Miracle-dz/UniFAR-FTUs huggingface dataset:
Sampling biases obscure the early diversification of the largest living vertebrate group

Extant ray-finned fishes (Actinopterygii) dominate marine and freshwater environments, yet their spatiotemporal diversity dynamics following their origin in the Palaeozoic are poorly understood. Previous studies investigate face-value patterns of richness, with only qualitative assessment of potential biases acting on the Palaeozoic actinopterygian fossil record. Here, we investigate palaeogeographic trends and apply richness estimation techniques to a recently-assembled occurrence database for Palaeozoic ray-finned fishes. We reconstruct patterns of local richness of Palaeozoic actinopterygians, alongside sampling standardised estimates of ‘global’ diversity. We identify substantial fossil record biases, such as geographic bias in the sampling of actinopterygian occurrences centred around Europe and North America. Similarly, estimates of diversity are skewed by extreme unevenness in the abundance distributions of occurrences, reflecting past taxonomic practices and historical biases in sampling. Increasing sampling of poorly represented regions and expanding sampling beyond the literature to include museum collection data will be critical in obtaining accurate estimates of Palaeozoic actinopterygian diversity. In conjunction, applying diversity estimation techniques to well-sampled regional subsets of the ‘global’ dataset may identify accurate local diversity trends.
Computational modeling of TGF-β2:TβRI:TβRII receptor complex assembly as mediated by the TGF-β co-receptor betaglycan

Transforming growth factor-β1, -β2, and -β3 (TGF-β1, -β2, and -β3) are secreted signaling ligands that play essential roles in tissue development, tissue maintenance, immune response, and wound healing. TGF-β homodimers signal by assembling a heterotetrameric complex comprised of two type I receptor (TβRI):type II receptor (TβRII) pairs. TGF-β1 and TGF-β3 signal with high potency due to their high affinity for TβRII, which engenders high affinity binding of TβRI through a composite TGF-β:TβRII binding interface. However, TGF-β2 binds TβRII 200-500 more weakly than TβRII and signals with lower potency compared to TGF-β1 and -β3. Remarkably, potency of TGF-β2 is increased to that of TGF-β1 and -β3 in the presence of an additional membrane-bound co-receptor, known as betaglycan (BG), even though betaglycan does not directly participate in the signaling mechanism and is displaced as the signaling receptors, TβRI and TβRII, bind. To determine the role of betaglycan in the potentiation of TGF-β2 signaling, we developed deterministic computational models with different modes of betaglycan binding and varying cooperativity between receptor subtypes. The models, which were developed using published kinetic rate constants for known quantities and optimization to determine unknown quantities, identified conditions for selective enhancement of TGF-β2 signaling. The models provide support for additional receptor binding cooperativity that has been hypothesized, but not evaluated in the literature. The models further showed that betaglycan binding to TGF-β2 ligand through two domains provides an effective mechanism for transfer to the signaling receptors that has been tuned to efficiently promote assembly of the TGF-β2(TβRII)2(TβRI)2 signaling complex.
Cellular And Molecular Effects Of Understudied Kinase Pregnancy-Upregulated Non-Ubiquitous Calcium-Calmodulin Dependent Kinase (PNCK) In Renal Cell Carcinoma

Renal Cell Carcinoma (RCC) is a uniformly fatal disease when advanced. While immunotherapy and tyrosine kinase inhibitor-based combinations are associated with improved progression-free and overall survival, the majority of patients eventually develop treatment resistance and succumb to progressive, refractory disease. This underscores the urgent need to identify novel, non-canonical RCC targets for drug development. Through a comprehensive pan-cancer, pan-kinome analysis of the Cancer Genome Atlas (TCGA), the understudied kinase, pregnancy upregulated non-ubiquitous calcium-calmodulin dependent kinase (PNCK) was identified as the most differentially overexpressed kinase in RCC. PNCK mRNA was significantly overexpressed in RCC tissues compared to adjacent normal tissue, and its overexpression correlated with tumor T-stage grade and poor disease specific survival in both clear cell and papillary RCCs. PNCK overexpression in VHL mutant and VHL wild type RCC cell lines was associated with increased CREB phosphorylation, as well as increased cell proliferation and cell cycle progression. PNCK down-regulation, conversely, was associated with inhibition of CREB phosphorylation, decreased cell proliferation, cell cycle arrest and increased apoptosis, with differential effects observed between VHL mutant and VHL wild type cell lines. Pathway analyses in PNCK knockdown cells showed significant down regulation of hypoxia and angiogenesis pathways, as well as modulation of pathways promoting cell cycle arrest and apoptosis. The above results demonstrate for the first time the biological role of PNCK, an understudied kinase, in renal cell carcinoma and validate PNCK as a potential novel target for drug development in this fatal disease.
Real-time single-molecule observation of chaperone-assisted protein folding

The ability of Hsp70 molecular chaperones to remodel the conformation of their clients is central to their biological function; however, questions remain regarding the precise molecular mechanisms by which Hsp70 machinery interacts with the client and how this contributes towards efficient protein folding. Here, we used Total Internal Reflection Fluorescence (TIRF) microscopy and single-molecule Fluorescence Resonance Energy Transfer (smFRET) to temporally observe the conformational changes that occur to individual firefly luciferase (Fluc) proteins as they are folded by the bacterial Hsp70 system. For the first time, we observed multiple cycles of chaperone binding-and-release to an individual client during refolding and that high rates of chaperone cycling improves refolding yield. Furthermore, we demonstrate that DnaJ remodels misfolded proteins via a conformational selection mechanism whereas DnaK resolves misfolded states via mechanical unfolding. This study illustrates that the temporal observation of chaperone-assisted folding enables the elucidation of key mechanistic details inaccessible using other approaches.
Deep reinforcement learning for optimal experimental design in biology

The field of optimal experimental design uses mathematical techniques to determine experiments that are maximally informative from a given experimental setup. Here we apply a technique from artificial intelligence—reinforcement learning—to the optimal experimental design task of maximizing confidence in estimates of model parameter values. We show that a reinforcement learning approach performs favourably in comparison with a one-step ahead optimisation algorithm and a model predictive controller for the inference of bacterial growth parameters in a simulated chemostat. Further, we demonstrate the ability of reinforcement learning to train over a distribution of parameters, indicating that this approach is robust to parametric uncertainty. 1 Author summary Biological systems are often complex and typically exhibit non-linear behaviour, making accurate model parametrisation difficult. Optimal experimental design tools help address this problem by identifying experiments that are predicted to provide maximally accurate parameter estimates. In this work we use reinforcement learning, an artificial intelligence method, to determine such experiments. Our simulation studies show that this approach allows uncertainty in model parameterisation to be directly incorporated into the search for optimal experiments, opening a practical avenue for training an experimental controller without confident knowledge of the system’s parameter values. We present this method as complementary to existing optimisation approaches and we anticipate that artificial intelligence has a fundamental role to play in the future of optimal experimental design.
Corynebacterium matruchotii fitness enhancement of adjacent streptococci by multiple mechanisms

Polymicrobial biofilms are present in many environments particularly in the human oral cavity where they can prevent or facilitate the onset of disease. While recent advances have provided a clear picture of both the constituents and their biogeographical arrangement, it is still unclear what mechanisms of interaction occur between individual species in close proximity within these communities. In this study we investigated two mechanisms of interaction between the highly abundant supragingival plaque (SUPP) commensal Corynebacterium matruchotii and Streptococcus mitis which are directly adjacent in vivo. We discovered that C. matruchotii enhanced the fitness of streptococci dependent on its ability to detoxify streptococcal-produced hydrogen peroxide and its ability to oxidize lactate also produced by streptococci. We demonstrate that the fitness of adjacent streptococci was linked to that of C. matruchotii and that these mechanisms support the previously described “corncob” arrangement between these species but that this is favorable only in aerobic conditions. Further we utilized scanning electrochemical microscopy (SECM) to quantify lactate production and consumption between individual bacterial cells for the 1st time, revealing that lactate oxidation provides a fitness benefit to S. mitis and not pH mitigation. This study describes mechanistic interactions between two highly abundant human commensals that can explain their observed in vivo spatial arrangements and suggest a way by which they may help preserve a healthy oral bacterial community.
Sequence-based pH-dependent prediction of protein solubility using CamSol

Solubility is a property of central importance for the use of proteins in research and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are resource-intensive and time-consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the extension of one of such methods, CamSol, to include in the predictions the effect of the pH of the solubility. We illustrate the accuracy of the pH-dependent predictions on a variety of antibodies and other proteins.
Reduction-based Solving of Multi-agent Pathfinding on Large Maps Using Graph Pruning

Multi-agent pathfinding is the problem of finding collision-free paths for a set of agents. Solving this problem optimally is computationally hard, therefore many techniques based on reductions to other formalisms were developed. In comparison to search-based techniques, the reduction-based techniques fall behind on large maps even for a small number of agents. To combat this phenomenon, we propose several strategies for pruning vertices off large instances that will most likely not be used by agents. First, we introduce these strategies conceptually and prove which of them maintain completeness and optimality. Eventually, we conduct an exhaustive evaluation and show that graph pruning strategies make reduction-based solvers comparable to search-based techniques on large maps while maintaining their advantage on small dense maps.
Multi-Agent Path Finding for Precedence-Constrained Goal Sequences

With the rising demand for deploying robot teams in autonomous warehouses and factories, the Multi-Agent Path Finding (MAPF) problem has drawn more and more attention. The classical MAPF problem and most of its variants focus on navigating agent teams to goal locations while avoiding collisions. However, they do not take into account any precedence constraints that agents should respect when reaching their goal locations. Planning with precedence constraints is important for real-world multi-agent systems. For example, a mobile robot can only pick up a package at a station after it has been delivered by another robot. In this paper, we study the Multi-Agent Path Finding with Precedence Constraints (MAPF-PC) problem, in which agents need to visit sequences of goal locations while satisfying precedence constraints between the goal locations. We propose two algorithms for solving this problem systematically: Conflict-Based Search with Precedence Constraints (CBS-PC) is complete and optimal, and Priority-Based Search with Precedence Constraints (PBS-PC) is incomplete but more efficient in finding near-optimal solutions in practice. Our experimental results show that CBS-PC scales to dozens of agents and hundreds of goal locations and precedence constraints, and PBS-PC scales to hundreds of agents, around one thousand goal locations, and hundreds of precedence constraints.
Intelligent Communication over Realistic Wireless Networks in Multi-Agent Cooperative Games

In MARL, communication among agents is essential to establish cooperation. Over the realistic wireless network, many factors can affect transmission reliability, especially considering that the wireless network condition varies with agents’ mobility. We propose a framework that improves the intelligence of communication over realistic wireless networks in two fundamental aspects: (1) When : Agents learn the timing of communication based on message importance and wireless channel condition. We further propose a communication lagging technique to make the training end-to-end differentiable. (2) What : Agents augment message contents with wireless network measurements. The messages improve both the game and communication actions of the agents. Experiments on a standard environment show that compared with state-of-the-art, our framework enables more intelligent collaboration and thus achieves significantly better game performance, convergence speed and communication efficiency.
Ensemble and Incremental Learning for Norm Violation Detection

The use of norms to guide and coordinate interactions has gained tremendous attention in the multiagent community. However, as the interest moves towards dynamic socio-technical systems, where human and software agents interact and interactions are required to adapt to the human’s changing needs, new challenges arise. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or that their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning the meaning of a norm violation from the limited interaction data. For this, we propose a framework that learns and updates the meaning of a norm violation from interaction data through a combination of ensemble and incremental learning techniques. Ensemble learning handles the imbalance class distribution of the interaction stream, and incremental learning is used to continuously update the ensemble models as community members interact, which is an essential feature to keep the ensemble models in accordance with the latest community view on the meaning of norm violation. We evaluate the proposed approach in the use case of Wikipedia article edits, where interactions revolve around editing articles and the norm in question is prohibiting vandalism. Results show that the proposed framework can learn the meaning of a norm violation in a setting with data imbalance and concept drift.
AdLeap-MAS: An Open-source Multi-Agent Simulator for Ad-hoc Reasoning

Ad-hoc reasoning models are recurrently used to solve some of our daily tasks. Intending to avoid worthless investments or spend valuable resources, these smart systems requires a proper evaluation before acting in the real-world. In this paper, we demonstrate AdLeap-MAS , a novel framework focused on enabling quick and easy testing of smart algorithms in ad-hoc reasoning domains
Priced Gerrymandering

We initiate the study of the gerrymandering problem when changing the district of a voter incurs a certain cost. In this problem, the input is a set of voters having votes over a set of alternatives, a graph on the voters, a partition of voters into connected districts, a cost of every voter for changing her district, a budget, and a target winner. We need to compute if the given partition can be modi-fied so that (i) the target alternative wins the resulting election, (ii) the modification is budget feasible, and (iii) every new district is connected. We study four natural variants of the above problem – the graph on the voters being arbitrary vs complete graph (corre-sponds to removing the connectivity requirement for districts) and the cost of moving every voter being uniform vs non-uniform. We show that all the four problems are NP -complete even under quite restrictive scenarios. Hence, our results show that district based elections are quite resistant under this new kind of electoral attack. We complement our intractability results by showing that two of our problems admit polynomial-time algorithms if the budget or the number of districts is a constant.
Towards Multi-Agent Interactive Reinforcement Learning for Opportunistic Software Composition in Ambient Environments

In order to manage the ever-growing number of devices present in modern and future ambient environments, as well as their dynamics and openness, we aim to propose a distributed multi-agent system that learns, in interaction with a human user, what would be their preferred applications given the services available. The goal of this Ph.D. thesis is to focus on the interaction between a reinforcement learning system and the human user, to improve the system’s learning capabilities as well as the user’s ease with the system, and ultimately build a working prototype, usable by end-users.
Statistical and Computational Models for Whole Word Morphology

The purpose of this thesis is to provide an unsupervised machine learning approach for language morphology, in which the latter is modeled as string transformations on whole words, rather than the segmentation of words into smaller structural units. The choice of a transformation-based morphology model is motivated mainly by the realization that most Natural Language Processing methods treat words as basic units of language, and thus the application of morphological analysis is usually centered at describing relations between words, rather than processing subword units. On the other hand, segmentational approaches sometimes require answering questions that are both difficult and irrelevant for most applications, such as whether to segment the German word fähig ‘able’ into fäh-ig or the English suicide into sui-cide. Furthermore, non-concatenative phenomena like German umlaut are not covered by segmentational approaches. The whole-word-based model of morphology employed in this work is rooted in linguistic theories rejecting the notion of morpheme, particularly Whole Word Morphology. In this theory, it is words that are minimal elements of the language combining form and meaning. Morphology is expressed in terms of patterns describing a regular interrelation in form and meaning within pairs of words. Such formulation naturally results in a representation of the lexicon as a graph, in which words are vertices and pairs of words related via a productive, systematic pattern are connected with an edge. The contribution of the present thesis is twofold. Firstly, I provide a computational model for Whole Word Morphology. Its foundation is a formal definition of morphological rule as a function mapping strings to sets of strings (as the application of a rule to a word may result in multiple outcomes or no outcome). The rules are formulated in terms of patterns, which consist of alternating constant elements – describing parts of the word that are affected by the rule – and variable elements,
Sympathy-based Reinforcement Learning Agents

As artificial agents become increasingly prevalent in our daily lives, it becomes imperative to equip them with an awareness of societal norms; specifically, the ability to account for and be considerate towards others they may cohabit with. In this work, we explore the ability for an agent trained through reinforcement learning to exhibit sympathetic behaviours towards another (independent) agent in the environment. We propose to achieve such behaviours by first inferring the reward function of the independent agent, through inverse reinforcement learning, and subsequently learning a policy based on a sympathetic reward function - a convex combination of the inferred rewards and the agent’s own rewards. The corresponding weighting is determined by a sympathy function which is computed based on the estimated return of the agent’s current action relative to that of all possible actions it could have taken. We evaluate our approach on adversarial as well as assistive environment settings, and demonstrate the ability of our sympathetic agent to perform well at its own goal, while simultaneously giving due consideration to another agent in its environment. We also empirically examine and report the sensitivity of our agent’s performance to the hyperparameters introduced in our proposed framework.
Quantitative Group Trust: A Two-Stage Verification Approach

This paper is about modeling and verifying quantitative group trust. We present a formal analysis of this concept that allows us to express and reason about trust in multi-agent systems in a quantitative setting. We introduce GTL, the graded branching temporal logic that includes operators for quantitative aspects of trust within a group. A two-stage verification procedure of the logic is presented. The first stage is a transformation procedure, and the second stage is an indirect procedure that uses an existing model checking algorithm. Theoretical results about the soundness, completeness and complexity of the procedure are presented.
Graphical Representation Enhances Human Compliance with Principles for Graded Argumentation Semantics

We examined principles of graded argumentation semantics (independence, anonymity, void precedence, and maximality) to explore if (a) they realistically model human reasoning, (b) graphical representation of arguments facilitates compliance with the principles, (c) there is a positive correlation between compliance with different principles, and (d) this compliance is related to cognitive reflection, need for cognition and faith in intuition. The participants ( 𝑁 = 96) were randomly assigned to one of two experimental conditions - the graph group was presented with textual and graphical representations, while the second group was presented only with textual arguments. Our results indicate that there are major differences in the compliance with the several argumentation principles studied in this paper. However, compliance with argumentation principles was consistently better and more consistent in the graph group. Moreover, cognitive reflection correlated with compliance to some principles, but only in the graph group.
Reinforcement Learning for Traffic Signal Control Optimization: A Concept for Real-World Implementation

The improvement of traffic control is one of the most important but also most ambitious goals in the field of urban traffic today. Due to the domain’s long history, new methods must first establish themselves in practice and, above all, demonstrate reasonable and robust optimization results. Potential methods which came up in recent years are Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL). In research, the use of RL/MARL for traffic optimization is widely spread. However, it has not yet managed to make it from simulations into practical implementation due to: (1) The lack of real data for the online estimation of the state space. (2) The compatibility to real controllers. (3) The necessity of guarantees to ensure the resilience of the controls. Enabled by a project developing testbeds and practical approaches to optimize traffic through AI, we present a concept to close this gap to the online control of real networks and to overcome the stated issues.