The genomic origins of the world's first farmers.

The precise genetic origins of the first Neolithic farming populations in Europe and Southwest Asia, as well as the processes and the timing of their differentiation, remain largely unknown. Demogenomic modeling of high-quality ancient genomes reveals that the early farmers of Anatolia and Europe emerged from a multiphase mixing of a Southwest Asian population with a strongly bottlenecked western hunter-gatherer population after the last glacial maximum. Moreover, the ancestors of the first farmers of Europe and Anatolia went through a period of extreme genetic drift during their westward range expansion, contributing highly to their genetic distinctiveness. This modeling elucidates the demographic processes at the root of the Neolithic transition and leads to a spatial interpretation of the population history of Southwest Asia and Europe during the late Pleistocene and early Holocene.

Mechanochemical Principles of Spatial and Temporal Patterns in Cells and Tissues.

Patterns are ubiquitous in living systems and underlie the dynamic organization of cells, tissues, and embryos. Mathematical frameworks have been devised to account for the self-organization of biological patterns, most famously the Turing framework. Patterns can be defined in space, for example, to form stripes; in time, such as during oscillations; or both, to form traveling waves. The formation of these patterns can have different origins: purely chemical, purely mechanical, or a combination of the two. Beyond the variety of molecular implementations of such patterns, we emphasize the unitary principles associated with them, across scales in space and time, within a general mechanochemical framework. We illustrate where such mechanisms of pattern formation arise in biological systems from cellular to tissue scales, with an emphasis on morphogenesis. Our goal is to convey a picture of pattern formation that draws attention to the principles rather than solely to specific molecular mechanisms.

Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs.

Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g., deletion) or complex (e.g., chromothripsis) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,778 tumor whole-genome sequences, we uncovered three novel complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early-replicating regions, superenhancers, and breast or ovarian cancers. Rigma comprise "chasms" of low-JCN deletions enriched in late-replicating fragile sites and gastrointestinal carcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold-back inversions associated with expressed protein-coding fusions, breakend hypermutation, and acral, but not cutaneous, melanomas. Clustering of tumors according to genome graph-derived features identified subgroups associated with DNA repair defects and poor prognosis.

Insertions and Deletions Target Lineage-Defining Genes in Human Cancers.

Certain cell types function as factories, secreting large quantities of one or more proteins that are central to the physiology of the respective organ. Examples include surfactant proteins in lung alveoli, albumin in liver parenchyma, and lipase in the stomach lining. Whole-genome sequencing analysis of lung adenocarcinomas revealed noncoding somatic mutational hotspots near VMP1/MIR21 and indel hotspots in surfactant protein genes (SFTPA1, SFTPB, and SFTPC). Extrapolation to other solid cancers demonstrated highly recurrent and tumor-type-specific indel hotspots targeting the noncoding regions of highly expressed genes defining certain secretory cellular lineages: albumin (ALB) in liver carcinoma, gastric lipase (LIPF) in stomach carcinoma, and thyroglobulin (TG) in thyroid carcinoma. The sequence contexts of indels targeting lineage-defining genes were significantly enriched in the AATAATD DNA motif and specific chromatin contexts, including H3K27ac and H3K36me3. Our findings illuminate a prevalent and hitherto unrecognized mutational process linking cellular lineage and cancer.

A Functional Dissection of the mRNA and Locally Synthesized Protein Population in Neuronal Dendrites and Axons.

Neurons are characterized by a complex morphology that enables the generation of subcellular compartments with unique biochemical and biophysical properties, such as dendrites, axons, and synapses. To sustain these different compartments and carry a wide array of elaborate operations, neurons express a diverse repertoire of gene products. Extensive regulation at both the messenger RNA (mRNA) and protein levels allows for the differentiation of subcellular compartments as well as numerous forms of plasticity in response to variable stimuli. Among the multiple mechanisms that control cellular functions, mRNA translation is manipulated by neurons to regulate where and when a protein emerges. Interestingly, transcriptomic and translatomic profiles of both dendrites and axons have revealed that the mRNA population only partially predicts the local protein population and that this relation significantly varies between different gene groups. Here, we describe the space that local translation occupies within the large molecular and regulatory complexity of neurons, in contrast to other modes of regulation. We then discuss the specialized organization of mRNAs within different neuronal compartments, as revealed by profiles of the local transcriptome. Finally, we discuss the features and functional implications of both locally correlated-and anticorrelated-mRNA-protein relations both under baseline conditions and during synaptic plasticity.

The cell biology of neurogenesis: toward an understanding of the development and evolution of the neocortex.

Neural stem and progenitor cells have a central role in the development and evolution of the mammalian neocortex. In this review, we first provide a set of criteria to classify the various types of cortical stem and progenitor cells. We then discuss the issue of cell polarity, as well as specific subcellular features of these cells that are relevant for their modes of division and daughter cell fate. In addition, cortical stem and progenitor cell behavior is placed into a tissue context, with consideration of extracellular signals and cell-cell interactions. Finally, the differences across species regarding cortical stem and progenitor cells are dissected to gain insight into key developmental and evolutionary mechanisms underlying neocortex expansion.

The probability of epidemic burnout in the stochastic SIR model with vital dynamics.

We present an approach to computing the probability of epidemic "burnout," i.e., the probability that a newly emergent pathogen will go extinct after a major epidemic. Our analysis is based on the standard stochastic formulation of the Susceptible-Infectious-Removed (SIR) epidemic model including host demography (births and deaths) and corresponds to the standard SIR ordinary differential equations (ODEs) in the infinite population limit. Exploiting a boundary layer approximation to the ODEs and a birth-death process approximation to the stochastic dynamics within the boundary layer, we derive convenient, fully analytical approximations for the burnout probability. We demonstrate-by comparing with computationally demanding individual-based stochastic simulations and with semi-analytical approximations derived previously-that our fully analytical approximations are highly accurate for biologically plausible parameters. We show that the probability of burnout always decreases with increased mean infectious period. However, for typical biological parameters, there is a relevant local minimum in the probability of persistence as a function of the basic reproduction number [Formula: see text]. For the shortest infectious periods, persistence is least likely if [Formula: see text]; for longer infectious periods, the minimum point decreases to [Formula: see text]. For typical acute immunizing infections in human populations of realistic size, our analysis of the SIR model shows that burnout is almost certain in a well-mixed population, implying that susceptible recruitment through births is insufficient on its own to explain disease persistence.

Climate change is poised to alter mountain stream ecosystem processes via organismal phenological shifts.

Climate change is affecting the phenology of organisms and ecosystem processes across a wide range of environments. However, the links between organismal and ecosystem process change in complex communities remain uncertain. In snow-dominated watersheds, snowmelt in the spring and early summer, followed by a long low-flow period, characterizes the natural flow regime of streams and rivers. Here, we examined how earlier snowmelt will alter the phenology of mountain stream organisms and ecosystem processes via an outdoor mesocosm experiment in stream channels in the Eastern Sierra Nevada, California. The low-flow treatment, simulating a 3- to 6-wk earlier return to summer baseflow conditions projected under climate change scenarios in the region, increased water temperature and reduced biofilm production to respiration ratios by 32%. Additionally, most of the invertebrate species explaining community change (56% and 67% of the benthic and emergent taxa, respectively), changed in phenology as a consequence of the low-flow treatment. Further, emergent flux pulses of the dominant insect group (Chironomidae) almost doubled in magnitude, benefitting a generalist riparian predator. Changes in both invertebrate community structure (composition) and functioning (production) were mostly fine-scale, and response diversity at the community level stabilized seasonally aggregated responses. Our study illustrates how climate change in vulnerable mountain streams at the rain-to-snow transition is poised to alter the dynamics of stream food webs via fine-scale changes in phenology-leading to novel predator-prey "matches" or "mismatches" even when community structure and ecosystem processes appear stable at the annual scale.

Breakthrough-induced loop formation in evolving transport networks.

Transport networks, such as vasculature or river networks, provide key functions in organisms and the environment. They usually contain loops whose significance for the stability and robustness of the network is well documented. However, the dynamics of their formation is usually not considered. Such structures often grow in response to the gradient of an external field. During evolution, extending branches compete for the available flux of the field, which leads to effective repulsion between them and screening of the shorter ones. Yet, in remarkably diverse processes, from unstable fluid flows to the canal system of jellyfish, loops suddenly form near the breakthrough when the longest branch reaches the boundary of the system. We provide a physical explanation for this universal behavior. Using a 1D model, we explain that the appearance of effective attractive forces results from the field drop inside the leading finger as it approaches the outlet. Furthermore, we numerically study the interactions between two fingers, including screening in the system and its disappearance near the breakthrough. Finally, we perform simulations of the temporal evolution of the fingers to show how revival and attraction to the longest finger leads to dynamic loop formation. We compare the simulations to the experiments and find that the dynamics of the shorter finger are well reproduced. Our results demonstrate that reconnection is a prevalent phenomenon in systems driven by diffusive fluxes, occurring both when the ratio of the mobility inside the growing structure to the mobility outside is low and near the breakthrough.

Unraveling the mesoscale organization induced by network-driven processes.

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance-which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.

Stochastic machine learning via sigma profiles to build a digital chemical space.

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

Milestoning estimators of dissipation in systems observed at a coarse resolution.

Many nonequilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such "lumped" observables display memory, and in many cases, the irreversible character of the underlying microscopic dynamics becomes blurred, e.g., when the projection hides dissipative cycles. As a result, the observations appear less irreversible, and it is very challenging to infer the degree of broken time-reversal symmetry. Here we show, contrary to intuition, that by ignoring parts of the already coarse-grained state space we may-via a process called milestoning-improve entropy-production estimates. We present diverse examples where milestoning systematically renders observations "closer to underlying microscopic dynamics" and thereby improves thermodynamic inference from lumped data assuming a given range of memory, and we hypothesize that this effect is quite general. Moreover, whereas the correct general physical definition of time reversal in the presence of memory remains unknown, we here show by means of physically relevant examples that at least for semi-Markov processes of first and second order, waiting-time contributions arising from adopting a naive Markovian definition of time reversal generally must be discarded.

The "4 + 1" strategy fabrication of iron single-atom catalysts with selective high-valent iron-oxo species generation.

Single-atom catalysts (SACs) with atomic dispersion active sites have exhibited huge potentials in peroxymonosulfate (PMS)-based Fenton-like chemistry in water purification. However, four-N coordination metal (MN4) moieties often suffer from such problems as low selectivity and narrow workable pH. How to construct SACs in a controllable strategy with optimized electronic structures is of great challenge. Herein, an innovative strategy (i.e., the "4 + 1" fabrication) was devised to precisely modulate the first-shell coordinated microenvironment of FeN4 SAC using an additional N (SA-FeN5). This leads to almost 100% selective formation of high-valent iron-oxo [Fe(IV)═O] (steady-state concentration: 2.00 × 10-8 M) in the SA-FeN5/PMS system. In-depth theoretical calculations unveil that FeN5 configuration optimizes the electron distribution of monatomic Fe sites, which thus fosters PMS adsorption and reduces the energy barrier for Fe(IV)═O generation. SA-FeN5 was then attached to polyvinylidene difluoride membrane for a continuous flow device, showing long-term abatement of the microcontaminant. This work furnishes a general strategy for effective PMS activation and selective high-valent metal-oxo species generation by high N-coordination number regulation in SACs, which would provide guidance in the rational design of superior environmental catalysts for water purification.

The searchable chromosome.

To date, genome structure and dynamics have been studied mostly independently; their interplay is a notable blind spot of the field. Brückner, Chen, et al. recently demonstrated an integrated experimental approach sensitive to both, uncovering a striking robustness of enhancer-promoter search times (dynamics) to changes in genomic separation (structure).

Principles, functions, and biological implications of m6A in plants.

Over the past decade, N 6-methyladenosine (m6A) has emerged as a prevalent and dynamically regulated modification across the transcriptome; it has been reversibly installed, removed, and interpreted by specific binding proteins, and has played crucial roles in molecular and biological processes. Within this scope, we consolidate recent advancements of m6A research in plants regarding gene expression regulation, diverse physiologic and pathogenic processes, as well as crop trial implications, to guide discussions on challenges associated with and leveraging epitranscriptome editing for crop improvement.

Polymer folding through active processes recreates features of genome organization.

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate. Moreover, chromatin motion varies with genomic coordinate, pointing toward a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers nonequilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward exploring whether active mechanisms contribute to shaping genome conformations, we discuss a data-driven approach.

Olfactory search with finite-state controllers.

Long-range olfactory search is an extremely difficult task in view of the sparsity of odor signals that are available to the searcher and the complex encoding of the information about the source location. Current algorithmic approaches typically require a continuous memory space, sometimes of large dimensionality, which may hamper their optimization and often obscure their interpretation. Here, we show how finite-state controllers with a small set of discrete memory states are expressive enough to display rich, time-extended behavioral modules that resemble the ones observed in living organisms. Finite-state controllers optimized for olfactory search have an immediate interpretation in terms of approximate clocks and coarse-grained spatial maps, suggesting connections with neural models of search behavior.

Plants sum and subtract stimuli over different timescales.

Mounting evidence suggests that plants engage complex computational processes to quantify and integrate sensory information over time, enabling remarkable adaptive growth strategies. However, quantitative understanding of these computational processes is limited. We report experiments probing the dependence of gravitropic responses of wheat coleoptiles on previous stimuli. First, building on a mathematical model that identifies this dependence as a form of memory, or a filter, we use experimental observations to reveal the mathematical principles of how coleoptiles integrate multiple stimuli over time. Next, we perform two-stimulus experiments, informed by model predictions, to reveal fundamental computational processes. We quantitatively show that coleoptiles respond not only to sums but also to differences between stimuli over different timescales, constituting evidence that plants can compare stimuli-crucial for search and regulation processes. These timescales also coincide with oscillations observed in gravitropic responses of wheat coleoptiles, suggesting shoots may combine memory and movement in order to enhance posture control and sensing capabilities.

Universal Poisson statistics of a passive tracer diffusing in dilute active suspensions.

The statistics of a passive tracer immersed in a suspension of active particles (swimmers) is derived from first principles by considering a perturbative expansion of the tracer interaction with the microscopic swimmer field. To first order in the swimmer density, the tracer statistics is shown to be exactly represented by a spatial Poisson process combined with independent tracer-swimmer scattering events, rigorously reducing the multiparticle dynamics to two-body interactions. The Poisson representation is valid in any dimension, for arbitrary interaction forces and for a large class of swimmer dynamics. The framework not only allows for the systematic calculation of the tracer statistics in various dynamical regimes but highlights in particular surprising universal features that are independent of the swimmer dynamics such as a time-independent velocity distribution.

Quantifying heterogeneity to drug response in cancer-stroma kinetics.

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.