Miracle-dz/UniFAR-FTUs huggingface dataset:
Making sense of disorder: Investigating intrinsically disordered proteins in the tardigrade proteome via a computational approach

Tardigrades, also known as water bears, are a phylum of microscopic metazoans with the extraordinary ability to endure environmental extremes. When threatened by suboptimal habitat conditions, these creatures enter a suspended animation-like state called cryptobiosis, in which metabolism is diminished, similar to hibernation. In this state, tardigrades benefit from enhanced extremotolerance, withstanding dehydration efficiently for years at a time in a type of cryptobiosis called anhydrobiosis. Recent studies have demonstrated that the tardigrade proteome is at the heart of cryptobiosis. Principally, intrinsically disordered proteins (IDPs) and tardigrade-specific intrinsically disordered proteins (TDPs) are known to help protect cell function in the absence of water. Importantly, TDPs have been successfully expressed in cells of other species experimentally, even protecting human tissue against stress in vitro. However, previous work has failed to address how to strategically identify TDPs in the tardigrade proteome holistically. The overarching purpose of this current study, consequently, was to generate a list of IDPs/TDPs associated with tardigrade cryptobiosis that are high-priority for further investigation. Firstly, a novel database containing 44,836 tardigrade proteins from 338 different species was constructed to consolidate and standardize publicly available data. Secondly, a support vector machine (SVM) was created to sort the newly constructed database entries on the binary basis of disorder (i.e., IDP versus non-IDP). Features of this model draw from disorder metrics and literature curation, correctly classifying 160 of the 171 training set proteins (~93.6%). Of the 5,415 putative IDPs/TDPs our SVM identified, we present 82 (30 having confident subclass prediction and 52 having experimental detection in previous studies). Subsequently, the role each protein might play in tardigrade resilience is discussed. By and large, this supervised machine learning classifier represents a promising new approach for identifying IDPs/TDPs, opening doors to harness the tardigrade’s remarkable faculties for biomaterial preservation, genetic engineering, astrobiological research, and ultimately, the benefit of humankind.
Guiding Irregular Nuclear Morphology on Nanopillar Array for Malignancy Differentiation in Tumor cells

For more than a century, abnormal nuclei in tumor cells, presenting subnuclear invaginations and folds on the nuclear envelope, have been known to be associated with high malignancy and poor prognosis. However, current nuclear morphology analysis focuses on the features of the entire nucleus, overlooking the malignancy-related subnuclear features in nanometer scale. The main technical challenge is to probe such tiny and randomly distributed features inside cells. We here employ nanopillar arrays to guide subnuclear features into ordered patterns enabling their quantification as a strong indicator of cell malignancy. Both breast and liver cancer cells were validated, as well as the quantification of nuclear abnormality heterogeneity. The alterations of subnuclear patterns were also explored as effective readouts for drug treatment. We envision this nanopillar-enabled quantification of subnuclear abnormal features in tumor cells opens a new angle in characterizing malignant cells and studying the unique nuclear biology in cancer. Teaser A nanopillar-based assay quantifying the abnormal nuclear morphology in tumor cells at single-cell level.
Tiltrotor Descent Aerodynamics: A Small-Scale Experimental Investigation of Vortex Ring State

An experimental investigation was completed in the NASA Ames 80- by 120-Foot Wind Tunnel with the objective of studying the aerodynamic characteristics of a tiltrotor at high descent angles in helicopter mode. A specific objective was to determine whether tiltrotors behave differently from helicopter rotors when operating in the Vortex Ring State (VRS). A single, 4-foot diameter, 3-bladed rotor, with twist and solidity similar to that of rotors on current tiltrotor aircraft, was tested with an image plane to simulate the mean effect of a second rotor. Rotor performance data were obtained over a wide range of simulated descent conditions, both with and without a semispan wing. Results of this study are presented, including changes in both mean and oscillatory rotor thrust, which are reasonably consistent with helicopter rotor VRS characteristics. Large reductions in mean rotor thrust were found in VRS. These were associated with high levels of low-frequency rotor thrust fluctuations. The image plane had a significant effect on the results, while the wing had a relatively small effect. Implications of these results for the safe operation of tiltrotor aircraft are discussed.
A critical test of deep convolutional neural networks’ ability to capture recurrent processing in the brain using visual masking

Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent processing remain unclear. In this paper, we tested a form of recurrence in deep residual networks (ResNets) to capture recurrent processing signals in the human brain. Though ResNets are feedforward networks, they approximate an excitatory additive form of recurrence. Essentially, this form of recurrence consists of repeating excitatory activations in response to a static stimulus. Here, we used ResNets of varying depths (reflecting varying levels of recurrent processing) to explain electroencephalography (EEG) activity within a visual masking paradigm. Sixty-two humans and fifty artificial agents (10 ResNet models of depths - 4, 6, 10, 18 and 34) completed an object categorization task. We show that deeper networks (ResNet-10, 18 and 34) explained more variance in brain activity compared to shallower networks (ResNet-4 and 6). Furthermore, all ResNets captured differences in brain activity between unmasked and masked trials, with differences starting at ∼98ms (from stimulus onset). These early differences indicated that EEG activity reflected ‘pure’ feedforward signals only briefly (up to ∼98ms). After ∼98ms, deeper networks showed a significant increase in explained variance which peaks at ∼200ms, but only within unmasked trials, not masked trials. In summary, we provided clear evidence that excitatory additive recurrent processing in ResNets captures some of the recurrent processing in humans. Significance statement The challenge of modeling recurrent processes is not trivial and the operationalization of recurrent processing is highly contested. In this paper, we tested the ability of deep residual networks (ResNets) to explain recurrent processes in the human brain. Though ResNets are feedforward networks, they have been shown to equate operations in recurrent neural networks. In this study, we show that deeper networks explained more variance in brain activity than shallower networks. However, all networks still performed far from the noise ceiling. Thus, we conclude that recurrent processing in ResNets captures a form of recurrent processing in humans though other types of recurrent processing (inhibition, multiplicative) that are not present in current regular deep neural networks (alexnet, cornet, resnet) are necessary for building better visual models.
The evolution of coordination: a phylogenetic meta-analysis and systematic review

To solve many cooperative problems, humans must have evolved the ability to solve physical problems in their environment by coordinating their actions. There have been many studies conducted across multiple different species regarding coordinating abilities. These studies aim to provide data which will help illuminate the evolutionary origins of cooperative problem solving and coordination. However, it is impossible to make firm conclusions about the evolutionary origins of coordinating abilities without a thorough comparative analysis of the existing data. Furthermore, there may be certain aspects of the literature that make it very difficult to confidently address evolutionary and meta-analytic questions. This study aimed to rectify this by using meta-analysis, phylogenetic analysis, and systematic review to analyse the data already obtained across multiple studies, and to assess the reliability of this data. We found that many studies did not provide the information necessary for meta analysis, or were not comparable enough to other studies to be included in analyses, meaning meta analyses were underpowered or could not be conducted due to low samples of both studies and different species. Overall, we found that many studies reported small positive effects across studies, but the standard errors of these effects frequently traversed zero.
The predictive value of double-stranded RNA for A-to-I editing detection

Motivation Adenosine-to-inosine (A-to-I) RNA editing, a crucial reaction for many processes that contribute to transcriptome plasticity, is both widely common across the transcriptome and difficult to predict due to a lack of distinctive genomic characteristics that can be obtained and analyzed computationally. An exception to this is the secondary structure of RNA molecules, which has been shown to have a major impact on the selectivity and specificity of the enzymes responsible for A-to-I editing. Yet, this information is rarely used for the task of editing site prediction. Results Here, we demonstrated the value of using base-pairing probabilities of RNA nucleotides to classify genomic sites as A-to-I RNA editing sites, using large-scale truth data which we compiled and make available for use in training future models. Our analysis suggests that the span of four bases from –2 (upstream) to +1 (downstream) of a putative editing site is most informative in this regard. A classifier trained on base-pairing probabilities alone performed with a positive predictive value (PPV) of 0.68, a negative predictive value (NPV) of 0.64, and an area under the receiver operating characteristic curve (AUC) of 0.71. By identifying structure-related features that are informative for detecting A-to-I RNA editing sites and quantifying their predictive value, this work advances our understanding of A-to-I editing determinants. Availability All source codes and data are available at https://github.com/Ally-s-Lab/P-BEP
Neural network for the prediction of treatment response in Triple Negative Breast Cancer *

The automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested cross-validation procedure for hyper-parameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.
On Making Determinations of Quality in Teaching

This article examines the notion of quality teaching, exploring its conceptual, empirical and normative properties. We begin by analyzing the concept of teaching, separating it into its task sense (what teachers try to do) and its achievement sense (the student learning that teachers foster). The analysis suggests that any determination of quality in teaching must account for both the worthiness of the activity (good teaching) as well as the realization of intended outcomes (successful teaching). Good teaching is not the same as successful teaching, nor does one logically entail the other. For teaching to be both good and successful, it must be conjoined with factors well beyond the range of control of the classroom teacher. The analysis of the concept of teaching is then used to explore three programs of research on teaching: process-product, cognitive science, and constructivist. The article concludes with an analysis of the policy implications of this explication of quality teaching.
Is Teaching Really by the Book?

There is a widely held belief that classroom instruction in elementary school is dominated by textbooks. Indeed, there is little doubt that textbooks are present in most classrooms most of the time. ~ But the assumption that textbooks drive instruction because they are ubiquitous may not be tenable. Exactly what the presence of textbooks signals about their use has not been adequately studied or analyzed. In this essay I shall review the limited research literature on this topic, raise some questions that need answers, and present some preliminary findings about textbook use in actual classrooms.
Lattice piecewise affine approximation of explicit linear model predictive control

In this paper, the lattice piecewise affine (PWA) approximation of explicit linear model predictive control (MPC) is proposed. The training data consists of the state samples and corresponding affine control laws, based on which the lattice PWA approximation is constructed. The proposed approximation is identical to the explicit MPC control law in unique-order (UO) regions containing the sample points as interior points, thus resemble the explicit MPC control law in a large number of regions. Through simplifying the terms and literals in the lattice PWA approximation, both the storage requirement and online computation complexity are largely decreased. The performance of the proposed approximation strategy is tested through a simulation example, and the result shows that with a moderate number of sample points, the lattice PWA approximation is very close to the explicit MPC control law.
Co-optimization of the Spooling Motion and Cross-Current Trajectory of an Energy-Harvesting Marine Hydrokinetic Kite

This paper examines the problem of simultaneously optimizing the spooling and cross-current flight trajectories of a tethered underwater energy harvesting kite. This work is motivated by the potential of tethered kites to provide attractive levelized costs of electricity, especially when cross-current motion is exploited in order to maximize energy harvesting. The literature explores trajectory optimization for tethered energy harvesting, for both airborne and marine hydrokinetic energy systems. However, the simultaneous co-optimization of both the spooling and cross-current trajectories of a marine hydrokinetic kite remains relatively unexplored. The paper formulates this co-optimization problem using a 3 degree-of-freedom kite model, coupled with an inelastic tether. A Fourier series expansion is then used for solving this co-optimization problem, and the key features of the resulting optimal trajectory are analyzed. Significant energy harvesting is achieved through co-optimization, approaching theoretical maximum power for cross-current systems.
Linearly Convergent Distributed Optimization Methods with Compressed Innovation

Data compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks. In this work, we propose a novel communication-efficient linearly convergent distributed (COLD) algorithm with compressed innovation—the difference between a model and its estimate. COLD supports a class of quantizers with δ-contraction property. For strongly convex distributed problems, we explicitly quantify in theory how the compression affects the linear convergence rate. To the best of our knowledge, we are the first to achieve linear convergence in distributed optimization allowing biased and δ-contracted compressors, which is a typical case in practice. Numerical experiments validate our theoretical results.
Communication Delay-Aware Network Topology Adaptation for Cooperative Control of Vehicular Platoons

Communication-enabled Cooperative Adaptive Cruise Control (CACC) is an important technology to improve the safety and traffic capacity of vehicle platoons. However, most existing CACC research considers a conventional communication delay and fixed network topology. When the network is attacked, the stability of the system will be affected. In this paper, a CACC system considering dynamic network topologies and communication delay jitters is proposed. A multi-vehicle look ahead network topology of CACC is designed to investigate the relationship between communication delay and the optimal network topology. Control parameters are obtained by solving Algebraic Riccati equations. Moreover, the relationship between the minimum inter-vehicle spacing and the communication delay is analyzed and derived under different network topologies. Finally, the performance of the proposed CACC system with adaptive network topologies is evaluated by simulations. Simulation results demonstrate that the proposed CACC system outperforms the H infinity synthesis-based controller considering the fixed network topologies. The robustness against communication failure of the proposed system, such as the DeGrading of Service (DGoS) attack, can be significantly improved, which can reduce the minimum safety headway buffer and further support better mobility of vehicles.
Finite-time stabilization under state constraints

The problem of finite-time stabilization of linear systems under state and control input constraints in the presence of external disturbances is addressed. The proposed control is designed such that the closed-loop system is 1) superstable, when system trajectories risk to violate the state constraints, and 2) homogeneous with a negative degree, otherwise. While the former property ensures the fulfillment of the state and control constraints, the latter implies finite-time stability. The robustness of the control scheme with respect to disturbances is studied. Theoretical results are supported by numerical simulations.
A Time-delay Approach for Sampled-data and Delayed Scalar Extremum Seeking*

This paper proposes a constructive approach for sampled-data delayed extremum seeking (ES) by using two time-delay approaches: one to averaging and another one to sampled-data control. We first investigate the continuous-time ES with square wave dithers and then expand the proposed time-delay method to its sampled-data implementation with constant delay. By transforming the ES system to a time-delay system, we have developed an improved stability analysis via a novel Lyapunov-Krasovskii functional (LKF). We derive the practical stability conditions in terms of linear matrix inequalities (LMIs) for the resulting time-delay system. Under the assumption of some known bounds on the extremum value of the map and its Hessian, the time-delay approach offers a quantitative calculation on the upper bounds of the dither-sampling period and the constant delays that the ES system is able to tolerate, as well as the ultimate bound of the extremum seeking error. The proposed method also provides a bound on the initial deviation starting from which the solution is ultimately bounded.
Supervisory Control for Stabilization under Multiple Local Average Payoff Constraints

This work investigates stabilization of discrete event systems via supervisory control under a series of quantitative constraints. Every event in the system model is weighted by a vector which represents payoffs of quantitative variables associated by the event. Multidimensional weight flows are generated when events occur successively. The supervisor aims to drive the generated strings to a reach set of target states to stabilize the system. Meanwhile, the supervisor is also responsible for regulating the weight flows so as to guarantee that at each dimension, the average weight every a certain number of events does not fall below a given threshold. Next, the formulated supervisory control problem is transformed to a two-player game between the supervisor and the environment on a specially defined game structure. Then we specify the objective of the game and synthesize game winning supervisors, which turn out to provably solve the proposed problem.
Loss-Averse Behavior May Destabilize Nash Equilibrium: Generalized Stability Results for Noncooperative Agents

The generalized stability results of a Nash equilibrium in two-agent noncooperative systems are investigated with loss-averse consideration and quadratic payoffs. In this system, each agent is driven by the pseudo-gradient dynamics with piecewise constant sensitivity parameters for the situations of losing payoffs and gaining payoffs. Based on the mode analysis, the sufficient and necessary conditions under which agents’ state converge to the Nash equilibrium are derived. We make a bifurcation analysis for the loss-aversion-based noncooperative system to observe how the loss-averse behaviors change the stability property of the Nash equilibrium. It is found that the loss-averse behaviors may destabilize the Nash equilibrium. We present a sufficient condition of robust stability under which the loss-averse behaviors never destabilize the Nash equilibrium for any sensitivity parameters. A numerical example is provided to illustrate the effectiveness of our results.
No-Regret Distributed Learning in Two-Network Zero-Sum Games

We consider a distributed learning problem in a two-network zero-sum game with finite action sets, where the agents within each network is connected through time-varying directed graphs and the agents from distinct networks are connected by time-varying bipartite graphs. Each agent in a network has its own cost function and can receive information from its neighbors, while the networks have no global decision-making capability. We propose a distributed multiplicative weights algorithm to compute a mixed-strategy Nash equilibrium. We first establish a sublinear regret bound on the sequence of iterates for each agent. Additionally, we study the time-averaged sequence of iterates and prove its convergence to the set of mixed-strategy Nash equilibria with suitably selected diminishing step-sizes.
Discrete-time Output Regulation and Visuomotor Adaptation

We consider a disturbance rejection problem for discrete-time LTI systems with a known plant and unknown exosystem, and we utilize adaptive internal models to solve this problem. The main application is short-term visuomotor adaptation, a subconscious brain process taking place over repetitive trials and elicited by a visual error closely following the execution of a movement. Our model is vetted by recovering results from visuomotor experiments involving removal or loss of measurements during adaptation.
Synthesis of interconnected control systems under reachability specifications

In this work we synthesize constrained control laws in a compositional way to enforce a reachability specification for an interconnected control system. We first synthesize for each system a controller enforcing a local reachability specification using linear feasibility programs. Then within the framework of parametric assume-guarantee contracts, which encode the behavior of a control system in a parametric domain, we establish a small gain theorem guaranteeing the overall specification after interconnecting the systems together. The resulting controller could then be implemented in a decentralized, distributed, or mixed setup. We also show the effectiveness of our approach with a room temperature control example.