Effects of simulated gut pH environment on bacterial composition and pheromone production of Dendroctonus valens.

Bark beetles are an economically and ecologically important insect group, with aggregation behavior and thus host colonization success depends on pheromone-mediated communication. For some species, such as the major invasive forest pest in China, red turpentine beetle (Dendroctonus valens), gut microbiota participates in pheromone production by converting tree monoterpenes into pheromone products. However, how variation in gut microenvironment, such as pH, affects the gut microbial composition, and consequently pheromone production, is unknown. In this study, we fed wild caught D. valens with 3 different pH media (main host diet with natural pH of 4.7; a mildly acidic diet with pH 6 mimicking the beetle gut pH; and highly acidic diet with pH 4), and measured their effects on the gut pH, bacterial community and production of the main aggregation and anti-aggregation pheromone (verbenone). We further tested the verbenone production capacity of 2 gut bacterial isolates in different pH environments (pH 6 and 4). Compared to natural state or main host diet, feeding on less acidic diet (pH 6) diluted the acidity of the gut, whereas feeding on highly acidic diet (pH 4) enhanced it. Both changes in gut pH reduced the abundance of dominant bacterial genera, resulting in decreased verbenone production. Similarly, the highest pheromone conversion rate of the bacterial isolates was observed in pH mimicking the acidity in beetle gut. Taken together, these results indicate that changes in gut pH can affect gut microbiota composition and pheromone production, and may therefore have the potential to affect host colonization behavior.

Multi-energy CT material decomposition using graph model improved CNN.

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications.

Effects of different wheat bran fermentation sources on growth performance, nutrient digestibility, serum antioxidant capacity and fecal microbiota in growing pigs.

The present study aimed to evaluate the application of different wheat bran fermentation sources in growing pigs. A total of 320 pigs (43 ± 0.21 kg), were randomly allocated to 5 groups in a 21-d trial. The control group was fed a basal diet (CON) containing raw wheat bran, and the other four treatments were fed the diets in which the raw wheat bran in the basal diet was substituted with Aspergillus niger (WBA), Bacillus licheniformis (WBB), Candida utilis (WBC), and Lactobacillus plantarum (WBL) fermented wheat bran, respectively. The results showed that compared to the CON group, the crude fiber and pH values were decreased (p < 0.05), while the gross energy (GE), crude protein (CP), and lactic acid values were increased (p < 0.05) in all the wheat bran fermented by different strains. Compared with other treatments, feeding B. licheniformis fermented wheat bran had higher final weight, average daily gain, as well as lower feed-to-gain ratio. Compared with CON group, pigs fed with fermented wheat bran diets had higher dry matter, CP, and GE availability, serum total protein, albumin and superoxide dismutase levels, and fecal Lactobacillus counts, as well as lower malondialdehyde level and fecal Escherichia coli count. Collectively, our findings suggested that feeding fermented wheat bran, especially B. licheniformis fermented wheat bran, showed beneficial effects on the growth performance, nutrient digestibility, serum antioxidant capacity, and the gut microbiota structure of growing pigs.

Gut Bacteria of Wood-Boring Streltzoviella insularis and Their Involvement in Pinoresinol Degradation of Ash Tree (Fraxinus sp.) Host.

Insect gut microbiota have been widely reported to help the insects to overcome host tree defense. Streltzoviella insularis (Lepidoptera: Cossidae) is one of the most common wood borers in China, attacking various hosts, including ash trees (Fraxinus sp.), but little is known about its gut microbial associates and their involvement in host tree defense. We isolated gut bacteria of S. insularis larvae, analyzed their ability to degrade pinoresinol (a defense compound of ash trees) and cellulose, and identified pinoresinol degradation products. Larval mortality increased with increasing pinoresinol concentration (reflecting natural variation observed in the host trees). All the five detected gut bacteria isolates were able to degrade pinoresinol, two of which were also capable of cellulose degradation. Furthermore, gut bacteria were also shown to degrade pinoresinol via the gluconeogenesis pathway. These results suggest that S. insularis-associated microorganisms help to overcome host pinoresinol defense and possibly contribute to insects or gut microbial nutrition via carbohydrate synthesis.

The effect of inactivation of aldehyde dehydrogenase on pheromone production by a gut bacterium of an invasive bark beetle, Dendroctonus valens.

Semiochemical-based management strategies are important for controlling bark beetles, such as invasive Red Turpentine Beetle (Denroctonus valens), the causal agent for mass mortality of pine trees (Pinus spp.) in China. It has been previously shown that the pheromone verbenone regulates the attack density of this beetle in a dose-dependent manner and that the gut bacteria of D. valens are involved in verbenone production. However, molecular functional verification of the role of gut bacteria in the pheromone production of D. valens is still lacking. To better understand the molecular function of gut bacterial verbenone production, we chose a facultative anaerobic gut bacterium (Enterobacter xiangfangensis) of D. valens based on its strong ability to convert cis-verbenol to verbenone, as shown in our previous study, and investigated its transcriptomics in the presence or absence of cis-verbenol under anaerobic conditions (simulating the anoxic environment in the beetle's gut). Based on this transcriptome analysis, aldehyde dehydrogenase (ALDH1) was identified as a putative key gene responsible for verbenone production and was knocked-down by homologous recombination to obtain a mutant E. xiangfangensis strain. Our results show that these mutants had significantly decreased the ability to convert the monoterpene precursor to verbenone compared with the wild-type bacteria, indicating that ALDH1 is primarily responsible for verbenone conversion for this bacterium species. These findings provide further mechanistic evidence of bacterially mediated pheromone production by D. valens, add new perspective for functional studies of gut bacteria in general, and may aid the development of new gene silencing-based pest management strategies.

Road to entire insulation for resonances from a forced mechanical system.

The effective solution to avoid machinery damage caused by resonance has been perplexing the field of engineering as a core research direction since the resonance phenomenon was discovered by Euler in 1750. Numerous attempts have been performed to reduce the influence of resonance since the earlier of last century, by introducing a nonlinear structure or a closed-loop control system. However, the existed methodologies cannot eliminate resonance completely even extra problems were introduced inevitably, which means the technical choke-point of resonance-free remains unsolved. Here we propose a designable archetype model, which establishes a mapping between the mechanical properties and its structure. A general inverse method for structure construction is proposed based upon the required property for the system with quasi-zero stiffness of any designed finite order and the zero-stiffness properties. It is shown that an ellipse trajectory tracking of the designed model is the sufficient and necessary condition to satisfy the zero-stiffness property. Theoretical analysis shows that no resonant response happens in a zero-stiffness system to the full-band frequency excitation, or equivalently, the system can completely isolate the energy transfer between the load and environment, when the damping ratio approaches zero. Finally, an experimental rig for the prototype structure is built up according to the sufficient and necessary condition of the zero-stiffness system, for which the special dynamic behaviours are verified through experiments of frequency-sweep and random-vibration as well. Experimental results show that the prototype of the initial vibration isolation frequency of zero-stiffness system is much lower than 0.37 Hz, and the vibration attenuation of the proposed model is about 16.86 dB, 45.63 dB, and 112.37 dB at frequencies of 0.37 Hz, 1 Hz, and 10 Hz, respectively. The distinguished geometric structure of the zero-stiffness system leads to a new inspiration for the design of resonance-free in metamaterial unit and the inverse method can even adapt the design for a more targeted applications based on an arbitrary complex dynamic requirement.

A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.

PURPOSE: X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semi-supervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. METHODS: The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squared-error (MSE) loss and perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. RESULTS: The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. The ablation experiment shows that adding real-artifact CT images greatly improves the generalization ability of the network. CONCLUSIONS: The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network.

An x-ray crosstalk correction method using FCNN for a novel energy resolving scheme in spectral CT.

Spectral computed tomography has great potential for multi-energy imaging and anti-artifacts. The complete absorption-based energy resolving scheme of x-rays has been used for the integrity of detected information. However, this scheme is limited by the fact that the detector pixel thickness is high and fixed. Here, an energy resolving scheme is proposed using the crosstalk correction method for the incomplete absorption detection of x-rays. A fully connected neural network (FCNN)-based method was used to correct the difference caused by internal x-ray crosstalk of the edge-on detector. The energy and spatial features of the data which is collected in layers were combined to establish the mapping between the ideal data and the data with crosstalk at the pre-processing stage. Thereafter, to reconstruct the stable and highly accurate energy-resolving equations, the layers with low relative energy difference were selected and grouped together to reduce the accumulation difference. The experiment results demonstrate the feasibility of this energy resolving scheme. The differences caused by crosstalk can be suppressed through the proposed FCNN-based method. The resolving accuracy can be further improved by grouping more layers at forward positions in the pixel. Moreover, this improvement can be observed in the reconstructed images with reduced artifacts and improved quality.

A material decomposition method for dual-energy CT via dual interactive Wasserstein generative adversarial networks.

PURPOSE: Dual-energy computed tomography (DECT) is highly promising for material characterization and identification, whereas reconstructed material-specific images are affected by magnified noise and beam-hardening artifacts. Although various DECT material decomposition methods have been proposed to solve this problem, the quality of the decomposed images is still unsatisfactory, particularly in the image edges. In this study, a data-driven approach using dual interactive Wasserstein generative adversarial networks (DIWGAN) is developed to improve DECT decomposition accuracy and perform edge-preserving images. METHODS: In proposed DIWGAN, two interactive generators are used to synthesize decomposed images of two basis materials by modeling the spatial and spectral correlations from input DECT reconstructed images, and the corresponding discriminators are employed to distinguish the difference between the generated images and labels. The DECT images reconstructed from high- and low-energy bins are sent to two generators separately, and each generator synthesizes one material-specific image, thereby ensuring the specificity of the network modeling. In addition, the information from different energy bins is exploited through the feature sharing of two generators. During decomposition model training, a hybrid loss function including L1 loss, edge loss, and adversarial loss is incorporated to preserve the texture and edges in the generated images. Additionally, a selector is employed to define the generator that should be trained in each iteration, which can ensure the modeling ability of two different generators and improve the material decomposition accuracy. The performance of the proposed method is evaluated using digital phantom, XCAT phantom, and real data from a mouse. RESULTS: On the digital phantom, the regions of bone and soft tissue are strictly and accurately separated using the trained decomposition model. The material densities in different bone and soft-tissue regions are near the ground truth, and the error of material densities is lower than 3 mg/ml. The results from XCAT phantom show that the material-specific images generated by directed matrix inversion and iterative decomposition methods have severe noise and artifacts. Regarding to the learning-based methods, the decomposed images of fully convolutional network (FCN) and butterfly network (Butterfly-Net) still contain varying degrees of artifacts, while proposed DIWGAN can yield high quality images. Compared to Butterfly-Net, the root-mean-square error (RMSE) of soft-tissue images generated by the DIWGAN decreased by 0.01 g/ml, whereas the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the soft-tissue images reached 31.43 dB and 0.9987, respectively. The mass densities of the decomposed materials are nearest to the ground truth when using the DIWGAN method. The noise standard deviation of the decomposition images reduced by 69%, 60%, 33%, and 21% compared with direct matrix inversion, iterative decomposition, FCN, and Butterfly-Net, respectively. Furthermore, the performance of the mouse data indicates the potential of the proposed material decomposition method in real scanned data. CONCLUSIONS: A DECT material decomposition method based on deep learning is proposed, and the relationship between reconstructed and material-specific images is mapped by training the DIWGAN model. Results from both the simulation phantoms and real data demonstrate the advantages of this method in suppressing noise and beam-hardening artifacts.

An invasive beetle-fungus complex is maintained by fungal nutritional-compensation mediated by bacterial volatiles.

Mutualisms between symbiotic microbes and animals have been well documented, and nutritional relationships provide the foundation for maintaining beneficial associations. The well-studied mutualism between bark beetles and their fungi has become a classic model system in the study of symbioses. Despite the nutritional competition between bark beetles and beneficial fungi in the same niche due to poor nutritional feeding substrates, bark beetles still maintain mutualistic associations with beneficial fungi over time. The mechanism behind this phenomenon, however, remains largely unknown. Here, we demonstrated the bark beetle Dendroctonus valens LeConte relies on the symbiotic bacterial volatile ammonia, as a nitrogen source, to regulate carbohydrate metabolism of its mutualistic fungus Leptographium procerum to alleviate nutritional competition, thereby maintaining the stability of the bark beetle-fungus mutualism. Ammonia significantly reduces competition of L. procerum for carbon resources for D. valens larval growth and increases fungal growth. Using stable isotope analysis, we show the fungus breakdown of phloem starch into D-glucose by switching on amylase genes only in the presence of ammonia. Deletion of amylase genes interferes with the conversion of starch to glucose. The acceleration of carbohydrate consumption and the conversion of starch into glucose benefit this invasive beetle-fungus complex. The nutrient consumption-compensation strategy mediated by tripartite beetle-fungus-bacterium aids the maintenance of this invasive mutualism under limited nutritional conditions, exacerbating its invasiveness with this competitive nutritional edge.

Effect of Oxygen on Verbenone Conversion From cis-Verbenol by Gut Facultative Anaerobes of Dendroctonus valens.

Since its introduction from North America, Dendroctonus valens LeConte has become a destructive forest pest in China. Although gut aerobic bacteria have been investigated and some are implicated in beetle pheromone production, little is known about the abundance and significance of facultative anaerobic bacteria in beetle gut, especially with regards to effects of oxygen on their role in pheromone production. In this study, we isolated and identified gut bacteria of D. valens adults in an anaerobic environment, and further compared their ability to convert cis-verbenol into verbenone (a multi-functional pheromone of D. valens) under different O2 concentrations. Pantoea conspicua, Enterobacter xiangfangensis, Staphylococcus warneri were the most frequently isolated species among the total of 10 species identified from beetle gut in anaerobic conditions. Among all isolated species, nine were capable of cis-verbenol to verbenone conversion, and the conversion efficiency increased with increased oxygen concentration. This O2-mediated conversion of cis-verbenol to verbenone suggests that gut facultative anaerobes of D. valens might play an important role in the frass, where there is higher exposure to oxygen, hence the higher verbenone production. This claim is further supported by distinctly differential oxygen concentrations between gut and frass of D. valens females.

Piecewise linear approach to an archetypal oscillator for smooth and discontinuous dynamics.

In a recent paper we examined a model of an arch bridge with viscous damping subjected to a sinusoidally varying central load. We showed how this yields a useful archetypal oscillator which can be used to study the transition from smooth to discontinuous dynamics as a parameter, alpha, tends to zero. Decreasing this smoothness parameter (a non-dimensional measure of the span of the arch) changes the smooth load-deflection curve associated with snap-buckling into a discontinuous sawtooth. The smooth snap-buckling curve is not amenable to closed-form theoretical analysis, so we here introduce a piecewise linearization that correctly fits the sawtooth in the limit at alpha=0. Using a Hamiltonian formulation of this linearization, we derive an analytical expression for the unperturbed homoclinic orbit, and make a Melnikov analysis to detect the homoclinic tangling under the perturbation of damping and driving. Finally, a semi-analytical method is used to examine the full nonlinear dynamics of the perturbed piecewise linear system. A chaotic attractor located at alpha=0.2 compares extremely well with that exhibited by the original arch model: the topological structures are the same, and Lyapunov exponents (and dimensions) are in good agreement.

Archetypal oscillator for smooth and discontinuous dynamics.

We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the dynamics from the standard one. In particular, the velocity flow suffers a jump in crossing from one well to another, caused by the loss of local hyperbolicity due to the collapse of the stable and unstable manifolds of the stationary state. In the presence of damping and external excitation, the system has coexisting attractors and also a chaotic saddle which becomes a chaotic attractor when a smoothness parameter drops to zero. This attractor can bifurcate to a high-period periodic attractor or a chaotic sea with islands of quasiperiodic attractors depending on the strength of damping.