Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the search of learning classifier systems through interpretable feature clustering

Learning Classifier Systems (LCS) are a well-known machine learning method, producing sets of interpretable rules in order to solve a variety of problems. Despite this, a common issue that these systems run into is the creation of unhelpful rules, caused by having multiple features in the data representing similar areas of knowledge. While we can logically know that these rules will not be useful in conjunction with each other, this is much more difficult for the algorithm to innately know. This paper presents an exploration into using clustering algorithms for feature selection in LCS, selecting features that represent each major cluster of feature information. Combined with the innate power of LCS at finding nonlinear decision boundaries, these selected features can achieve results close to that of the full feature set while reducing the training time required to reach those results. The feature selection performed is highly interpretable, allowing for different features to be selected while maintaining the information spread in the feature subset. Learning Classifier Systems (LCS) [1] are an Evolutionary Computation method that learns a population of rules in order to perform tasks. One common application for LCS systems is classification, where each rule makes an individual mapping of the class from an input, and these heuristics are combined to form an overall class prediction.
Towards evolutionary self-optimization of large multi-agent systems

The development of IoT technologies raises the question of their self-optimization. For this purpose we propose an approach based on a multi-agent interpretation, modeling discrete space and time by Cellular Automata (CA), and applying a game-theoretical model known as spatial Prisoner's Dilemma (SPD). A 2D space is occupied by agents which belong to species with certain strategies. Each agent participates in games with neighbors and its goal is to maximize its cumulated payoff. Species competing for space apply locally a mechanism of evolutionary selection, where the cumulated payoff is considered as fitness. As a result of the competition, more profitable species replace less profitable ones. While agents act locally to maximize their incomes we study conditions of emerging collective behavior measured by the global average total payoff of which the players are not aware. We show that collective behavior based on achieving in a fully distributed way a Nash equilibrium (NE) can emerge if some conditions of the game are fulfilled, in particular when an "income sharing mechanism" is introduced.
Exploiting landscape features for fitness prediction in university timetabling

A small but growing number of papers have shown that landscape metrics can be useful for performance prediction, usually on classic unconstrained problems. In this paper, we consider the Curriculum-Based Course Timetabling problem, a heavily constrained problem known to have very neutral landscapes, and extract over 100 instance and landscape features to construct prediction models. An Iterated Local Search is used to sample the landscape, and the performance of both Simulated Annealing and a Hybrid Local Search algorithm are predicted using linear regression. Using as few as 4 features obtained via feature selection, our simple models are able to accurately predict the final fitness for either approach with an R-squared of approximately 0.95.
Multi-objective path planning for environmental monitoring using an autonomous surface vehicle

Intelligent environmental monitoring is an exciting and impactful use case of autonomous vehicles. In this work, we explore measuring water quality parameters via a battery-powered Autonomous Surface Vehicle to obtain an accurate spatial model of the quality of a water resource while minimizing the distance traveled (alternatively, time, or battery consumption). We develop an objective function for this class of problems and propose an evolutionary multi-objective optimization algorithm that selects the measurement locations while using a quick novel construction heuristic for determining the path. Our method leverages new crossover and mutation operators that seem to be of independent interest. We demonstrate the efficacy of the proposed approach in obtaining results for two different waterbodies: Lake Ypacarai in Paraguay, and Mar Menor in Spain.
Social Capital and Civil Society in Public Policy, Social Change, and Welfare

Abstract Mainstream economics reduces concepts of social capital and civil society to means for profit and competition, reproducing inequality and power. Alternative approaches identify social capital with state-society synergy relations and generalized networks of cooperation, which enable the civil society to promote solidarity, democracy, public policy, social change, and welfare. Similarly, Post-Keynesian Institutionalism recognizes the creative role of the state, democratic institutions, and civic values. However, questions remain regarding how social groups and the state interact and promote change. This article seeks to fill that void by connecting these analyses with the literature on the commons and participatory and deliberative democracy.
Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication

Pulse shaping for coherent optical fiber communication has been an active area of research for the past decade. Most of the early schemes are based on classic Nyquist pulse shaping that was originally intended for linear channels. The best known classic scheme, the split digital back-propagation (DBP), uses joint pre-distortion and post equalization and hence, a nonlinear transmitter (TX); it, however, suffers from spectral broadening on the fiber due to the Kerr-effect. With the advent of deep learning in communications, it has been realized that an Autoencoder can learn to communicate efficiently over the optical fiber channel, jointly optimizing geometric constellations and pulse shaping - while also taking into account linear and nonlinear impairments such as chromatic dispersion and Kerr-nonlinearity. E.g., arXiv:2006.15027 shows how an Autoencoder can learn to mitigate spectral broadening due to the Kerr-effect using a trainable linear TX. In this paper, we extend this linear architectural template to a scalable nonlinear pulse shaping consisting of a Convolutional Neural Network at both transmitter and receiver. By introducing a novel $\gamma$-lifting training procedure tailored to the nonlinear optical fiber channel, we achieve stable Autoencoder convergence to pulse shapes reaching information rates outperforming the classic split DBP reference at high input powers.
Delivery Model Innovation: Insights From Infrastructure Projects

This special issue seeks to bring together contributions of a nascent community of scholars studying infrastructure delivery models; what they are, how they are created, and how they change over time and across institutional fields. This area of research is crucial to advancing the field of project management and, with its links to neighboring fields such as innovation, organizational theory, and strategic management, is a fertile ground for developing new insights and knowledge. Before we introduce the articles that make up the special issue, we describe the policy environment and drivers for innovation in infrastructure delivery models, situate infrastructure delivery models within project management scholarship, and provide some conceptual scaffolding for considering the nature of innovation in delivery models. We then turn to a discussion of how each article in the special issue advances our knowledge of delivery models and project management. Drawing inspiration from the articles, we conclude by sketching out the building blocks and core conceptual components of a delivery model, and laying the foundations for a more nuanced comparison of existing, emerging, and novel delivery models over time and across institutional fields.
Adera: A drug repurposing workflow for neuro-immunological investigations using neural networks

Drug repurposing in the context of neuro-immunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and rather focuses on discovering new usage for known medications. Neuro-immunological diseases such as Alzheimer’s, Parkinson, multiple sclerosis and depression include various pathologies that resulted from the interaction between the central nervous system and the immune system. However, repurposing of medications is hindered by the vast amount of information that needs mining. To challenge the need for repurposing known medications for neuro-immunological diseases, we built a deep neural network named Adera to perform drug repurposing. The model uses two deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network we explored the usage of two different loss function, binary cross entropy and means square error (MSE). Furthermore, we investigated the effect of ten different network architecture with each loss function. Our results show that for the binary cross entropy loss function, the best architecture consists of a two layers of convolution neural network and it achieves a loss of less than 0.001. In the case of MSE loss function a shallow network using aRelu activation achieved an accuracy of over 98 % and loss of 0.001. Additionally, Adera was able to predict various drug repurposing targets in agreement with DRUG Repurposing Hub. These results establish the ability of Adera to repurpose with high accuracy drug candidates that can shorten the development of the drug cycle. The software could be downloaded from https://github.com/michel-phylo/ADERA1.
REM sleep has minute-scale rhythms in mice and humans: A non-binary continuum between phasic and tonic microstates

Rapid eye movement sleep (REM) is believed to have a binary temporal structure with “phasic” and “tonic” microstates, defined by motoric activity versus quiescence, respectively. However, we observed in mice that the frequency of theta activity (a marker of rodent REM) fluctuates in a non-binary fashion, with the extremes of that fluctuation correlating with phasic-type and tonic-type facial motricity. This demonstrates that phasic and tonic REM rather represent ends of a continuum. These cycles of brain physiology and facial movement occurred at 0.01-0.06 Hz, or infraslow frequencies, and affected cross-frequency coupling and neuronal assembly activity in the neocortex, suggesting network functional impact. We then confirmed that humans also demonstrate non-binary phasic/tonic microstates, with continuous 0.01-0.04 Hz respiratory rate cycles matching the incidence of eye movements. Thus, we discovered a fundamental property of REM, which can yield new insights into our understanding of sleep health.
Neuronal glutamate transporters control reciprocal inhibition and gain modulation in D1 medium spiny neurons

Understanding the function of glutamate transporters has broad implications for explaining how neurons integrate information and relay it through complex neuronal circuits. Most of what is currently known about glutamate transporters, specifically their ability to maintain glutamate homeostasis and limit glutamate diffusion away from the synaptic cleft, is based on studies of glial glutamate transporters. By contrast, little is known about the functional implications of neuronal glutamate transporters. The neuronal glutamate transporter EAAC1 is widely expressed throughout the brain, particularly in the striatum, the primary input nucleus of the basal ganglia, a region implicated with movement execution and reward. Here, we show that EAAC1 limits synaptic excitation onto a population of striatal medium spiny neurons identified for their expression of D1 dopamine receptors (D1-MSNs). In these cells, EAAC1 also contributes to strengthen lateral inhibition from other D1-MSNs. Together, these effects contribute to reduce the gain of the input-output relationship and increase the offset at increasing levels of synaptic inhibition in D1-MSNs. By reducing the sensitivity and dynamic range of action potential firing in D1-MSNs, EAAC1 limits the propensity of mice to rapidly switch between behaviors associated with different reward probabilities.
Contrasting roles of landscape compositions in shaping functional traits of arthropod community in subtropical vegetable fields

Agricultural intensification and land use transformation are among the main driving forces of the unprecedented decline of biodiversity and ecosystem services in croplands. Trait-based approaches provide a unique framework to detect the potential mechanisms of how this intensification affects biodiversity and alter ecosystem services. However, the potential relationship between arthropod traits and various types of habitats is still poorly understood, especially in subtropical vegetable agroecosystems. Here, we conducted a trait-based approach to evaluate the variable roles of different habitats on functional trait diversity and the structure of the arthropod community in brassica vegetable crops. Twenty-three conventional cruciferous vegetables fields were sampled over two years in three regions in Fujian, China. We found that the increasing proportion of non-brassica vegetable plantations and water bodies negatively affected the functional diversity of arthropods, whereas forest and grassland habitats showed a positive correlation, indicating habitat filtering for certain traits or trait combinations. This study demonstrates the importance of landscape composition as an ecological filter for vegetable arthropod community, and identifies how the proportion of different habitats selected for or against specific functional traits. Our findings support that increasing forest and grassland areas adjacent to vegetable fields can play a vital role in promoting the functional diversity of arthropod communities. Since the natural enemy assemblages supported by these habitats bear combinations of diverse traits adapted to disturbance, they have the potential to enhance pest suppression in the highly variable environment of vegetable crops.
ATP-competitive and allosteric inhibitors induce differential conformational changes at the autoinhibitory interface of Akt

The protein kinase Akt is a master regulator of pro-growth signalling in the cell. Akt is activated through its targeted recruitment to phosphoinositides, leading to disruption of the autoinhibitory interface between the kinase and pleckstrin homology (PH) domains. Hyper activation of Akt is common in oncogenic transformation, with multiple oncogenic activating mutants identified in Akt. This has led to the development of potent and selective ATP-competitive and allosteric inhibitors for Akt. Paradoxically, some ATP-competitive Akt inhibitors cause hyperphosphorylation of Akt. Here, using hydrogen deuterium exchange mass spectrometry (HDX-MS), we interrogated the conformational changes upon binding to the Akt active site inhibitor A-443654, and the Akt allosteric inhibitor MK-2206. We compared the conformational changes that occurred for each inhibitor under three different states of Akt: i-inactive monophosphorylated, ii-partially active tris-phosphorylated [T308, T450, S473], and iii-fully activated, tris-phosphorylated bound to PIP3 membranes. The allosteric MK-2206 inhibitor results in large scale allosteric conformational changes in all states, and restricts membrane binding through sequestration of the PH domain. Binding of the A-443654 inhibitor led to large scale allosteric conformational changes in both the monophosphorylated and phosphorylated states, leading to an alteration in the autoinhibitory PH-kinase interface. We also observed increased protection in the PH domain upon membrane binding in the presence of A-443654, suggesting that the PH domain is more accessible for membrane binding. This work provides unique insight into the autoinhibitory conformation of the PH and kinase domain and dynamic conformational changes induced by Akt inhibitors, and has important implications for the design of Akt targeted therapeutics.
A core UPS molecular complement implicates unique endocytic compartments at the parasite–host interface in Giardia lamblia

ABSTRACT Unconventional protein secretion (UPS) plays important roles in cell physiology. In contrast to canonical secretory routes, UPS does not generally require secretory signal sequences and often bypasses secretory compartments such as the ER and the Golgi apparatus. Giardia lamblia is a protist parasite with reduced subcellular complexity which releases several proteins, some of them virulence factors, without canonical secretory signals. This implicates UPS at the parasite–host interface. No dedicated machinery nor mechanism(s) for UPS in Giardia are currently known, although speculations on the involvement of endocytic organelles called PV/PECs, have been put forth. To begin to address the question of whether PV/PECs are implicated in virulence-associated UPS and to define the composition of molecular machinery involved in protein release, we employed affinity purification and mass spectrometry, coupled to microscopy-based subcellular localization and signal correlation quantification to investigate the interactomes of 11 reported unconventionally secreted proteins, all predicted to be cytosolic. A subset of these are associated with PV/PECs. Extended and validated interactomes point to a core PV/PECs-associated UPS machinery, which includes uncharacterized and Giardia-specific coiled-coil proteins and NEK kinases. Finally, a subset of the alpha-giardin protein family was enriched in all PV/PECs-associated protein interactomes, highlighting a previously unappreciated role for these proteins at PV/PECs and in UPS. Taken together, our results provide the first characterization of a virulence-associated UPS protein complex in Giardia lamblia at PV/PECs, suggesting a novel link between these primarily endocytic and feeding organelles and UPS at the parasite–host interface.
Bacterial pattern recognition in C. elegans by a nuclear hormone receptor

Pattern recognition of bacterial products by host receptors is essential for innate immunity in many metazoans. Curiously, the nematode lineage lost canonical mechanisms of bacterial pattern recognition. Whether other immune receptors evolved in their place is not known. Here, we characterize the first bacterial pattern recognition receptor and its natural ligand in the nematode Caenorhabditis elegans. We show that the C. elegans nuclear hormone receptor NHR-86/HNF4 senses phenazine-1-carboxamide (PCN), a metabolite produced by pathogenic strains of Pseudomonas aeruginosa. PCN binds to the ligand-binding domain of NHR-86/HNF4, a ligand-gated transcription factor, and activates an anti-pathogen transcriptional program in intestinal epithelial cells that provides protection against P. aeruginosa. These data de-orphan a nuclear hormone receptor and demonstrate that surveillance of metabolite signals from bacteria allows nematodes to identify virulent pathogens in their environment that are poised to cause disease.
Synthetic yeast chromosome XI design enables extrachromosomal circular DNA formation on demand

We describe construction of the 660 kilobase synthetic yeast chromosome XI (synXI) and reveal how synthetic redesign of non-coding DNA elements impact the cell. To aid construction from synthesized 5 to 10 kilobase DNA fragments, we implemented CRISPR-based methods for synthetic crossovers in vivo and used these methods in an extensive process of bug discovery, redesign and chromosome repair, including for the precise removal of 200 kilobases of unexpected repeated sequence. In synXI, the underlying causes of several fitness defects were identified as modifications to non-coding DNA, including defects related to centromere function and mitochondrial activity that were subsequently corrected. As part of synthetic yeast chromosome design, loxPsym sequences for Cre-mediated recombination are inserted between most genes. Using the GAP1 locus from chromosome XI, we show here that targeted insertion of these sites can be used to create extrachromosomal circular DNA on demand, allowing direct study of the effects and propagation of these important molecules. Construction and characterization of synXI has uncovered effects of non-coding and extrachromosomal circular DNA, contributing to better understanding of these elements and informing future synthetic genome design.
A BUGS Implementation of a Kriged Kalman Filter Model

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Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies

T-cell receptor-mimetic antibodies (TCRms) targeting disease-associated peptides presented by Major Histocompatibility Complexes (pMHCs) are set to become a major new drug modality. However, we lack a general understanding of how TCRms engage pMHC targets, which is crucial for predicting their specificity and safety. Several new structures of TCRm:pMHC complexes have become available in the past year, providing sufficient initial data for a holistic analysis of TCRms as a class of pMHC binding agents. Here, we profile the complete set of TCRm:pMHC complexes against representative TCR:pMHC complexes to quantify the TCR-likeness of their pMHC engagement. We find that intrinsic molecular differences between antibodies and TCRs lead to fundamentally different roles for their heavy/light chains and Complementarity-Determining Region loops during antigen recognition. The idiotypic properties of antibodies may increase the likelihood of TCRms engaging pMHCs with less peptide selectivity than TCRs. However, the pMHC recognition features of some TCRms, including the two TCRms currently in clinical trials, can be remarkably TCR-like. The insights gained from this study will aid in the rational design and optimisation of next-generation TCRms.