It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, multicellularity and development, the threshold nature of computations in cognitive systems, and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.

Wind tunnel data of time-averaged aerodynamic forces and moments are used to simulate the trajectory of a store separating from a rectangular cavity at supersonic speeds. The aerodynamic database serving as the primary input for the simulations was developed over a series of test entries in the PolysonicWind Tunnel (PSWT) using a Captive Trajectory System (CTS) operated in grid mode. The wind tunnel data were obtained at Mach 2 and 4 for a generic store with a slenderness ratio of 8 and a cavity with a length-to-depth ratio of 4.5. Methods for interpolation and extrapolation of the experimental data used to expand the database are presented. A fourth-order Runge-Kutta scheme is utilized to numerically integrate the six-degrees-of-freedom (6DOF) equations of motion for an axisymmetric projectile. Store separation trajectories consisting of the translational positions (x,y,z) and angular positions (phi,theta,psi) are shown for several cases which demonstrate the effects of ejection velocity, initial pitch angle and pitch rate, and freestream Mach number.

Measurements were made using a buried acoustic receiving array and a mobile sound projector, to study the underlying physical processes in the penetration of sound into sandy ocean sediments, particularly at shallow grazing angles. The experiments took place in a sandy sediment off the west coast of Florida, as part of the Sediment Acoustics Experiment (SAX99). The buried array was insonified by a wide band sound source carried on a remotely operated vehicle (ROV). Data were recorded at several positions and grazing angles over a period of 4 weeks. The discrimination between refraction and scattering processes was of particular interest. To this end, an array processing method was used that employed coherent superposition to distinguish between refracted and scattered signals. The latter should be incoherent. Variable focusing was used to sharpen the distinction between refraction from a distant source and scattered signals from scatterers in the near-field of the array. Sound waves entering the sediment at steep angles were clearly refracted. In the case of shallow grazing angles, the results were much more complicated. This is a component of a multi-disciplinary effort to study sediment acoustics.

Fig A. ROC curves for classifiers with different parameters built from the BaliBASE dataset, tested against the rProt dataset. Parameters shown here are segment boundaries (length threshold), TWC intensity for detection of positive positions (intensity threshold), and what percentage gaps should be used for removal of alignment columns (gap threshold). Each subplot represents different combination of the intensity and length thresholds. Colored lines within subplots represent different gap thresholds. Cutting only alignment positions with more than 80–90% gaps produce better distinction between true positive and true negatives. Complete data including all tested parameters and datasets is available in S2 Dataset. Fig B. ROC curves of training classifiers with different penalties and gamma parameters. The first four subplots (A-D) test different penalties and the last four test different gamma values (E-H). Each subplot represents a different dataset that was used for training and testing. (A) and (E) are PROSITE, (B) and (F) are BaliBASE, (C) and (G) are INDELible, (D) and (H) are rProtein dataset. For testing each dataset was split in 3 folds. Each fold produces an ROC curve, we plot the mean of the three results as single curve and plot the standard deviation of the true positive rate as a shaded region around it. Complete data is available in S3 Dataset. Fig C. ROC curves generated from HHalign alignments from the four datasets: BaliBASE, rProtein, INDELible, and PROSITE. Colored lines within subplots represent different gap thresholds used for column exclusion. Fig D. Comparison of structural mapping between Zebra2 and TwinCons. A) Zebra2 results and B) TwinCons results from sequence alignment for uL2 between archaeal and bacterial sequences mapped on the E. coli uL2 structure from PDB 4V9D [22]. C) Zebra2 results and D) TwinCons results from the same sequence alignment mapped on the P. furiosus uL2 structure from PDB 4V6U [36]. In panels A) and C) red indicates signatures. In panels B) and D) dark green indicates alignment positions with high conservation of residues, purple indicates signature positions, gray indicates heavily gapped regions in the composite alignment. Orange circles indicate signature positions. Fig E. TwinCons mapped for a short α-helix region in uL2 with analogous sequence between Bacteria and Archaea. Residues depicted here are listed in Table C in S1 Appendix. (A) stick representation for E. coli uL2. (B) stick representation for P. furiosus uL2. (C) cartoon representation of E. coli uL2. (D) cartoon representation of P. furiosus uL2. (E) and (F) show different angle for the E. coli and P. furiosus uL2. Conserved residues are colored green, signatures are colored purple, and random positions are white. Heavily gapped regions, present in a single group, are colored gray. Figure generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Fig F. TwinCons segment with significant sequence similarity between (A, B) bL33 and (C, D) aL42. The segment is shown with full opacity cartoon, non-segment regions are shown with transparent cartoon. Conserved residues are colored green, signatures are colored purple, and random positions are white. Heavily gapped regions, present in a single group, are colored gray. Segment definitions are available in S6 Dataset. Figure generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Fig G. TwinCons score for Archaea and Bacteria composite alignments of the small and large subunits. (A) Secondary structure of the P. furiosus 16S rRNA with mapped TwinCons. (B) Secondary structure of the P. furiosus 5S and 23S rRNAs with mapped TwinCons. (C) Surface representation of the 16S rRNA for P. furiosus ribosome. (D) Surface representation of the 5S and 23S rRNAs for P. furiosus ribosome in crown view. Both the small and large subunits are shown from the subunit interface direction. Gray indicates heavily gapped regions, present only in bacterial or archaeal sequences; dark green indicates highly conserved regions between both bacterial and archaeal sequences; dark purple indicates signature regions between bacterial and archaeal sequences; white indicates sequence variable regions. In panels (A) and (B) blue numbers indicate helical numbering and ribosomal domains are indicated with brown. Panels (A) and (B) are generated with RiboVision, panels (C) and (D) are generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Fig H. TwinCons signatures differ based on the substitution matrix used. TwinCons results mapped on (A) metacaspase, (C) caspase, and (B) β-sheet superimposition of both structures, using the Blosum62 matrix. TwinCons results mapped on (D) metacaspase, (F) caspase, and (E) β-sheet superimposition of both structures, using structure-informed substitution matrices. A position with differing result is highlighted between panels (B) and (D) with red. Set of residues, representing the composite alignment column for the highlighted position, are shown between (B) and (E). Structure-informed matrices produce stronger signature signal between the two groups for this alignment position. Structures are generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Fig I. Distribution of TwinCons scores from the E. coli rRNA, based on three composite alignments between Archaeal and Bacterial sequences of 23S, 16S, and 5S rRNA. (A) Histogram of TwinCons scores showing three peaks of distribution around the minimum score, score zero, and the maximum score. (B) Scatter plot of TwinCons scores with group assignment by k-means clustering algorithm. The y-axis holds randomly assigned values and is only illustrative. Scores from different groups are colored with the viridis gradient. The red and green lines indicate the calculated thresholds of the groups spanning the lowest (red) and highest (green) scores. Thresholds calculated from each composite alignment are available in Table B in S1 Appendix. Fig J. TwinCons segments with significant sequence similarity between the P-loop domains of (A, C) aIF5 and (B, D) EF-Tu. Segments are shown with full opacity cartoon, while non-segment regions are shown with transparent cartoon. GDP from the EF-Tu structure is shown with sticks. Conserved residues are colored green, signatures are colored purple, and random positions are white. Heavily gapped regions, present in a single group, are colored gray. Segment definitions are available in S6 Dataset. Figure generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Fig K. TwinCons segment with significant sequence similarity between bS1 and domain 7 of RNAP mapped on the RNAP7 structure. (A) and (B) two views of the segment mapped on the RNAP7 structure. Segment is shown with full opacity cartoon, while non-segment regions are shown with transparent cartoon. Conserved residues are colored green, signatures are colored purple, and random positions are white. Heavily gapped regions, present in a single group, are colored gray. Segment definitions are available in S6 Dataset. Figure generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Fig L. TwinCons segment with significant sequence similarity between bL34 and aL37. (A) representation of E. coli bL34, (B) representation of P. furiosus aL37, (C) 90-degree rotation view of E. coli bL34, and (D) 90-degree rotation view of P. furiosus aL37. The segment is shown with full opacity cartoon, non-segment regions are shown with transparent cartoon. Conserved residues are colored green, signatures are colored purple, and random positions are white. Heavily gapped regions, present in a single group, are colored gray. Segment definitions are available in S6 Dataset. Figure generated with PyMOL. PDB IDs and chains used for the figure are available in Table E in S1 Appendix. Table A. Substitution matrices available for TwinCons calculation and references for full descriptions. Table B. TwinCons thresholds calculated with 5 k-clusters for different subsets of rRNA. First two rows, tagged with ‘ribosome’, include sequences from the 23S, 5S, and 16S. Entries tagged with LSU include sequences from the 23S and 5S. Entries tagged with SSU include only rRNA from the 16S rRNA. TwinCons was calculated against the Archaea-Bacteria composite alignment of the rRNA. Standard deviations were calculated after repeating the calculation 100 times. Full script used to generate this data can be found at https://github.com/LDWLab/TWC_distribution. Table C. TwinCons and ConSurf statistics for α-helical region in uL2. Positions with low Shannon entropy, low ConSurf score, and high TwinCons score are detected as highly conserved. Positions with TwinCons below -0.6 are detected as signature positions. Signature positions detected with TwinCons, that are detected as conserved by ConSurf are highlighted with blue. Table D. Composite alignments used in sequence similarity analysis. Table E. Protein and rRNA structures used to map sequence similarity analysis. When multiple PDBs are used in a single row they are separated by a semicolon. When multiple chains are used from a single PDB they are separated by &. (PDF)

Uniting the conceptual foundations of the physical sciences and biology, this groundbreaking multidisciplinary book explores the origin of life as a planetary process. Combining geology, geochemistry, biochemistry, microbiology, evolution and statistical physics to create an inclusive picture of the living state, the authors develop the argument that the emergence of life was a necessary cascade of non-equilibrium phase transitions that opened new channels for chemical energy flow on Earth. This full colour and logically structured book introduces the main areas of significance and provides a well-ordered and accessible introduction to multiple literatures outside the confines of disciplinary specializations, as well as including an extensive bibliography to provide context and further reading. For researchers, professionals entering the field or specialists looking for a coherent overview, this text brings together diverse perspectives to form a unified picture of the origin of life and the ongoing organization of the biosphere.

Uniting the conceptual foundations of the physical sciences and biology, this groundbreaking multidisciplinary book explores the origin of life as a planetary process. Combining geology, geochemistry, biochemistry, microbiology, evolution and statistical physics to create an inclusive picture of the living state, the authors develop the argument that the emergence of life was a necessary cascade of non-equilibrium phase transitions that opened new channels for chemical energy flow on Earth. This full colour and logically structured book introduces the main areas of significance and provides a well-ordered and accessible introduction to multiple literatures outside the confines of disciplinary specializations, as well as including an extensive bibliography to provide context and further reading. For researchers, professionals entering the field or specialists looking for a coherent overview, this text brings together diverse perspectives to form a unified picture of the origin of life and the ongoing organization of the biosphere.

Contents 1. Introduction 1.1. A workshop and this document 1.2. Framing origins of life science 1.2.1. What do we mean by the origins of life (OoL)? 1.2.2. Defining life 1.2.3. How should we characterize approaches to OoL science? 1.2.4. One path to life or many? 2. A Strategy for Origins of Life Research 2.1. Outcomes—key questions and investigations 2.1.1. Domain 1: Theory 2.1.2. Domain 2: Practice 2.1.3. Domain 3: Process 2.1.4. Domain 4: Future studies 2.2. EON Roadmap 2.3. Relationship to NASA Astrobiology Roadmap and Strategy documents and the European AstRoMap  Appendix I  Appendix II  Supplementary Materials  References

In water depths on the order of a wavelength, sound propagates with considerable involvement of the bottom, whose velocities and attenuation vary with depth into the sediment. In order to study propagation in these types of environments, experiments were conducted in Currituck Sound on the Outer Banks of North Carolina using a Combustive Sound Source (CSS) and bottom mounted hydrophones and geophones as receivers. The CSS was deployed at a depth of approximately 1 meter and generated transient signals, several wavelengths long, at frequencies around 300 Hz. The results are used to determine transmission loss in water depths of approximately 3 meters, as well as to examine the generation and propagation of Sholte type interface waves. The measurements are compared to numerical models generated with a two-dimensional finite-element code. [Work supported by the U.S. Army Engineer Research and Development Center. Permission to publish was granted by Director, Geotechnical & Structures Laboratory.]

The U.S. Army Engineer Research & Development Center installed a Fiber Optic Sensor System (FOSS) at the Field Research Facility (FRF) in Duck, NC as a test-bed for evaluating FOSS performance in measuring ambient acoustic and seismic noise in a coastal environment. The sensor system consists of a buried fiber optic cable with a length of approximately 3 km, which wends its way through the sand dunes and along the beach. An optical interrogator contains a pulsed laser which injects light into an optical fiber within the cable and receives Rayleigh backscattered signals from it. The system, which is able to detect and locate seismic activity along the entire length with a 10 m resolution, is being used to study ocean wave generated noise. The FRF also maintains and operates a real-time cross-shore directional wave array, consisting of five bottom-mounted acoustic wave gauges installed at 2 to 11 m depths. The main focus of the work presented here compares FOSS data with directional wave measurements made with the cross-shore array under a variety of conditions. Data collected with other sensor systems (e.g., vertical long-period seismometer, infrasound microphone, anemometers, rain gauges, high resolution video) were also used in this comparison.

In this summer project, we sought to derive in a principled way an expression for the entropy of a classical system which would be independent of assumptions of equilibrium. The chosen system, as simple model of polymer growth is taken from Esposito et. al [1]. It was chosen for the phenomenological property that in certain regimes of parameter space, energy can be extracted from the environment through the growth of a disordered solid. The model is formulated as a stochastic process, taken through the formal development laid out by Smith in [2], and the resulting expression for the entropy is manipulated appropriately to elucidate its structure. The results are, at this point, so complex as to be enlightening, but we expect that further investigation will yield an understanding of appropriate simplifying assumptions, and hope to eventually derive an expression that is generally valid for the system and has a clear conceptual interpretation.

Commonly observed patterns typically follow a few distinct families of probability distributions. Over one hundred years ago, Karl Pearson provided a systematic derivation and classification of the common continuous distributions. His approach was phenomenological: a differential equation that generated common distributions without any underlying conceptual basis for why common distributions have particular forms and what explains the familial relations. Pearson's system and its descendants remain the most popular systematic classification of probability distributions. Here, we unify the disparate forms of common distributions into a single system based on two meaningful and justifiable propositions. First, distributions follow maximum entropy subject to constraints, where maximum entropy is equivalent to minimum information. Second, different problems associate magnitude to information in different ways, an association we describe in terms of the relation between information invariance and measurement scale. Our framework relates the different continuous probability distributions through the variations in measurement scale that change each family of maximum entropy distributions into a distinct family. From our framework, future work in biology can consider the genesis of common patterns in a new and more general way. Particular biological processes set the relation between the information in observations and magnitude, the basis for information invariance, symmetry and measurement scale. The measurement scale, in turn, determines the most likely probability distributions and observed patterns associated with particular processes. This view presents a fundamentally derived alternative to the largely unproductive debates about neutrality in ecology and evolution.

Most evolutionary biologists cherish Darwin's theory of natural selection (NS) as the process of adaptive evolution more than 140 years after publication of his first book on the subject. However, in the past few decades the study of self-organization (SO) in complex dynamical systems has suggested that adaptation may occur through intrinsic reorganization without NS. In this study, we attempt to describe the logical framework that relates the general process of SO to the specific process of NS. We describe NS as a mechanism that coordinates the coevolution of species in an ecosystem to effectively capture, process and dissipate solar energy into the earth's shadow. Finally, we conclude that NS is an emergent process founded on the same thermodynamic imperatives that are thought to underlie all SO. This perspective suggests that the theory of self-organizing systems offers a broader physical context in which to understand the process of NS, rather than contesting it. It even suggests the possibility that there may be a physical basis for understanding the origin of the process of NS. Rather than being merely a fluke of nature, the origin of NS that may be driven by energy flows across gradients.

We review an emerging body of work by physicists addressing questions of economic organization and function. We suggest that, beyond simply employing models familiar from physics to economic observables, remarkable regularities in economic data may suggest parts of social order that can usefully be incorporated into, and in turn can broaden, the conceptual structure of physics.

This is the second of a pair of papers considering acoustic multiple scattering and localization in fluidsaturated porous solids. Such systems are represented by the Biot effective medium, and spatially d-function correlated disorder is incorporated by replica-field methods. In the companion paper it was shown that a preferred form of the wavelength-scale scattering operator, coupling primarily to slow waves, is selected by a universal renormalization-group flow toward strong coupling. The simplification afforded by a single scattering interaction is used here to define both Ginzburg-Landau and coherent-potential descriptions of the localized density of states. It is found that, as long as the Biot medium is the appropriate background, a description of localized slow and extended fast waves is possible, but that fast waves may not localize within the weakcoupling limits of the models. Further, coupling between slow and fast waves contributes an apparent dissipation that can inhibit slow wave localization. Finally, relations are found between fast-wave coherence length and the slow noise spectrum, which make fast waves a potential probe of a slow-localized volume.

This is the first of two papers that develop a theory of perturbative acoustic scattering and localization in fluid-saturated porous solids. The Blot effective-medium theory is used as the general model of porous-medium acoustics, and replica field functional integrals are introduced to incorporate spatially delta -function correlated fluctuations of the Biot parameters. This paper develops the renormalization group and frequency-dependent definition of the effective medium in the limit of inviscid saturating fluid. (Generalized) density fluctuations are found to create an RG-relevant effective coupling, which renormalizes to a universal form in an asymptotic limit of large flow range. A scattering-induced dispersion is also identified, which, in a simple example assuming superfluid helium as saturant, serves as an experimental probe of heterogeneity in the Biot tortuosity parameter.

Experimental properties of a new form of creep dynamics are reported, as manifest in a variety of sandstones, limestone, and concrete. The creep is a recovery behavior, following the sharp drop in elastic modulus induced either by nonlinear acoustic straining or rapid temperature change. The extent of modulus recovery is universally proportional to the logarithm of the time after source discontinuation in all samples studied, over a scaling regime covering at least 10(3) s. Comparison of acoustically and thermally induced creep suggests a single origin based on internal strain, which breaks the symmetry of the inducing source.

Signals collected from a buried acoustic receiving array in a sandy sediment off the west coast of Florida, as part of the Sediment Acoustics Experiment (SAX99), were analyzed to determine the penetration processes. The two main candidate processes are refraction and scattering. Wave front coherence and direction of arrival, as well as sound speed measurements, provide important clues concerning the processes involved. The sound was projected by a wideband sound source carried on a remotely operated vehicle (ROV) in the band 10 to 100 kHz. Coherent and incoherent processing methods were explored. [Work supported by the Office of Naval Research, Ocean Acoustics.]