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Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., “angina–myocardial infarction–heart failure” in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

Advanced manufacturing equipment heavily relies on gearboxes for optimal performance, especially in heavy-duty applications like industrial robot joints and advanced machine tools where precision and stiffness are paramount. While harmonic and cycloid gearboxes are common for their low backlash characteristic, conventional 3 K planetary gears excel in efficiency, torque density and stiffness but they are rarely utilized in high precision scenario due to backlash issue. In this study, a deformable planet carrier system (DPCS) is proposed for backlash restrain by center distance compensation. The DPCS incorporates a self-lock design, preserving low backlash feature while upholding load capacity and stiffness, meeting the demands of precision industrial equipment. To illustrate its principles, statics models demonstrate its force analysis, and FEM simulations reveal its self-lock feature. Backlash compensation model theoretically analyzes its anti-backlash effect from kinematics and geometry aspects. To validate its feasibility, the first prototype is manufactured and designated as H3K-40K-104. Experiment result prove that prototype achieves significant low backlash characteristic, with the maximum dynamic transmission error (DTE) of 20.5 arcsec and backlash of 6.561 arcsec. Stiffness hysteresis curve indicates a minimal lost motion of 20.01 arcsec. This paper provides an effective method to greatly refine precision performance in 3 K gear trains.

Abstract Background Open tibial fractures often include severe bone loss and soft tissue defects and requires complex reconstructive operations. However, the optimal treatment is unclear. Methods This retrospective study enrolled patients with Gustilo type III open tibial fractures from January 2018 to January 2021 to assess the clinical utility of Masquelet technique together with microsurgical technique as a combined strategy for the treatment of open tibial fractures. The demographics and clinical outcomes including bone union time, infection, nonunion and other complications were recorded for analysis. The bone recovery quality was evaluated by the AOFAS Ankle-Hindfoot Scale score and the Paley criteria. Results We enrolled 10 patients, the mean age of the patients and length of bone defects were 31.7 years (range, 23–45 years) and 7.5 cm (range, 4.5–10 cm) respectively. Bone union was achieved for all patients, with an average healing time of 12.2 months (range, 11–16 months). Seven patients exhibited a bone healing time of less than 12 months, whereas 3 patients exhibited a bone healing time exceeding 12 months. No significant correlation was found between the length of bone loss and healing time. In addition, no deep infection or nonunion was observed, although 2 patients experienced wound fat liquefaction with exudates and 1 patient presented with a bloated skin flap. The average AOFAS Ankle-Hindfoot Scale score was 80.5 (range, 74–85), and all patients were evaluated as good or exellent based on the Paley criteria. Conclusions Our study indicated that the use of the Masquelet technique and the microsurgical technique as a combined strategy is safe and effective for the treatment of Gustilo type III open tibial fractures.

In coal mining, rock strata are fractured under cyclic loading and unloading to form fracture channels. Fracture channels are the main flow narrows for gas. Therefore, expounding the flow conductivity of fracture channels in rocks on fluids is significant for gas flow in rock strata. In this regard, graded incremental cyclic loading and unloading experiments were conducted on sandstones with different initial stress levels. Then, the three-dimensional models for fracture channels in sandstones were established. Finally, the fracture channel percentages were used to reflect the flow conductivity of fracture channels. The study revealed how the particle size distribution of fractured sandstone affects the formation and expansion of fracture channels. It was found that a smaller proportion of large blocks and a higher proportion of small blocks after sandstone fails contribute more to the formation of fracture channels. The proportion of fracture channels in fractured rock can indicate the flow conductivity of those channels. When the proportion of fracture channels varies gently, fluids flow evenly through those channels. However, if the proportion of fracture channels varies significantly, it can greatly affect the flow rate of fluids. The research results contribute to revealing the morphological evolution and flow conductivity of fracture channels in sandstone and then provide a theoretical basis for clarifying the gas flow pattern in the rock strata of coal mines.

ABSTRACT The diamondback moth (DBM), Plutella xylostella, has successfully adapted to the potent chemical defenses of Brassicaceae plants that deter most other herbivores. Gut bacteria are increasingly recognized as key to the biology of many species but their role in DBM adaptation to plant defense compounds is not well known. In this study, the secondary metabolites of radish seedlings, rich in flavonoids, were identified by liquid chromatography-mass spectrometry. These secondary metabolites reduced the larval growth of DBM lacking gut bacteria. The effect was rapidly eclipsed by the re-introduction of gut microbiota, which was dominated by Enterobacter (Proteobacteria). Similarly, while treatment with the flavonoid kaempferol adversely affected growth and extended the development time, these were alleviated by the re-introduction of Enterobacter sp. EbPXG5 (EbPXG5) to the DBM gut. EbPXG5 not only degrades kaempferol both in vitro and DBM gut, but is also shown to colonize the gut epithelium, forming a protective biofilm. Genomic sequencing of EbPXG5 showed that metabolic genes were the most abundant, especially those involved in xenobiotic degradation, and the metabolism of terpenoids and polyketides, which could participate in the degradation of plant secondary metabolites such as kaempferol. Overall, our results showed that EbPXG5 is a bacterium common in the gut of DBM larvae and has the in vitro and in vivo capacity to detoxify a major secondary metabolite that is produced in brassica plants as a defense against herbivores. This insect-bacterial association may be an important contributor to the status of DBM as a major pest of brassica crops worldwide.IMPORTANCEIn this study, we identify an important role of gut bacteria in mediating the adaptation of diamondback moth (DBM) to plant secondary metabolites. We demonstrate that kaempferol’s presence in radish seedlings greatly reduces the fitness of DBM with depleted gut biota. Reinstatement of gut biota, particularly Enterobacter sp. EbPXG5, improved insect performance by degrading kaempferol. This bacterium was common in the larval gut of DBM, lining the epithelium as a protective film. Our work highlights the role of symbiotic bacteria in insect herbivore adaptation to plant defenses and provides a practical and mechanistic framework for developing a more comprehensive understanding of insect-gut microbe-host plant co-evolution.

Plutella xylostella (L.), commonly known as the diamondback moth, is currently a major worldwide pest. Gut bacteria play an important role in the physiology and insecticide resistance of P. xylostella, but little is known about the sources and transmission routes of its gut bacteria. In this study, traditional microbial culture methods were used to analyze the sources and transmission modes of gut bacteria in P. xylostella, which could help develop pest control strategies based on gut bacteria. The main findings are as follows: gut bacterial diversity was significantly higher in P. xylostella-fed radish sprouts than those fed an artificial diet, indicating a potential association between gut bacteria and food bacteria. In addition, sequence analysis revealed the isolation of Enterobacter sp., Pantoea sp., Cedecea sp., and Pseudomonas sp. from both radish sprouts and P. xylostella. Importantly, Enterobacter sp. was found in all tested samples (radish sprouts, gut of P. xylostella, ovaries, and eggs), suggesting that bacteria acquired from food could be transferred from the gut to the ovaries and eggs. This was confirmed through experiments, which also showed that eggs could carry bacteria and transmit them to the gut, indicating vertical transmission of gut bacteria via eggs. Furthermore, the 3rd instar larvae of P. xylostella with and without gut bacteria were mixed and raised until the 4th instar. Then, we found that all the gut of the 4th instar larvae carried the same bacteria, indicating that the gut bacteria of P. xylostella can be horizontally transmitted through social behavior. This study lays a foundation for further exploration of the sources, transmission, and coevolution of the host of gut bacteria in P. xylostella, and provides new insights into pest control strategies based on the source and transmission of gut bacteria.

With the great concern of our country and the continuous development of the epidemic, the development of smart campus is getting faster and faster, the safety of teachers and students becomes more and more important. To ensure the safety of users, the first step is to control at the doors. Usually, the access control method is used in computer system to protect the documents and data, few people use it at doors, but it’s a very effective way to improve safety. So we design a two-factor authentication protocol to verity the user’s identity, and improve the attribute-based access control (ABAC) model to fit the smart campus. We analyze the protocol theoretically and verify its security. Compare with others, our scheme can be more efficient and safer.

Cas9 fused to DNA damage repair proteins can improve rates of gene knock-in but the chimeric protein is often large. Here the authors fuse Cas9 to a minimal Brex27 motif from BRCA2 consisting of thirty-six amino acids to enhance the efficacy and safety of gene editing.

The diamondback moth, Plutella xylostella is a cosmopolitan agricultural pest. By analyzing 532 genomes from 114 populations around the world, the authors find evidence supporting a South American origin of this moth, and look for signatures of positive selection.

Netflow log files commonly contain massive transfer records in tiny time interval, making analytical works complex and burdensome. By combining human cognition abilities with computerized techniques, visual analytics systems have become efficient tools for showing network states and locating abnormal behaviors. However, traditional visual analytics systems tend to be designed for solving certain problems and unable to synthesize various types of data sources. Despite recent advances in network security visualization, academia still starves for a proper solution to visualize IPv4 address behavior modes and IPv4 connection patterns within limited drawing space. Thus, we propose a visual analytics system called `Owleyes' which reprocesses Netflow log data with simple statistical operations in basic dimensions and fulfills the aforementioned requirements with proper novel graphs such as `sunburst-hive-plot graph' (SHG) and link-wheel graph (LW). The SHG provides a stable and comparable means of visualizing connection patterns efficiently in a limited drawing space. The LW represents the hourly connection counts of main ports in a specific IPv4 connection during one day. With the use case dealing with the ChinaVis 2016 Challenge I data, the efficiency and practicability of Owleyes are demonstrated.

Convolutional neural network-based methods are attracting increasing attention in steganalysis. However, steganalysis for content-adaptive image steganography in the spatial domain is still a difficult problem. In this paper, a new convolutional neural network-based steganalysis approach was proposed with two contributions. 1) By adding more convolutional layers in the lower part of the model, we proposed a new arrangement of convolutional layers and pooling layers, which can process the local information better than the existing CNN models in steganalysis. 2) By adding the global average pooling layer before the softmax layer instead of using global average pooling before the fully connected layer, the global average pooling was placed in a better position for steganalysis. Two state-of-the-art steganographic algorithms in the spatial domain, namely, WOW and S-UNIWARD, were used to evaluate the effectiveness of our model. The experimental results on BOSSbase showed that the proposed CNN could obtain better steganalysis performance than YeNet across all tested algorithms when the payloads were 0.2, 0.3, and 0.4 bpp.

Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN) used a convolutional neural network to obtain high-quality crowd density map estimation and integrate the density map to get the estimated headcount. To obtain better performance on crowd counting, an improved crowd counting method based on SaCNN was proposed in this paper. The spread parameter, i.e., the standard variance, of geometry-adaptive Gaussian kernel used in SaCNN was optimized to generate a higher quality ground truth density map for training. The absolute count loss with weight 4e-5 was used to jointly optimize with the density map loss to improve the network generalization ability for crowd scenes with few pedestrians. Also, a random cropping method was applied to improve the diversity of training samples to enhance network generalization ability. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than those of SaCNN.

Parasites attack the host insects and possibly impact the host-gut microbiota, which leads to provision of a suitable host environment for parasites’ development. However, little is known about whether and how the parasitic wasp Cotesia vestalis alters the gut microbiota of the host Plutella xylostella. In this study, 16S rDNA microbial profiling, combined with a traditional isolation and culture method, were used to assess changes in the bacterial microbiome of parasitized and non-parasitized hosts at different developmental stages of C. vestalis larvae. Parasitization affected both the diversity and structure of the host-gut microbiota, with a significant reduction in richness on the sixth day post parasitization (6 DPP) and significant differences in bacterial structure between parasitized and non-parasitized hosts on the third day. The bacterial abundance of host-gut microbiota changed significantly as the parasitization progressed, resulting in alteration of potential functional contribution. Notably, the relative abundance of the predominant family Enterobacteriaceae was significantly decreased on the third day post-parasitization. In addition, the results of traditional isolation and culture of bacteria indicated differences in the bacterial composition between the three DPP and CK3 groups, as with 16S microbial profiling. These findings shed light on the interaction between a parasitic wasp and gut bacteria in the host insect during parasitization.

The exfoliation of bulk two-dimensional metal–organic framework (MOF) into few-layered nanosheets has attracted much attention recently. In this work, an environmental-friendly route has been developed for layered-MOF (MAMS-1) delamination using deep eutectic solvent (DES), which is more sustainable and efficient alternative than conventional organic solvents for MOF nanosheet preparation. Under sonication condition, DES as solvents, the highest exfoliation rate of MAMS-1 is up to 70% with two host layers via poly(vinylpyrrolidone) (PVP) surfactant-assisted method. The presence of tert-butyl exteriors and the atomically thickness endow the MOF nanosheets stable suspension for at least one month. Due to the 2D structure and excellent stability, MAMS-1 nanosheet (MAMS-1-NS) was chosen as a good candidate to encapsulate Eu3+ cations. The obtained Eu3+@MAMS-1-NS acts as a multi-responsive luminescent sensor through fluorescence quenching, and can specifically recognize Fe3+ (LOD = 0.40 μM, KSV = 1.05 × 105 M−l), Hg2+ (LOD = 0.038 μM, KSV = 5.78 × 106 M−l), Cr2O72− (LOD = 0.33 μM, KSV = 1.55 × 105 M−l) and MnO4− (LOD = 0.088 μM, KSV = 4.49 × 105 M−l). Compared with bulk Eu3+@MAMS-1, the sensitivity of Eu3+@MAMS-1-NS is greatly improved owing to its ultrathin nanosheet morphology and highly accessible active sites on the surface.

Endophytic fungi are important in diverse plant functions but knowledge of the factors that shape assemblages of these symbionts is lacking. Here, using a culture-dependent approach, we report 4,178 endophytic fungal isolates representing 16 orders isolated from stems, roots and leaves of three cruciferous plant species, Chinese cabbage (Brassica rapa L.), radish (Raphanus sativus L.) and white cabbage (B. olerocea L.), collected from 21 focal fields with different landscape contexts and pesticide uses during four seasons (summer, autumn, winter and spring). The colonization rate of fungi was found to be most strongly affected by season, plant identity and plant tissue. The colonization was highest during autumn, followed by summer, spring and lowest during winter. The colonization was highest in B. olerocea (53.2%), followed by B. rapa (42.6%), and lowest in R. sativus (35.0%). The colonization was highest in stems (51.9%) in all plant types, followed by leaves (42.4%) and roots (37.5%). Hypocreales was the dominant order (33.3% of all the isolates), followed by Glomerellales (26.5%), Eurotiales (12.1%), Pleosporales (9.8%) and Capnodiales (6.0%). Fungal endophyte abundance (number of isolates) followed the same pattern as colonization rate, while species richness varied with season and host plant tissue. Ordination analyses showed that the abundance and richness of Hypocreales, Eurotiales and Sordariales were associated with plant roots, while Capnodiales, Pleosporales and Trichosphaeriales were associated with spring. Other environmental factors, elevation, and the proportions of grassland, forest, orchard and waterbodies in the surrounding landscape also exerted effects within some categories of other main effects or for certain fungal taxa. Our results indicate that while fungal endophyte communities of crucifer crops vary strongly with the season, they are also strongly structured by plant identity and plant tissue, to a lesser extent by pesticide use and only weakly by landscape composition. The understanding of the ecological roles of fungal endophytes could contribute to habitat management and consequently improve crop pest management.

Insect gut microbes play important roles in host feeding, digestion, immunity, growth and development. Spodoptera litura is an important agricultural pest distributed of global importance. In the present study, diversity and functions of the gut bacteria in S. litura are investigated based on the approaches of metagenomics and denaturing gradient gel electrophoresis (DGGE). The results showed that the gut bacterial diversity of S. litura reared on taro leaves or an artificial diet, were similar at the phylum level, as both were mainly composed of Proteobacteria, but differed significantly at the order level. Spodoptera litura reared on taro leaves (Sl-tar) had gut biota mainly comprised of Enterobacteriales and Lactobacillales, while those reared on artificial diet (Sl-art) predominantly contained Pseudomonadales and Enterobacteriales, suggesting that gut bacteria composition was closely related to the insect’s diet. We found that feeding and growth of S. litura were significantly reduced when individuals were treated with antibiotics, but could be both restored to a certain extent after reimporting gut bacteria, indicating that gut bacteria are important for feeding, digestion, and utilization of food in S. litura. Metagenomic sequencing of gut microbes revealed that the gut bacteria encode a large number of enzymes involved in digestion, detoxification, and nutrient supply, implying that the gut microbes may be essential for improving the efficiency of food utilization in S. litura.

Software products in the market are changing due to changes in business processes, technology, or new requirements from the customers. Maintainability of legacy systems has always been an inspiring task for the software companies. In order to determine whether the software requires maintainability by reverse engineering or by forward engineering approach, a system assessment was done from diverse perspectives: quality, business value, type of errors, etc. In this research, the changes required in the existing software components of the legacy system were identified using a supervised learning approach. New interfaces for the software components were redesigned according to the new requirements and/or type of errors. Software maintainability was measured by applying a machine learning technique, i.e., Naïve Bayes classifier. The dataset was designed based on the observations such as component state, successful or error type in the component, line of code of error that exists in the component, component business value, and changes required for the component or not. The results generated by the Waikato Environment for Knowledge Analysis (WEKA) software confirm the effectiveness of the introduced methodology with an accuracy of 97.18%.

In our previous research, the fitness cost of resistance of the diamondback moth (DBM), Plutella xylostella found in insecticide-resistant DBM (Rc-DBM) under heat stress was based on heavier damage to wing veins when compared to insecticide-susceptible DBM (Sm-DBM). To investigate the molecular mechanism of the damage to the veins between Rc- and Sm-DBM, the full-length sequences of two related genes involved in the development of wing veins, fringe (Px-fng) and engrailed (Px-en) of DBM were cloned, and the mRNA expressions of both Px-fng and Px-en were studied. The Px-fng and Px-en cDNA contained 1038 bp and 1152 bp of open reading frames (ORFs), respectively, which encoded a putative protein comprising 345 and 383 amino acids with a calculated molecular weight of 39.59 kDa and 42.69 kDa. Significantly down regulated expressions of Px-fng and Px-en under heat stress were found in pupae and adults of Rc-DBM compared to Sm-DBM, and a result of higher damage to wing veins in Rc-DBM under heat stress. Based on RNAi experiments, significant inhibitions on expressions of Px-fng and Px-en in both Sm-DBM and Rc-DBM were found when the pupae were infected by dsFng or dsEn. Corresponding to these, infections of dsFng or dsEn resulted in significant decrease of eclosion rate and increase malformation rate of DBM. Our results suggest that the higher damage of wing veins in DBM might be related to the heavier inhibitions of Px-fng and Px-en expression, and the Px-fng and Px-en are involved in the development of wings and veins.

Mammalian telomere lengths are primarily regulated by telomerase, a ribonucleoprotein consisting of a reverse transcriptase (TERT) and an RNA subunit (TERC). TERC is constitutively expressed in all cells, whereas TERT expression is temporally and spatially regulated, such that in most adult somatic cells, TERT is inactivated and telomerase activity is undetectable. Most tumor cells activate TERT as a mechanism for preventing progressive telomere attrition to achieve proliferative immortality. Therefore, inactivating TERT has been considered to be a promising means of cancer therapy. Here we applied the CRISPR/Cas9 gene editing system to target the TERT gene in cancer cells. We report that disruption of TERT severely compromises cancer cell survival in vitro and in vivo. Haploinsufficiency of TERT in tumor cells is sufficient to result in telomere attrition and growth retardation in vitro. In vivo, TERT haploinsufficient tumor cells failed to form xenograft after transplantation to nude mice. Our work demonstrates that gene editing-mediated TERT knockout is a potential therapeutic option for treating cancer.

The development of insecticide resistance in insect pests is a worldwide concern and elucidating the underlying mechanisms is critical for effective crop protection. Recent studies have indicated potential links between insect gut microbiota and insecticide resistance and these may apply to the diamondback moth, Plutella xylostella (L.), a globally and economically important pest of cruciferous crops. We isolated Enterococcus sp. (Firmicutes), Enterobacter sp. (Proteobacteria), and Serratia sp. (Proteobacteria) from the guts of P. xylostella and analyzed the effects on, and underlying mechanisms of insecticide resistance. Enterococcus sp. enhanced resistance to the widely used insecticide, chlorpyrifos, in P. xylostella, while in contrast, Serratia sp. decreased resistance and Enterobacter sp. and all strains of heat-killed bacteria had no effect. Importantly, the direct degradation of chlorpyrifos in vitro was consistent among the three strains of bacteria. We found that Enterococcus sp., vitamin C, and acetylsalicylic acid enhanced insecticide resistance in P. xylostella and had similar effects on expression of P. xylostella antimicrobial peptides. Expression of cecropin was down-regulated by the two compounds, while gloverin was up-regulated. Bacteria that were not associated with insecticide resistance induced contrasting gene expression profiles to Enterococcus sp. and the compounds. Our studies confirmed that gut bacteria play an important role in P. xylostella insecticide resistance, but the main mechanism is not direct detoxification of insecticides by gut bacteria. We also suggest that the influence of gut bacteria on insecticide resistance may depend on effects on the immune system. Our work advances understanding of the evolution of insecticide resistance in this key pest and highlights directions for research into insecticide resistance in other insect pest species.

Herbivore specialists adapt to feed on a specific group of host plants by evolving various mechanisms to respond to plant defenses. Insects also possess complex gut microbiotas but their potential role in adaptation is poorly understood. Our previous study of the genome of diamondback moth, Plutella xylostella, revealed an intrinsic capacity to detoxify plant defense compounds, which is an important factor in its success as a pest. Here we expand on that work with a complete taxonomic and functional profile of the P. xylostella gut microbiota obtained by metagenomic sequencing. Gene enrichment in the metagenome, accompanied by functional identification, revealed an important role of specific gut bacteria in the breakdown of plant cell walls, detoxification of plant phenolics, and synthesis of amino acids. Microbes participating in these pathways mainly belonged to three highly abundant bacteria: Enterobacter cloacae, Enterobacter asburiae, and Carnobacterium maltaromaticum. Results show that while the gut microbial community may be complex, a small number of functionally active species can be disproportionally important. The presence of specific enzymes in the microbiota community, such as supporting amino acid synthesis, digestion and detoxification functions, demonstrates the beneficial interactions between P. xylostella and its gut microbiota. These interactions can be potential targets for manipulation to provide novel pest management approaches.

Vehicle detection is a challenging task in computer vision. In recent years, numerous vehicle detection methods have been proposed. Since the vehicles may have varying sizes in a scene, while the vehicles and the background in a scene may be with imbalanced sizes, the performance of vehicle detection is influenced. To obtain better performance on vehicle detection, a multi-scale vehicle detection method was proposed in this paper by improving YOLOv2. The main contributions of this paper include: (1) a new anchor box generation method Rk-means++ was proposed to enhance the adaptation of varying sizes of vehicles and achieve multi-scale detection; (2) Focal Loss was introduced into YOLOv2 for vehicle detection to reduce the negative influence on training resulting from imbalance between vehicles and background. The experimental results upon the Beijing Institute of Technology (BIT)-Vehicle public dataset demonstrated that the proposed method can obtain better performance on vehicle localization and recognition than that of other existing methods.

BackgroundInsect midgut microbiota is important in host nutrition, development and immune response. Recent studies indicate possible links between insect gut microbiota and resistance to biological and chemical toxins. Studies of this phenomenon and symbionts in general have been hampered by difficulties in culture-based approach. In the present study, DNA sequencing was used to examine the midgut microbiota of diamondback moth (DBM), Plutella xylostella (L.), a destructive pest that attacks cruciferous crops worldwide. Its ability to develop resistance to many types of synthetic insecticide and even Bacillus thuringiensis toxins makes it an important species to study.Methodology/principal findingsBacteria of the DBM larval midgut in a susceptible and two insecticide (chlorpyrifos and fipronil) resistant lines were examined by Illumina sequencing sampled from an insect generation that was not exposed to insecticide. This revealed that more than 97% of the bacteria were from three orders: Enterobacteriales, Vibrionales and Lactobacillales. Both insecticide-resistant lines had more Lactobacillales and the much scarcer taxa Pseudomonadales and Xanthomonadales with fewer Enterobacteriales compared with the susceptible strain. Consistent with this, a second study observed an increase in the proportion of Lactobacillales in the midgut of DBM individuals from a generation treated with insecticides.Conclusions/significanceThis is the first report of high-throughput DNA sequencing of the entire microbiota of DBM. It reveals differences related to inter- and intra-generational exposure to insecticides. Differences in the midgut microbiota among susceptible and insecticide-resistant lines are independent of insecticide exposure in the sampled generations. While this is consistent with the hypothesis that Lactobacillales or other scarcer taxa play a role in conferring DBM insecticide resistance, further studies are necessary to rule out other possibilities. Findings constitute the basis for future molecular work on the functions of insect midgut microbiota taxa and their possible role in conferring host resistance to toxins.

Glioblastoma Multiforme (GBM) cells are highly invasive, infiltrating into the surrounding normal brain tissue, making it impossible to completely eradicate GBM tumors by surgery or radiation. Increasing evidence also shows that these migratory cells are highly resistant to cytotoxic reagents, but decreasing their migratory capability can re-sensitize them to chemotherapy. These evidences suggest that the migratory cell population may serve as a better therapeutic target for more effective treatment of GBM. In order to understand the regulatory mechanism underlying the motile phenotype, we carried out a genome-wide RNAi screen for genes inhibiting the migration of GBM cells. The screening identified a total of twenty-five primary hits; seven of them were confirmed by secondary screening. Further study showed that three of the genes, FLNA, KHSRP and HCFC1, also functioned in vivo, and knocking them down caused multifocal tumor in a mouse model. Interestingly, two genes, KHSRP and HCFC1, were also found to be correlated with the clinical outcome of GBM patients. These two genes have not been previously associated with cell migration.

Conventional carbon capture and storage technologies and carbon capture, utilization and storage technologies mainly target CO2 emissions from fixed sources, while direct air capture CO2 (DAC) technology, as an emerging negative carbon emission technology, can capture CO2 emissions from distributed sources and further reduce the global atmospheric CO2 concentration. In this paper, the development process of DAC's typical liquid absorption process, solid adsorption process and the construction of relevant demonstration projects are introduced, the technical characteristics of emerging DAC processes are analyzed, and the key equipment schemes and future development trends of existing DAC processes are discussed. The DAC liquid absorption process has the characteristics of low cost of absorbent raw materials and high selectivity, which can realize a large-scale continuous capture, but high energy consumption in the regeneration process. The DAC solid adsorption process has the characteristics of modularity, low investment cost, and relatively low energy consumption in the regeneration process, but requires a regular replacement of adsorption materials and maintenance of adsorption equipment, which is suitable for small-scale DAC application scenarios. Two typical DAC process absorption/adsorption materials are reviewed. In the DAC electric oscillation adsorption process, CO2 is chemically captured in the solid electrode, and CO2 desorption is achieved by changing the polarity of the solid electrode with applied electric field. This process has a higher efficiency than that of the heat or pressure-based separation process. The CO2 in the air is selected through the DAC separation membrane to achieve an efficient carbon capture. The DAC process achieves the CO2 absorption and desorption through the change of humidity, which breaks through the high energy consumption limit of conventional variable temperature/pressure swing adsorption. The DAC bio-absorption process absorbs and fixes CO2 through the photosynthesis of algae organisms. The DAC process based on bifunctional catalyst can capture and catalyze CO2 in one integrated process, saving the transportation and storage cost of CO2 capture. The key devices of DAC liquid absorption process are air contactor, particle reactor, calciner and curing device, among which the core of air contactor development is to improve gas-liquid contact efficiency, reduce water loss during spray process and reduce equipment corrosion, while the key of particle reactor and curing development is to improve the contact efficiency of solid-liquid two-phase materials and the solid-liquid separation efficiency after reaction. The DAC solid adsorption process is composed of a modular device consisting of an induced air module, an adsorption/regeneration module, an energy supply regeneration module and a CO2 compression module. The core of optimizing the adsorption module is to improve the gas-solid mass transfer rate, adjust the CO2 capture efficiency and reduce the pressure drop. It is very important to select suitable regeneration system or use clean energy based on the process requirements of different application scenarios, optimize DAC process and develop high-performance DAC core devices.

In recent years, with the rapid development of big data and artificial intelligence technology, data-driven automatic driving vehicle lane change intention recognition has become an active research area in the transportation field. Numerous studies have reported innovative and practical research results. However, this field still presents common technical challenges, such as accurately identifying the lane change process, handling missing lane change labels, and addressing imbalanced data categories. These issues remain the focal points of current research. This paper aims to classify and organize various data-driven methods, mainly focusing on lane change intention recognition methods based on traditional machine learning, deep learning, and ensemble learning. In the academic community, two primary approaches exist for identifying lane change behavior. The first approach mainly focuses on the vehicle not crossing the lane line, which is suitable for early recognition of the driver’s intention to change lanes. The second approach focuses on the actual crossing of lane markings by vehicles, which is often considered the complete lane change process. In academic research on lane change intention annotation, the selection of fixed time windows and heading angle thresholds plays a crucial role in the accuracy of annotation. These parameters affect the accurate recognition of lane change behavior and are directly related to the stability and reliability of autonomous driving and intelligent transportation system performance. Therefore, researchers have conducted in-depth investigations on the impact of these two parameters on annotation accuracy. To identify the optimal fixed time window and heading angle threshold, researchers have used the grid search optimization algorithm. This method performs well in fixed driving scenarios by traversing all possible parameter combinations and selecting the optimal parameters according to preset evaluation criteria. However, in practical applications, driving scenarios often exhibit diversity and complexity. Different driving environments, road conditions, and driving styles can impact the recognition of lane change intentions. Therefore, achieving adaptive parameter adjustment so that the annotation algorithm maintains high accuracy across various driving scenarios remains a challenging problem. To address the issue of imbalanced data categories in lane changing, researchers adopt two strategies. The first strategy involves adjusting the data sampling method, and under-sampling and oversampling techniques are used to balance the number of samples in each category. The second strategy involves the use of classification models with strong adaptability to imbalanced data, such as ensemble learning algorithms or cost-sensitive learning models, to maintain good classification performance.