A primary aim of microbial ecology is to determine patterns and drivers of community distribution, interaction, and assembly amidst complexity and uncertainty. Microbial community composition has been shown to change across gradients of environment, geographic distance, salinity, temperature, oxygen, nutrients, pH, day length, and biotic factors 1-6 . These patterns have been identified mostly by focusing on one sample type and region at a time, with insights extra polated across environments and geography to produce generalized principles. To assess how microbes are distributed across environments globally-or whether microbial community dynamics follow funda mental ecological 'laws' at a planetary scale-requires either a massive monolithic cross environment survey or a practical methodology for coordinating many independent surveys. New studies of microbial environments are rapidly accumulating; however, our ability to extract meaningful information from across datasets is outstripped by the rate of data generation. Previous meta analyses have suggested robust gen eral trends in community composition, including the importance of salinity 1 and animal association 2 . These findings, although derived from relatively small and uncontrolled sample sets, support the util ity of meta analysis to reveal basic patterns of microbial diversity and suggest that a scalable and accessible analytical framework is needed.The Earth Microbiome Project (EMP, http://www.earthmicrobiome. org) was founded in 2010 to sample the Earth's microbial communities at an unprecedented scale in order to advance our understanding of the organizing biogeographic principles that govern microbial commu nity structure 7,8 . We recognized that open and collaborative science, including scientific crowdsourcing and standardized methods 8 , would help to reduce technical variation among individual studies, which can overwhelm biological variation and make general trends difficult to detect 9 . Comprising around 100 studies, over half of which have yielded peer reviewed publications (Supplementary Table 1), the EMP has now dwarfed by 100 fold the sampling and sequencing depth of earlier meta analysis efforts 1,2 ; concurrently, powerful analysis tools have been developed, opening a new and larger window into the distri bution of microbial diversity on Earth. In establishing a scalable frame work to catalogue microbiota globally, we provide both a resource for the exploration of myriad questions and a starting point for the guided acquisition of new data to answer them. As an example of using this Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of r...

Salt stress is a major environmental stress that affects plant growth and development. Plants are sessile and thus have to develop suitable mechanisms to adapt to high-salt environments. Salt stress increases the intracellular osmotic pressure and can cause the accumulation of sodium to toxic levels. Thus, in response to salt stress signals, plants adapt via various mechanisms, including regulating ion homeostasis, activating the osmotic stress pathway, mediating plant hormone signaling, and regulating cytoskeleton dynamics and the cell wall composition. Unraveling the mechanisms underlying these physiological and biochemical responses to salt stress could provide valuable strategies to improve agricultural crop yields. In this review, we summarize recent developments in our understanding of the regulation of plant salt stress.

Both diet and host phylogeny shape the gut microbial community, and separating out the effects of these variables can be challenging. In this study, high-throughput sequencing was used to evaluate the impact of diet and phylogeny on the gut microbiota of nine colobine monkey species (N = 64 individuals). Colobines are leaf-eating monkeys that fare poorly in captivity-often exhibiting gastrointestinal (GI) problems. This study included eight Asian colobines (Rhinopithecus brelichi, Rhinopithecus roxellana, Rhinopithecus bieti, Pygathrix nemaeus, Nasalis larvatus, Trachypithecus francoisi, Trachypithecus auratus, and Trachypithecus vetulus) and one African colobine (Colobus guereza). Monkeys were housed at five different captive institutes: Panxi Wildlife Rescue Center (Guizhou, China), Beijing Zoo, Beijing Zoo Breeding Center, Singapore Zoo, and Singapore Zoo Primate Conservation Breeding Center. Captive diets varied widely between institutions, but within an institution, all colobine monkey species were fed nearly identical or identical diets. In addition, four monkey species were present at multiple captive institutes. This allowed us to parse the effects of diet and phylogeny in these captive colobines. Gut microbial communities clustered weakly by host species and strongly by diet, and overall, colobine phylogenetic relationships were not reflected in gut microbiota analyses. Core microbiota analyses also identified several key taxa-including microbes within the Ruminococcaceae and Lachnospiraceae families-that were shared by over 90% of the monkeys in this study. Microbial species within these families include many butyrate producers that are important for GI health. These results highlight the importance of diet in captive colobines.

This work focuses on developing a novel adsorbent for CO2 capture, by coating polyethylenimine (PEI) on glass fiber matrix and using epichlorohydrin (ECH) as cross-linking agent. The physicochemical properties of the fibrous adsorbent were characterized. The CO2 adsorption capacity was evaluated. Factors that affect the adsorption capacity of the fibrous adsorbent were studied. The experimental results show that this fibrous PEI adsorbent exhibits a much higher adsorption capacity for CO2 compared with another PEI fiber prepared in our previous work, which employed epoxy resin as the cross-linking agent. A CO2 adsorption capacity as high as 4.12 mmol CO2/g of adsorbent was obtained for this fibrous PEI adsorbent at 30 degrees C, equal to 13.56 mmol CO2/g of PEI, with a PEI/ECH ratio of 20:1. The adsorbent can be completely regenerated at 120 degrees C.

Many colobine species-including the endangered Guizhou snub-nosed monkey (Rhinopithecus brelichi) are difficult to maintain in captivity and frequently exhibit gastrointestinal (GI) problems. GI problems are commonly linked to alterations in the gut microbiota, which lead us to examine the gut microbial communities of wild and captive R. brelichi. We used high-throughput sequencing of the 16S rRNA gene to compare the gut microbiota of wild (N = 7) and captive (N = 8) R. brelichi. Wild monkeys exhibited increased gut microbial diversity based on the Chao1 but not Shannon diversity metric and greater relative abundances of bacteria in the Lachnospiraceae and Ruminococcaceae families. Microbes in these families digest complex plant materials and produce butyrate, a short chain fatty acid critical to colonocyte health. Captive monkeys had greater relative abundances of Prevotella andBacteroides species, which degrade simple sugars and carbohydrates, like those present in fruits and cornmeal, two staples of the captive R. brelichi diet. Captive monkeys also had a greater abundance of Akkermansia species, a microbe that can thrive in the face of host malnutrition. Taken together, these findings suggest that poor health in captive R. brelichi may be linked to diet and an altered gut microbiota.

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Intrusion detection system (IDS) can effectively identify anomaly behaviors in the network; however, it still has low detection rate and high false alarm rate especially for anomalies with fewer records. In this paper, we propose an effective IDS by using hybrid data optimization which consists of two parts: data sampling and feature selection, called DO_IDS. In data sampling, the Isolation Forest (iForest) is used to eliminate outliers, genetic algorithm (GA) to optimize the sampling ratio, and the Random Forest (RF) classifier as the evaluation criteria to obtain the optimal training dataset. In feature selection, GA and RF are used again to obtain the optimal feature subset. Finally, an intrusion detection system based on RF is built using the optimal training dataset obtained by data sampling and the features selected by feature selection. The experiment will be carried out on the UNSW-NB15 dataset. Compared with other algorithms, the model has obvious advantages in detecting rare anomaly behaviors.

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.