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The gut-brain axis is evident in modulating neuropsychiatric diseases including autism spectrum disorder (ASD). Chromosomal 16p11.2 microduplication 16p11.2dp/+ is among the most prevalent genetic copy number variations (CNV) linked with ASD. However, the implications of gut microbiota status underlying the development of ASD-like impairments induced by 16p11.2dp/+ remains unclear. To address this, we initially investigated a mouse model of 16p11.2dp/+, which exhibits social novelty deficit and repetitive behavior characteristic of ASD. Subsequently, we conducted a comparative analysis of the gut microbial community and metabolomic profiles between 16p11.2dp/+ and their wild-type counterparts using 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC/MS). Our microbiota analysis revealed structural dysbiosis in 16p11.2dp/+ mice, characterized by reduced biodiversity and alterations in species abundance, as indicated by α/β-diversity analysis. Specifically, we observed reduced relative abundances of Faecalibaculum and Romboutsia, accompanied by an increase in Turicibacter and Prevotellaceae UCG_001 in 16p11.2dp/+ group. Metabolomic analysis identified 19 significantly altered metabolites and unveiled enriched amino acid metabolism pathways. Notably, a disruption in the predominantly histamine-centered neurotransmitter network was observed in 16p11.2dp/+ mice. Collectively, our findings delineate potential alterations and correlations among the gut microbiota and microbial neurotransmitters in 16p11.2dp/+ mice, providing new insights into the pathogenesis of and treatment for 16p11.2 CNV-associated ASD.

Shale gas, as an important unconventional natural gas resource, is the main force to increase natural gas reserves and production in the future. For shale gas with huge resources, it is particularly important to accurately evaluate its development potential and realize scale benefit development. Given the importance of shale gas production evaluation in the context of digital transformation of the oil and gas industry, this paper presents a systematic review and examination of the application of data-driven technology in shale gas production evaluation to provide an overview of their current status. With the deepening of shale gas development theory and the maturity of data-driven technology, the existing data-driven technology has made great progress in shale gas production evaluation. It is worth noting that the application of these technologies in actual production needs to be further refined to assess shale gas production more accurately in various production demand scenarios, thereby effectively guiding production optimization and development plans. Therefore, in view of the limitations of the application of data-driven technology in shale gas production evaluation, this paper puts forward some possible priorities and trends in the future, aiming to optimize the application of data-driven technology in shale gas production evaluation in the future as much as possible.

Salmon bone soup was prepared by ultrasonic-assisted heating, and the cooking process was optimized by response surface test using soluble proteins, sensory score, and amino acid nitrogen as the indicators. Furthermore, the taste characteristics difference between fish bone soups obtained by ultrasonic-assisted heating and traditional heating was investigated by electronic tongue, amino acid automatic analyzer and high performance liquid chromatography. The results showed that the optimum conditions of salmon bone soup prepared by ultrasonic-assisted heating were as follows: Ratio of material to water 1:2 g/mL, ultrasonic time 96 min, and ultrasonic power 800 W. The soluble protein, amino nitrogen, and sensory score of fish bone soup boiled under the above conditions were 192.30 mg/mL, 0.036 g/100 mL, and 8.43, respectively, which was close to the predicted value of the model. Compared with the fishbone soup prepared by the traditional heating process, the ultrasonic-assisted heating facilitated the intensity of umami, richness, and salty taste, decreased the intensity of sourness, and increased the content of taste nucleotides and umami amino acids. In conclusion, ultrasonic-assisted cooking can improve the overall taste characteristics of salmon bone soup and enhance the flavor quality.

Shale gas reservoirs, as representative reservoirs in the Sichuan Basin, have attracted widespread attention regarding development. Using gas reservoir numerical simulation to assist development has greatly improved the work efficiency of workers. However, traditional gas reservoir numerical simulation is widely criticized for its inability to effectively integrate with geological and engineering factors. In this study, we proposed a geological engineering integration method that considers pre-fracturing parameters. We further applied it to a typical well (N03) in a certain block of the Sichuan Basin. The reliability of the method was determined through historical fitting. Based on the N03 geological model, the optimization range of fracturing construction parameters in adjacent areas was determined with the goal of maximizing EUR. Recommended values for widely distributed construction parameter combinations of Class II reservoirs were provided through orthogonal analysis. The influence order of fracturing construction parameters is (1) sand addition strength, (2) cluster spacing, (3) construction displacement, (4) fracture fluid strength, and (5) horizontal segment length. Finally, we compared the simulated data with the actual case. The results showed that an integrated numerical simulation method including geological and engineering factors can comprehensively and accurately assist in reservoir development.

Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods. Based on traditional image processing techniques, this paper proposes a new hybrid image denoising model. The block-batching and 3-D filtering (BM3D) algorithm is used to obtain the first denoised image. The weighted kernel norm minimization (WNNM) and non-subsampled shearlet transform (NSST) algorithms are successively adopted to get the second denoised image. By the gradient domain guided filtering, the texture information of the first denoised image is extracted to enhance the details of the second denoised image. Specially, we propose the adaptive iterative NSST algorithm based on the improved soft thresholding, in order to solve the problems about the discontinuity of the hard thresholding and the constant deviation of the soft thresholding. Our approach can not only attenuate excessive smoothing, but also restore the natural appearance of the image. Experiments are conducted to demonstrate that our proposed method enjoys PSNR and SSIM performance gains over several deep learning denoising methods.

The issue of hypersonic boundary layer transition prediction is a critical aerodynamic concern that must be addressed during the aerodynamic design process of high-speed vehicles. In this context, we propose an advanced mesoscopic method that couples the gas kinetic scheme (GKS) with the Langtry–Menter transition model, including its three high-speed modification methods, tailored for accurate predictions of high-speed transition flows. The new method incorporates the turbulent kinetic energy term into the Maxwellian velocity distribution function, and it couples the effects of high-speed modifications on turbulent kinetic energy within the computational framework of the GKS solver. This integration elevates both the transition model and its high-speed enhancements to the mesoscopic level, enhancing the method’s predictive capability. The GKS-coupled mesoscopic method is validated through a series of test cases, including supersonic flat plate simulation, multiple hypersonic cone cases, the Hypersonic International Flight Research Experimentation (HIFiRE)-1 flight test, and the HIFiRE-5 case. The computational results obtained from these cases exhibit favorable agreement with experimental data. In comparison with the conventional Godunov method, the new approach encompasses a broader range of physical mechanisms, yielding computational results that closely align with the true physical phenomena and marking a notable elevation in computational fidelity and accuracy. This innovative method potentially satisfies the compelling demand for developing a precise and rapid method for predicting hypersonic boundary layer transition, which can be readily used in engineering applications.

Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space into a latent space or employing environmental action masks to reduce the action possibilities. Nevertheless, these methods often lack interpretability or rely on expert knowledge. In this study, we introduce a novel method for automatically reducing the action space in environments with discrete action spaces while preserving interpretability. The proposed approach learns state-specific masks with a dual purpose: (1) eliminating actions with minimal influence on the MDP and (2) aggregating actions with identical behavioral consequences within the MDP. Specifically, we introduce a novel concept called Bisimulation Metrics on Actions by States (BMAS) to quantify the behavioral consequences of actions within the MDP and design a dedicated mask model to ensure their binary nature. Crucially, we present a practical learning procedure for training the mask model, leveraging transition data collected by any RL policy. Our method is designed to be plug-and-play and adaptable to all RL policies, and to validate its effectiveness, an integration into two prominent RL algorithms, DQN and PPO, is performed. Experimental results obtained from Maze, Atari, and μRTS2 reveal a substantial acceleration in the RL learning process and noteworthy performance improvements facilitated by the introduced approach.

The imperative for swift and intelligent decision making in production scheduling has intensified in recent years. Deep reinforcement learning, akin to human cognitive processes, has heralded advancements in complex decision making and has found applicability in the production scheduling domain. Yet, its deployment in industrial settings is marred by large state spaces, protracted training times, and challenging convergence, necessitating a more efficacious approach. Addressing these concerns, this paper introduces an innovative, accelerated deep reinforcement learning framework—VSCS (Variational Autoencoder for State Compression in Soft Actor–Critic). The framework adeptly employs a variational autoencoder (VAE) to condense the expansive high-dimensional state space into a tractable low-dimensional feature space, subsequently leveraging these features to refine policy learning and augment the policy network’s performance and training efficacy. Furthermore, a novel methodology to ascertain the optimal dimensionality of these low-dimensional features is presented, integrating feature reconstruction similarity with visual analysis to facilitate informed dimensionality selection. This approach, rigorously validated within the realm of crude oil scheduling, demonstrates significant improvements over traditional methods. Notably, the convergence rate of the proposed VSCS method shows a remarkable increase of 77.5%, coupled with an 89.3% enhancement in the reward and punishment values. Furthermore, this method substantiates the robustness and appropriateness of the chosen feature dimensions.

ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is a major bacterial pathogen that causes hospital- and community-acquired infections. Owing to its multidrug resistance, it is imperative to develop new antimicrobial agents to treat MRSA infections. In this study, using genome mining analysis and a culture-based screening method to detect bacteriocin activity, we screened a strain, Bacillus sp. TL12, which harbored a putative leaderless bacteriocin gene cluster (bac gene cluster) and exhibited potent anti-MRSA activity. The antimicrobial agents, products of the bac gene cluster, were purified and identified as four novel leaderless bacteriocins: bacin A1, A2, A3, and A4. Bacin A2 was evaluated as a representative antimicrobial agent and showed remarkable antimicrobial activity against S. aureus, MRSA, and the foodborne pathogens Listeria monocytogenes and Bacillus cereus. Mechanistic experiments revealed that bacin A2 damaged cell membranes and exhibited bactericidal activity against MRSA. Bacin A2 effectively inhibited the formation of S. aureus and MRSA biofilms (>0.5× MIC) and killed the cells in their established biofilms (>4× MIC). The hemolytic and NIH/3T3 cytotoxicity assay results for bacin A2 confirmed its biosafety. Thus, bacins have potential as alternative antimicrobial agents for treating MRSA infections. IMPORTANCE Methicillin-resistant Staphylococcus aureus (MRSA) is a major human pathogen that is difficult to treat because of its resistance to several widely used antibiotics. The present study aimed to identify novel anti-MRSA bacteriocins in a prominent producer of bacteriocins, Bacillus cereus group. Four novel leaderless bacteriocins, bacin A1, A2, A3, and A4, which show potent bactericidal effect against S. aureus and MRSA, were identified in Bacillus sp. TL12. Moreover, bacins inhibited biofilm formation and killed cells in the established biofilms of S. aureus and MRSA. These findings suggest that bacins are promising alternatives to treat MRSA infections.

In order to obtain high-quality images, it is very important to remove noise effectively and retain image details reasonably. In this paper, we propose a residual UNet denoising network that adds the attention-guided filter and multi-scale feature extraction blocks. We design a multi-scale feature extraction block as the input block to expand the receiving domain and extract more useful features. We also develop the attention-guided filter block to hold the edge information. Further, we use the global residual network strategy to model residual noise instead of directly modeling clean images. Experimental results show our proposed network performs favorably against several state-of-the-art models. Our proposed model can not only suppress the noise more effectively, but also improve the sharpness of the image.

The Bozi 3 gas reservoir in the Tarim Basin is a typical ultra-deep fractured tight sandstone gas reservoir, in which the main formations are the Cretaceous Bashijiqike Formation and the Baxigai Formation. The reservoir has the characteristics of deep burial, tight reservoir matrix, well-developed fractures, large differences in well productivity, and regional pressure distribution. These characteristics bring development challenges in well deployment, production allocation, gas production rate optimization, and production improvement. The main reservoir of Bozi 3 is deposited in the braided river delta, fan delta, and shallow lake environment with adequate provenance and a strong hydrodynamic environment, which provides favorable depositional conditions for the formation of thick sandbodies. Although the buried depth of the Bozi 3 gas reservoir is over 6,000 m, it has undergone an evolutionary process of long-term shallow burial in the early stage and rapid deep burial in the late stage, which protects the primary pore, and the finally formed reservoir has the characteristics of an extra-low pore and ultra-low permeability pore structure. Late tectonic movements caused the Bozi 3 gas reservoir to experience strong uplift under a strong nappe-thrust environment, forming a large number of high-angle fractures around the faults. Although the fracture distribution is inhomogeneous, tensile fractures at the top anticline have a low-filling degree and good effectiveness, which effectively improve Bozi 3 reservoir properties. Through analysis of the reservoir characteristics and influencing factors of the Bozi 3 gas reservoir, it is concluded that the difference in reservoir characteristics is the main reason for the large difference in production of adjacent wells and zonal distribution of formation pressure at the early development. This study can also provide a reliable geological basis for well deployment, production allocation rationalization, and gas production rate optimization in the study area.

Gas reservoir numerical simulation is an important method to optimize the development strategy of shale gas reservoirs which has been influenced by the multi-stage fracture. The regular fracture network model was used to build a conventional numerical simulation, in which it was difficult to show the true situation of fracture propagation. However, the physical parameters not only affect the production, but also influence the stimulation effect; moreover, the quality of the fracturing effect also affects the production which causes the input and out parameters to be inaccurate. To solve this problem, the process simulation must be completed from geology to engineering to gas reservoir. The main controlling factors of production are identified with geological and engineering factors such as horizontal stage length, the volume of fracturing fluid, well spacing, production allocation, and proppant mass. Therefore, on the basis of the integrated simulation method of a hydraulic fracturing network simulation and an unstructured grid high-precision numerical simulation, this paper builds an integrated numerical simulation of a shale gas reservoir coupled with geology and engineering to optimize the development strategy with production as the target. Taking four wells of a platform as an example, the EUR (estimated ultimate recovery) has increased by 25% after the optimization of the development strategy.

Bacillus amyloliquefaciens is a gram-positive, nonpathogenic, endospore-forming, member of a group of free-living soil bacteria with a variety of traits including plant growth promotion, production of antifungal and antibacterial metabolites, and production of industrially important enzymes. We have attempted to reconstruct the biogeographical structure according to functional traits and the evolutionary lineage of B. amyloliquefaciens using comparative genomics analysis. All the available 96 genomes of B. amyloliquefaciens strains were curated from the NCBI genome database, having a variety of important functionalities in all sectors keeping a high focus on agricultural aspects. In-depth analysis was carried out to deduce the orthologous gene groups and whole-genome similarity. Pan genome analysis revealed that shell genes, soft core genes, core genes, and cloud genes comprise 17.09, 5.48, 8.96, and 68.47%, respectively, which demonstrates that genomes are very different in the gene content. It also indicates that the strains may have flexible environmental adaptability or versatile functions. Phylogenetic analysis showed that B. amyloliquefaciens is divided into two clades, and clade 2 is further dived into two different clusters. This reflects the difference in the sequence similarity and diversification that happened in the B. amyloliquefaciens genome. The majority of plant-associated strains of B. amyloliquefaciens were grouped in clade 2 (73 strains), while food-associated strains were in clade 1 (23 strains). Genome mining has been adopted to deduce antimicrobial resistance and virulence genes and their prevalence among all strains. The genes tmrB and yuaB codes for tunicamycin resistance protein and hydrophobic coat forming protein only exist in clade 2, while clpP, which codes for serine proteases, is only in clade 1. Genome plasticity of all strains of B. amyloliquefaciens reflects their adaption to different niches.

Understanding the adaptative behaviors of dominant desert-steppe species under varying levels of grazing-related environmental degradation is an important step in elucidating the survival strategies and environmental response mechanisms of desert plants. In this study, the Xilamuren desert steppe in Northwest China was used as a case study location to systematically investigate the adaptative traits of three dominant plant species (Stipa breviflora, Cleistogenes squarrosa, and Convolvulus ammannii) from the perspectives of plant morphology, leaf structure, and photosynthesis. As degradation intensified, the adaptive changes in the three dominant plant species manifested in the following ways: 1) adjustment of substance allocation strategy, such that biomass was preferentially concentrated in the leaves and roots for photosynthesis and resource acquisition; 2) miniaturization, including reduced plant height, reduced leaf size, and leaf narrowing; 3) thickening of the leaves, epidermis, and cuticle to increase water storage and resist illumination and temperature elevation and thickening of the midrib veins to increase water and nutrient transportation; and 4) increased water-use efficiency in Stipa breviflora, which showed a stronger capacity to adjust to water stress than Cleistogenes squarrosa and Convolvulus ammannii. The results suggested that the morphological, structural, and functional adjustments of desert plants were related to the development of drought-resistance mechanisms through water-use strategies, including increased water absorption and water-use efficiency and decreased water consumption. These results enrich the existing knowledge of the environmental adaptation mechanisms of dominant plants in desert steppes, providing a scientific basis for the restoration of desert steppe ecologies and the formulation of suitable grazing policies.

Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

ABSTRACT Consumer demand for “fresh food” with no chemical preservatives has prompted researchers to pay more attention to natural antimicrobial peptides such as bacteriocins. Nisin is currently the most widely used food biopreservative among the bacteriocins; however, its applications are restricted due to its low stability at neutral and alkaline pH values. Circular bacteriocins have potent antimicrobial activity against foodborne pathogens, show exceptional stability, and have great potential to be developed as biopreservatives. Here, we take advantage of the precursor peptides of 15 reported circular bacteriocins to devise an in silico approach to identify potential circular bacteriocins in sequenced microbial genomes. A total of nearly 7,000 putative precursor peptides were identified from 86 species of bacteria and further classified into 28 groups based on their amino acid similarity. Among the groups, 19 showed low similarity (less than 50%) to any known precursor peptide of circular bacteriocins. One novel circular bacteriocin in group 11, cerecyclin, showed the highest identity (34%) to the known circular bacteriocin enterocin NKR-5-3B and was selected for verification. Cerecyclin showed antimicrobial activity against several Gram-positive bacteria, inhibited the outgrowth of Bacillus cereus spores, and did not exhibit hemolysis activity. Moreover, it showed 4-fold- to 8-fold-higher antimicrobial activity against B. cereus and Listeria monocytogenes than nisin A. Cerecyclin also had increased stability compared to nisin A under neutral or alkaline conditions. This work not only identified a promising food biopreservative but also provided a rich source for novel circular bacteriocins. IMPORTANCE Circular bacteriocins are promising biopreservatives, and it is important to identify more novel circular bacteriocins to enhance the current arsenal of antimicrobials. In this study, we used an in silico approach to identify a large number of novel circular bacteriocins and classified these bacteriocins into 28 groups rather than the 2 groups that were described in previous studies. Nineteen groups were novel and had low similarity (less than 50%) to any known precursor peptides of circular bacteriocins; this finding greatly expands the awareness of the novelty and diversity of circular bacteriocins. A novel circular bacteriocin which we named cerecyclin was identified in the B. cereus group; this circular bacteriocin had great antimicrobial activity against some foodborne pathogens and showed extreme stability. This study not only identified a promising food biopreservative but also provided a rich source for the identification of novel circular bacteriocins and the development of new biopreservatives.

The visualization of near infrared hyperspectral images is valuable for quick view and information survey, whereas methods using band selection or dimension reduction fail to produce good colors as reasonable as corresponding multispectral images. In this paper, an end-to-end neural network of hyperspectral visualization is proposed, based on the convolutional neural networks, to transform a hyperspectral image of hundreds of near infrared bands to a three-band image. Supervised learning is used to train the network where multispectral images are targeted to reconstruct naturally looking images. Each pair of the training images shares the same geographic location and similar moments. The generative adversarial framework is used with an adversarial network to improve the training of the generating network. In the experimental procedure, the proposed method is tested for the near infrared bands of EO-1 Hyperion images with LandSat-8 images as the benchmark, which is compared with five state-of-the-art visualization algorithms. The experimental results show that the proposed method performs better in producing naturally looking details and colors for near infrared hyperspectral images.

ABSTRACT Understanding the genetic basis of host shifts is a key genomic question for pathogen and parasite biology. The Bacillus cereus group, which encompasses Bacillus thuringiensis and Bacillus anthracis, contains pathogens that can infect insects, nematodes, and vertebrates. Since the target range of the essential virulence factors (Cry toxins) and many isolates is well known, this group presents a powerful system for investigating how pathogens can diversify and adapt to phylogenetically distant hosts. Specialization to exploit insects occurs at the level of the major clade and is associated with substantial changes in the core genome, and host switching between insect orders has occurred repeatedly within subclades. The transfer of plasmids with linked cry genes may account for much of the adaptation to particular insect orders, and network analysis implies that host specialization has produced strong associations between key toxin genes with similar targets. Analysis of the distribution of plasmid minireplicons shows that plasmids with orf156 and orf157, which carry genes encoding toxins against Lepidoptera or Diptera, were contained only by B. thuringiensis in the specialized insect clade (clade 2), indicating that tight genome/plasmid associations have been important in adaptation to invertebrate hosts. Moreover, the accumulation of multiple virulence factors on transposable elements suggests that cotransfer of diverse virulence factors is advantageous in terms of expanding the insecticidal spectrum, overcoming insect resistance, or through gains in pathogenicity via synergistic interactions between toxins. IMPORTANCE Population genomics have provided many new insights into the formation, evolution, and dynamics of bacterial pathogens of humans and other higher animals, but these pathogens usually have very narrow host ranges. As a pathogen of insects and nematodes, Bacillus thuringiensis, which produces toxins showing toxicity to many orders of insects and other invertebrates, can be used as a model to study the evolution of pathogens with wide host ranges. Phylogenomic analysis revealed that host specialization and switching occur at the level of the major clade and subclade, respectively. A toxin gene co-occurrence network indicates that multiple toxins with similar targets were accumulated by the same cell in the whole species. This accumulation may be one of the strategies that B. thuringiensis has used to fight against host resistance. This kind of formation and evolution of pathogens represents a different path used against multiple invertebrate hosts from that used against higher animals.

Due to the rapidly increasing prevalence of multidrug-resistant bacterial strains, the need for new antimicrobial drugs to treat infections has become urgent. Bacteriocins, which are antimicrobial peptides of bacterial origin, are considered potential alternatives to conventional antibiotics and have attracted widespread attention in recent years. Among these bacteriocins, lantibiotics, especially two-component lantibiotics, exhibit potent antimicrobial activity against some clinically relevant Gram-positive pathogens and have potential applications in the pharmaceutical industry. In this study, we characterized a novel two-component lantibiotic termed thusin that consists of Thsα, Thsβ and Thsβ' (mutation of Thsβ, A14G) and that was isolated from a B. thuringiensis strain BGSC 4BT1. Thsα and Thsβ (or Thsβ') exhibit optimal antimicrobial activity at a 1:1 ratio and act sequentially to affect target cells, and they are all highly thermostable (100°C for 30 min) and pH tolerant (pH 2.0 to 9.0). Thusin shows remarkable efficacy against all tested Gram-positive bacteria and greater activities than two known lantibiotics thuricin 4A-4 and ticin A4, and one antibiotic vancomycin against various bacterial pathogens (Bacillus cereus, Listeria monocytogenes, Staphylococcus aureus (MRSA), Staphylococcus sciuri, Enterococcus faecalis, and Streptococcus pneumoniae). Moreover, thusin is also able to inhibit the outgrowth of Bacillus cereus spores. The potent antimicrobial activity of thusin against some Gram-positive pathogens indicates that it has potential for the development of new drugs.