Search for: All records

We present 17 new 10Be erosion rates from southern Peru sampled across an extreme orographic rainfall gradient. Using a rainfall-weighted variant of the normalized channel steepness index, ksnQ, we show that channel steepness values, and thus topography, are adjusted to spatially varying rainfall. Rocks with similar physical characteristics define distinct relationships between ksnQ and erosion rate (E), suggesting ksnQ is also resolving lithologic variations in erodibility. However, substantial uncertainty exists in parameters describing these relationships. By combining our new data with 38 published erosion rates from Peru and Bolivia, we collapse the range of compatible parameter values and resolve robust, nonlinear ksnQ–E relationships suggestive of important influences of erosional thresholds, rock properties, sediment characteristics, and temporal runoff variability. In contrast, neither climatic nor lithologic effects are clear using the traditional channel steepness metric, ksn. Our results highlight that accounting for spatial rainfall variations is essential for disentangling the multiple influences of climate, lithology, and tectonics common in mountain landscapes, which is a necessary first step toward greater understanding of how these landscapes evolve.

 

Abstract Model systems are an essential resource in cancer research. They simulate effects that we can infer into humans, but come at a risk of inaccurately representing human biology. This inaccuracy can lead to inconclusive experiments or misleading results, urging the need for an improved process for translating model system findings into human-relevant data. We present a process for applying joint dimension reduction (jDR) to horizontally integrate gene expression data across model systems and human tumor cohorts. We then use this approach to combine human TCGA gene expression data with data from human cancer cell lines and mouse model tumors. By identifying the aspects of genomic variation joint-acting across cohorts, we demonstrate how predictive modeling and clinical biomarkers from model systems can be improved. 

Silicate weathering and organic carbon (OC) burial in soil regulate atmospheric CO2, but their influence on each other remains unclear. Generally, OC oxidation can generate acids that drive silicate weathering, yet clay minerals that form during weathering can protect OC and limit oxidation. This poses a conundrum where clay formation and OC preservation either compete or cooperate. Debate remains about their relative contributions because quantitative tools to simultaneously probe these processes are lacking while those that exist are often not measured in concert. Here we demonstrate that Li isotope ratios of sediment, commonly used to trace clay formation, can help constrain OC cycling. Measurements of river suspended sediment from two watersheds of varying physiography and analysis of published data from Hawaii soil profiles show negative correlations between solid-phase d7Li values and OC content, indicating the association of clay mineral formation with OC accumulation. Yet, the localities differ in their ranges of d7Li values and OC contents, which we interpret with a model of soil formation. We find that temporal trends of Li isotopes and OC are most sensitive to mineral dissolution/clay formation rates, where higher rates yield greater OC stocks and lower d7Li values. Whereas OC-enhanced dissolution primarily dictates turnover times of OC and silicate minerals, clay protection distinctly modifies soil formation pathways and is likely required to explain the range of observations. These findings underscore clay mineral formation, driven primarily by bedrock chemistry and secondarily by climate, as a principal modulator of weathering fluxes and OC accumulation in soil. 

A synthetic biology approach toward constructing an RNA-based genome expands our understanding of living things and opens avenues for technological advancement. For the precise design of an artificial RNA replicon either from scratch or based on a natural RNA replicon, understanding structure–function relationships of RNA sequences is critical. However, our knowledge remains limited to a few particular structural elements intensively studied so far. Here, we conducted a series of site-directed mutagenesis studies of yeast narnaviruses ScNV20S and ScNV23S, perhaps the simplest natural autonomous RNA replicons, to identify RNA elements required for maintenance and replication. RNA structure disruption corresponding to various portions of the entire narnavirus genome suggests that pervasive RNA folding, in addition to the precise secondary structure of genome termini, is essential for maintenance of the RNA replicon in vivo. Computational RNA structure analyses suggest that this scenario likely applies to other “narna-like" viruses. This finding implies selective pressure on these simplest autonomous natural RNA replicons to fold into a unique structure that acquires both thermodynamic and biological stability. We propose the importance of pervasive RNA folding for the design of RNA replicons that could serve as a platform for in vivo continuous evolution as well as an interesting model to study the origin of life. 

Microarchitectural side-channels enable an attacker to exfiltrate information via the observable side-effects of a victim’s execution. Obfuscating mitigation schemes have recently gained in popularity for their appealing performance characteristics. These schemes, including randomized caches and DRAM traffic shapers, limit, but do not completely eliminate, side-channel leakage. An important (yet under-explored) research challenge is the quantitative study of the security effectiveness of these schemes, identifying whether these obfuscating schemes help increase the security level of a system, and if so, by how much. In this paper, we address this research challenge by presenting Metior, a comprehensive model to quantitatively evaluate the effectiveness of obfuscating side-channel mitigations. Metior offers a way to reason about the flow of information through obfuscating schemes. Metior builds upon existing information theoretic approaches, allowing for the comprehensive side-channel leakage evaluation of active attackers, real victim applications, and state-ofthe-art microarchitectural obfuscation schemes. We demonstrate the use of Metior in the concrete leakage evaluation of three microarchitectural obfuscation schemes (fully-associative random replacement caches, CEASER-S, and Camouflage), identifying unintuitive leakage behaviours across all three schemes. 

Accounting for climate unlocks potential to disentangle primary factors controlling the evolution of mountain topography. 

We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific op timizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatica lly bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optim izations using SAM, and show SAM's ability to represent dataflow hardware. 

In this didactic paper, we present a theoretical modeling framework, called theG-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. TheG-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build theG-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basicGfunction models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.