Essay: Exploring the Physics of Basic Medical Research.

The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics community, which have revolutionized medical diagnostics and patient care. However, fundamental medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the human body that are based on physical principles and yet govern our health over a vast range of length and time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how, and mindset of the physics community and that the pursuit of basic research in medicine is compatible with the mission of physics. Part of a series of Essays that concisely present author visions for the future of their field.

Assembly theory explains and quantifies selection and evolution.

Scientists have grappled with reconciling biological evolution1,2 with the immutable laws of the Universe defined by physics. These laws underpin life's origin, evolution and the development of human culture and technology, yet they do not predict the emergence of these phenomena. Evolutionary theory explains why some things exist and others do not through the lens of selection. To comprehend how diverse, open-ended forms can emerge from physics without an inherent design blueprint, a new approach to understanding and quantifying selection is necessary3-5. We present assembly theory (AT) as a framework that does not alter the laws of physics, but redefines the concept of an 'object' on which these laws act. AT conceptualizes objects not as point particles, but as entities defined by their possible formation histories. This allows objects to show evidence of selection, within well-defined boundaries of individuals or selected units. We introduce a measure called assembly (A), capturing the degree of causation required to produce a given ensemble of objects. This approach enables us to incorporate novelty generation and selection into the physics of complex objects. It explains how these objects can be characterized through a forward dynamical process considering their assembly. By reimagining the concept of matter within assembly spaces, AT provides a powerful interface between physics and biology. It discloses a new aspect of physics emerging at the chemical scale, whereby history and causal contingency influence what exists.

Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns.

Ultrafast laser irradiation can induce spontaneous self-organization of surfaces into dissipative structures with nanoscale reliefs. These surface patterns emerge from symmetry-breaking dynamical processes that occur in Rayleigh-Bénard-like instabilities. In this study, we demonstrate that the coexistence and competition between surface patterns of different symmetries in two dimensions can be numerically unraveled using the stochastic generalized Swift-Hohenberg model. We originally propose a deep convolutional network to identify and learn the dominant modes that stabilize for a given bifurcation and quadratic model coefficients. The model is scale-invariant and has been calibrated on microscopy measurements using a physics-guided machine learning strategy. Our approach enables the identification of experimental irradiation conditions for a desired self-organization pattern. It can be generally applied to predict structure formation in situations where the underlying physics can be approximately described by a self-organization process and data is sparse and nontime series. Our Letter paves the way for supervised local manipulation of matter using timely controlled optical fields in laser manufacturing.

Confirmation, or pursuit-worthiness? Lessons from J. J. Sakurai's 1960 theory of the strong force for the debate on non-empirical physics.

Over the last few decades, our theories of fundamental physics have become increasingly detached from empirical data. Recently, Richard Dawid has argued that the progressive separation of theory from experiment is concomitant with a number of changes in the methodology of the discipline. More precisely, Dawid has argued that the new methods of fundamental physics amount to a form of non-empirical confirmation, and that physical theories may therefore be confirmed even in the absence of empirical data. In this paper, I critically engage with Dawid's views on non-empirical physics. My main target is the excessively central role that, in my view, the notion of non-empirical confirmation plays on Dawid's analysis. I will therefore argue that, while non-empirical methods may legitimately be employed in physics, those are not always deployed with the purpose of confirming scientific theories. Non-empirical arguments may also be used in order to ground pragmatic choices regarding what theories deserve to be further developed-and this is an aspect of the work that non-empirical methods perform that cannot be solely understood in terms of Dawid's notion of non-empirical confirmation. I support these claims by making use of a case-study from the early history of particle physics. The case-study concerns a theory of the strong force that J. J. Sakurai introduced in 1960. As we shall see, both the genesis of Sakurai's theory as well as the arguments that he used to defend it provide direct support for my own views on the role that non-empirical methods play in physics. Finally, I conclude the paper by introducing a notion that I believe is useful in making sense of the manner in which the pragmatic and the epistemic dimensions of non-empirical reasoning relate to each other, namely the notion of a cognitive attitude.

Model-Free Measurement of Local Entropy Production and Extractable Work in Active Matter.

Time-reversal symmetry breaking and entropy production are universal features of nonequilibrium phenomena. Despite its importance in the physics of active and living systems, the entropy production of systems with many degrees of freedom has remained of little practical significance because the high dimensionality of their state space makes it difficult to measure. Here we introduce a local measure of entropy production and a numerical protocol to estimate it. We establish a connection between the entropy production and extractability of work in a given region of the system and show how this quantity depends crucially on the degrees of freedom being tracked. We validate our approach in theory, simulation, and experiments by considering systems of active Brownian particles undergoing motility-induced phase separation, as well as active Brownian particles and E.coli in a rectifying device in which the time-reversal asymmetry of the particle dynamics couples to spatial asymmetry to reveal its effects on a macroscopic scale.

Intrinsic Pathology of Self-Interacting Vector Fields.

We show that self-interacting vector field theories exhibit unphysical behavior even when they are not coupled to any external field. This means any theory featuring such vectors is in danger of being unphysical, an alarming prospect for many proposals in cosmology, gravity, high energy physics, and beyond. The problem arises when vector fields with healthy configurations naturally reach a point where time evolution is mathematically ill defined. We develop tools to easily identify this issue, and provide a simple and unifying framework to investigate it.

Evolution, physics, and education.

Evolution is a universal phenomenon of nature, bio, and non-bio. Evolution belongs in physics and general education. This step clarifies and enhances the transmission and retention of knowledge. Teaching and learning are the mental activity of grasping a new subject as something familiar, like an older and wider body of knowledge. The new subject is evolution, and the older and wider is physics. The older is accepted because it is based on morphing images available for observation and testing during a human lifetime (e.g., dendrites of river basins, lightning, snowflakes). Physical images can be predicted, tested, confirmed, and condensed into principles with the power to predict directionality and future evolution. The teaching of this method is illustrated with examples from animal locomotion and vehicle configuration, speed, and path.

Stability analysis and novel solutions to the generalized Degasperis Procesi equation: An application to plasma physics.

In this work two kinds of smooth (compactons or cnoidal waves and solitons) and nonsmooth (peakons) solutions to the general Degasperis-Procesi (gDP) equation and its family (Degasperis-Procesi (DP) equation, modified DP equation, Camassa-Holm (CH) equation, modified CH equation, Benjamin-Bona-Mahony (BBM) equation, etc.) are reported in detail using different techniques. The single and periodic peakons are investigated by studying the stability analysis of the gDP equation. The novel compacton solutions to the equations under consideration are derived in the form of Weierstrass elliptic function. Also, the periodicity of these solutions is obtained. The cnoidal wave solutions are obtained in the form of Jacobi elliptic functions. Moreover, both soliton and trigonometric solutions are covered as a special case for the cnoidal wave solutions. Finally, a new form for the peakon solution is derived in details. As an application to this study, the fluid basic equations of a collisionless unmagnetized non-Maxwellian plasma is reduced to the equation under consideration for studying several nonlinear structures in the plasma model.

Physics meets biology: The joining of two forces to further our understanding of cellular function.

Some biological questions are tough to solve through standard molecular and cell biological methods and naturally lend themselves to investigation by physical approaches. Below, a group of formally trained physicists discuss, among other things, how they apply physics to address biological questions and how physical approaches complement conventional biological approaches.

Phase separation during blood spreading.

Blood pools can spread on several types of substrates depending on the surrounding environment and conditions. Understanding the influence of these parameters on the spreading of blood pools can provide crime scene investigators with useful information. The focus of the present study is on phase separation, that is, when the serum spreads outside the main blood pool. For this purpose, blood pools with constant initial masses on wooden floors that were either varnished or not were created at ambient temperatures of [Formula: see text], [Formula: see text], and [Formula: see text] with a relative humidity varying from 20 to 90%. The range [Formula: see text] to [Formula: see text] covers almost all worldwide indoor cases. The same whole blood from the same donor was used for all experiments. As a result, an increase in relative humidity was found to result in an increase in the final pool area. In addition, at the three different experimental temperatures, the serum spread outside the main pool at relative humidity levels above 50%. This phase separation is more significant on varnished substrates, and does not lead to any changes in the drying morphology. This phenomenon is explained by the competition between coagulation and evaporation.

A mathematician's view of the unreasonable ineffectiveness of mathematics in biology.

This paper discusses, from a mathematician's point of view, the thesis formulated by Israel Gelfand, one of the greatest mathematicians of the 20th century, and one of the pioneers of mathematical biology, about the unreasonable ineffectiveness of mathematics in biology as compared with the obvious success of mathematics in physics. The author discusses the limitations of the mainstream mathematics of today when it is used in biology. He suggests that some emerging directions in mathematics have potential to enhance the role of mathematics in biology.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences.

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

Gradient-based training and pruning of radial basis function networks with an application in materials physics.

Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms.

The Physics of Magnetic Resonance Imaging Safety.

Magnetic resonance (MR) imaging relies on a strong static magnetic field in conjunction with careful orchestration of pulsed linear gradient magnetic fields and radiofrequency magnetic fields in order to generate images. The interaction of these fields with patients as well as materials with magnetic or conducting properties can be a source of risk in the MR environment. This article provides a basic review of the physical underpinnings of the primary risks in MR imaging to foster development of intuition with respect to both patient and risk management in the MR environment.

Inference of chromosome 3D structures from GAM data by a physics computational approach.

The combination of modelling and experimental advances can provide deep insights for understanding chromatin 3D organization and ultimately its underlying mechanisms. In particular, models of polymer physics can help comprehend the complexity of genomic contact maps, as those emerging from technologies such as Hi-C, GAM or SPRITE. Here we discuss a method to reconstruct 3D structures from Genome Architecture Mapping (GAM) data, based on PRISMR, a computational approach introduced to find the minimal polymer model best describing Hi-C input data from only polymer physics. After recapitulating the PRISMR procedure, we describe how we extended it for treating GAM data. We successfully test the method on a 6 Mb region around the Sox9 gene and, at a lower resolution, on the whole chromosome 7 in mouse embryonic stem cells. The PRISMR derived 3D structures from GAM co-segregation data are finally validated against independent Hi-C contact maps. The method results to be versatile and robust, hinting that it can be similarly applied to different experimental data, such as SPRITE or microscopy distance data.

Physics and Biology (of Chromosomes).

Advances in molecular biology, optics, genetics, and bioinformatics have opened the door to mapping, in molecular detail, processes inside living cells. With the ability to observe the individual moving parts of cellular machinery, concepts formerly confined to physics are entering mainstream biology. This article discusses a few ideas of this sort related to chromosome biology, to illustrate what kinds of insights physics might yet bring to our understanding of living systems.