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ObjectiveThis study used network Meta-analysis to compare the effects of different transcranial magnetic stimulation (TMS) modalities on the effectiveness and long-term validity of improving cognitive function in Parkinson’s patients.MethodsComputer searches of the Cochrane Library, PubMed, Web of Science, Embass, CNKI and Wanfang Data were conducted to collect randomized controlled clinical studies on TMS to improve cognitive function in Parkinson’s patients published from the time of library construction to December 2023.ResultsA total of 22 studies and 1,473 patients were included, comprising 5 interventions: high frequency repetitive transcranial magnetic stimulation (HF-rTMS), low frequency repetitive transcranial magnetic stimulation (LF-rTMS), intermittent theta burst stimulation (iTBS), sham stimulation and conventional rehabilitation therapy (CRT). Network Meta-analysis showed that the ranking results of different TMS intervention modalities in terms of MoCA scores were: HF-rTMS > LF-rTMS > iTBS > sham > CRT, the ranking results of different TMS intervention modalities in terms of MMSE scores were: HF-rTMS > LF-rTMS > sham > CRT. The effect of TMS on improving Parkinsonian cognitive function lasted for 1 month compared to the no-stimulation group.ConclusionTMS has some long-term sustained effects on improving cognitive function in Parkinson’s patients. HF-rTMS is more effective in improving cognitive function in Parkinson’s patients.Systematic review registration: https://www.crd.york.ac.uk/PROSPERO, identifier: CRD42023463958.

This study investigates the distribution, morphology, and potential functions of antennal sensilla in various wasp species, including Dolichovespula flora, D. intermedia, Vespula structor, Vl. vulgaris, Provespa barthelemyi, Vespa bicolor, V. ducalis, V. mocsaryana, and V. velutina var. nigothorax. The study thoroughly analyzes the antennal structure of these species, representing all four genera of the yellow-jacket and hornet subfamily Vespinae. Using scanning electron microscopy (SEM), the study identifies a total of nineteen types of sensilla, including sensilla trichodea (ST-I, ST-II, ST-III), sensilla campaniform (SCF-I, SCF-II, SCF-III), pit organs (SCO-I, SCO-II, and SA), sensilla placodea (SP-I, SP-II), sensilla chaetica (SCH-I, SCH-II), sensilla basiconica (SB-I, SB-II), sensilla agmon (SAG-I, SAG-II), and sensilla coelocapitular (SCA). Additionally, tyloids were observed in the males of seven species, except for Vl. structor and Vl. vulgaris. The study provides insights into these sensilla types’ morphology, abundance, and distribution. It discusses the variations in sensilla morphology among different species and the presence of gender-specific sensilla. This study provides new data about the morphology and distribution patterns of sensilla and tyloid.

The phototactic behavior of insects is commonly used to manage pest populations in practical production. However, this elusive behavior is not yet fully understood. Investigating whether the opsin genes play a crucial role in phototaxis is an intriguing topic. Vespinae (Hymenoptera: Vespidae) are a common group of social wasps that are closely associated with human activities. Efficiently controlling wasp populations while maintaining ecological balance is a pressing global challenge that still has to be resolved. This research aims to explore the phototactic behavior and key opsin genes associated with Vespinae. We found significant differences in the photophilic rates of Vespula germanica and Vespa analis under 14 different light conditions, indicating that their phototactic behavior is rhythmic. The results also showed that the two species exhibited varying photophilic rates under different wavelengths of light, suggesting that light wavelength significantly affects their phototactic behavior. Additionally, the opsin genes of the most aggressive hornet, Vespa basalis, have been sequenced. There are only two opsin genes, one for UV light and the other for blue light, and Vespa basalis lacks long-wavelength visual proteins. However, they exhibit peak phototaxis for long-wavelength light and instead have the lowest phototaxis for UV light. This suggests that the visual protein genes have a complex regulatory mechanism for phototactic behavior in Vespinae. Additionally, visual protein sequences have a high degree of homology among Hymenoptera. Despite the hypotheses put forward by some scholars regarding phototaxis, a clear and complete explanation of insect phototaxis is still lacking to date. Our findings provide a strong theoretical basis for further investigation of visual expression patterns and phototactic mechanisms in Vespinae.

Abstract Gut microbiota is established to be associated with the diversity of gastrointestinal conditions, but information on the variation associated with music and gut microbes is limited. Current study revealed the impacts of music intervention during feeding on the growth performance and gut microbes of mice by using clinical symptoms and 16S rRNA sequencing techniques. The results showed that feeding mice with music had a significant increase in body weight after the 25th day. The Firmicutes and Proteobacteria were the most dominant phylum in the gut microbiota. Also, the relative abundance of the dominant bacteria was variable after musical intervention. In contrast to the control group, a significant decrease in alpha diversity analysis of gut bacterial microorganisms and Metastats analysis showed a significant increase in the relative abundance of 5 genera and one phylum after the music intervention. Moreover, the musical intervention during feeding caused modifications in the gut microbial composition of mice, as evidenced by an increase in the level of Firmicutes and Lactobacillus, while decreases the richness of pathogenic bacteria, e.g. Proteobacteria, Cyanobacteria and Muribaculaceae, etc. In summary, music intervention increased body weight and enhanced the abundance of beneficial bacteria by reducing the prevalence of pathogenic bacteria in gut microbiota of mice.

Abstract Mosquitoes of many species are key disease vectors, killing millions of people each year. Bacillus thuringiensis-based insecticide formulations are largely recognized as among the most effective, ecologically safe, and long-lasting methods of managing insect pests. New B. thuringiensis strains with high mosquito control effectiveness were isolated, identified, genetically defined, and physiologically characterized. Eight B. thuringiensis strains were identified and shown to carry endotoxin-producing genes. Using a scanning electron microscope, results revealed typical crystal forms of various shapes in B. thuringiensis strains. Fourteen cry and cyt genes were found in the strains examined. Although the genome of the B. thuringiensis A4 strain had twelve cry and cyt genes, not all of them were expressed, and only a few protein profiles were observed. The larvicidal activity of the eight B. thuringiensis strains was found to be positive (LC50: 1.4–28.5 g/ml and LC95: 15.3-130.3 g/ml). Bioassays in a laboratory environment demonstrated that preparations containing B. thuringiensis spores and crystals were particularly active to mosquito larvae and adults. These new findings show that the novel preparation containing B. thuringiensis A4 spores and crystals mixture might be used to control larval and adult mosquitoes in a sustainable and ecologically friendly manner.

A newly discovered species, Subancistrocerus clypeatus sp. nov., from China (Zhejiang) is described and illustrated. In addition, Subancistrocerus kankauensis (Schulthess-Rechberg) is redescribed and photographed after studying the type series. A key to the genus Subancistrocerus de Saussure, 1855 from China is presented. The distribution of this genus is briefly discussed.

Flight is a complex physiological process requiring precise coordination of muscular contraction. A key protein in insect flight is flightin, which plays an integral role in the flight muscles. This research sought to evaluate the flight competence of the social wasp V. basalis by characterizing the molecular components involved. Our study focused on Vespa basalis, one of the most dangerous hornet species, utilizing PCR to obtain a partial cDNA sequence of the flightin protein. We then employed phylogenetic and sequence analysis to gain insights into this protein in flight-related adaptations. The cDNA has an 1189-base pair sequence including an open reading frame (453 bp) encoding 150 amino acids. Analyzing the deduced amino acid sequence using an online tool revealed a molecular weight of 18.05 kDa, an isoelectric point of 5.84, four functional site patterns, and no transmembrane topology. We constructed a phylogenetic tree of flightin based on 38 species. Our analysis indicated that V. basalis is most closely related to V. mandarinia; this alignment is consistent with their similar aggressive behavior, but their evolutionary relationship, based on mitochondrial sequences, presents a contrast. These initial findings on the flightin gene in V. basalis lay the groundwork for future functional studies to elucidate its specific role in flight adaptations and explore its potential as a target for pest management strategies.

Abstract Background Accumulating evidence has revealed that the gut microbiota influences the effectiveness of immune checkpoint inhibitors (ICIs) in cancer patients. As a part of the human microbiome, Helicobacter pylori (H. pylori) was reported to be associated with reduced effectiveness of anti-PD1 immunotherapy in patients with non-small-cell lung cancer (NSCLC). Gastric cancer is more closely related to H. pylori, so we conducted a retrospective analysis to verify whether the association of H. pylori and effectiveness is applicable to advanced gastric cancer (AGC) patients. Material and methods AGC patients who had evidence of H. pylori and received anti-PD-1 antibodies were enrolled in the study. The differences in the disease control rate (DCR), overall survival (OS) and progression-free survival (PFS) between the H. pylori-positive group and the negative group were compared. Results A total of 77 patients were included in this study; 34 patients were H. pylori positive, and the prevalence of H. pylori infection was 44.2%. Compared with the H. pylori-negative group, patients in the H. pylori-positive group had a higher risk of nonclinical response to anti-PD-1 antibody, with an OR of 2.91 (95% CI: 1.13–7.50). Patients in the H. pylori-negative group had a longer OS and PFS than those in the positive group, with an estimated median OS of 17.5 months vs. 6.2 months (HR = 2.85, 95% CI: 1.70–4.78; P = 0.021) and a median PFS of 8.4 months vs. 2.7 months (HR = 3.11, 95% CI: 1.96–5.07, P = 0.008). Multivariate analysis indicated that H. pylori infection was independently associated with PFS (HR = 1.90, 95% CI: 1.10–3.30; P = 0.022). Conclusion Our study unveils for the first time that H. pylori infection is associated with the outcome of immunotherapy for AGC patients. Multicenter, large sample and prospective clinical studies are needed to verify the association.

Abstract Background Stool DNA (sDNA) tests and fecal immunochemical test (FIT) are used for the detection of colorectal cancer (CRC). Here we performed a novel evaluation using sDNA and FIT to assess their performance in CRC screening and monitoring in Hubei, China. Methods Stool samples were collected from a high-risk population in Hubei, China (n = 359). sDNA tests and FIT were performed to test for KRAS mutations, NDRG4 and BMP3 methylation, and check hemoglobin levels. The methylation in BMP3 and NDRG4 genes was detected by TaqMan PCR method from human fecal samples. KRAS gene mutation in human fecal DNA was tested using TaqMan probe and amplification-refractory mutation system method. The colloid gold method was used for detection of hemoglobin in fecal samples. Finally, a novel evaluation by software was used to calculate the comprehensive value of the combined results for CRC detection and monitoring. Results The sensitivity and specificity of the novel evaluation for early CRC (stage I and II), advanced adenoma (AA), and non-colon cancer neoplasm (NA) detection were 95.45% and 81.6%, 29.63% and 75.9%, and 23.08% and 75.17%, respectively. The receiver operating characteristic (ROC) curves of the combined value for the above diseases were 0.945 ± 0.015, 0.543 ± 0.055, and 0.547 ± 0.038, respectively. The levels of the novel evaluation were not significantly associated with the pathology and stage (P > 0.05). In 20 out of 22 CRC patients, the novel evaluation of sDNA and FIT had decreased below threshold (

Abstract Lung adenocarcinoma (LUAD) is a highly prevalent cancer with high mortality. Immune resistance and tumor metastasis are the pivotal factors for the promotion of LUAD. CircRNAs have been revealed a crucial pre-clinical diagnostic and therapeutic potentials in LUAD. Herein, we identify a novel circRNA (circ_0004140), derived from the oncogene YAP1, which is up-regulated in LUAD. The high expression of circ_0004140 is correlated with poor prognosis and CTL cells dysfunction in LUAD patients. Knockdown of circ_0004140 regulated LUAD cells proliferation, migration, and apoptosis. Mechanistically, circ_0004140 served as a sponge of miR-1184 targeting C-C motif chemokine ligand 22(CCL22). Overexpression of CCL22 reversed the inhibitory effect induced by si-circ_0004140 on cells proliferation and migration. Moreover, we also revealed that elevated circ_ooo4140 was related to cytotoxic lymphocyte exhaustion, and a combination therapy of C-021 (CCL22/CCR4 axis inhibitor) and anti-PD-1 attenuated LUAD promotion and immune resistance. In conclusion, circ_0004140 may drive resistance to anti-PD-1 immunotherapy, providing a novel potential therapeutic target for LUAD treatment.

Lactococcus lactis subsp. lactis is a food bacterium that has been utilized for decades in food fermentation and the development of high-value industrial goods. Among these, nisin, which is produced by several strains of L. lactis subsp. lactis, plays a crucial role as a food bio-preservative. The gene expression for nisin synthesis was evaluated using qPCR analysis. Additionally, a series of re-transformations of the strain introducing multiple copies of the nisA and nisRK genes related to nisin production were developed. The simultaneous expression of nisA and nisZ genes was used to potentiate the effective inhibition of foodborne pathogens. Furthermore, qPCR analysis indicated that the nisA and nisRK genes were expressed at low levels in wild-type L. lactis subsp. lactis. After several re-transformations of the strain with the nisA and nisRK genes, a high expression of these genes was obtained, contributing to improved nisin production. Also, co-expression of the nisA and nisZ genes resulted in extremely effective antibacterial action. Hence, this study would provide an approach to enhancing nisin production during industrial processes and antimicrobial activity.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

Background: Post-hepatectomy liver failure (PHLF) remains a complication with the potential risk of mortality for hepatocellular carcinoma (HCC) patients. The systemic inflammatory response (SIR) has been demonstrated to be associated with a bad prognosis of liver cirrhosis and tumors. This study aims to evaluate the incremental prognostic value of inflammatory markers in predicting PHLF in patients with HCC. Methods: Clinical characteristics and variables were retrospectively collected in 2824 patients diagnosed with HCC who underwent radical hepatectomy from the First Medical Center of the General Hospital of the People’s Liberation Army. A recently published prognostic model for PHLF was used as the reference model. The increase in AUC (ΔAUC), integrated discrimination improvement (IDI), and the continuous version of the net reclassification improvement (NRI) were applied for quantifying the incremental value of adding the inflammatory markers to the reference model. A p value < 0.05 was considered statistically significant. Results: The reference PHLF model showed acceptable prediction performance in the current cohort, with an AUC of 0.7492 (95%CI, 0.7191–0.7794). The calculated ΔAUC associated with procalcitonin (PCT) was the only one that was statistically significant (p < 0.05), with a value of 0.0044, and demonstrated the largest magnitude of the increase in AUC. The continuous NRI value associated with the systemic immune-inflammation index (SII) was 35.79%, second only to GPS (46.07%). However, the inflammatory markers of the new models with statistically significant IDI only included WBC count, lymphocyte count, and SII. IDI associated with SII, meanwhile, was the maximum (0.0076), which was consistent with the performance of using the ΔAUC (0.0044) to assess the incremental value of each inflammatory variable. Conclusions: Among a wide range of inflammatory markers, only PCT and SII have potential incremental prognostic value for predicting PHLF in patients with radical resectable HCC.

Autonomous underwater vehicles (AUVs) are important in areas such as underwater scientific research and underwater resource collection. However, AUVs suffer from data portability and energy portability problems due to their physical size limitation. In this work, an acoustic guidance method for underwater docking is proposed to solve the problem of persistent underwater operation. A funnel docking station and an autonomous remotely operated vehicle (ARV) are used as the platform for designing the guidance algorithms. First, the underwater docking guidance is divided into three stages: a long-range approach stage, a mid-range adjustment stage and a short-range docking stage. Second, the relevant guidance strategy is designed for each stage to improve the docking performance. Third, a correction method based on an ultra-short baseline (USBL) system is proposed for the ARV’s estimate of the depth, relative position and orientation angle of the docking station. To verify the feasibility of the docking guidance method, in this work, tests were performed on a lake and in a shallow sea. The success rate of autonomous navigation docking on the lake was 4 out of 7. The success rate of acoustic guidance docking on the lake and in the shallow sea were 11 out of 14 and 6 out of 8, respectively. The experimental results show the effectiveness of the docking guidance method in lakes and shallow seas.

Abstract Stress caused by pathogens strongly damages plants. Developing products to control plant disease is an important challenge in sustainable agriculture. In this study, a heat-killed endophytic bacterium (HKEB), Bacillus aryabhattai, is used to induce plant defense against fungal and bacterial pathogens, and the main defense pathways used by the HKEB to activate plant defense are revealed. The HKEB induced high protection against different pathogens through the salicylic and jasmonic acid pathways. We report the presence of gentisic acid in the HKEB for the first time. These results show that HKEBs may be a useful tool for the management of plant diseases.

Graph data are widely collected and exploited by organizations, providing convenient services from policy formation and market decisions to medical care and social interactions. Yet, recent exposures of private data abuses have caused huge financial and reputational costs to both organizations and their users, enabling designing efficient privacy protection mechanisms a top priority. Local differential privacy (LDP) is an emerging privacy preservation standard and has been studied in various fields, including graph data aggregation. However, existing research studies of graph aggregation with LDP mainly provide single edge privacy for pure graph, leaving heterogeneous graph data aggregation with stronger privacy as an open challenge. In this paper, we take a step toward simultaneously collecting mixed attributed graph data while retaining intrinsic associations, with stronger local differential privacy protecting more than single edge. Specifically, we first propose a moderate granularity attributewise local differential privacy (ALDP) and formulate the problem of aggregating mixed attributed graph data as collecting two statistics under ALDP. Then we provide mechanisms to privately collect these statistics. For the categorical-attributed graph, we devise a utility-improved PrivAG mechanism, which randomizes and aggregates subsets of attribute and degree vectors. For heterogeneous graph, we present an adaptive binning scheme (ABS) to dynamically segment and simultaneously collect mixed attributed data, and extend the prior mechanism to a generalized PrivHG mechanism based on it. Finally, we practically optimize the utility of the mechanisms by reducing the computation costs and estimation errors. The effectiveness and efficiency of the mechanisms are validated through extensive experiments, and better performance is shown compared with the state-of-the-art mechanisms.

In nature, plants interact with a wide range of microorganisms, and most of these microorganisms could induce growth through the activation of important molecular pathways. The current study evaluated whether the endophytic bacterium Bacillus aryabhattai encourages plant growth and the transcriptional changes that might be implicated in this effect. The endophytic bacterium promotes the growth of Arabidopsis and tobacco plants. The transcriptional changes in Arabidopsis plants treated with the bacterium were also identified, and the results showed that various genes, such as cinnamyl alcohol dehydrogenase, apyrase, thioredoxin H8, benzaldehyde dehydrogenase, indoleacetaldoxime dehydratase, berberine bridge enzyme-like and gibberellin-regulated protein, were highly expressed. Also, endophytic bacterial genes, such as arginine decarboxylase, D-hydantoinase, ATP synthase gamma chain and 2-hydroxyhexa-2,4-dienoate hydratase, were activated during the interaction. These findings demonstrate that the expression of novel plant growth-related genes is induced by interaction with the endophytic bacterium B. aryabhattai and that these changes may promote plant growth in sustainable agriculture.

Introduction: Inflammation plays a crucial role in cancers, and the advanced lung cancer inflammation index (ALI) is considered to be a potential factor reflecting systemic inflammation.Objectives: This work aimed to explore the prognostic value of the ALI in metastatic non–small cell lung cancer (NSCLC) and classify patients according to risk and prognosis.Methods: We screened 318 patients who were diagnosed with stage IV NSCLC in Hubei Cancer Hospital from July 2012 to December 2013. The formula for ALI is body mass index (BMI, kg/m2) × serum albumin (Alb, g/dl)/neutrophil–lymphocyte ratio (NLR). Categorical variables were analyzed by the chi-square test or Fisher’s exact test. The overall survival (OS) rates were analyzed by the Kaplan–Meier method and plotted with the R language. A multivariate Cox proportional hazard model was used to analyze the relationship between ALI and OS.Results: According to the optimal cut-off value determined by X-tile software, patients were divided into two groups (the ALI

Background: Recently, many studies have suggested that bilirubin is associated with the prognosis of colorectal cancer (CRC). Conversely, there is substantial evidence that lactate dehydrogenase (LDH) levels are associated with the prognosis of cancer. Therefore, we sought to find a novel marker based on the above to predict prognosis in patients with resectable CRC.Methods: A total of 702 patients from Hubei Cancer Hospital were included. The whole population was randomly divided into training (n = 491) and testing (n = 211) cohorts. Next, we established a new index based on direct bilirubin, indirect bilirubin and LDH levels. Chi-square tests, Kaplan-Meier survival analyses, and Cox regression analyses were used to evaluate prognosis. The prediction accuracies of models for overall survival (OS) and disease-free survival (DFS) were estimated through Harrell’s concordance index (C-index) and the Brier score.Results: The median DFS duration was 32 months (range: 0–72.6 months), whereas the median OS duration was 35 months (range: 0 months–73.8 months). In addition, a new indicator, (DIR.LDH) (HR: 1.433; 95% CI, 1.069–1.920) could independently predict outcomes in CRC patients. Moreover, the module based on DIR. LDH was found to have exceptional performance for predicting OS and DFS. The C-index of the nomogram for OS was 0.802 (95% CI, 0.76–0.85) in the training cohort and 0.829 (95% CI, 0.77–0.89) in the testing cohort. The C-index of the nomogram for DFS was 0.774 (95% CI, 0.74–0.81) in the training cohort and 0.775 (95% CI, 0.71–0.84) in the testing cohort.Conclusion: We successfully established a novel module to guide clinical decision-making for CRC.

The trajectory planning algorithm of underwater vehicle near-bottom cruise is important to scientific investigation, industrial inspection, and military affairs. An autonomous underwater vehicle (AUV) often faces the problems of complex underwater environment and large cruise area in a real environment, and some robots must hide themselves during the cruise. However, to the best of our knowledge, few studies have focused on trajectory planning algorithms for AUVs with multiple constraints on large-scale maps. The currently used algorithms are not effective at solving length-constraint problems, and the mainstream trajectory planning algorithms for robots cannot be directly applied to the needs of underwater vehicle sailing near the bottom. Therefore, we present a bounded ridge-based trajectory planning algorithm (RA*) for an AUV to go on a near-bottom cruise. In the algorithm, we design a safety map based on a spherical structure to ensure the safe operation of the robot. In addressing the length-constraint problem and large-scale map planning problem, this paper proposes a two-stage framework for RA*, which designs map compression and parallel computation using a coarse-fine planning framework to solve the large-scale trajectory planning problem and uses a bounded search method to meet the trajectory planning requirements of length constraint. In this study, experiments based on the virtual ocean ridge are conducted, and the results validate the effectiveness and efficiency of the proposed RA* with MCPC algorithm framework.

For aquaculture resource evaluation and ecological environment monitoring, the automatic detection and identification of marine organisms is critical; however, due to the low quality of underwater images and the characteristics of underwater biological detection, the lack of abundant features can impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environments. Therefore, the goal of this study was to perform object detection in underwater environments. This study developed a novel method for capturing feature information by adding the convolutional block attention module (CBAM) to the YOLOv5 backbone network. The interference of underwater organism characteristics in object characteristics decreased and the output object information of the backbone network was enhanced. In addition, a self-adaptive global histogram stretching algorithm (SAGHS) was designed to eliminate degradation problems, such as low contrast and color loss, that are caused by underwater environmental features in order to restore image quality. Extensive experiments and comprehensive evaluations using the URPC2021 benchmark dataset demonstrated the effectiveness and adaptivity of the proposed methods. Additionally, this study conducted an exhaustive analysis of the impacts of training data on performance.

The identification and use of endophytic bacteria capable of triggering plant growth is an important aim in sustainable agriculture. In nature, plants live in alliance with multiple plant growth-promoting endophytic microorganisms. In the current study, we isolated and identified a new endophytic bacterium from a wild plant species Glyceria chinensis (Keng). The bacterium was designated as a Bacillus altitudinis strain using 16S rDNA sequencing. The endophytic B. altitudinis had a notable influence on plant growth. The results of our assays revealed that the endophytic B. altitudinis raised the growth of different plant species. Remarkably, we found transcriptional changes in plants treated with the bacterium. Genes such as maturase K, tetratricopeptide repeat-like superfamily protein, LOB domain-containing protein, and BTB/POZ/TAZ domain-containing protein were highly expressed. In addition, we identified for the first time an induction in the endophytic bacterium of the major facilitator superfamily transporter and DNA gyrase subunit B genes during interaction with the plant. These new findings show that endophytic B. altitudinis could be used as a favourable candidate source to enhance plant growth in sustainable agriculture.

Background: Inflammatory factors and nutritional status are critical to the prognosis of colorectal cancer patients. This study aimed to investigate the prognostic value of the combination of preoperative lymphocytes, albumin, and neutrophils (LANR) in patients with resectable colorectal cancer.Methods: A total of 753 patients with pathologically diagnosed primary colorectal cancer were included in the study. The value of LANR was defined as follows: LANR, lymphocyte × albumin/neutrophil. The ROC curve, subgroup analysis and Cox proportional hazard regression analysis were used to assess the prognostic value of LANR in overall survival and progression-free survival.Results: The median age of the patients was 60 years (range 52–67 years). In overall survival, the area under the curve of LANR was 0.6276, and the HR (95% CI) was 0.551 (0.393–0.772). And in progression-free survival, the area under the curve of LANR was 0.5963, and the HR (95% CI) was 0.697 (0.550–0.884). The results indicate that preoperative LANR may be a reliable predictor of overall and progression-free survival in resectable colorectal cancer patients.Conclusions: LANR is an important prognostic indicator for patients with resectable colorectal cancer, and it can also provide a reference for clinicians and patients to choose a treatment plan.

This study tackles the semi‐supervised segmentation task for the objects that have large motion or appearance change in a video sequence, which is very challenging to the existing methods of video object segmentation (VOS). In this study, a novel adaptive approach is presented, named adaptive convolutional neural network for large change VOS, which determines when and how to fine‐tune the convolutional neural network through the motion metric and the appearance metric among consecutive video frames. Additionally, a lightweight optimisation algorithm for the predictive binary mask is introduced which is effective for pixel prediction by eliminating the discrete points cluster. To illustrate the advantages of this approach, experiments have been performed on four VOS datasets, which demonstrate that the proposed method is highly effective and could achieve the state‐of‐the‐art on these datasets.

The potential of using autonomous underwater vehicles (AUVs) for underwater exploration is confined by its limited on-board battery energy and data storage capacity. This problem has been addressed using docking systems by underwater recharging and data transfer for AUVs. In this paper, we propose a vision-based framework by addressing the detection and pose estimation problems for short-range underwater docking using these systems. For robust and credible detection of docking stations, we propose a convolutional neural network called docking neural network (DoNN). For accurate pose estimation, a perspective-n-point algorithm is integrated into our framework. In order to examine our framework in underwater docking tasks, we collected a dataset of 2D images, named underwater docking images dataset (UDID), which is the first publicly available underwater docking dataset to the best of our knowledge. In the field experiments, we first evaluate the performance of DoNN on the UDID and its deformed variations. Next, we examine the pose estimation module by ground and underwater experiments. At last, we integrate our proposed vision-based framework with an ultra-short baseline acoustic sensor, to demonstrate the efficiency and accuracy of our framework by performing experiments in a lake. The experimental results show that the proposed framework is able to detect docking stations and estimate their relative pose more efficiently and successfully, compared with the state-of-the-art baseline systems.

This paper considers the control of one unmanned semi-submersible vehicle (USV), named BQ-01, where the submersible depth cannot exceed a few meters in the presence of environmental disturbances. Consequently, the depth control performance measures and the rejection of disturbances, such as those due to sea waves, must be within pre-determined ranges to ensure maximum use of such vehicles. This paper develops a control scheme that combines the state feedback control and rule-based supervision for depth control, where the entries in the feedback control matrix are adjusted by rules formulated from the state errors in the USV diving plane. Surge speed also plays an important role in adjusting control parameters because of its coupled relationship with the state depth of the diving plane. The results from sea trials are given and confirm the potential of the design.

Following the development of autonomous underwater vehicles (AUVs), multiple trajectory-based submarine target information collection constitutes one of the key technologies that significantly influence underwater information collection ability and deployment efficiency. In this paper, we propose an underwater information collection AUV, O-AUV, that can perceive the omnidirectional area and could detect a larger area than the traditional AUV. A 3D sensing model for the O-AUV is proposed to describe the complex underwater information collection spaces. Thereafter, a cube-based environment model involving candidate observation point calculation methods are suggested to adapt the O-AUV model. A voyage cost map is also built according to the multi-AUV path planning for a common submarine mission that must traverse numerous mission targets in complex environments through the R-Dijkstra algorithm. Specifically, the voyage planning problem is solved through a critical algorithm called ANSGA (accelerated NSGA-II algorithm), which in turn, is developed by modifying the non-dominated sorting genetic algorithm (NSGA-II) to accelerate the optimization rate for the Pareto solution. Experiments are carried out in MATLAB, and the results verify the validity of the proposed O-AUV+ANSGA algorithm framework.

Congenital hypogonadotropic hypogonadism (CHH) patients with cryptorchidism history usually have poor spermatogenesis outcome, while researches focusing on this population are rare. This study retrospectively evaluated gonadotropin-induced spermatogenesis outcome in CHH patients with cryptorchidism (n = 40). One hundred and eighty-three CHH patients without cryptorchidism were served as control. All patients received combined gonadotropins therapy (HCG and HMG) and were followed up for at least 6 months. The median follow-up period was 24 (15, 33) months (totally 960 person-months). Sperm (>0/ml) initially appeared in semen at a median of estimated 24 months (95% confidence interval (CI) 17.8–30.2). Twenty (20/40, 50%) patients succeeded in producing sperms, and the average time to produce first sperm was 19 ± 8 months. Five pregnancies were achieved in 9 (5/9, 56%) couples who desired for children. Compared with CHH patients without cryptorchidism (n = 183), cryptorchid patients had longer median time for sperm appearance in semen (24 months vs. 15 months, P

It is important for underwater wireless sensor networks (UWSNs) to satisfy the diverse monitoring demands in harsh and perilous three-dimensional underwater environments. After the monitoring missions and demands transform, a large number of underwater event coverage holes will appear. Traditional network repair strategies cannot be applied to the ever-changing underwater monitoring missions and the harsh multi-constrained three-dimensional underwater environments. Multiple autonomous underwater vehicles (multi-AUVs) have strong adaptability and flexibility in perilous and harsh three-dimensional underwater environments. First, an underwater event coverage hole (UECH) repair model under various constraints is proposed. Next, a multi-agent event coverage hole repair algorithm (MECHR), which combines multi-agent strategy with diversity archive strategy, is proposed to repair UECHs in UWSNs. The presented algorithm symmetrically completes subtasks through information exchange and interactive operations with other agents. Unlike existing repair strategies, the MECHR algorithm can effectively repair a large number of UECHs resulted by the transformations in underwater monitoring scenes and demands. The MECHR algorithm can adapt to a wide range of harsh scenes and multi-constrained three-dimensional underwater environments. Eventually, the effect of the MECHR algorithm is verified through underwater repair simulation experiments, which can adapt to the constantly changing three-dimensional underwater monitoring environments.

An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.

We propose an acoustic-based framework for automatically homing an Autonomous Underwater Vehicle (AUV) to the fixed docking station (F-DS) and mobile docking station (M-DS). The proposed framework contains a simultaneous localization method of AUV and docking station (DS) and a guidance method based on the position information. The Simultaneous localization and mapping (SLAM) algorithm is not available as the statistical characteristics of the measurement error of the observation system are unknown. To solve this problem, we propose a data pre-processing method. Firstly, the measurement error data of acoustic sensor are collected. Then, We propose a Variational Auto-Encoder (VAE) based Gaussian mixture model (GMM) for estimating the statistical characteristics of measurement error. Finally, we propose a support vector regression (SVR) algorithm to fit the non-linear relationship between the statistical characteristics of measurement error and its corresponding working distance. We adopt a guidance method based on line-of-sight (LOS) and path tracking method for homing an AUV to the fixed docking station (F-DS) and mobile docking station (M-DS). The lake experimental data are used to verify the performance of the localization with the estimated statistical characteristics of measurement error.

The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.