Miracle-dz/UniFAR-FTUs huggingface dataset:
Collagen polarization provides a structural memory for the elongation of epithelial anlage

Branched epithelial networks are fundamental features of many organs in the body. The biogenesis of these networks involves distinct processes where multicellular aggregates elongate and branch. In this report we focus on understanding how the extracellular matrix contributes to the process of elongation. Using mammary epithelial organotypic cultures we found that collagen 1, but not a basement membrane extract, induces the formation of elongated multicellular aggregates. Indeed, isotropic aggregates, used as models of epithelial anlage, broke symmetry and elongated when transplanted into collagen 1 gels; this was accompanied by reorganization of collagen fibrils into bundles that were polarized around the elongating aggregates. By applying external stretch as a cell-independent way to reorganize the ECM gels, we found that collagen polarization itself can induce and guide the direction of aggregate elongation. This critically involves cell proliferation, which is selectively enhanced in the regions of anlage that elongate, and requires β1-integrin and ERK signaling. We propose that collagen polarization promotes anlage elongation by providing a structural memory of the initial axis that is generated when aggregates break symmetry.
Closed-loop automated reaching apparatus (CLARA) for interrogating motor systems

Objective: Personalized neurostimulation is a rapidly expanding category of therapeutics for a broad range of indications. Development of these innovative neurological devices requires high-throughput systems for closed-loop stimulation of model organisms, while monitoring physiological signals and complex, naturalistic behaviors. To address this need, we developed CLARA, a closed-loop automated reaching apparatus. Approach: Using breakthroughs in computer vision, CLARA integrates fully-automated, markerless kinematic tracking of multiple features we use to classify animal behavior and precisely deliver neural stimulation based on behavioral outcomes. CLARA is compatible with advanced neurophysiological tools, enabling the testing of neurostimulation devices and identification of novel neurological biomarkers. Results: The CLARA system tracks unconstrained skilled reach behavior in 3D at 150hz without physical markers. The system fully automates trial initiation and pellet delivery and is capable of accurately delivering stimulation in response to trial outcome with sub-quarter second latency. Mice perform the skilled reach task in the CLARA system at a proficiency similar to manually trained animals. Kinematic data from the CLARA system provided novel insights into the dynamics of reach consistency over the course of learning, suggesting that changes are driven entirely by unsuccessful reach accuracy. Additionally, using the closed-loop capabilities of CLARA, we demonstrate that vagus nerve stimulation (VNS) delivered on reach success improves skilled reach performance and increases reach trajectory consistency in healthy animals. Significance: The CLARA system is the first mouse behavior apparatus that uses markerless pose tracking to provide real-time closed-loop stimulation in response to the outcome of an unconstrained motor task. Additionally, we demonstrate that the CLARA system was essential for our finding that VNS given after successful completion of a motor task improves performance in healthy animals. This approach has high translational relevance for developing neurostimulation technology based on complex human behavior.
Positive supercoiling favors transcription elongation through lac repressor-mediated DNA loops

Some proteins, like the lac repressor (LacI), mediate long-range loops that alter DNA topology and create torsional barriers. During transcription, RNA polymerase generates supercoiling that may facilitate passage through such barriers. We monitored E. coli RNA polymerase progress along templates in conditions that prevented, or favored, LacI-mediated DNA looping. Tethered particle motion measurements revealed that RNA polymerase paused longer at unlooped LacI obstacles or those barring entry to a loop than those barring exit from the loop. Enhanced dissociation of a LacI roadblock by the positive supercoiling generated ahead of a transcribing RNA polymerase within a torsion-constrained DNA loop may be responsible. In support of this, RNA polymerase transcribed a looped segment more slowly than an unlooped segment and paused more briefly at LacI obstacles on positively supercoiled templates, especially under increased tension (torque). Positive supercoiling propagating ahead of polymerase appears to facilitate elongation along topologically-complex, protein-coated templates.
Are Adaptive Chemotherapy Schedules Robust? A Three-Strategy Stochastic Evolutionary Game Theory Model

We investigate the robustness of adaptive chemotherapy schedules over repeated cycles and a wide range of tumor sizes. We introduce a non-stationary stochastic three-component fitness-dependent Moran process to quantify the variance of the response to treatment associated with multidrug adaptive schedules that are designed to mitigate chemotherapeutic resistance in an idealized (well-mixed) setting. The finite cell (N tumor cells) stochastic process consists of populations of chemosensitive cells, chemoresistant cells to drug 1, and chemoresistant cells to drug 2, and the drug interactions can be synergistic, additive, or antagonistic. First, the adaptive chemoschedule is determined by using the N → ∞ limit of the finite-cell process (i.e. the adjusted replicator equations) which is constructed by finding closed treatment response loops (which we call evolutionary cycles) in the three component phase-space. The schedules that give rise to these cycles are designed to manage chemoresistance by avoiding competitive release of the resistant cell populations. To address the question of how these cycles are likely to perform in practice over large patient populations with tumors across a range of sizes, we then consider the statistical variances associated with the approximate stochastic cycles for finite N, repeating the idealized adaptive schedule over multiple periods. For finite cell populations, the error distributions remain approximately multi-Gaussian in the principal component coordinates through the first three cycles, with variances increasing exponentially with each cycle. As the number of cycles increases, the multi-Gaussian nature of the distribution breaks down due to the fact that one of the three subpopulations typically saturates the tumor (competitive release) resulting in treatment failure. This suggests that to design an effective and repeatable adaptive chemoschedule in practice will require a highly accurate tumor model and accurate measurements of the subpopulation frequencies or the errors will quickly (exponentially) degrade its effectiveness, particularly when the drug interactions are synergistic. Possible ways to extend the efficacy of the stochastic cycles in light of the computational simulations are discussed. Prepared for Special Issue Understanding the Evolutionary Dynamics and Ecology of Cancer Treatment Resistance, Ed. D. Basanta, Cancers (2021)
Phosphate transporter genes: genome-wide identification and characterization in Camelina sativa

Phosphorus is known as a key element associated with growth, energy, and cell signaling. In plants, phosphate transporters (PHTs) are responsible for moving and distributing phosphorus in cells and organs. PHT genes have been genome-wide identified and characterized in various plant species, however, these genes have not been widely identified based on available genomic data in Camellia sativa, which is an important oil seed plant. In the present study, we found 66 PHT genes involved in phosphate transporter/translocate in C. sativa. The recognized genes belonged to PHTs1, PHTs2, PHTs4, PHOs1, PHO1 homologs, glycerol-3-PHTs, sodium dependent PHTs, inorganic PHTs, xylulose 5-PHTs, glucose-6-phosphate translocators, and phosphoenolpyruvate translocators. Our finding revealed that PHT proteins are divers based on their physicochemical properties such as Isoelectric point (pI), molecular weight, GRAVY value, and exon-intron number(s). Besides, the expression profile of PHT genes in C. sativa based on RNA-seq data indicate that PHTs are involved in response to abiotic stresses such as cold, drought, salt, and cadmium. The tissue specific expression high expression of PHO1 genes in root tissues of C. sativa. In additions, four PHTs, including a PHT4;5 gene, a sodium dependent PHT gene, and two PHO1 homolog 3 genes were found with an upregulation in response to aforementioned studied stresses. In the current study, we found that PHO1 proteins and their homologs have high potential to post-translation modifications such as N-glycosylation and phosphorylation. Besides, different cis-acting elements associated with response to stress and phytohormone were found in the promoter region of PHT genes. Overall, our results show that PHT genes play various functions in C. Sativa and regulate Camellia responses to external and intracellular stimuli. The results can be used in future studies related to the functional genomics of C. sativa.
Newtonian mechanics in scale of minutia

This article commemorates Newton's contributions to mechanics in small scale. In particular, it deals with Newtonian slow viscous action of fluids in narrow conjunctions leading to hydrodynamics. It is shown that the Newtonian continuum relies on some bulk properties of fluids as opposed to their molecular interactions. When the latter and surface energy effects become dominant, the interaction potentials deviate from the Newtonian continuum. A plethora of largely empirically based force laws are used to describe conjunctional behaviours in nanoscale, usually lightly loaded. Some of these force laws are described, and their applicability to nanoconjunctions of very small devices and some biological systems is noted. In general, a thorough understanding of all the involved kinetics is required. Representative problems in soft nanoscale contacts in normal (humid) atmosphere are highlighted in the article. It is shown that contact load/adhesion depends on several key parameters including surface roughness, surface free energy, atmospheric moisture, and normal approach velocity.
Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components, and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogenous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.
Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory network

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer-target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the most updated knowledge on chromatin three-dimensional (3D) architecture, which is organized in a hierarchy of structural domains. Here we introduce a paradigm shift based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work i) we develop a computational and statistical framework to reconstruct a comprehensive ETG regulatory network leveraging functional genomics data; ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy; and iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.
PuBliCiTy: Python Bioimage Computing Toolkit

The Python Bioimage Computing Toolkit (PuBliCiTy) is an evolving set of functions, scripts, and classes, written primarily in Python, to facilitate the analysis of biological images, of two or more dimensions, from electron or light microscopes. While the early development was guided by the goal of replacing an existing internal code-base with Python code, the effort later came to include novel tools, specially in the areas of machine learning infrastructure and model development. The toolkit is built on top of the so-called python data science stack, which includes numpy, scipy, scikit-image, scikit-learn, and pandas. It also contains some deep learning models, written in TensorFlow and PyTorch, and a web-app for image annotation, which uses Flask as the web framework. The main features of the toolkit are: (1) simplifying the interface of some routinely used functions from underlying libraries; (2) providing helpful tools for the analysis of large images; (3) providing a web interface for image annotation, which can be used remotely and on tablets with pencils; (4) providing machine learning model implementations that are easy to read, train, and deploy – written in a way that minimizes complexity for users without a computer science or software development background. The source code is released under an MIT-like license at github.com/hms-idac/PuBliCiTy. Details, tutorials, and up-to-date documentation can be found at the project’s page as well. Project page github.com/hms-idac/PuBliCiTy
MXtrodes: MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation

Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a novel class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti3C2 MXene and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials, and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in a number of applications ranging from mapping large scale neuromuscular networks in humans to delivering cortical microstimulation in small animal models. Finally, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.
Two-Dimensional DOA Estimation of MIMO Radar Coherent Source Based on Toeplitz Matrix Set Reconstruction

In order to realize the high-precision direction of arrival (DOA) estimation of the coherent source of two-dimensional multipleinput and multiple-output (MIMO) radar, a solution is given by combining Toeplitz matrix set reconstruction. MIMO radar obtains a larger aperture with fewer arrays. Traditional two-dimensional reconstruction Toeplitz-like algorithms use part of the information in the construction of two correlation matrices or covariance matrices to construct the Toeplitz matrix when performing two-dimensional coherent source DOA estimation, which makes the information utilization incomplete and requires additional denoising processing. To solve the above problems, this paper proposes an improved Toeplitz matrix set reconstruction algorithm based on the two-dimensional reconstruction Toeplitz class algorithm. -e complete array element receiving signal vector is used to construct two Toeplitz matrix sets containing complete information, and then their conjugate transposes. Multiply and sum to correct the matrix to obtain a full-rank matrix, so as to achieve the purpose of decoherence and combine the traditional ESPRIT algorithm to perform two one-dimensional reconstruction processing through rotation invariance and then perform angle matching to achieve two-dimensional coherent signal angle estimation, while avoiding additional denoising processing. Finally, the simulation results of the cross array and the L-shaped array verify the effectiveness of the algorithm in this paper and further extend it to the two-dimensional MIMO radar array model and compare it with the traditional ESPRIT-like algorithm and the REC-FBSS-ESPRIT algorithm. In comparison, the algorithm in this paper has better performance under the conditions of successful resolution, DOA estimation accuracy, and low signal-to-noise ratio.
A New Password- and Position-Based Authenticated Key Exchange

Password-based authenticated key exchange is a popular method for secure authentication and key exchange. With the wide application of unmanned aerial vehicles, position information has also become an important factor in authentication. In this paper, we present a new key exchange protocol, which firstly realizes dual authentication for both password and position, and we propose two applicable scenarios for the PPAKE mechanism: one is unmanned aerial vehicle authentication, and the other one is authentication in the military base. By adding position authentication, the reliability of authentication has improved, and the difficulty of adversarial attacks also increases. Any arbitrary adversary who can listen, tamper, and sendmessages can only perform an online attack for password guessing at a specified position. Finally, we provide security proofs under the defined model.
Influencing Moral Behavior Through Mere Observation of Robot Work: Video-based Survey on Littering Behavior

Can robots influence the moral behavior of humans by simply doing their job? Robots have been considered as replacement for humans in repetitive jobs in public spaces, but what could that mean for the behavior of the surrounding people is not known. In this work we were interested to see how people change their behavior when they observe either a robot or a person do a morally-laden task. In particular, we studied the influence of seeing a robot or a human picking up and discarding garbage on the observer’s willingness to litter or to pick up garbage. The study was done as a video-based survey. Results show that while observing a person clean up does make people less keen to litter, this effect is not present when people watch a robot doing the same action. Moreover, people appear to feel less guilty about littering if they observed a robot doing the cleaning up than in the case when they watched a human cleaner. CCS CONCEPTS•. Human-centered computing → Empirical studies in HCI; • Computer systems organization → Robotics.
Category Recognition in E-Commerce using Sequence-to-Sequence Hierarchical Classification

E-commerce platforms often use a predefined structured hierarchy of product categories. Apart from helping buyers sort between different product types, listing categorization is also critical for multiple downstream tasks, including the platform's main listing search. Traditionally, when creating a new listing, sellers need to assign the product they sell to a single category. However, the high diversity of product types in the platform, along with the hierarchy's low level of granularity result in tens of thousands of different possible categories that sellers need to pick from. This, in turn, creates a unique classification challenge, especially for sellers with a large number of listings. Moreover, the expected cost of making a category classification error is high, as it can impact the likelihood that their listing will get discovered by relevant buyers, and eventually sold. To help with the challenge of category recognition we present CatReComm - an interactive real-time system that is generalized to provide category recommendations in different e-commerce scenarios. We present results from using the system for two main sub-tasks - listing and search-query category recognition, and demonstrate an end-to-end scenario of the former one. The system uses a convolutional sequence-to-sequence approach, and to the best of our knowledge, is the first to use this approach for category recognition. We define a new metric for evaluating this model which captures the hierarchical characteristics of the data and supports displaying multiple classification results. Finally, our experimental results show the effectiveness and efficiency on real-world data.