivity from anatomy, finding that it reproduces the targeting of PV+ and VIP+ interneurons only with explicitly added specificity, but the one of Sst+ neurons even without. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. A 211,712 neuron subvolume of the model has been made freely and openly available to the community.
Developmental noise and phenotypic plasticity are correlated in Drosophila simulans

Non-genetic variation is the phenotypic variation induced by the differential expression of a genotype in response to varying environmental cues and is broadly categorized into two types: phenotypic plasticity and developmental noise. These variation aspects have been suggested to play an important role in adaptive evolution; however, the mechanisms by which these two types of non-genetic variations influence the evolutionary process are currently poorly understood. Using a machine-learning based phenotyping tool, we independently quantified the phenotypic plasticity and developmental noise in the wing morphological traits of a fruit fly Drosophila simulans. Utilizing a rearing experiment, we demonstrated plastic responses in both wing size and shape as well as non-zero heritability of both phenotypic plasticity and developmental noise, which suggests that adaptive phenotypic plasticity can evolve via genetic accommodation in the wing morphology of D. simulans. We found a positive correlation between phenotypic plasticity and developmental noise, while the correlation between the plastic response to three kinds of environmental factors that were examined (nutrient condition, temperature, and light–dark cycle) were poor. These results suggest that phenotypic plasticity and developmental noise contribute to evolvability in a similar manner, however, the mechanisms that underlie the correspondence between these two variation types remains to be elucidated. Lay Summary Non-genetic variations consist of phenotypic plasticity and developmental noise, and these variations have been suggested to influence the direction and the rate of evolution. However, the role of phenotypic plasticity and developmental noise in evolutionary process is still poorly understood. Using a rearing experiment, we examined the heritability of plasticity and developmental noise, the correlation of the strength of plastic response to three kinds of environmental factors, and the relationship between plasticity and developmental noise in wing size and wing shape in Drosophila simulans. We found that the degree of phenotypic plasticity and developmental noise were heritable, and positively correlated with each other. Our results suggest that there two non-genetic variations dependently affect the direction and the rate of evolution together.
Ploidy-specific transcriptomes shed light on the heterogeneous identity and metabolism of developing pericarp cells

Endoreduplication, during which cells increase their DNA content through successive rounds of full genome replication without cell division, is the major source of endopolyploidy in higher plants. Endoreduplication plays pivotal roles in plant growth and development and is associated with the activation of specific transcriptional programs that are characteristic to each cell type, thereby defining their identity. In plants, endoreduplication is found in numerous organs and cell types and especially in agronomically valuable ones, such as the fleshy fruit (pericarp) of tomato presenting high ploidy levels. We used the tomato pericarp tissue as a model system to explore the transcriptomes associated with endoreduplication progression during fruit growth. We confirmed that expression globally scales with ploidy level and identified sets of genes differentially expressed when comparing ploidy levels at a specific developmental stage. We found that non-endoreduplicated cells are defined by cell division state and cuticle synthesis while endoreduplicated cells are mainly defined by their metabolic activity changing rapidly over time. By combining this dataset with publicly available spatiotemporal pericarp expression data, we proposed a map describing the distribution of ploidy levels within the pericarp. These transcriptome-based predictions were validated by quantifying ploidy levels within the pericarp tissue. This in situ ploidy quantification revealed the dynamic progression of endoreduplication and its cell layer specificity during early fruit development. In summary, the study sheds light on the complex relationship between endoreduplication, cell differentiation, and gene expression patterns in the tomato pericarp. Significance statement The progression of endoreduplication is very dynamic during early fruit development and displays cell layer specific patterns. The integration of ploidy distribution maps with ploidy-specific transcriptome data revealed that gene expression in the pericarp is controlled in a ploidy-specific manner during the early stages of tomato fruit development, resulting in the spatialization of transcriptional programs.
Predictive microbial community changes across a temperature gradient

A central challenge in community ecology is predicting the effects of abiotic factors on community assembly. In particular, microbial communities play a central role in the ecosystem, but we do not understand how changing factors like temperature are going to affect community composition or function. One of the challenges is that we do not understand the mechanistic impacts of temperature on different metabolic strategies, nor how this metabolic plasticity could impact microbial interactions. Dissecting the contribution of environmental factors on microbial interactions in natural ecosystems is hindered by our understanding of microbial physiology and our ability to disentangle interactions from sequencing data. Studying the self-assembly of multiple communities in synthetic environments, here we are able to predict changes in microbial community composition based on metabolic responses of each functional group along a temperature gradient. This research highlights the importance of metabolic plasticity and metabolic trade-offs in predicting species interactions and community dynamics across abiotic gradients.
Unraveling cell differentiation mechanisms through topological exploration of single-cell developmental trajectories

Understanding the circuits that control cell differentiation is a fundamental problem in developmental biology. Single-cell RNA sequencing has emerged as a powerful tool for investigating this problem. However, the reconstruction of developmental trajectories is based on the assumption that cell states traverse a tree-like structure, which may bias our understanding of critical developmental mechanisms. To address this limitation, we developed a framework, TopGen, that enables identifying topological signatures of functional biological circuits as persistent homology groups in transcriptome space. First, we show that TopGen can identify genetic drivers of topological structures in simulated datasets. We then applied our approach to more than ten single-cell developmental atlases and found that topological transcriptome spaces are predominantly path-connected and only sometimes simply connected. Finally, we applied TopGen to analyze gene expression patterns in topological loops representing stem-like, transdifferentiation, and convergent cell circuits, found in C. elegans, H. vulgaris, and N. vectensis, respectively. Our results show that some essential differentiation mechanisms use non-trivial topological motifs, and that these motifs can be conserved in a cell-type–specific manner. Thus, our approach to studying the topological properties of developmental transcriptome atlases opens new possibilities for understanding cell development and differentiation.
Community structure of heritable viruses in a Drosophila-parasitoids complex

The diversity and phenotypic impacts related to the presence of heritable bacteria in insects have been extensively studied in the last decades. On the contrary, heritable viruses have been overlooked for several reasons, including technical ones. This is regrettable because of the size of this gap knowledge and because case study indicate that viruses may have profound impact on the functionning of individuals and communities. Additionally, the factors that may shape viral communities are poorly known, except in some very specific viral-insect systems. Here we analyze the community structure of heritable viruses in a multi-hosts-multi-parasitoids community. Drosophilidae and their larval and pupal parasitoids were sampled in two locations, two years and two seasons. After two lab generations, putative DNA and RNA viruses were purified and sequenced. Our analysis revealed the presence of 53 viruses (including 37 new viruses), the great majority of which were RNA viruses. The "species" factor was by far the most significant one, explaining more than 50% of the variance in viral structure. Additionally, parasitoids had a higher number of heritable viruses compared to their hosts, suggesting that this lifestyle favours the association with viruses. Finally, our community-level survey challenged previous interpretation concerning the host range of some previously described viruses.
Stabilization of strict‐feedback nonlinear systems with input delay using closed‐loop predictors

In this paper, we consider the control problem of strict‐feedback nonlinear systems with time‐varying input and output delays. The approach is based on the usual observer/predictor/feedback approach, but the novelty is the use of the closed‐loop dynamics in the predictor. This approach allows to develop two designs, an instantaneous predictor and a delay differential equation‐based predictor, that both attain the same performance in terms of system trajectories and input signal as in the case with no delays. The design based on delay differential equations allows to build a cascade of predictors to deal with arbitrarily large delay bounds. The resulting controller is much simpler to implement than classical infinite‐dimensional predictors, and it is robust with respect to actuation and measurement disturbances. We illustrate the approach with an application to the control of a chaotic system with input delay. Copyright © 2016 John Wiley & Sons, Ltd.
Simulation of obstacle overcoming by processive molecular motors

: This work aims to study the dynamics of KIF1A in a noise-driven ratchet potential. KIF1A molecule is a single-headed kinesin which is specific to axonal transport in neurons. Recent studies have shown that the capability of this kind of motor to overcome an obstacle or a traffic jam that impedes the advance of a vesicle is strongly related to several neurodegenerative diseases like Alzheimer’s. We model the interaction and displacement of the motor with a two state Brownian ratchet potential in two dimensions. With the numerical simulation of this model we are able to give an accurate prediction of the overcoming time of an obstacle, modelled as a Gaussian potential, as a first step towards a better understanding of the interplay between traffic dysfunctions and neurodegenerative diseases.
On generalized S-quasinormal and generalized subnormal subgroups of finite groups

ABSTRACT Let G be a finite group and H a subgroup of G. We say that H is a generalized subnormal (respectively generalized S-quasinormal) subgroup of G if H = ⟨A,B⟩ for some modular subgroup A and subnormal (respectively S-quasinormal) subgroup B of G. If , where Mi is a maximal subgroup of Mi−1 for all i = 1,…,n, then Mn (n>0) is an n-maximal subgroup of G. In this paper, we study finite groups whose n-maximal subgroups are generalized subnormal or generalized S-quasinormal.
New design strategies for ultra-specific CRISPR-Cas13a-based RNA-diagnostic tools with single-nucleotide mismatch sensitivity

The pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and new Cas13a variants for easier-to-implement Cas13-based diagnostics. KEY POINTS Certain positions in the Cas13a crRNA-target-RNA duplex are particularly sensitive to mismatches. Understanding Cas13a’s allosteric activation pathway allowed us to develop novel high-fidelity Cas13a variants. These Cas13a variants and crRNA design strategies achieve superior discrimination of SARS-CoV-2 strains. GRAPHICAL ABSTRACT New strategies to improve Cas13a RNA-detection specificity developed via mismatch tolerance profiling, uncovering features that modulate specificity, and structure-guided engineering of LbuCas13a.
Intersubject representational similarity analysis uncovers the impact of state anxiety on multivariate activation patterns in the human extrastriate cortex

The current study used functional magnetic resonance imaging (fMRI) and showed that state anxiety modulated extrastriate cortex activity in response to emotionally-charged visual images. State anxiety and neuroimaging data from 53 individuals were subjected to an intersubject representational similarity analysis (ISRSA), wherein the geometries between neural and behavioral data are compared. This analysis identified the extrastriate cortex (fusiform gyrus and area MT) to be the sole regions whose activity patterns covaried with state anxiety. Importantly, we show that this brain-behavior association is revealed when treating state anxiety data as a multidimensional response pattern, rather than a single composite score. This suggests that ISRSA using multivariate distances may be more sensitive in identifying the shared geometries between self-report questionnaires and brain imaging data. Overall, our findings demonstrate that a transient state of anxiety may influence how visual information – especially those relevant to the valence dimension – is processed in the extrastriate cortex.
Textured Enhanced Image De-noising using Fast Wavelet Transform

The digital imaging technologies possess very hasty development that uses Giga-pixels to store an image. Image de-noising algorithms plays a significant role in the restoration process. In an image the texture regions are homogeneous and are composed of local descriptor, a trade off exist between visual quality of image and the enhanced texture regions. In the existing paper the Gradient Histogram Preservation (GHP) method on the enhanced image regions have a limitation where it cannot be directly applied to nonadditive noise removal such as Multiplicative Poisson Noise (MPN) and Signal-Dependent Noise (SDN) to overcome the limitation Fast Wavelet Transform is used. In this work an image is first added with different types of noise like Additive White Gaussian Noise (AWGN), Salt and Pepper Noise, Poisson Noise, Signal Dependent noise and Flicker Noise, the noisy image is restored using filters, next the enhanced texture region of the image is chosen which is blurred or deformed and the fine details of the texture is obtained using Fast Wavelet Transform (FWT). The proposed work is analyzed in Frequency domain by considering various parameters like Peak Signal to Noise Ratio (PSNR), Correlation Factor (CF) and Standard Deviation (SD) and the quality of the enhanced region of the image is improved to the best level than the conventional noise removal algorithms.
A Generalized Conditional Mean Spectrum and Its Application for Intensity-Based Assessments of Seismic Demands

When the response of a structure is influenced significantly by multiple vibration modes, the seismic demands determined by response history analyses with ground motions selected to match a single conditional mean spectrum (CMS) may be unconservative for several response quantities. An existing solution to this problem is to determine the demand as the maximum of mean demands from several CMSs. To reduce the computational effort while maintaining accuracy and precision, we introduce an alternative engineering solution that is based on a simplified version of the generalized CMS, where two specified conditioning spectral accelerations share a common hazard level. vector-valued disaggregation is developed herein to provide a single earthquake scenario for the target spectrum. The results from a realistic case study suggest that the proposed spectrum provides seismic demands that are as accurate and precise as those obtained from analyzing the structure with multiple CMSs while simultaneously reducing the computational effort by a factor of 2 or more.
AutoCollage

The paper defines an automatic procedure for constructing a visually appealing collage from a collection of input images. The aim is that the resulting collage should be representative of the collection, summarising its main themes. It is also assembled largely seamlessly, using graph-cut, Poisson blending of alpha-masks, to hide the joins between input images. This paper makes several new contributions. Firstly, we show how energy terms can be included that: encourage the selection of a representative set of images; that are sensitive to particular object classes; that encourage a spatially efficient and seamless layout. Secondly the resulting optimization poses a search problem that, on the face of it, is computationally in-feasible. Rather than attempt an expensive, integrated optimization procedure, we have developed a sequence of optimization steps, from static ranking of images, through region of interest optimization, optimal packing by constraint satisfaction, and lastly graph-cut alpha-expansion. To illustrate the power of AutoCollage, we have used it to create collages of many home photo sets; we also conducted a user study in which AutoCollage outperformed competitive methods.
The positivity of Schrödinger operators and the hypoellipticity of second order degenerate elliptic operators

The purpose of this paper is to consider the hypoellipticity of second order infinitely degenerate elliptic operators, from the viewpoint of the uncertainty principle, in other words, the positivity of Schrodinger operators. Reciprocally, we shall study the positivity of Schrodinger operators, taking into account the case where the potential vanishes infinitely.
Learning Dense Correspondences between Photos and Sketches

Humans effortlessly grasp the connection be-tween sketches and real-world objects, even when these sketches are far from realistic. Moreover, human sketch understanding goes beyond categorization — critically, it also entails understanding how individual elements within a sketch correspond to parts of the physical world it represents. What are the computational ingredients needed to support this ability? Towards answering this question, we make two contributions: first, we introduce a new sketch-photo correspondence benchmark, PSC6k , containing 150K annotations of 6250 sketch-photo pairs across 125 object categories, augmenting the existing Sketchy dataset (Sangkloy et al., 2016) with fine-grained
Quantum theory of complex ultracold collisions

This thesis reports on a variety of calculations on cold and ultracold scattering, with a broad theme of how best to consider and understand complex systems in simple ways. Firstly, we investigate quantum defect theory. We demonstrate that it is not only an excellent model for simple systems, but can also provide simple predictions of the \emph{range} of possible behaviours for complex systems, in particular for a model of collisional losses. These predictions agree well with expensive coupled-channels calculations in cases where the full calculations also predict only the range of possible behaviours. Secondly, we consider effects relating to thermalisation of cold and ultracold gases. We show that considering the correct transport cross section, $\sigma_\eta^{(1)}$, is important for determination of scattering lengths and their signs by interspecies thermalisation. This cross section is also important to the understanding of high-quality simulations of sympathetic cooling in a microwave trap, which suggest Rb is likely to be a good coolant for CaF. We also correct an error in the interpretation of previous results for sympathetic cooling in a magnetic trap, showing this may work from over 100 mK for Li+CaF and many Kelvin when using atomic hydrogen as a coolant. Thirdly, we study quantum chaos in ultracold collisions. We find very clear and strong signs of chaos in Li+CaH. We also show that a more strongly coupled system, Li+CaF, is \emph{not} fully chaotic and that there is unexpected structure in the levels of chaos as the CaF rotational constant is varied. We also show that signatures of chaos can emerge in a very simple atom-atom system, Yb(${}^1S_0$)+Yb(${}^3P_2$), which interacts on only two Born-Oppenheimer potentials. Finally, we examine the idea that metastable states in 2-body scattering greatly enhance 3-body recombination at ultracold temperatures. We attempt to put it on a more rigorous theoretical grounding by considering Smith's collision lifetime and related quantities, but those are shown to lack clear interpretations in the ultracold regime. We therefore consider 3-body scattering theory and arrive at some general conclusions about how we expect such 2-body features to appear in 3-body scattering and suggest possible ways forward.
Nonlinear generalized Lie triple derivation on triangular algebras

ABSTRACT Let ℛ be a commutative ring with identity and let 𝔄 = Tri(𝒜,ℳ,ℬ) be a triangular algebra consisting of unital algebras 𝒜,ℬ over ℛ and an (𝒜,ℬ)-bimodule ℳ which is faithful as a left 𝒜-module as well as a right ℬ-module. In this paper, we prove that under certain assumptions every nonlinear generalized Lie triple derivation GL:𝔄→𝔄 is of the form GL = δ+τ, where δ:𝔄→𝔄 is an additive generalized derivation on 𝔄 and τ is a mapping from 𝔄 into its center which annihilates all Lie triple products [[x,y],z].