Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

Mathematical Modelling of Gene Regulatory Networks

This research includes the use of an artificial intelligence algorithm, which is one of the algorithms of biological systems which is the algorithm of genetic regulatory networks (GRNs), which is a dynamic system for a group of variables representing space within time. To construct this biological system, we use (ODEs) and to analyze the stationarity of the model we use Euler's method. And through the factors that affect the process of gene expression in terms of inhibition and activation of the transcription process on DNA, we will use TF transcription factors. The current research aims to use the latest methods of the artificial intelligence algorithm. To apply Gene Regulation Networks (GRNs), we used a program (MATLAB2020), which provides facilitation to the most important biological concepts for building this biological interaction

How “is” shapes “ought” for folk-biological concepts

Knowing which features are frequent among a biological kind (e.g., that most zebras have stripes) shapes people’s representations of what category members are like (e.g., that typical zebras have stripes) and normative judgments about what they ought to be like (e.g., that zebras should have stripes). In the current work, we ask if people’s inclination to explain why features are frequent is a key mechanism through which what “is” shapes beliefs about what “ought” to be. Across four studies (N = 591), we find that frequent features are often explained by appeal to feature function (e.g., that stripes are for camouflage), that functional explanations in turn shape judgments of typicality, and that functional explanations and typicality both predict normative judgments that category members ought to have functional features. We also identify the causal assumptions that license inferences from feature frequency and function, as well as the nature of the normative inferences that are drawn: by specifying an instrumental goal (e.g., camouflage), functional explanations establish a basis for normative evaluation. These findings shed light on how and why our representations of how the natural world is shape our judgments of how it ought to be.

Sigmund Freud and Carl Jung on Myths and Archetypes of the Collective Unconscious: Unnoticed Similarity

Sigmund Freud’s psychoanalysis and Carl Gustav Jung’s analytical psychology are different in many ways and some of their differences are extremely crucial. It is widely believed that one of the most obvious examples of this intellectual confrontation is the difference between Freud’s and Jung’s views on mythology. Proponents of this view believe that Jung was much more interested in mythological issues and his theory of myth became much deeper and more developed than Freud’s one. In particular, it is believed that Freud focused exclusively on the individual’s psyche, while Jung allegedly reached the true origins of mythmaking in the collective unconscious, which is the sediment of the vast historical experience of mankind. The article shows that such statements do not reflect the real situation but just the point of view, which Jung began to spread after his break-up with Freud. In fact, the founder of psychoanalysis had a steady and deep interest in mythology. The manifestation of this interest was the formation of “psycho-analytics” of myth – a specific area of research, which in the early years of the psychoanalytic movement was joined by several first psychoanalysts, including Franz Riklin, Karl Abraham, Otto Rank, Ernest Jones, and Jung himself. It is essential that both Freud and Jung, before and after the break-up in 1913, have been and remain the supporters of the consideration of a man and culture through the prism of certain biological concepts of that time. Those are the principle of inheritance of acquired properties (Lamarckism) and the idea that ontogenesis recapitulates phylogeny (“biogenetic law”). Based on Lamarckian-biogenetic assumptions, both Freud and Jung saw the origins of mythology in the collective historical experience of mankind. The article demonstrates that the image of Oedipus and the associated motives of incest and parricide play almost the same role in Freud’s (and Freudian) model of mythmaking as the archetypes of the collective unconscious in Jung’s (and Jungian) concept of myth.

The Modes of Dysregulation of the Proto-Oncogene T-Cell Leukemia/Lymphoma 1A

Incomplete biological concepts in lymphoid neoplasms still dictate to a large extent the limited availability of efficient targeted treatments, which entertains the mostly unsatisfactory clinical outcomes. Aberrant expression of the embryonal and lymphatic TCL1 family of oncogenes, i.e., the paradigmatic TCL1A, but also TML1 or MTCP1, is causally implicated in T- and B-lymphocyte transformation. TCL1A also carries prognostic information in these particular T-cell and B-cell tumors. More recently, the TCL1A oncogene has been observed also in epithelial tumors as part of oncofetal stemness signatures. Although the concepts on the modes of TCL1A dysregulation in lymphatic neoplasms and solid tumors are still incomplete, there are recent advances in defining the mechanisms of its (de)regulation. This review presents a comprehensive overview of TCL1A expression in tumors and the current understanding of its (dys)regulation via genomic aberrations, epigenetic modifications, or deregulation of TCL1A-targeting micro RNAs. We also summarize triggers that act through such transcriptional and translational regulation, i.e., altered signals by the tumor microenvironment. A refined mechanistic understanding of these modes of dysregulations together with improved concepts of TCL1A-associated malignant transformation can benefit future approaches to specifically interfere in TCL1A-initiated or -driven tumorigenesis.

Principles and biological concepts of heredity before Mendel

The knowledge of the history of a subject stimulates understanding. As we study how other people have made scientific breakthroughs, we develop the breadth of imagination that would inspire us to make new discoveries of our own. This perspective certainly applies to the teaching of genetics as hallmarked by the pea experiments of Mendel. Common questions students have in reading Mendel’s paper for the first time is how it compares to other botanical, agricultural, and biological texts from the early and mid-nineteenth centuries; and, more precisely, how Mendel’s approach to, and terminology for debating, topics of heredity compare to those of his contemporaries? Unfortunately, textbooks are often unavailing in answering such questions. It is very common to find an introduction about heredity in genetic textbooks covering Mendel without mentions of preceding breeding experiments carried out in his alma mater. This does not help students to understand how Mendel came to ask the questions he did, why he did, or why he planned his pea studies the way he did. Furthermore, the standard textbook “sketch” of genetics does not allow students to consider how discoveries could have been framed and inspired so differently in various parts of the world within a single historical time. In our review we provide an extended overview bridging this gap by showing how different streams of ideas lead to the eventual foundation of particulate inheritance as a scientific discipline. We close our narrative with investigations on the origins of animal and plant breeding in Central Europe prior to Mendel in Kőszeg and Brno, where vigorous debates touched on basic issues of heredity from the early eighteenth-century eventually reaching a pinnacle coining the basic questions: What is inherited and how is it passed on from one generation to another?

Natural Selection beyond Life? A Workshop Report

Natural selection is commonly seen not just as an explanation for adaptive evolution, but as the inevitable consequence of “heritable variation in fitness among individuals”. Although it remains embedded in biological concepts, such a formalisation makes it tempting to explore whether this precondition may be met not only in life as we know it, but also in other physical systems. This would imply that these systems are subject to natural selection and may perhaps be investigated in a biological framework, where properties are typically examined in light of their putative functions. Here we relate the major questions that were debated during a three-day workshop devoted to discussing whether natural selection may take place in non-living physical systems. We start this report with a brief overview of research fields dealing with “life-like” or “proto-biotic” systems, where mimicking evolution by natural selection in test tubes stands as a major objective. We contend the challenge may be as much conceptual as technical. Taking the problem from a physical angle, we then discuss the framework of dissipative structures. Although life is viewed in this context as a particular case within a larger ensemble of physical phenomena, this approach does not provide general principles from which natural selection can be derived. Turning back to evolutionary biology, we ask to what extent the most general formulations of the necessary conditions or signatures of natural selection may be applicable beyond biology. In our view, such a cross-disciplinary jump is impeded by reliance on individuality as a central yet implicit and loosely defined concept. Overall, these discussions thus lead us to conjecture that understanding, in physico-chemical terms, how individuality emerges and how it can be recognised, will be essential in the search for instances of evolution by natural selection outside of living systems.

How Biological Concepts and Evolutionary Theories Are Inspiring Advances in Machine Intelligence

Since its advent in the mid-twentieth century, the field of artificial intelligence (AI) has been heavily influenced by biology. From the structure of the brain to evolution by natural selection, core biological concepts underpin many of the fundamental breakthroughs in modern AI. Here, focusing specifically on artificial neural networks (ANNs) that have become commonplace in machine learning, we show the numerous connections between theories based on coevolution, multi-level selection, modularity and competition and related developments in ANNs. Our aim is to illuminate the valuable but often overlooked inspiration biologists have provided AI research and to spark future contributions at this intersection of biology and computer science. Although recent advances in AI have been swift, many significant challenges remain requiring innovative solutions. Thankfully, biology in all its forms still has a lot to teach us, especially when trying to create truly intelligent machines.

Exploring Teachers' Views on the Impact of an Online Distance Learning Course on Their Self-Efficacy Beliefs

This paper reports on an online distance learning course that emphasizes the improvement of the self-efficacy beliefs of in-service elementary school teachers in teaching biological concepts. The course utilizes digital educational content and Open Educational Resources (OERs) and focuses on the interaction, peer support, and peer teaching into an online learning environment. In the design framework of the course, we investigated the educational needs of teachers and took into consideration the findings of other studies. A total of 251 teachers were enrolled in the online distance learning course and 142 completed it. Quantitative and qualitative data was collected in November 2020 through the bio-STEBI-A instrument and the posts in the forums of the course. The quantitative results revealed an improvement in both subscales of bio-STEBI-A, which were also confirmed by the qualitative ones, that underline the course, thus contributing to the improvement of self-efficacy beliefs of in-service elementary school teachers in teaching biological concepts. Recommendations are made for future research.