Turbulence is a ubiquitous phenomenon in nature, and it is well known that turbulence couples energy input to dissipation by cascade processes. Plasma turbulence play a critical role in tokamak confinement. Magnetized plasma turbulence is quasi 2D, anisotropic, wave like and two fluid (i.e. electrons and ions) in structure. Thus, weakly collisional plasma turbulence can mediate electron and ion energy transfer. The issue of anomalous electron and ion energy coupling is particularly important for low collisionality, electron heated plasmas, such as ITER. In this work, we reconsider the classic problem of turbulent heating and energy transfer pathways in drift wave turbulence. The total turbulent heating, composed of quasilinear electron cooling, quasilinear ion heating, nonlinear ion heating and zonal flow frictional heating, is analyzed. In Chapter 2, the electron and ion energy exchange via linear wave and particle resonance will be computed. To address net heating, we show the turbulent heating in an annulus arises due to a wave energy flux differential across this region. We show this net heating is proportional to the Reynolds work on the zonal flow. Zonal flow friction heats ions, thus the turbulence and zonal flow interaction enters as an important energy transfer channel. Since zonal flows are nonlinearly generated, it follows that we should apply weak turbulence theory to calculate the nonlinear ion turbulent heating via the virtual mode resonance in the electron drift wave turbulence, which will be discussed in Chapter 3. We defines a new collisionless turbulent energy transfer channel through nonlinear Landau damping in the electron and ion energy coupling process. The result shows that nonlinear ion heating can exceed quasilinear ion heating, so that nonlinear heating becomes the principal collisionless wave energy dissipation channel in electron drift wave turbulence. This follows since the beat mode resonates with the bulk of the ion distribution, in contrast to the linear resonance which is located on the tail. This result also suggests that zonal flow shearing is not necessarily the only saturation mechanism of importance, especially for very low collisionality. This observation brings a new perspective on electron heat transport where ions, play a role as an energy "sink" in a collisionless plasma, such as ITER. In addition, it is shown that the electron turbulent energy transfer to ions in a collisionless plasma can be the same order as electron heat transport losses. Thus, it is necessary to consider the influence of collisionless energy transfer to determine the total energy budget in ITER

Commonly used methods for inferring phylogenies were designed before the emergence of high-throughput sequencing and can generally not accommodate the challenges associated with noisy, diploid sequencing data. In many applications, diploid genomes are still treated as haploid through the use of ambiguity characters; while the uncertainty in genotype calling-arising as a consequence of the sequencing technology-is ignored. In order to address this problem, we describe two new probabilistic approaches for estimating genetic distances: distAngsd-geno and distAngsd-nuc, both implemented in a software suite named distAngsd. These methods are specifically designed for next-generation sequencing data, utilize the full information from the data, and take uncertainty in genotype calling into account. Through extensive simulations, we show that these new methods are markedly more accurate and have more stable statistical behaviors than other currently available methods for estimating genetic distances-even for very low depth data with high error rates.

Hybrid fuzzing that combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, state-of-the-art hybrid fuzzing systems deploy 'optimal concolic testing' and 'demand launch' strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to unrealistic or oversimplified assumptions. Further, we propose a novel 'discriminative dispatch' strategy and design a probabilistic hybrid fuzzing system to better utilize the capability of concolic execution. Specifically, we design a Monte Carlo-based probabilistic path prioritization model to quantify each path's difficulty, and then prioritize them for concolic execution. Our model assigns the most difficult paths to concolic execution. We implement a prototype named DigFuzz and evaluate our system with two representative datasets and real-world programs. Results show that the concolic execution in DigFuzz outperforms than those in state-of-the-art hybrid fuzzing systems in every major aspect. In particular, the concolic execution in DigFuzz contributes to discovering more vulnerabilities (12 versus 5) and producing more code coverage (18.9 versus 3.8 percent) on the CQE dataset than the concolic execution in Driller.

Background

Microbiota play important roles in the gastrointestinal tract (GIT) of dairy cattle as the communities are responsible for host health, growth, and production performance. However, a systematic characterization and comparison of microbial communities in the GIT of cattle housed in different management units on a modern dairy farm are still lacking. We used 16S rRNA gene sequencing to evaluate the fecal bacterial communities of 90 dairy cattle housed in 12 distinctly defined management units on a modern dairy farm.

Results

We found that cattle from management units 5, 6, 8, and 9 had similar bacterial communities while the other units showed varying levels of differences. Hutch calves had a dramatically different bacterial community than adult cattle, with at least 10 genera exclusively detected in their samples but not in non-neonatal cattle. Moreover, we compared fecal bacteria of cattle from every pair of the management units and detailed the number and relative abundance of the significantly differential genera. Lastly, we identified 181 pairs of strongly correlated taxa in the community, showing possible synergistic or antagonistic relationships.

Conclusions

This study assesses the fecal microbiota of cattle from 12 distinctly defined management units along the production line on a California dairy farm. The results highlight the similarities and differences of fecal microbiota between cattle from each pair of the management units. Especially, the data indicate that the newborn calves host very different gut bacterial communities than non-neonatal cattle, while non-neonatal cattle adopt one of the two distinct types of gut bacterial communities with subtle differences among the management units. The gut microbial communities of dairy cattle change dramatically in bacterial abundances at different taxonomic levels along the production line. The findings provide a reference for research and practice in modern dairy farm management.

Water availability has historically been one of the most significant threats to African regional social and economic well-being. Over the Sahel region, a megadrought during the 1960s and 1970s induced by an abrupt and substantial rainfall reduction caused widespread famine and death. The postdrought recovery, which is still ongoing, has been characterized by gradual increases in rainfall, but with dramatic fluctuations. The large negative human impacts, slow recovery, and variability raise important questions of why and how rainfall dynamics evolve and interact with other components of the regional climate system. Here we provide an observational assessment of rainfall interactions mechanisms (informed by directional transfer of information entropy) that regulate Sahel rainfall. We quantitatively demonstrate that (1) sea surface temperature over the Gulf of Guinea controls moisture advection and transport to the West Sahel region and (2) strong bidirectional interactions exist between local vegetation dynamics and rainfall patterns. Then we assess the directional interaction patterns from nine state-of-the-art Earth System Models (ESMs). We find that most ESMs are able to represent either the unidirectional control of sea surface temperature on precipitation or the bidirectional interaction between vegetation and precipitation. However, none of the ESMs represents both interactive patterns. The GFDL and IPSL-CM5A-LR models successfully reproduced observed patterns over ~50% of the West Sahel region but were not accurate in reproducing observed precipitation regional trends or interannual variation. We propose that the directional information transfer is a powerful mechanistic benchmark to assess model fidelity at the process level.

Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90g% higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2Combining double low line0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

Heat waves (HWs) are among the most damaging climate extremes to human society. Climate models consistently project that HW frequency, severity, and duration will increase markedly over this century. For urban residents, the urban heat island (UHI) effect further exacerbates the heat stress resulting from HWs. Here we use a climate model to investigate the interactions between the UHI and HWs in 50 cities in the United States under current climate and future warming scenarios. We examine UHI2m (defined as urban-rural difference in 2m-height air temperature) and UHIs (defined as urban-rural difference in radiative surface temperature). Our results show significant sensitivity of the interaction between UHI and HWs to local background climate and warming scenarios. Sensitivity also differs between daytime and nighttime. During daytime, cities in the temperate climate region show significant synergistic effects between UHI and HWs in current climate, with an average of 0.4 K higher UHI2m or 2.8 K higher UHIs during HWs than during normal days. These synergistic effects, however, diminish in future warmer climates. In contrast, the daytime synergistic effects for cities in dry regions are insignificant in the current climate, but emerge in future climates. At night, the synergistic effects are similar across climate regions in the current climate, and are stronger in future climate scenarios. We use a biophysical factorization method to disentangle the mechanisms behind the interactions between UHI and HWs that explain the spatial-temporal patterns of the interactions. Results show that the difference in the increase of urban versus rural evaporation and enhanced anthropogenic heat emissions (air conditioning energy use) during HWs are key contributors to the synergistic effects during daytime. The contrast in water availability between urban and rural land plays an important role in determining the contribution of evaporation. At night, the enhanced release of stored and anthropogenic heat during HWs are the primary contributors to the synergistic effects.

Mutations in the BRCA2 gene are associated with sporadic and familial cancer, cause genomic instability and sensitize cancer cells to inhibition by the poly(ADP-ribose) polymerase (PARP). Here we show that human pluripotent stem cells (hPSCs) with one copy of BRCA2 deleted can be used to annotate variants of this gene and to test their sensitivities to PARP inhibition. By using Cas9 to edit the functional BRCA2 allele in the locally haploid hPSCs and in fibroblasts differentiated from them, we characterized essential regions in the gene to identify permissive and loss-of-function mutations. We also used Cas9 to directly test the function of individual amino acids, including amino acids encoded by clinical BRCA2 variants of uncertain significance, and identified alleles that are sensitive to PARP inhibitors used as a standard of care in BRCA2-deficient cancers. Locally haploid human pluripotent stem cells can facilitate detailed structure-function analyses of genes and the rapid functional evaluation of clinically observed mutations.

African and South American (ASA) wildfires account for more than 70g% of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire-climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire-climate relationships in historical and future predictions.