A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution.

The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios.

Arbidol inhibits human esophageal squamous cell carcinoma growth in vitro and in vivo through suppressing ataxia telangiectasia and Rad3-related protein kinase.

Human esophageal cancer has a global impact on human health due to its high incidence and mortality. Therefore, there is an urgent need to develop new drugs to treat or prevent the prominent pathological subtype of esophageal cancer, esophageal squamous cell carcinoma (ESCC). Based upon the screening of drugs approved by the Food and Drug Administration, we discovered that Arbidol could effectively inhibit the proliferation of human ESCC in vitro. Next, we conducted a series of cell-based assays and found that Arbidol treatment inhibited the proliferation and colony formation ability of ESCC cells and promoted G1-phase cell cycle arrest. Phosphoproteomics experiments, in vitro kinase assays and pull-down assays were subsequently performed in order to identify the underlying growth inhibitory mechanism. We verified that Arbidol is a potential ataxia telangiectasia and Rad3-related (ATR) inhibitor via binding to ATR kinase to reduce the phosphorylation and activation of minichromosome maintenance protein 2 at Ser108. Finally, we demonstrated Arbidol had the inhibitory effect of ESCC in vivo by a patient-derived xenograft model. All together, Arbidol inhibits the proliferation of ESCC in vitro and in vivo through the DNA replication pathway and is associated with the cell cycle.

The Terrestrial Biosphere Model Farm.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Nuplazid suppresses esophageal squamous cell carcinoma growth in vitro and in vivo by targeting PAK4.

BACKGROUND: Due to the high recurrence and low 5-year survival rates of esophageal squamous cell carcinoma (ESCC) after treatment, the discovery of novel drugs for recurrence chemoprevention is of particular importance. METHODS: We screened the FDA-approved drug library and found that Nuplazid, an atypical antipsychotic that acts as an effective 5-HT 2 A receptor inverse agonist, could potentially exert anticancer effects in vitro and in vivo on ESCC. RESULTS: Pull-down results indicated that Nuplazid binds with p21-activated kinase 4 (PAK4), and a kinase assay showed that Nuplazid strongly suppressed PAK4 kinase activity. Moreover, Nuplazid exhibited inhibitory effects on ESCC in vivo. CONCLUSIONS: Our findings indicate that Nuplazid can suppress ESCC progression through targeting PAK4.

Enhancement of microwave absorption performance of porous carbon induced by Ce (CO3) OH.

In recent years, electromagnetic pollution has become more and more serious, resulting in a very negative impact on people's health. Therefore, it is important to develop efficient microwave absorbers to reduce electromagnetic pollution. Here, we construct a novel absorbing material of the polymer gel-derived porous carbon decorated by rare earth compounds (Ce (CO3) OH). When the thickness is 2.2 mm, the composite exhibits excellent microwave absorption performance with the optimal RLmin value and EAB reached up to -47.67 dB and 5.52 GHz, respectively, covering the Ku band. The high-efficiency microwave absorption is mainly attributed to the synergistic effect of dipole polarization, defect polarization and interfacial polarization. This work not only provides a new view for designing superior absorber materials, but also lay a foundation for their real applications.

Tegaserod Maleate Inhibits Esophageal Squamous Cell Carcinoma Proliferation by Suppressing the Peroxisome Pathway.

Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are the two major types of esophageal cancer (EC). ESCC accounts for 90% of EC. Recurrence after primary treatment is the main reason for poor survival. Therefore, recurrence prevention is a promising strategy for extending the 5-year survival rate. Here, we found tegaserod maleate could inhibit ESCC proliferation both in vivo and in vitro. Proteomics analysis revealed that tegaserod maleate suppressed the peroxisome signaling pathway, in which the key molecules peroxisome membrane protein 11B (PEX11B) and peroxisome membrane protein 13 (PEX13) were downregulated. The immunofluorescence, catalase activity assay, and reactive oxygen species (ROS) confirmed that downregulation of these proteins was related to impaired peroxisome function. Furthermore, we found that PEX11B and PEX13 were highly expressed in ESCC, and knockout of PEX11B and PEX13 further demonstrated the antitumor effect of tegaserod maleate. Importantly, tegaserod maleate repressed ESCC tumor growth in a patient-derived xenograft (PDX) model in vivo. Our findings conclusively demonstrated that tegaserod maleate inhibits the proliferation of ESCC by suppressing the peroxisome pathway.

Gridded daily weather data for North America with comprehensive uncertainty quantification.

Access to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

Author Correction: Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mefloquine Inhibits Esophageal Squamous Cell Carcinoma Tumor Growth by Inducing Mitochondrial Autophagy.

Esophageal squamous cell carcinoma (ESCC) has a worldwide impact on human health, due to its high incidence and mortality. Therefore, identifying compounds to increase patients' survival rate is urgently needed. Mefloquine (MQ) is an FDA-approved anti-malarial drug, which has been reported to inhibit cellular proliferation in several cancers. However, the anti-tumor activities of the drug have not yet been completely defined. In this study, mass spectrometry was employed to profile proteome changes in ESCC cells after MQ treatment. Sub-cellular localization and gene ontology term enrichment analysis suggested that MQ treatment mainly affect mitochondria. The KEGG pathway enrichment map of down-regulated pathways and Venn diagram indicated that all of the top five down regulated signaling pathways contain four key mitochondrial proteins (succinate dehydrogenase complex subunit C (SDHC), succinate dehydrogenase complex subunit D, mitochondrially encoded cytochrome c oxidase III and NADH: ubiquinone oxidoreductase subunit V3). Meanwhile, mitochondrial autophagy was observed in MQ-treated KYSE150 cells. More importantly, patient-derived xenograft mouse models of ESCC with SDHC high expression were more sensitive to MQ treatment than low SDHC-expressing xenografts. Taken together, mefloquine inhibits ESCC tumor growth by inducing mitochondrial autophagy and SDHC plays a vital role in MQ-induced anti-tumor effect on ESCC.

Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century.

Terrestrial vegetation removes CO2 from the atmosphere; an important climate regulation service that slows global warming. This 119 Pg C per annum transfer of CO2 into plants-gross primary productivity (GPP)-is the largest land carbon flux globally. While understanding past and anticipated future GPP changes is necessary to support carbon management, the factors driving long-term changes in GPP are largely unknown. Here we show that 1901 to 2010 changes in GPP have been dominated by anthropogenic activity. Our dual constraint attribution approach provides three insights into the spatiotemporal patterns of GPP change. First, anthropogenic controls on GPP change have increased from 57% (1901 decade) to 94% (2001 decade) of the vegetated land surface. Second, CO2 fertilization and nitro gen deposition are the most important drivers of change, 19.8 and 11.1 Pg C per annum (2001 decade) respectively, especially in the tropics and industrialized areas since the 1970's. Third, changes in climate have functioned as fertilization to enhance GPP (1.4 Pg C per annum in the 2001 decade). These findings suggest that, from a land carbon balance perspective, the Anthropocene began over 100 years ago and that global change drivers have allowed GPP uptake to keep pace with anthropogenic emissions.

Global vegetation biomass production efficiency constrained by models and observations.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Land carbon models underestimate the severity and duration of drought's impact on plant productivity.

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0-6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.

Enhanced peak growth of global vegetation and its key mechanisms.

The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPPmax and NDVImax). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVImax variation is evenly explained by expanding croplands (21%), rising CO2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPPmax to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.

Observed positive vegetation-rainfall feedbacks in the Sahel dominated by a moisture recycling mechanism.

Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region's proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative of amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.

Global patterns of drought recovery.

Drought, a recurring phenomenon with major impacts on both human and natural systems, is the most widespread climatic extreme that negatively affects the land carbon sink. Although twentieth-century trends in drought regimes are ambiguous, across many regions more frequent and severe droughts are expected in the twenty-first century. Recovery time-how long an ecosystem requires to revert to its pre-drought functional state-is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO2 fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth's climate system) and second, that drought impacts (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

Hyperspectral Study of Estimating Nitrification Microorganism in Wetland Soils.

Nitrogen cycle is an important process in the circle of soil ecosystem elements, and nitrification has significant effect on soil nitrogen cycling. The main completer of nitrification is nitrification microbial communities. Soil microorganisms are vital components of wetland ecosystem. They can indicate the variations of wetland ecological environment, and this helps us to have the correct understanding of nitrogen cycle and pollution purification function in wetland ecosystem. This paper tries to study nitrification microbial communities in wetland soils from the perspective of hyperspectral remote sensing technology, based on the monitoring mechanisms of soil nitrogen spectrum. The study explores hyperspectral estimation techniques for nitrification microbial communities in wetland soils, and it can provide a new technical approach to estimate the temporal and spatial distribution of nitrification microbial communities. The study adopted most probable number method (MPN) to count the numbers of ammonia oxidizing bacteria and nitrite oxidizing bacteria respectively, which were main completers of two independent stages in nitrification. And the total results of both count measurements were used as the values of soil nitrification microorganisms for each sampling area. The estimation models of nitrification microorganism and total nitrogen in wetland soils were developed respectively using spectral transformation techniques, such as log-transformed spectra (LR), first derivative (FD), second derivative (SD), continuum removal (CR) and band depth (BD), and modeling methods, such as stepwise multiple linear regression (SMLR) and partial least-squares regression (PLSR) based on the bootstrap technology. The results indicated that the selected estimation bands of nitrification microorganism and total nitrogen were close (especially for original spectral data (R) and SD spectra) when the modeling method of bootstrap SMLR was used. Compared to the bootstrap SMLR, the bootstrap PLSR achieved higher accuracies for estimating nitrification microorganism and total nitrogen in wetland soils. The spectral transformation technique of SD combined with the modeling method of bootstrap PLSR yielded the highest estimation accuracy to predict nitrification microorganism in wetland soils. The CR spectral data combined with bootstrap PLSR produced the highest estimation accuracy to predict total nitrogen content in wetland soils.

Bridging Theory with Practice: An Exploratory Study of Visualization Use and Design for Climate Model Comparison.

Evaluation methodologies in visualization have mostly focused on how well the tools and techniques cater to the analytical needs of the user. While this is important in determining the effectiveness of the tools and advancing the state-of-the-art in visualization research, a key area that has mostly been overlooked is how well established visualization theories and principles are instantiated in practice. This is especially relevant when domain experts, and not visualization researchers, design visualizations for analysis of their data or for broader dissemination of scientific knowledge. There is very little research on exploring the synergistic capabilities of cross-domain collaboration between domain experts and visualization researchers. To fill this gap, in this paper we describe the results of an exploratory study of climate data visualizations conducted in tight collaboration with a pool of climate scientists. The study analyzes a large set of static climate data visualizations for identifying their shortcomings in terms of visualization design. The outcome of the study is a classification scheme that categorizes the design problems in the form of a descriptive taxonomy. The taxonomy is a first attempt for systematically categorizing the types, causes, and consequences of design problems in visualizations created by domain experts. We demonstrate the use of the taxonomy for a number of purposes, such as, improving the existing climate data visualizations, reflecting on the impact of the problems for enabling domain experts in designing better visualizations, and also learning about the gaps and opportunities for future visualization research. We demonstrate the applicability of our taxonomy through a number of examples and discuss the lessons learnt and implications of our findings.

Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions.

Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land-atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process-based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century-long (1901-2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project and two observation-based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111 Pg C (1 Pg = 1015 g) with a median value of 1158 Pg C in 2010. The models estimate a broad range of Rh from 35 to 69 Pg C yr-1 with a median value of 51 Pg C yr-1 during 2001-2010. The largest uncertainty in SOC stocks exists in the 40-65°N latitude whereas the largest cross-model divergence in Rh are in the tropics. The modeled SOC change during 1901-2010 ranges from -70 Pg C to 86 Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble-estimated mean residence time of SOC shows a reduction of 3.4 years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO2 and nitrogen deposition over intact ecosystems increased SOC stocks-even though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.

Toward verifying fossil fuel CO2 emissions with the CMAQ model: motivation, model description and initial simulation.

UNLABELLED: Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO2 spatiotemporal variability and verify CO2 fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO2. This paper presents methods, input data, and initial results for CO2 simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere-atmosphere exchange, and meteorology in regulating the spatial distribution of CO2 near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO2 concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver CO, the CMAQ model using hourly varying, high-resolution CO2 fossil-fuel emissions from the Vulcan inventory and Carbon Tracker optimized NEE reproduced the observed diurnal profile of CO2 reasonably well but with a low bias in the early morning. The spatial distribution of CO2 was found to correlate with NO(x), SO2, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO2 observations and understand CO2 variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO2 source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO2 spatiotemporal variability and various uncertainties in the future. IMPLICATIONS: Atmospheric CO2 has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO2 variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO2 emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO2 as an air pollutant.