Terrestrial hydrological controls on land surface phenology of African savannas and woodlands

This paper presents a continental-scale phenological analysis of African savannas and woodlands. We apply an array of synergistic vegetation and hydrological data records from satellite remote sensing and model simulations to explore the influence of rainy season timing and duration on regional land surface phenology and ecosystem structure. We find that (i) the rainy season onset precedes and is an effective predictor of the growing season onset in African grasslands. (ii) African woodlands generally have early green-up before rainy season onset and have a variable delayed senescence period after the rainy season, with this delay correlated nonlinearly with tree fraction. These woodland responses suggest their complex water use mechanisms (either from potential groundwater use by relatively deep roots or stem-water reserve) to maintain dry season activity. (iii) We empirically find that the rainy season length has strong nonlinear impacts on tree fractional cover in the annual rainfall range from 600 to 1800 mm/yr, which may lend some support to the previous modeling study that given the same amount of total rainfall to the tree fraction may first increase with the lengthening of rainy season until reaching an “optimal rainy season length,” after which tree fraction decreases with the further lengthening of rainy season. This nonlinear response is resulted from compound mechanisms of hydrological cycle, fire, and other factors. We conclude that African savannas and deciduous woodlands have distinctive responses in their phenology and ecosystem functioning to rainy season. Further research is needed to address interaction between groundwater and tropical woodland as well as to explicitly consider the ecological significance of rainy season length under climate change

Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales

Gross ecosystem productivity (GEP) in tropical forests varies both with the environment and with biotic changes in photosynthetic infrastructure, but our understanding of the relative effects of these factors across timescales is limited. Here, we used a statistical model to partition the variability of seven years of eddy covariance-derived GEP in a central Amazon evergreen forest into two main causes: variation in environmental drivers (solar radiation, diffuse light fraction, and vapor pressure deficit) that interact with model parameters that govern photosynthesis and biotic variation in canopy photosynthetic light-use efficiency associated with changes in the parameters themselves. Our fitted model was able to explain most of the variability in GEP at hourly (R2 = 0.77) to interannual (R2 = 0.80) timescales. At hourly timescales, we found that 75% of observed GEP variability could be attributed to environmental variability. When aggregating GEP to the longer timescales (daily, monthly, and yearly), however, environmental variation explained progressively less GEP variability: At monthly timescales, it explained only 3%, much less than biotic variation in canopy photosynthetic light-use efficiency, which accounted for 63%. These results challenge modeling approaches that assume GEP is primarily controlled by the environment at both short and long timescales. Our approach distinguishing biotic from environmental variability can help to resolve debates about environmental limitations to tropical forest photosynthesis. For example, we found that biotically regulated canopy photosynthetic light-use efficiency (associated with leaf phenology) increased with sunlight during dry seasons (consistent with light but not water limitation of canopy development) but that realized GEP was nonetheless lower relative to its potential efficiency during dry than wet seasons (consistent with water limitation of photosynthesis in given assemblages of leaves). This work highlights the importance of accounting for differential regulation of GEP at different timescales and of identifying the underlying feedbacks and adaptive mechanisms

Simulated sensitivity of African terrestrial ecosystem photosynthesis to rainfall frequency, intensity, and rainy season length

There is growing evidence of ongoing changes in the statistics of intra-seasonal rainfall variability over large parts of the world. Changes in annual total rainfall may arise from shifts, either singly or in a combination, of distinctive intra-seasonal characteristics –i.e. rainfall frequency, rainfall intensity, and rainfall seasonality. Understanding how various ecosystems respond to the changes in intra-seasonal rainfall characteristics is critical for predictions of future biome shifts and ecosystem services under climate change, especially for arid and semi-arid ecosystems. Here, we use an advanced dynamic vegetation model (SEIB-DGVM) coupled with a stochastic rainfall/weather simulator to answer the following question: how does the productivity of ecosystems respond to a given percentage change in the total seasonal rainfall that is realized by varying only one of the three rainfall characteristics (rainfall frequency, intensity, and rainy season length)? We conducted ensemble simulations for continental Africa for a realistic range of changes (−20% ~ +20%) in total rainfall amount. We find that the simulated ecosystem productivity (measured by gross primary production, GPP) shows distinctive responses to the intra-seasonal rainfall characteristics. Specifically, increase in rainfall frequency can lead to 28% more GPP increase than the same percentage increase in rainfall intensity; in tropical woodlands, GPP sensitivity to changes in rainy season length is ~4 times larger than to the same percentage changes in rainfall frequency or intensity. In contrast, shifts in the simulated biome distribution are much less sensitive to intra-seasonal rainfall characteristics than they are to total rainfall amount. Our results reveal three major distinctive productivity responses to seasonal rainfall variability—'chronic water stress', 'acute water stress' and 'minimum water stress' - which are respectively associated with three broad spatial patterns of African ecosystem physiognomy, i.e. savannas, woodlands, and tropical forests

Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

DDX21, a Host Restriction Factor of FMDV IRES-Dependent Translation and Replication

In cells, the contributions of DEAD-box helicases (DDXs), without which cellular life is impossible, are of utmost importance. The extremely diverse roles of the nucleolar helicase DDX21, ranging from fundamental cellular processes such as cell growth, ribosome biogenesis, protein translation, protein–protein interaction, mediating and sensing transcription, and gene regulation to viral manipulation, drew our attention. We designed this project to study virus–host interactions and viral pathogenesis. A pulldown assay was used to investigate the association between foot-and-mouth disease virus (FMDV) and DDX21. Further insight into the DDX21–FMDV interaction was obtained through dual-luciferase, knockdown, overexpression, qPCR, and confocal microscopy assays. Our results highlight the antagonistic feature of DDX21 against FMDV, as it progressively inhibited FMDV internal ribosome entry site (IRES) -dependent translation through association with FMDV IRES domains 2, 3, and 4. To subvert this host helicase antagonism, FMDV degraded DDX21 through its non-structural proteins 2B, 2C, and 3C protease (3Cpro). Our results suggest that DDX21 is degraded during 2B and 2C overexpression and FMDV infection through the caspase pathway; however, DDX21 is degraded through the lysosomal pathway during 3Cpro overexpression. Further investigation showed that DDX21 enhanced interferon-beta and interleukin-8 production to restrict viral replication. Together, our results demonstrate that DDX21 is a novel FMDV IRES trans-acting factor, which negatively regulates FMDV IRES-dependent translation and replication

NSs Filament Formation Is Important but Not Sufficient for RVFV Virulence In Vivo

Rift Valley fever virus (RVFV) is a mosquito-borne phlebovirus that represents as a serious health threat to both domestic animals and humans. The viral protein NSs is the key virulence factor of RVFV, and has been proposed that NSs nuclear filament formation is critical for its virulence. However, the detailed mechanisms are currently unclear. Here, we generated a T7 RNA polymerase-driven RVFV reverse genetics system based on a strain imported into China (BJ01). Several NSs mutations (T1, T3 and T4) were introduced into the system for investigating the correlation between NSs filament formation and virulence in vivo. The NSs T1 mutant showed distinct NSs filament in the nuclei of infected cells, the T3 mutant diffusively localized in the cytoplasm and the T4 mutant showed fragmented nuclear filament formation. Infection of BALB/c mice with these NSs mutant viruses revealed that the in vivo virulence was severely compromised for all three NSs mutants, including the T1 mutant. This suggests that NSs filament formation is not directly correlated with RVFV virulence in vivo. Results from this study not only shed new light on the virulence mechanism of RVFV NSs but also provided tools for future in-depth investigations of RVFV pathogenesis and anti-RVFV drug screening.</p