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Abstract Background Food-borne trematodiases (FBTs), mainly encompassing clonorchiasis, fascioliasis, fasciolopsiasis, opisthorchiasis, and paragonimiasis, is a neglected public health problem, particularly in the WHO South-East Asia and the Western Pacific regions. This study evaluates the global, regional, and national disease burden of FBTs from 1990 to 2021 and projects trends to 2030, underscore the need for targeted prevention and control. Methods Using the Global Burden of Disease 2021 database, the crude and the age-standardized prevalence rate (ASPR) and age-standardized prevalence disability-adjusted life years rate (ASDR) of FBTs at the global, regional and national level from 1990 to 2021 were described. The pivotal years of trend changes were identified using joinpoint regression analysis. The effects of age, period, cohort on FBTs prevalence and correlation with the sociodemographic index (SDI) was analyzed. Finally, the worldwide disability-adjusted life years (DALYs) for FBTs, projected up to 2030 using the Bayesian age-period-cohort model, were analyzed. Results In 2021, 44,466,329 FBTs cases [95% uncertainty interval (UI): 40,017,217, 50,034,921], and 998,028 DALYs [95% UI: 569,766, 1,638,112] were estimated across 17 countries. The Western Pacific region exhibited the highest ASPR and ASDR, with the values of 1649.26 (95% UI: 1461.95, 1881.64) and 36.54 (95% UI: 19.77, 64.16), respectively. From 1990 to 2021, Lao PDR, Thailand, and the Philippines showed the most substantial declines in FBTs, while Kazakhstan had the largest average annual percentage change in DALYs (− 6.60, 95% UI: − 7.10, − 6.10). High-middle and middle SDI countries exhibited higher burden, with ASDR values of 28.03 (95% UI: 15.41, 48.73) and 16.63 (95% UI: 9.32, 27.68), respectively. The disease burden was greater among males, peaking in the 50–59 age group. The projected ASDR in 2030 is 13.10 for males and 8.40 for females. Conclusions FBTs remain a public health threat, with the global ASDR projected to remain stable, showing only a slight decrease by 2030. Low-income countries face ambiguous mortality rates and underestimated disease burdens, highlighting the need for improved surveillance. To achieve the 2030 NTD goal, comprehensive surveillance and integrated strategies derived using a One Health approach should be prioritized to control FBTs effectively. Graphical Abstract

Pipe-like formations are vertical, complex and shape-diverse subsurface structures. The accurate seismic interpretation is essential for understanding their fluid dynamics and environmental impacts. However, conventional seismic exploration techniques struggle to accurately resolve their critical areas due to a general decline in imaging quality. This challenge is significant due to their three-dimensional nature, as some exaggerated forms impact seismic imaging, despite appearing similar in 2D slices. This limited perspective is commonly used in the oil and gas industry. This study uses a forward solution based on ray-tracing modeling specifically designed for illumination studies to help understand the impact of boundary on illumination variation, complemented by robust geometric models and elastic information derived from reasonable interpretations. We explore the impact of boundary edge curvature on illumination. The findings indicate that low curvature edges allow more rays to penetrate deeper into the boundary areas, potentially achieving higher illumination. The potential distribution of low-illumination or shadow zones are then proposed on the horizontal attribute at the top of the root area, which may explain why internal structures are often poorly imaged. This suggests the possibility of internal ray preclusion, leading to local multiples that cannot be effectively received. This research supports and enhances the understanding of why conventional seismic methods have difficulty in fully addressing imaging quality for such complex structures. It provides a thoughtful basis for further geological interpretation of this and similar vertical structures under the constraints of seismic imaging technology.

In modern manufacturing industry, in order to adapt to changes in the general environment, the manufacturing industry must improve production efficiency. To this end, this article introduces an improved genetic algorithm based on rule selection to tackle the nondeterministic polynomial hard problem stemming from inventory fibre resources and fibre selection principles in optical cable production. The algorithm aims to maximize inventory score and minimize fibre segmentation rate. It employs a permutation encoding approach to link the genetic algorithm with fibre allocation solutions and applies a self-attention mechanism to determine subset solution weight within each solution. To boost the recombination of favourable gene segments from different chromosomes, a rule optimization strategy is integrated into the crossover operation based on the weights. This operations enhance the algorithm's global search capability and convergence speed. A feasibility repair strategy is then used to inspect and rectify chromosomes, preventing the generation of illegal solutions. The legitimate mutation operation, founded on weight optimization rules, effectively reduces the algorithm's running time by avoiding illegal solutions. By leveraging actual production data from an optical cable manufacturer for simulation, the experimental results confirm the effectiveness of the improved genetic algorithm in addressing the fibre allocation problem. Comparative simulations with the unimproved genetic algorithm and a stepwise greedy algorithm underscore the superiority of the improved genetic algorithm in resolving the fibre allocation problem.

The boundary conditions in thermal simulation of the spindle system are very complicated and the empirically calculated ones will usually result in a significant discrepancy between experiment and simulation. Thus, the optimal boundary conditions were identified by treating it as an inverse optimization problem in this paper. Moreover, to advance the optimization efficiency, a reduced order model is established to replace the time-consuming thermal simulation of the spindle system. By superposition of truncated eigenmodes and accurately predicted modal coefficients, the reduced order model can reconstruct the field very similar to thermal simulation. Experimental reconstruction of both the temperature field and thermal deformation field by the reduced order model demonstrated its at least 360 times speedup with 99.8 % accuracy compared to the actual thermal simulation. With the accurate reduced order model as a lightweight digital twin and using the differential evolution algorithm, three types of boundary conditions, i.e., the heat generation rates, the convective heat transfer coefficients and the thermal contact resistances, under the shaft rotation speed of 4,000r/min were identified within 11s. The maximum temperature simulation error was reduced from 85.6 % to 6.6 % and the thermal deformation simulation error was reduced from 60.9 % to 10.8 %.

IntroductionWild rodents can serve as reservoirs or carriers of E. bieneusi, thereby enabling parasite transmission to domestic animals and humans. This study aimed to investigate the prevalence of E. bieneusi in wild rodents from the Inner Mongolian Autonomous Region and Liaoning Province of China. Moreover, to evaluate the potential for zoonotic transmission at the genotype level, a genetic analysis of the isolates was performed.MethodsA total of 486 wild rodents were captured from two provinces in China. Polymerase chain reaction (PCR) was performed to amplify the vertebrate cytochrome b (cytb) gene in the fecal DNA of the rodents to detect their species. The genotype of E. bieneusi was determined via PCR amplification of the internal transcribed spacer (ITS) region of rDNA. The examination of genetic characteristics and zoonotic potential requires the application of similarity and phylogenetic analysis.ResultsThe infection rates of E. bieneusi in the four identified rodent species were 5.2% for Apodemus agrarius (n = 89), 4.5% for Cricetulus barabensis (n = 96), 11.3% for Mus musculus (n = 106), and 38.5% for Rattus norvegicus (n = 195). Infection was detected at an average rate of 17.4% among 486 rodents. Of the 11 identified genotypes, nine were known: SHR1 (detected in 32 samples), D (30 samples), EbpA (9 samples), PigEbITS7 (8 samples), HNR-IV (6 samples), Type IV (5 samples), HNR-VII (2 samples), HNH7 (1 sample), and HNPL-V (1 sample). Two novel genotypes were also discovered, NMR-I and NMR-II, each comprising one sample. The genotypes were classified into group 1 and group 13 via phylogenetic analysis.DiscussionBased on the initial report, E. bieneusi is highly prevalent and genetically diverse in wild rodents residing in the respective province and region. This indicates that these animals are crucial for the dissemination of E. bieneusi. Zoonotic E. bieneusi-carrying animals present a significant hazard to local inhabitants. Therefore, it is necessary to increase awareness regarding the dangers presented by these rodents and reduce their population to prevent environmental contamination.

IntroductionWild rodents are key hosts for Cryptosporidium transmission, yet there is a dearth of information regarding their infection status in the Inner Mongolian Autonomous Region and Liaoning Province of China. Therefore, the present study was conducted to determine the prevalence and genetic characteristics of Cryptosporidium among wild rodents residing in these two provinces.MethodsA total of 486 rodents were captured, and fresh feces were collected from each rodent’s intestine for DNA extraction. Species identification of rodents was performed through PCR amplification of the vertebrate cytochrome b (cytb) gene. To detect the presence of Cryptosporidium in all fecal samples, PCR analysis and sequencing of the partial small subunit of the ribosomal RNA (rRNA) gene were performed.ResultsFour species of rodents were identified: Rattus norvegicus, Mus musculus, Apodemus agrarius, and Cricetulus barabensis. Positive results for Cryptosporidium were obtained for 9.2% (18/195), 6.6% (7/106), 5.6% (5/89), and 6.3% (6/96) of these rodents, respectively, with an average infection rate of 7.4% (36/486). The identification revealed the presence of five Cryptosporidium species, C. ubiquitum (n = 8), C. occultus (n = 5), C. muris (n = 2), C. viatorum (n = 1), and C. ratti (n = 1), along with two Cryptosporidium genotypes: Rat genotype III (n = 10) and Rat genotype IV (n = 9).DiscussionBased on the molecular evidence presented, the wild rodents investigated were concurrently infected with zoonotic (C. muris, C. occultus, C. ubiquitum and C. viatorum) as well as rodent-adapted (C. ratti and Rat genotype III and IV) species/genotypes, actively participating in the transmission of cryptosporidiosis.

The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth.

IntroductionWeeds are one of the main factors affecting crop growth, making weed control a pressing global problem. In recent years, interest in intelligent mechanical weed-control equipment has been growing. MethodsWe propose a semantic segmentation network, RDS_Unet, based on corn seedling fields built upon an improved U-net network. This network accurately recognizes weeds even under complex environmental conditions, facilitating the use of mechanical weeding equipment for reducing weed density. Our research utilized field-grown maize seedlings and accompanying weeds in expansive fields. We integrated the U-net semantic segmentation network, employing ResNeXt-50 for feature extraction in the encoder stage. In the decoder phase, Layer 1 uses deformable convolution with adaptive offsets, replacing traditional convolution. Furthermore, concurrent spatial and channel squeeze and excitation is incorporated after ordinary convolutional layers in Layers 2, 3, and 4. ResultsCompared with existing classical semantic segmentation models such as U-net, Pspnet, and DeeplabV3, our model demonstrated superior performance on our specially constructed seedling grass semantic segmentation dataset, CGSSD, during the maize seedling stage. The Q6mean intersection over union (MIoU), precision, and recall of this network are 82.36%, 91.36%, and 89.45%, respectively. Compared to those of the original network, the proposed network achieves improvements of 5.91, 3.50, and 5.49 percentage points in the MIoU, precision, and recall, respectively. The detection speed is 12.6 frames per second. In addition, ablation experiments further confirmed the impactful contribution of each improvement component on the overall semantic segmentation performance. DiscussionThis study provides theoretical and technical support for the automated operation of intelligent mechanical weeding devices.

To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.

ABSTRACT The fertile land in Heilongjiang Province of China is suitable for rice cultivation, but this area is susceptible to low temperature and chilling injury, which is prevented by planting rice varieties suitable for GP that is an important measure. However, selection based on rice traits is vulnerable to environmental influences and takes a long time, and selection based on molecular markers may result in progeny recombination and lack of reliability. Therefore, an efficient accurate and intelligent identification method for rice growth period is urgently needed. In this study, machine learning and deep learning methods in Python were used to analyze the Raman spectra of 6 rice varieties in three accumulated temperature region of Heilongjiang Province. 1) In machine learning, Principal Component Analysis (PCA) was adopted for feature extraction, in combination with Support Vector Machine (SVM) classification models suitable for nonlinear data sets for identification, the identification rate was 93.33% and the type of this experimental data set was determined to be discrete. 2) In deep learning, Continuous Wavelet Transform (CWT) methods was adopted for data preprocessing, combined with the Convolutional Neural Networks (CNN) model with its own feature extraction, with the highest accuracy of 94.82%, which was higher than the PCA+SVM identification model. 3) Based on the method mentioned in 2), in order to improve the feature extraction ability of the model as a whole, Convolutional Block Attention Module (CBAM) was used to improve the CNN identification model for the first time for one-dimensional data sets, and the highest identification rate was 98.28%, which was better than the PCA+SVM identification model. 3) In the verification test, Raman spectral information of 4 rice varieties was brought into the constructed CWT+CNN-CBAM identification model for identification, and the identification results were as high as 94.79%. The experimental results showed that the CWT visualization data processing method based on Raman technology combined with the CNN identification model of CBAM with improved feature extraction ability in deep learning achieved the best identification results, which could provide an efficient, accurate and intelligent method for the identification of different growth period of rice varieties in Heilongjiang Province.

Abstract The difficulty of feature extraction and the small sample size are two challenges in the field of mechanical fault diagnosis for a long time. Here we propose an intelligent mechanical fault diagnosis method for scenario with small sample datasets. This method can not only diagnose bearing faults but also gear faults, and has strong generalization performance. We use convolutional neural network to realize automatic feature extraction. Through sliding window scanning, one sample set is expanded to three sub-sample sets with different scales to meet the needs of deep learning training. Three convolutional networks are used to extract the features of the subsets respectively to ensure that their useful features are fully extracted. After feature extraction, the feature is reconstructed through feature splicing. Because of the unique advantages of SVM in dealing with small sample sets, we use SVM to classify the reconstructed features. We use the bearing data set collected by Case Western Reserve University in the United States, the bearing fault data set collected by Xi'an Jiaotong University in China, and the gearbox fault data collected by the University of Connecticut in the United States to conduct experiments. The experimental results show that the accuracy of training, validation and testing of the proposed method on the three data sets all reach 100%. This proves that our method can not only tackle the two challenges, but also has high fault diagnosis accuracy and strong generalization performance. It is hoped that our proposed method can contribute to the development of mechanical fault diagnosis.

Abstract Background Lysine lactylation (Kla) is a novelposttranslational modification (PTM) identified in histone and nonhistone proteins of several eukaryotic cells that directly activates gene expression and DNA replication. However, very little is known about the scope and cellular distribution of Kla in apicomplexan parasites despite its significance in public and animal health care. Methods Toxoplasma gondii, the causative agent of toxoplasmosis, is an obligate intracellular apicomplexan parasite that can infect different nucleated cell types of animals and humans. We used this parasite as a model organism and extracted the total protein of tachyzoites to produce the first global lysine lactylome profile of T. gondii through liquid chromatography–tandem mass spectrometry. We also investigated the level and localization of the Kla protein in T. gondii using western blotting and the indirect fluorescent antibody test (IFA), respectively. Results A total of 983 Kla sites occurring on 523 lactylated proteins were identified in the total protein extracted from Toxoplasma tachyzoites, the acute toxoplasmosis-causing stage. Bioinformatics analysis revealed that the lactylated proteins were evolutionarily conserved and involved in a wide variety of cellular functions, such as energy metabolism, gene regulation and protein biosynthesis. Subcellular localization analysis and IFA results further revealed that most of the lactylated T. gondii proteins were localized in the nucleus, indicating the potential impact of Kla on gene regulation in the T. gondii model. Notably, an extensive batch of parasite-specific proteins unique to phylum Apicomplexa is lactylated in T. gondii. Conclusions This study revealed that Kla is widespread in early dividing eukaryotic cells. Lactylated proteins, including a batch of unique parasite proteins, are involved in a remarkably diverse array of cellular functions. These valuable data will improve our understanding of the evolution of Kla and potentially provide the basis for developing novel therapeutic avenues. Graphical Abstract

The mitochondrial electron transport chain (ETC) of apicomplexan parasites differs considerably from the ETC of the animals that these parasites infect, and is the target of numerous anti-parasitic drugs. The cytochrome c oxidase complex (Complex IV) of the apicomplexan Toxoplasma gondii ETC is more than twice the mass and contains subunits not found in human Complex IV, including a 13 kDa protein termed TgApiCox13. TgApiCox13 is homologous to a human iron-sulfur (Fe-S) cluster-containing protein called the mitochondrial inner NEET protein (HsMiNT) which is not a component of Complex IV in humans. Here, we establish that TgApiCox13 is a critical component of Complex IV in T. gondii, required for complex activity and stability. Furthermore, we demonstrate that TgApiCox13, like its human homolog, binds two Fe-S clusters. We show that the Fe-S clusters of TgApiCox13 are critical for ETC function, having an essential role in mediating Complex IV integrity. Our study provides the first functional characterisation of an Fe-S protein in Complex IV.

BackgroundToxoplasmosis caused by Toxoplasma gondii is a globally distributed zoonosis. Most infections appear asymptomatic in immunocompetent individuals, but toxoplasmosis can be fatal in fetuses and immunocompromised adults. There is an urgent need to research and develop effective and low-toxicity anti-T. gondii drugs because of some defects in current clinical anti-T. gondii drugs, such as limited efficacy, serious side effects and drug resistance.MethodsIn this study, 152 autophagy related compounds were evaluated as anti-T. gondii drugs. The activity of β-galactosidase assay based on luminescence was used to determine the inhibitory effect on parasite growth. At the same time, MTS assay was used to further detect the effects of compounds with over 60% inhibition rate on host cell viability. The invasion, intracellular proliferation, egress and gliding abilities of T. gondii were tested to assess the inhibitory effect of the chosen drugs on the distinct steps of the T. gondii lysis cycle.ResultsThe results showed that a total of 38 compounds inhibited parasite growth by more than 60%. After excluding the compounds affecting host cell activity, CGI-1746 and JH-II-127 were considered for drug reuse and further characterized. Both CGI-1746 and JH-II-127 inhibited tachyzoite growth by 60%, with IC50 values of 14.58 ± 1.52 and 5.88 ± 0.23 μM, respectively. TD50 values were 154.20 ± 20.15 and 76.39 ± 14.32 μM, respectively. Further research found that these two compounds significantly inhibited the intracellular proliferation of tachyzoites. Summarize the results, we demonstrated that CGI-1746 inhibited the invasion, egress and especially the gliding abilities of parasites, which is essential for the successful invasion of host cells, while JH-II-127 did not affect the invasion and gliding ability, but seriously damaged the morphology of mitochondria which may be related to the damage of mitochondrial electron transport chain.DiscussionTaken together, these findings suggest that both CGI-1746 and JH-II-127 could be potentially repurposed as anti-T. gondii drugs, lays the groundwork for future therapeutic strategies.

Histone deacetylase (HDAC) inhibitors (HDACis) have been widely used in plants to investigate the role of histone acetylation, particularly the function of HDACs, in the regulation of development and stress response. However, how histone acetylation is involved in rice (Oryza sativa L.) disease resistance has hardly been studied. In this paper, four HDACis including Sodium butyrate (NaBT), Suberoylanilide Hydroxamic Acid (SAHA), LBH-589 and Trichostatin A (TSA) were used to treat rice seedlings at different concentrations before inoculation of Magnaporthe oryzae. We found that only 10mM NaBT treatment can significantly enhanced rice blast resistance. However, treatment of the four HDACis all increased global histone acetylation but at different sites, suggesting that the inhibition selectivity of these HDACis is different. Notably, the global H3K9ac level was dramatically elevated after both NaBT and LBH589 treatment although LBH589 could not enhance rice blast resistance. This indicates that the HDACs they inhibit target different genes. In accordance with the phenotype, transcriptomic analysis showed that many defense-related genes were up-regulated by NaBT treatment. Up-regulation of the four genes bsr-d1, PR10B, OsNAC4, OsKS4 were confirmed by RT-qPCR. ChIP-qPCR results revealed that H3K9ac level on these genes was increased after NaBT treatment, suggesting that these defense-related genes were repressed by HDACs. In addition, by promoter motif analysis of the genes that induced by both NaBT treatment and rice blast infection, we found that the motifs bound by ERF and AHL transcription factors (TFs) were the most abundant, which demonstrates that ERF and AHL proteins may act as the candidate TFs that recruit HDACs to defense-related genes to repress their expression when plants are not infected by rice blast.

Metformin is a first-line drug for type 2 diabetes, and its anticancer effects have also been widely studied in recent years. The Sonic hedgehog (Shh) signaling pathway is involved in the initiation and progression of medulloblastoma. In order to develop a new treatment strategy for medulloblastoma (MB), this study investigated the inhibitory effect of metformin on MB and the underlying mechanism of metformin on the Shh signaling pathway. The effect of metformin on proliferation was evaluated by the cell counting kit-8 (CCK-8) test and colony formation experiment. The effect of metformin on metastasis was assessed by the scratch-wound assay and transwell invasion assay. Cell cycle and apoptosis were evaluated by flow cytometry, and the associated proteins were examined by western blotting. The mRNA and protein expression levels related to the Shh pathway were measured by quantitative PCR, western blotting, and immunofluorescence staining. The xenograft murine model was carried out to evaluate the anticancer effect of metformin on medulloblastoma in vivo. Metformin inhibited proliferation and metastasis of the Shh subgroup MB cell line, and the inhibitory effect on proliferation was related to apoptosis and the block of the cell cycle at the G0/G1 phase. Animal experiments showed that metformin inhibits medulloblastoma growth in vivo. Moreover, metformin decreased mRNA and protein expression levels of the Shh pathway, and this effect was reversed by the AMP-activated protein kinase (AMPK) siRNA. Furthermore, the pro-apoptotic and cell cycle arrest effects of metformin on Daoy cells could be reversed by the Shh pathway activators. Our findings demonstrated that metformin could inhibit medulloblastoma progression in vitro and in vivo, and this effect was associated with AMPK-mediated inhibition of the Shh signaling pathway in vitro studies.

Abstract Background Enterocytozoon bieneusi, a microsporidian species, is a zoonotic pathogen found in both humans and animals. Here, we determined the prevalence, explored the different genotypes of E. bieneusi in wild rhesus macaques (Macaca mulatta) (Hainan Island of China), and assessed their zoonotic potential. Methods We collected 173 fecal specimens from wild rhesus macaques living in Nanwan Monkey Island, Hainan, China. Subsequently, we identified and genotyped E. bieneusi using nested PCR analysis amplification of the internal transcribed spacer region (ITS) of the rRNA gene. Lastly, a neighbor-joining tree was built based on gene sequences from the ITS region of E. bieneusi. Results Of the 173 specimens from wild rhesus macaques, 26 (15%) were infected with E. bieneusi. We identified six genotypes of E. bieneusi, of which five were known: PigEBITS7 (n = 20), D (n = 2), Type IV (n = 1), Peru6 (n = 1), Henan-III (n = 1), and a novel genotype: HNM-IX (n = 1). From the phylogenetic analysis, the six genotypes identified here were all clustered into zoonotic group 1. Conclusion This study is the first report to detect E. bieneusi infection in wild rhesus macaques from Hainan, China. Human-pathogenic genotypes D, Henan-III, Peru6, PigEbITS7, and Type IV in the wild rhesus macaques support these animals infected with E. bieneusi have a public health significance.

Enterocytozoon bieneusi is a zoonotic pathogen commonly found in humans and animals all over the world. Here, we investigated the occurrence and genotype constitute of E. bieneusi among the individuals from Haikou city of Hainan, China. A total of 1,264 fecal samples of humans were collected, including 628 samples from patients with diarrhea (325 adults and 303 children) and 636 samples from the asymptomatic population (383 college students and 253 kindergarten children). E. bieneusi was detected using nested polymerase chain reaction (PCR) amplification of the internal transcribed spacer (ITS) region. A phylogenetic tree was constructed using a neighbor-joining tree construction method. The overall prevalence of E. bieneusi was 3.7% (47/1,264), while it was 5.6% in the patients with diarrhea (5.8% in adults and 5.3% in children) and 1.9% in the asymptomatic population (2.9% in college students and 0.4% in kindergarten children). The prevalence of E. bieneusi in humans with diarrhea was significantly higher than that in the asymptomatic population (χ2 = 36.9; P < 0.05). A total of 28 genotypes were identified, including ten known genotypes: CHG2 (n = 3), CHG3 (n = 5), CHG5 (n = 10), CM21 (n = 1), EbpA (n = 1), EbpC (n = 1), PigEBITS4 (n = 1), PigEBITS7 (n = 1), SHR1 (n = 4), Type IV (n = 2), and 18 novel genotypes (HNH-1 to HNH-18; one each). All these genotypes were categorized into three groups, including group 1 (n = 6), group 2 (n = 14), and group 13 (n = 8). This was the first study on the identification of E. bieneusi among humans in Hainan, China. The correlation between E. bieneusi infection and diarrhea was observed. The high diversity and distinctive distribution of E. bieneusi genotypes found in this study reflected the unique epidemic genetic characteristics of E. bieneusi in humans living in Hainan.

Type 2 diabetes mellitus (T2DM) is a major caused by insulin resistance with a relative deficiency in insulin secretion. Statistically, T2DM accounts for 90% of diabetes cases worldwide. We report the patient-specific human induced pluripotent stem cell line (iPSC) CSUASOi010-A by using Peripheral blood mononuclear cells (PBMCs) of a 62-year-old female from Type 2 diabetes mellitus (T2DM). Patient blood-derived cells were reprogrammed using the Sendai virus.

The inflammatory response after cerebral ischemia/reperfusion is an important cause of neurological damage and repair. After cerebral ischemia/reperfusion, microglia are activated, and a large number of circulating inflammatory cells infiltrate the affected area. This leads to the secretion of inflammatory mediators and an inflammatory cascade that eventually causes secondary brain damage, including neuron necrosis, blood-brain barrier destruction, cerebral edema, and an oxidative stress response. Activation of inflammatory signaling pathways plays a key role in the pathological process of ischemic stroke. Increasing evidence suggests that acupuncture can reduce the inflammatory response after cerebral ischemia/reperfusion and promote repair of the injured nervous system. Acupuncture can not only inhibit the activation and infiltration of inflammatory cells, but can also regulate the expression of inflammation-related cytokines, balance the effects of pro-inflammatory and anti-inflammatory factors, and interfere with inflammatory signaling pathways. Therefore, it is important to study the transmission and regulatory mechanism of inflammatory signaling pathways after acupuncture treatment for cerebral ischemia/reperfusion injury to provide a theoretical basis for clinical treatment of this type of injury using acupuncture. Our review summarizes the overall conditions of inflammatory cells, mediators, and pathways after cerebral ischemia/reperfusion, and discusses the possible synergistic intervention of acupuncture in the inflammatory signaling pathway network to provide a foundation to explore the multiple molecular mechanisms by which acupuncture promotes nerve function restoration.

Abstract Background Toxoplasma gondii is a zoonotic pathogen that causes toxoplasmosis and leads to serious public health problems in developing countries. However, current clinical therapeutic drugs have some disadvantages, such as serious side effects, a long course of treatment and the emergence of drug-resistant strains. The urgent need to identify novel anti-Toxoplasma drugs has initiated the effective strategy of repurposing well-characterized drugs. As a principled screening for the identification of effective compounds against Toxoplasma gondii, in the current study, a collection of 666 compounds were screened for their ability to significantly inhibit Toxoplasma growth. Methods The inhibition of parasite growth was determined using a luminescence-based β-galactosidase activity assay. Meanwhile, the effect of compounds on the viability of host cells was measured using CCK8. To assess the inhibition of the selected compounds on discrete steps of the T. gondii lytic cycle, the invasion, intracellular proliferation and egress abilities were evaluated. Finally, a murine infection model of toxoplasmosis was used to monitor the protective efficacy of drugs against acute infection of a highly virulent RH strain. Results A total of 68 compounds demonstrated more than 70% parasite growth inhibition. After excluding compounds that impaired host cell viability, we further characterized two compounds, NVP-AEW541 and GSK-J4 HCl, which had IC50 values for parasite growth of 1.17 μM and 2.37 μM, respectively. In addition, both compounds showed low toxicity to the host cell. Furthermore, we demonstrated that NVP-AEW541 inhibits tachyzoite invasion, while GSK-J4 HCl inhibits intracellular tachyzoite proliferation by halting cell cycle progression from G1 to S phase. These findings prompted us to analyse the efficacy of the two compounds in vivo by using established mouse models of acute toxoplasmosis. In addition to prolonging the survival time of mice acutely infected with T. gondii, both compounds had a remarkable ability to reduce the parasite burden of tissues. Conclusions Our findings suggest that both NVP-AEW541 and GSK-J4 could be potentially repurposed as candidate drugs against T. gondii infection.

Diabetic retinopathy (DR) is one of the most common and severe microvascular complications of diabetes, and the leading cause of preventable blindness in working-aged people. Here, we generated an induced pluripotent stem (iPS) cell line using blood-derived cells from a patient with DR. Peripheral blood mononuclear cells (PBMCs) were reprogrammed with Sendai virus.

Toxoplasmosis, caused by Toxoplasma gondii, is a common disease worldwide and could be severe and even fatal in immunocompromised individuals and fetuses. Limitation in current available treatment options drives the need to develop novel therapeutics. This study assessed the anti-T. gondii potential of 103 marine natural products. A luminescence-based β-galactosidase activity assay was used to screen the marine natural products library. Afterward, those compounds that displayed over 70% parasite inhibition ratio were further chosen to assess their cytotoxicity. Compounds exhibiting low cytotoxicity (≥80% cell viability) were applied to evaluate the inhibition efficacy on discrete steps of the T. gondii lytic cycle, including invasion, intracellular growth, and egress abilities as well as the cell cycle. We found that both estradiol benzoate and octyl gallate caused >70% inhibition of tachyzoite growth with IC50 values of 4.41 ± 0.94 and 5.66 ± 0.35 μM, respectively, and displayed low cytotoxicity with TD50 values of 34.11 ± 2.86 and 26.4 ± 0.98 μM, respectively. Despite their defects in inhibition of invasion and egress of tachyzoite, the two compounds markedly inhibited the tachyzoite intracellular replication. Flow cytometric analyses further suggested that the anti-T. gondii activity of estradiol benzoate, rather than octyl gallate, may be linked to halting cell cycle progression of tachyzoite from G1 to S phase. Taken together, these findings suggest that both estradiol benzoate and octyl gallate are potential inhibitors for anti-T. gondii infection and support the further exploration of marine natural products as a thinkable source of alternative and active agents against T. gondii.

ABSTRACT The apicoplast, which harbors key pathways involved in biosynthesis of vital metabolites, is a unique and essential nonphotosynthetic plastid organelle in apicomplexan parasites. Intriguingly, autophagy-related protein 8 (Atg8), a highly conserved eukaryotic protein, can localize to the outermost membrane of the apicoplast and modulate its inheritance in both Toxoplasma and Plasmodium parasites. The Atg8-Atg3 interaction plays a key role in Atg8 lipidation and localization, and our previously work in Toxoplasma has suggested that the core Atg8-family interacting motif (AIM) in TgAtg3, 239FADI242, and the R27 residue of TgAtg8 contribute to TgAtg8-TgAtg3 interaction in vitro. However, little is known about the function of this interaction or its importance in tachyzoite growth in Toxoplasma gondii. Here, we generated two complemented cell lines, TgAtg3F239A/I242A and TgAtg8R27E, based on the TgAtg3 and TgAtg8 conditional knockdown cell lines, respectively. We found that both mutant complemented cell lines were severely affected in terms of tachyzoite growth and displayed delayed death upon conditional knockdown of endogenous TgAtg3 or TgAtg8. Intriguingly, both complemented lines appeared to be defective in TgAtg8 lipidation and apicoplast inheritance. Moreover, we showed that the interaction of TgAtg8 and TgAtg3 is critical for TgAtg8 apicoplast localization. In addition, we found that the TgAtg3F239A/I242A complemented line exhibits an integral mitochondrial network upon ablation of endogenous TgAtg3, which is distinct from TgAtg3-depleted parasites with a fragmented mitochondrial network. Taken together, this work solidifies the contribution of the TgAtg8-TgAtg3 interaction to apicoplast inheritance and the growth of T. gondii tachyzoites. IMPORTANCE Toxoplasma gondiiis a widespread intracellular parasite infecting a variety of warm-blooded animals, including humans. Current frontline treatment of toxoplasmosis suffers many drawbacks, including toxicity, drug resistance, and failure to eradicate tissue cysts, underscoring the need to identify novel drug targets for suppression or treatment of toxoplasmosis. TgAtg8 is thought to serve multiple functions in lipidation and is considered essential to the growth and development of both tachyzoites and bradyzoites. Here, we show that Toxoplasma gondii has adapted a conserved Atg8-Atg3 interaction, required for canonical autophagy in other eukaryotes, to function specifically in apicoplast inheritance. Our finding not only highlights the importance of TgAtg8-TgAtg3 interaction in tachyzoite growth but also suggests that this interaction is a promising drug target for the therapy of toxoplasmosis.

Rice diseases are extremely harmful to rice growth, and achieving the identification and rapid classification of rice disease spots is an essential means to promote intelligent rice production. However, due to the large variety of rice diseases and the similar appearance of some rice diseases, the existing deep learning methods are less effective at classification and detection. Aiming at such problems, this paper took the spot images of five common rice diseases as the research object and constructed a rice disease data set containing 2500 images of rice bacterial blight, sheath blight, flax leaf spot, leaf streak and rice blast, including 500 images of each disease. An improved lightweight deep learning network model was proposed to realize the accurate identification of disease types and disease spots. A rice disease image classification network was designed based on the RlpNet (rice leaf plaque net) network model, Which is the underlying network, in addition to the YOLOv3 target detection network model in order to achieve the optimization of the feature extraction link, i.e., upsampling by transposed convolution and downsampling by dilated convolution. The improved YOLOv3 model was compared with traditional convolutional neural network models, including the AlexNet, GoogLeNet, VGG-16 and ResNet-34 models, for disease recognition, and the results showed that the average recall, average precision, average F1-score and overall accuracy of the network model for rice disease classification were 91.84%, 92.14%, 91.87% and 91.84%, respectively, which were all improved compared with the traditional algorithms. The improved YOLOv3 network model was compared with FSSD, Faster-RCNN, YOLOv3 and YOLOv4 for spot detection studies, and the results showed that it could achieve a mean average precision (mAP) of 86.72%, a detection rate (DR) of 93.92%, a frames per second (FPS) rate of 63.4 and a false alarm rate (FAR) of only 5.12%. In summary, the comprehensive performance of the proposed model was better than that of the traditional YOLOv3 algorithm, so this study provides a new method for rice disease identification and disease spot detection. It also had good performance in terms of the common detection and classification of multiple rice diseases, which provides some support for the common differentiation of multiple rice diseases and has some practical application value.

Enterocytozoon bieneusi is a microsporidian and zoonotic species. This study investigated the prevalence and distribution of E. bieneusi genotypes in farmed masked palm civets using nested PCR, as well as assessed their zoonotic potential by phylogenetic analysis of the ITS region of the rRNA region. Here, we collected 251 fecal specimens from farmed masked palm civets (Paguma larvata) from the Hainan Island, China. In total, 128 of 251 samples were positive for E. bieneusi, with an average infection rate of 51.0%. Seventeen genotypes were identified including 12 known genotypes—HNR-VI (n = 56), SHR1 (n = 45), SHW7 (n = 6), KIN-1 (n = 3), D (n = 3), New1 (n = 3), EbpC (n = 2), CHC5 (n = 1), CHG19 (n = 1), CHN4 (n = 1), EbpA (n = 1), and Henan-III (n = 1)—and five novel genotypes (HNPL-I to HNPL-II; one each). Phylogenetic analysis categorized these genotypes into two groups. Thirteen of them were members of the zoonotic group 1, and the remaining four genotypes were in group 12. This study has shown that the infection rates of E. bieneusi in masked palm civets from Hainan were relatively high and provide baseline data to control and prevent microsporidiosis in farm-related communities. Therefore, infections in masked palm civets with zoonotic genotypes D, EbpC, CHN4, EbpA, KIN-1, and Henan-III should be considered potential threats to public health.

A new neural network prediction model is proposed for predicting ship motion attitude with high accuracy. This prediction model is based on an adaptive dynamic particle swarm optimization algorithm (ADPSO) and bidirectional long short-term memory (BiLSTM) neural network, which is to optimize the hyperparameters of BiLSTM neural network by the proposed ADPSO algorithm. The ADPSO algorithm introduces dynamic search space strategy into the classical particle swarm optimization algorithm and adjusts the learning factor adaptively to balance the global and local search ability, so as to improve the optimization performance and improve its optimization effect in BiLSTM parameter optimization process. The results show that the model can obtain higher prediction accuracy and faster convergence speed, and has better prediction performance in the prediction of ship motion attitude.

The global water demand and supply situation is becoming increasingly severe due to water shortage and uneven distribution of water resources. The highest water demand in the energy sector is attributable to power generation. With cross-country and cross-continental power grid interconnections becoming a reality, electricity trading across countries and the creation of new opportunities for re-allocation of water resources are possible. This study expands the concept of virtual water and proposes a generalized virtual water flow in an interconnected power grid system to accurately estimate water resource benefits of clean power transmission from both the production and the consumption sides. By defining the water scarcity index as a price mechanism indicator, the benefits of water resources allocation through power grid interconnections are evaluated. Taking the Africa-Asia-Europe interconnection scenario as an example, the total water saving would amount to 88.95 million m3 by 2030 and 337.8 million m3 by 2050. This result shows that grid interconnections could promote the development of renewable energy and expand the benefits of available water resources. Keywords: Power grid interconnection, Virtual water, Water resources allocation

To ameliorate soil oxygen deficiencies around subsurface drip irrigation (SDI) drippers, aerated irrigation (AI) was introduced to supply aerated water to the soil through venturi installed in the SDI pipeline. The objectives of this study were to investigate the effect of AI on the soil respiration rate and the mechanism of regulation. The Daejeon experiment included two treatments: AI and unaerated SDI as a control check (CK), and used the National Soil Quality Zhanjiang Observation and Experiment Station as a platform to carry out a 2-year (2020–2021) positioning experiment. The effects on the soil respiration rate, soil temperature, soil water content, oxygen content, soil bacterial biomass and root biomass of the two treatments were established. The oxygen content, soil bacterial biomass and root biomass regression equation, using the partial least squares regression analysis (PLSR) algorithm and structural equation modeling (SEM), screened out the influence of soil respiration under AI treatment as the main soil environmental factor and driving mechanism of rate change. The results showed that compared with the control CK, the soil respiration rate, soil oxygen content, root biomass and soil bacterial biomass were significantly enhanced under AI treatment, the soil water content had a decreasing trend, and there was no significant difference in the effect on soil temperature between the different treatments. The regression fitting results showed that the soil respiration rate under both treatments was negatively correlated with soil temperature using a quadratic polynomial correlation, linearly correlated with the soil oxygen content, positively correlated with root biomass and soil bacterial biomass using power function and positively correlated with the soil water content using a cubic polynomial correlation. The PLSR and SEM results demonstrated that aerated irrigation technology could drive the increase in the soil respiration rate by changing the soil oxygen content, root biomass and bacterial biomass.

Obtaining the number of plants is the key to evaluating the effect of maize mechanical sowing, and is also a reference for subsequent statistics on the number of missing seedlings. When the existing model is used for plant number detection, the recognition accuracy is low, the model parameters are large, and the single recognition area is small. This study proposes a method for detecting the number of maize seedlings based on an improved You Only Look Once version 4 (YOLOv4) lightweight neural network. First, the method uses the improved Ghostnet as the model feature extraction network, and successively introduces the attention mechanism and k-means clustering algorithm into the model, thereby improving the detection accuracy of the number of maize seedlings. Second, using depthwise separable convolutions instead of ordinary convolutions makes the network more lightweight. Finally, the multi-scale feature fusion network structure is improved to further reduce the total number of model parameters, pre-training with transfer learning to obtain the optimal model for prediction on the test set. The experimental results show that the harmonic mean, recall rate, average precision and accuracy rate of the model on all test sets are 0.95%, 94.02%, 97.03% and 96.25%, respectively, the model network parameters are 18.793 M, the model size is 71.690 MB, and frames per second (FPS) is 22.92. The research results show that the model has high recognition accuracy, fast recognition speed, and low model complexity, which can provide technical support for corn management at the seedling stage.

Minimizing the use of platinum (Pt) in catalysts for the oxygen reduction reaction (ORR) is critical for the commercial application of fuel cells. Here, we report a highly active carbon-supported catalyst with a low loading of Pt nanoparticles (Pt/C-N, 7.13 wt% Pt), prepared using a small molecular Pt complex containing Pt−N4 moieties as the Pt precursor. It was confirmed that Pt nanoparticles with an average diameter of

The core of saline-alkali land improvement is planting suitable plants. Planting rice in saline-alkali land can not only effectively improve saline-alkali soil, but also increase grain yield. However, traditional identification methods for saline-alkali-tolerant rice varieties require tedious and time-consuming field investigations based on growth indicators by rice breeders. In this study, the visualization method of Python data processing was used to analyze the Raman spectroscopy of japonica rice in order to study a simple and efficient identification method of saline-alkali-tolerant japonica rice varieties. Three saline-alkali-tolerant japonica varieties and three saline-alkali-sensitive japonica varieties were collected from control and saline-alkali-treated fields, respectively, and the Raman spectra of 432 samples were obtained. The data preprocessing stage used filtering-difference method to process Raman spectral data to complete interference reduction and crests extraction. In the feature selection stage, scipy.signal.find_peaks (SSFP), SelectKBest (SKB) and recursive feature elimination (RFE) were used for machine feature selection of spectral data. According to the feature dimension obtained by machine feature selection, dataset partitioning by K-fold CV, the typical linear logistic regression (LR) and typical nonlinear support vector machine (SVM) models were established for classification. Experimental results showed that the typical nonlinear SVM identification model based on both RFE machine feature selection and six-fold CV dataset partitioning had the best identification rate, which was 94%. Therefore, the SVM classification model proposed in this study could provide help in the intelligent identification of saline-alkali-tolerant japonica rice varieties.

As an ultra-sensitivity sensor, a quartz crystal microbalance (QCM) could be used to quantitatively characterize the relation between QCM parameters and the characteristics of viscoelastic films. When a QCM coated with a viscoelastic thin film is in the gas phase, based on the constructive equation of the quartz crystal and ignoring the capacitance effect, the equivalent Butterworth–Van Dyke (BVD) model of the QCM and the explicit expression for its frequency shift are derived. The “extra mass effect” deduced by the complex modulus is also obtained. It is found that the tendency of the BVD model in this work agrees well with that of Voinova’s model, although both are derived in different ways. Meanwhile, it can be seen that the two above-mentioned models exhibit different characteristics when compared with Sauerbrey’s model and Arnau’s model. The BVD model will help analyze the properties of viscoelastic films in the gas phase.

Water is essential for crops and plays a vital role in olive (Olea europaea) growth. Three irrigation treatments, rain-fed (CK), flood irrigation (FI), and drip irrigation (DI), were applied from late November to late May in a 2-year study (Nov. 2015 to Oct. 2017) on two olive cultivars, Coratina and Koroneiki. Shoot growth, flower and fruit characteristics, and olive and oil yields were measured. Compared with CK, FI had significantly higher values of vegetative growth, olive and oil yields, moisture content, and oil content. Although the fruit weight, pulp rate, and oil content with DI were the lowest, our results support that DI had the greatest positive effects on olive vegetative growth, flowers, fruit set, and olive and oil yields. It is suggested that DI in winter and spring is the best irrigation strategy for olive productivity in southwest China.

Argon, a non-polar molecule, easily diffuses into deeper tissue and interacts with larger proteins, protein cavities, or even receptors. Some of the biological effects of argon, notably its activity as an antioxidant, have been revealed in animals. However, whether and how argon influences plant physiology remains elusive. Here, we provide the first report that argon can enable plants to cope with salinity toxicity. Considering the convenience of the application, argon gas was dissolved into water (argon-rich water (ARW)) to investigate the argon’s functioning in phenotypes of alfalfa seed germination and seedling growth upon salinity stress. The biochemical evidence showed that NaCl-decreased α/β-amylase activities were abolished by the application of ARW. The qPCR experiments confirmed that ARW increased NHX1 (Na+/H+ antiporter) transcript and decreased SKOR (responsible for root-to-shoot translocation of K+) mRNA abundance, the latter of which could be used to explain the lower net K+ efflux and higher K accumulation. Subsequent results using non-invasive micro-test technology showed that the argon-intensified net Na+ efflux and its reduced Na accumulation resulted in a lower Na+/K+ ratio. NaCl-triggered redox imbalance and oxidative stress were impaired by ARW, as confirmed by histochemical and confocal analyses, and increased antioxidant defense was also detected. Combined with the pot experiments in a greenhouse, the above results clearly demonstrated that argon can enable plants to cope with salinity toxicity via reestablishing ion and redox homeostasis. To our knowledge, this is the first report to address the function of argon in plant physiology, and together these findings might open a new window for the study of argon biology in plant kingdoms.

Blood viscosity measurements are crucial for the diagnosis of cardiovascular and hematological diseases. Traditional blood viscosity measurements have obvious limitations because of their expensive equipment usage and large sample consumption. In this study, blood viscosity was measured by the oscillating circuit method and impedance analysis method based on single QCM. In addition, the effectiveness of two methods with high precision and less sample is proved by the experiments. Moreover, compared to the result from a standard rotational viscometer, the maximum relative errors of the proposed oscillating circuit method and impedance analysis method are ±5.2% and ±1.8%, respectively. A reliability test is performed by repeated measurement (N = 5), and the result shows that the standard deviation about 0.9% of impedance analysis is smaller than that of oscillating circuit method. Therefore, the impedance analysis method is superior. Further, the repeatability of impedance analysis method was evaluated by regression analysis method, and the correlation coefficient R2 > 0.965 demonstrated that it had excellent reproducibility.

Browning has been the primary limitation in eggplant processing. This study investigates the molecular mechanism underlying fresh-cut eggplant fruit browning by observing the physicochemical characteristics of browning-resistant (‘F’) and browning-sensitive (‘36′) eggplant cultivars. Browning-related enzyme activity and gene expression (PPO, LOX, and PLD) were significantly higher in the ‘36′ eggplant, thereby enhancing the degree of browning, compared to the ‘F’ eggplant. The MDA content and O2− production rate progressively increased as browning increased, while the antioxidant capacity of the fruit decreased. The cutting injury significantly activated the expression of PAL, thereby inducing the accumulation of phenolic acids, while the PPO gene was significantly upregulated, which activated the activity of polyphenol oxidase. Our results showed that the oxidation of chlorogenic acids to chlorogenic quinones resulted in the occurrence of browning, which suggests chlorogenic acid as the main browning substrate in fresh-cut eggplant.

Abstract Background Persistent and uncontrollable intracranial hypertension (ICH) and difficulty in reducing Cryptococcus count are severe problems in cryptococcal meningitis (CM) patients. The therapeutic effects of ventriculoperitoneal shunts (VPS) in non-HIV CM patients are not fully known, and the procedure is somewhat unusual. Here, our study offers a review to investigate the role of VPS in non-HIV CM. Methods We retrospectively collected data on 23 non-HIV CM patients with and without ventriculomegaly from 2010 to 2016. Their demographic data, clinical manifestations, cerebrospinal fluid (CSF) features and outcomes were analysed. Results We found that non-HIV CM patients without ventriculomegaly were older, had earlier treatment times and had shorter symptom durations than CM patients with ventriculomegaly. In both groups, headache, vomiting, fever and loss of vision were the most common clinical features. CSF pressure and Cryptococcus count were significantly decreased after operation. VPS could provide sustained relief from ICH symptoms such as headache. 13% of patients had poor outcomes because of serious underlying disease, while 87% of patients had good outcomes. Conclusions The use of a VPS is helpful in decreasing ICH and fungal overload in non-HIV CM patients, and VPS should be performed before CM patients present with symptoms of severe neurological deficit.

Abstract Antibiotics in water have recently caused increasing concerns for public health and ecological environments. In this work, we demonstrated polydopamine-coated graphene oxide/Fe3O4 (PDA@GO/Fe3O4) imprinted nanoparticles coupled with magnetic separation for fast and selective removal of fluoroquinolone antibiotics in water. The nanoparticles were prepared by the self-polymerization of dopamine using sarafloxacin as a template. The imprinted PDA film of 10~20 nm uniformly covered the surface of GO/Fe3O4 providing selective binding sites. The nanoparticles showed rapid binding and a large capacity (70.9 mg/g). The adsorption data fitted well the Langmuir and pseudo-second order kinetic equations. The nanoparticles could be easily separated by a magnet following the adsorption and then regenerated by simple washing for repetitive adsorptions. The nanoparticles were successfully used for the removal of fluoroquinolone antibiotics in seawater, with removal efficiencies of more than 95%. The proposed strategy has potentials for efficient removal of antibiotics in environmental water.

A quartz crystal resonator (QCR) is an indispensable electronic component in the field of the modern electronics industry. By designing and depositing electrodes of different shapes and thicknesses on a quartz wafer with a certain fundamental frequency, the desired target frequency can be obtained. Affected by factors such as the deposition equipment, mask, wafer size and placement position, it is difficult to accurately obtain the target frequency at a given time, especially for mass-produced QCRs. In this work, a laser with a wavelength of 532 nm was used to thin the electrodes of a QCR with a fundamental frequency of 10 MHz. The electrode surface was etched through a preset processing pattern to form a processing method of local thinning of the electrode surface. At the same time, the effect of laser etching on silicon dioxide and resonator performance was analyzed. Satisfactory trimming frequency-increasing results were achieved, such as a frequency modulation accuracy of 1 ppm, frequency distribution with good consistency and equivalent parameters with small changes, by the laser partial etching of the resonator electrode. However, when the surface electrode was etched into using through-holes, the attenuation amplitude of the equivalent parameter became larger, especially in terms of the quality factor (Q), which decreased from 63 K to 1 K, and some resonators which had a serious frequency drift of >40%. In this case, a certain number of QCRs were no longer excited to vibrate, which was due to the disappearance of the piezoelectric effect caused by the local thermal phase change in the quartz wafer.

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

Toxoplasma gondii is a parasitic protist possessing a limited set of proteins involved in the autophagy pathway, a self-degradative machinery for protein and organelle recycling. This distant eukaryote has even repurposed part of this machinery, centered on protein ATG8, for a non-degradative function related to the maintenance of the apicoplast, a parasite-specific organelle. However, some evidence also suggest Toxoplasma is able to generate autophagic vesicles upon stress, and that some autophagy-related proteins, such as ATG9, might be involved solely in the canonical autophagy function. Here, we have characterised TgPROP1 and TgPROP2, two Toxoplasma proteins containing WD-40 repeat that can bind lipids for their recruitment to vesicular structures upon stress. They belong to the PROPPIN family and are homologues to ATG18/WIPI, which are known to be important for the autophagic process. We conducted a functional analysis of these two Toxoplasma PROPPINs. One of them is dispensable for normal in vitro growth, although it may play a role for parasite survival in specific stress conditions or for parasite fitness in the host, through a canonical autophagy-related function. The other, however, seems important for parasite viability in normal growth conditions and could be primarily involved in a non-canonical function. These divergent roles for two proteins from the same family illustrate the functional versatility of the autophagy-related machinery in Toxoplasma.

Ebola virus (EBOV) causes severe hemorrhagic fevers in humans, and no approved therapeutics or vaccine is currently available. Glycoprotein (GP) is the major protective antigen of EBOV, and can generate virus-like particles (VLPs) by co-expression with matrix protein (VP40). In this study, we constructed a recombinant Alphavirus Semliki Forest virus (SFV) replicon vector DREP to express EBOV GP and matrix viral protein (VP40). EBOV VLPs were successfully generated and achieved budding from 293 cells after co-transfection with DREP-based GP and VP40 vectors (DREP-GP+DREP-VP40). Vaccination of BALB/c mice with DREP-GP, DREP-VP40, or DREP-GP+DREP-VP40 vectors, followed by immediate electroporation resulted in a mixed IgG subclass production, which recognized EBOV GP and/or VP40 proteins. This vaccination regimen also led to the generation of both Th1 and Th2 cellular immune responses in mice. Notably, vaccination with DREP-GP and DREP-VP40, which produces both GP and VP40 antigens, induced a significantly higher level of anti-GP IgG2a antibody and increased IFN-γ secreting CD8+ T-cell responses relative to vaccination with DREP-GP or DREP-VP40 vector alone. Our study indicates that co-expression of GP and VP40 antigens based on the SFV replicon vector generates EBOV VLPs in vitro, and vaccination with recombinant DREP vectors containing GP and VP40 antigens induces Ebola antigen-specific humoral and cellular immune responses in mice. This novel approach provides a simple and efficient vaccine platform for Ebola disease prevention.

Abstract Background Anopheles anthropophagus, one of the most important mosquito-borne disease vectors in Asia, mainly takes blood meals from humans and transmits both malaria and filariae. MicroRNAs (miRNAs) are small non-coding RNAs, and play a critical role in many cellular processes, including development, differentiation, apoptosis and innate immunity. Methods We investigated the global miRNA expression profile of male and female adults of A. anthropophagus using illumina Hiseq2000 sequencing combined with Northern blot. Results By using the miRNAs of the closely-related species Anopheles gambiae and Aedes aegypti as reference, we obtained 102 miRNAs candidates out of 12.43 million raw sequencing reads for male and 16.51 million reads for female, with 81 of them found as known miRNAs in An. gambiae and/or Ae. aegypti, and the remaining 21 miRNAs were considered as novel. By analyzing the revised read count of miRNAs in male and female, 29 known miRNAs show sexual difference expression: >2-fold in the read count of the same miRNAs in male and female. Especially for miR-989, which is highly expressed in the female mosquitoes, but shows almost no detected expression in male mosquitoes, indicating that miR-989 may be involved in the physiological activity of female mosquito adults. The expression of four miRNAs in different growth stages of mosquito were further identified by Northern blot. Several miRNAs show the stage-specific expression, of which miR-2943 only expressed in the egg stage, suggesting that miR-2943 may be associated with the development of mosquito eggs. Conclusions The present study represents the first global characterization of An. anthropophagus miRNAs in sexual differences and stage-specific functions. A better understanding of the functions of these miRNAs will offer new insights in mosquito biology and has implications for the effective control of mosquito-borne infectious diseases.

Aqueous liquids have a wide range of applications in many fields. Basic physical properties like the density and the viscosity have great impacts on the functionalities of a given ionic liquid. For the millions kinds of existing liquids, only a few have been systematically measured with the density and the viscosity using traditional methods. However, these methods are limited to measure the density and the viscosity of an ionic liquid simultaneously especially in processing micro sample volumes. To meet this challenge, we present a new theoretical model and a novel method to separate density and viscosity measurements with single quartz crystal microbalance (QCM) in this work. The agreement of experimental results and theocratical calculations shows that the QCM is capable to measure the density and the viscosity of ionic liquids.

We have developed a novel optical biosensor device using recombinant methyl parathion hydrolase (MPH) enzyme immobilized on agarose by metal-chelate affinity to detect organophosphorus (OP) compounds with a nitrophenyl group. The biosensor principle is based on the optical measurement of the product of OP catalysis by MPH (p-nitrophenol). Briefly, MPH containing six sequential histidines (6× His tag) at its N-terminal was bound to nitrilotriacetic acid (NTA) agarose with Ni ions, resulting in the flexible immobilization of the bio-reaction platform. The optical biosensing system consisted of two light-emitting diodes (LEDs) and one photodiode. The LED that emitted light at the wavelength of the maximum absorption for p-nitrophenol served as the signal light, while the other LED that showed no absorbance served as the reference light. The optical sensing system detected absorbance that was linearly correlated to methyl parathion (MP) concentration and the detection limit was estimated to be 4 μM. Sensor hysteresis was investigated and the results showed that at lower concentration range of MP the difference got from the opposite process curves was very small. With its easy immobilization of enzymes and simple design in structure, the system has the potential for development into a practical portable detector for field applications.

As a salt-tolerant arbor tree species, Salix matsudana plays an important role in afforestation and greening in the coastal areas of China. To select superior Salix varieties that adapt to wide saline areas, it is of paramount importance to understand and identify the mechanisms of salt-tolerance at the level of the whole genome. Here, we describe a high-density genetic linkage map of S. matsudana that represents a good coverage of the Salix genome. An intraspecific F1 hybrid population was established by crossing the salt-sensitive "Yanjiang" variety as the female parent with the salt-tolerant "9901" variety as the male parent. This population, along with its parents, was genotyped by specific length amplified fragment sequencing (SLAF-seq), leading to 277,333 high-quality SLAF markers. By marker analysis, we found that both the parents and offspring were tetraploid. The mean sequencing depth was 53.20-fold for "Yanjiang", 47.41-fold for "9901", and 11.02-fold for the offspring. Of the SLAF markers detected, 42,321 are polymorphic with sufficient quality for map construction. The final genetic map was constructed using 6,737 SLAF markers, covering 38 linkage groups (LGs). The genetic map spanned 5,497.45 cM in length, with an average distance of 0.82 cM. As a first high-density genetic map of S. matsudana constructed from salt tolerance-varying varieties, this study will provide a foundation for mapping quantitative trait loci that modulate salt tolerance and resistance in Salix and provide important references for molecular breeding of this important forest tree.