How to align leg segments between the four groups of arthropods (insects, crustaceans, myriapods, and chelicerates) has tantalized researchers for over a century. By comparing the loss-of-function phenotypes of leg patterning genes in diverged arthropod taxa, including a crustacean, insects, and spiders, we show that all arthropod legs can be aligned in a one-to-one fashion. We propose a model wherein insects incorporated two proximal leg segments into the body wall, which moved the ancestral leg lobe (exite) up onto the back to later form wings. For myriapods and chelicerates with seven leg segments, it appears that one proximal leg segment was incorporated into the body wall. According to this model, the chelicerate exopod and the crustacean exopod emerge from different leg segments, and are therefore proposed to have arisen independently. A framework for how to align arthropod appendages now opens up a powerful system for studying the origins of novel structures, the plasticity of developmental fields, and convergent evolution.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

Understanding how novel structures arise is a central question in evolution. The carapace of the waterflea Daphnia is a bivalved “cape” of exoskeleton that surrounds the animal, and has been proposed to be one of many novel structures that arose through repeated co-option of genes that also pattern insect wings. To determine whether the Daphnia carapace is a novel structure, the expression of pannier, the Iroquois gene aurucan, and vestigial are compared between Daphnia, Parhyale, and Tribolium. The results suggest that the Daphnia carapace did not arise by cooption, but instead represents an elongated ancestral exite (lateral lobe or plate) that emerges from a proximal leg segment that was incorporated into the Daphnia body wall. The Daphnia carapace therefore appears to be homologous to the Parhyale tergal plate and the insect wing. In addition, the vg-positive region that gives rise to the Daphnia carapace also appears to be present in Parhyale and Tribolium, which do not form a carapace. Thus, rather than a novel structure resulting from gene co-option, the carapace appears to have arisen from an ancient, conserved developmental field that persists in a cryptic state in other arthropod lineages, but in Daphnia became elaborated into the carapace. Cryptic persistence of serially homologous developmental fields may thus be a general solution for the origin of many novel structures.

The question of how to conceive of the relation between the pregnant female and the embryo has become a recent focus of debate in the philosophical literature. Here we consider this problem in the context of current debates on individuality in the philosophy of biology by looking at how pregnancy is individuated in different disciplinary contexts. Firstly, we review different notions of biological individuality in the literature, examine how each of them applies to the case of pregnancy, and claim that recent work on the evolution of eutherian reproduction offers insights for new criteria for rethinking the individuality of pregnancy. Then, we reconstruct the main assumptions underlying the established biological account of pregnancy. Finally, we develop an alternative account based on the hypothesis that pregnancy is an evolved relational novelty and maintain that pregnant females are conceived as historical reproductive individuals. In the final section before the conclusions, we discuss how the historical reproductive biological individuality of pregnancy differs from, and coexists with, other views of individuality, and examine some of its consequences.

Sexual selection involving female choice or female preference (‘inter-sexual’ selection) or male-male competition (‘intra-sexual’ selection) is one of the key mechanisms for evolutionary diversification and speciation. In particular, sexual selection is recently suggested to be an important mode to drive the evolution of “novel” phenotype (i. e. “evolutionary novelty”). Despite extensive studies performed on sexually selected traits or male-specific ornaments (or weapon-like structures) with respect to their evolutionary origin and history and fitness benefits, relatively little is known about the molecular genetic mechanisms underlying their developmental process. However, with advances of genomic technologies (including whole transcriptome analysis using Next Generation Sequencing [NGS] techniques; RNA-Seq), progresses have been made to unveil the genetic background underpinning diverse sexually selected traits in different animal taxa. In the present review, we compile empirical data on the genes, genetic mechanisms or regulatory pathways underlying various sexually selected traits to explore whether the “common” genetic architectures shape the development and evolution of those traits across evolutionarily distant animal lineages. We show that the recruitment of pre-existing genetic network for a new purpose (i.e. gene network “co-option”) is rather widespread in the development and evolution of sexually selected traits, indicating that particular genes or gene-sets are repeatedly involved in different sexually selected traits. Information of genes or genetic mechanisms regulating the development of sexually selected traits would be an essential piece to complete a whole picture of the origin and evolution of sexually selected traits.

Deep learning models are part of the family of artificial neural networks and, as such, it suffers of catastrophic interference when they learn sequentially. In addition, most of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, in this article we propose the Self-Improving Generative Artificial Neural Network (SIGANN), a type of end-to-end Deep Neural Network system which is able to ease the catastrophic forgetting problem when leaning new classes. In this method, we introduce a novelty detection model to automatically detect samples of new classes, moreover an adversarial auto-encoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder; and a novelty detection module, implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN is able to retain previous knowledge with a gradual forgetfulness for each learning sequence. Moreover, SIGANN can detect new classes that are hidden in the data and, therefore, proceed with incremental class learning.

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to support lifelong learning. We provide our ideas about how to combine this optimization method with evolutionary algorithms in order to boost the development of specialized Artificial Neural Networks, which define the proprioceptive configuration of particular robotic units that are part of a swarm. We consider how optimization of the free-energy can promote the homeostasis of the swarm system, i.e. ensures that the system remains within its sensory boundaries throughout its active lifetime. We will show how complex distributed cognitive systems can be build in the form of hierarchical modular system, which consists of specialized micro-intelligent agents connected through information channels. We will also consider the co-evolution of various robotic swarm units, which can result in development of proprioception and a comprehensive awareness of the properties of the environment. And finally, we will give a brief outline of how this system can be implemented in practice and of our progress in this area.