Physiological and molecular mechanisms of the inhibitory effects of artemisinin on Microcystis aeruginosa and Chlorella pyrenoidosa.

Artemisinin, a novel plant allelochemical, has attracted attention for its potential selective inhibitory effects on algae, yet to be fully explored. This study compares the sensitivity and action targets of Microcystis aeruginosa (M. aeruginosa) and Chlorella pyrenoidosa (C. pyrenoidosa) to artemisinin algaecide (AMA), highlighting their differences. Results indicate that at high concentrations, AMA displaces the natural PQ at the QB binding site within M. aeruginosa photosynthetic system, impairing the D1 protein repair function. Furthermore, AMA disrupts electron transfer from reduced ferredoxin (Fd) to NADP+ by interfering with the iron-sulfur clusters in the ferredoxin-NADP+ reductases (FNR) domain of Fd. Moreover, significant reactive oxygen species (ROS) accumulation triggers oxidative stress and interrupts the tricarboxylic acid cycle, hindering energy acquisition. Notably, AMA suppresses arginine synthesis in M. aeruginosa, leading to reduced microcystins (MCs) release. Conversely, C. pyrenoidosa counters ROS accumulation via photosynthesis protection, antioxidant defenses, and by regulating intracellular osmotic pressure, accelerating damaged protein degradation, and effectively repairing DNA for cellular detoxification. Additionally, AMA stimulates the expression of DNA replication-related genes, facilitating cell proliferation. Our finding offer a unique approach for selectively eradicating cyanobacteria while preserving beneficial algae, and shed new light on employing eco-friendly algicides with high specificity.

Comparative transcriptome analysis of juniper branches infected by Gymnosporangium spp. highlights their different infection strategies associated with cytokinins.

BACKGROUND: Gymnosporangium asiaticum and G. yamadae can share Juniperus chinensis as the telial host, but the symptoms are completely different. The infection of G. yamadae causes the enlargement of the phloem and cortex of young branches as a gall, but not for G. asiaticum, suggesting that different molecular interaction mechanisms exist the two Gymnosporangium species with junipers. RESULTS: Comparative transcriptome analysis was performed to investigate genes regulation of juniper in responses to the infections of G. asiaticum and G. yamadae at different stages. Functional enrichment analysis showed that genes related to transport, catabolism and transcription pathways were up-regulated, while genes related to energy metabolism and photosynthesis were down-regulated in juniper branch tissues after infection with G. asiaticum and G. yamadae. The transcript profiling of G. yamadae-induced gall tissues revealed that more genes involved in photosynthesis, sugar metabolism, plant hormones and defense-related pathways were up-regulated in the vigorous development stage of gall compared to the initial stage, and were eventually repressed overall. Furthermore, the concentration of cytokinins (CKs) in the galls tissue and the telia of G. yamadae was significantly higher than in healthy branch tissues of juniper. As well, tRNA-isopentenyltransferase (tRNA-IPT) was identified in G. yamadae with highly expression levels during the gall development stages. CONCLUSIONS: In general, our study provided new insights into the host-specific mechanisms by which G. asiaticum and G. yamadae differentially utilize CKs and specific adaptations on juniper during their co-evolution.

Reference Genes Selection of Gymnosporangium yamadae during the Interaction with Apple Leaves.

Apple rust disease caused by Gymnosporangium yamadae is the one of the major threats to the development of the apple industry in China, but the pathogenic molecular mechanism of the disease remains unclear. It is imperative to screen out appropriate reference genes during the interaction between G. yamadae and apple leaves to analyze the gene expression patterns during the pathogenesis of G. yamadae. ACT, EF1, EF2, GAPDH, 40S, 60S, α-TUB, ß-TUB and UBCE3 were selected as candidate reference genes based on the transcriptomic dataset of G. yamadae. The expression levels were tested by real-time quantitative PCR during time-course infection of apple leaves and the expression stabilities were evaluated by △Ct method as well as by three software (NormFinder, geNorm and BestKeeper) and one web-based analysis software (RefFinder). The expression stability of the candidate reference genes was further validated by using the effector candidate gene Cluster-3395.48660 as the target gene in RT-qPCR. According to the results by △Ct and BestKeeper, 40S, EF2 and EF1 were the most stable reference genes, while EF1, EF2 and GAPDH were the most stable reference genes based on the NormFinder analysis result. The geNorm recommended the most stable genes EF1, EF2 and α-TUB as reference genes. Comprehensive analysis results of the RefFinder indicated EF1, EF2 and α-TUB were the most suitable genes. Based on these results, EF1, EF2 and α-TUB were considered as reference genes for analyzing the gene expression profiles of Cluster-3395.48660 in different infection stages, and the results were consistent with the transcriptome data. All the results suggest that the combination of EF1, EF2 and α-TUB proved to be acceptable reference genes during the interaction between G. yamadae and apple leaves.

Gut Microbiome Succession in Chinese Mitten Crab Eriocheir sinensis During Seawater-Freshwater Migration.

Biological migration is usually associated with disturbances and environmental changes that are key drivers in determining the diversity, community compositions, and function of gut microbiome. However, little is known about how gut microbiome is affected by disturbance such as salinity changes during migration from seawater to freshwater. Here, we tracked the gut microbiome succession of Chinese mitten crabs (Eriocheir sinensis) during their migrations from seawater to freshwater and afterward using 16S rDNA sequencing for 127 days, and explored the temporal patterns in microbial diversity and the underlying environmental factors. The species richness of gut microbiome showed a hump-shaped trend over time during seawater-freshwater migration. The community dissimilarities of gut microbiome increased significantly with day change. The turnover rate of gut microbiome community was higher during seawater-freshwater transition (1-5 days) than that in later freshwater conditions. Salinity was the major factor leading to the alpha diversity and community dissimilarity of gut microbiome during seawater-freshwater transition, while the host selection showed dominant effects during freshwater stage. The transitivity, connectivity, and average clustering coefficient of gut microbial co-occurrence networks showed decreased trends, while modularity increased during seawater-freshwater migration. For metabolic pathways, "Amino Acid Metabolism" and "Lipid Metabolism" were higher during seawater-freshwater transition than in freshwater. This study advances our mechanistic understanding of the assembly and succession of gut microbiota, which provides new insights into the gut ecology of other aquatic animals.

Gas flow through rough microchannels in the transition flow regime.

A multiple-relaxation-time lattice Boltzmann model of Couette flow is developed to investigate the rarified gas flow through microchannels with roughness characterized by fractal geometry, especially to elucidate the coupled effects of roughness and rarefaction on microscale gas flow in the transition flow regime. The results indicate that the surface roughness effect on gas flow behavior becomes more significant in rarefied gas flow with the increase of Knudsen number. We find the gas flow behavior in the transition flow regime is more sensitive to roughness height than that in the slip flow regime. In particular, the influence of fractal dimension on rarefied gas flow behavior is less significant than roughness height.

Series-nonuniform rational B-spline signal feedback: From chaos to any embedded periodic orbit or target point.

The self-controlling feedback control method requires an external periodic oscillator with special design, which is technically challenging. This paper proposes a chaos control method based on time series non-uniform rational B-splines (SNURBS for short) signal feedback. It first builds the chaos phase diagram or chaotic attractor with the sampled chaotic time series and any target orbit can then be explicitly chosen according to the actual demand. Second, we use the discrete timing sequence selected from the specific target orbit to build the corresponding external SNURBS chaos periodic signal, whose difference from the system current output is used as the feedback control signal. Finally, by properly adjusting the feedback weight, we can quickly lead the system to an expected status. We demonstrate both the effectiveness and efficiency of our method by applying it to two classic chaotic systems, i.e., the Van der Pol oscillator and the Lorenz chaotic system. Further, our experimental results show that compared with delayed feedback control, our method takes less time to obtain the target point or periodic orbit (from the starting point) and that its parameters can be fine-tuned more easily.

Impact of environmental factors and biological soil crust types on soil respiration in a desert ecosystem.

The responses of soil respiration to environmental conditions have been studied extensively in various ecosystems. However, little is known about the impacts of temperature and moisture on soils respiration under biological soil crusts. In this study, CO2 efflux from biologically-crusted soils was measured continuously with an automated chamber system in Ningxia, northwest China, from June to October 2012. The highest soil respiration was observed in lichen-crusted soil (0.93 ± 0.43 µmol m-2 s-1) and the lowest values in algae-crusted soil (0.73 ± 0.31 µmol m-2 s-1). Over the diurnal scale, soil respiration was highest in the morning whereas soil temperature was highest in the midday, which resulted in diurnal hysteresis between the two variables. In addition, the lag time between soil respiration and soil temperature was negatively correlated with the soil volumetric water content and was reduced as soil water content increased. Over the seasonal scale, daily mean nighttime soil respiration was positively correlated with soil temperature when moisture exceeded 0.075 and 0.085 m3 m-3 in lichen- and moss-crusted soil, respectively. However, moisture did not affect on soil respiration in algae-crusted soil during the study period. Daily mean nighttime soil respiration normalized by soil temperature increased with water content in lichen- and moss-crusted soil. Our results indicated that different types of biological soil crusts could affect response of soil respiration to environmental factors. There is a need to consider the spatial distribution of different types of biological soil crusts and their relative contributions to the total C budgets at the ecosystem or landscape level.

Recovering chaotic properties from small data.

Physical properties are obviously essential to study a chaotic system that generates discrete-time signals, but recovering chaotic properties of a signal source from small data is a very troublesome work. Existing chaotic models are weak in dealing with such case in that most of them need big data to exploit those properties. In this paper, geometric theory is considered to solve this problem. We build a smooth trajectory from series to implicitly exhibit the chaotic properties with series-nonuniform rational B-spline (S-NURBS) modeling method, which is presented by our team to model slow-changing chaotic time series. As for the part of validation, we reveal how well our model recovers the properties from both the statistical and the chaotic aspects to confirm the effectiveness of the model. Finally a practical chaotic model is built up to recover the chaotic properties contained in the Musa standard dataset, which is used in analyzing software reliability, thereby further proves the high credibility of this model in practical time series. The effectiveness of the S-NURBS modeling leads us to believe that it is really a feasible and worthy research area to study chaotic systems from geometric perspective. For this reason, we reckon that we have opened up a new horizon for chaotic system research.

Series-NonUniform Rational B-Spline (S-NURBS) model: a geometrical interpolation framework for chaotic data.

Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible.

Influence of disturbance on soil respiration in biologically crusted soil during the dry season.

Soil respiration (Rs) is a major pathway for carbon cycling and is a complex process involving abiotic and biotic factors. Biological soil crusts (BSCs) are a key biotic component of desert ecosystems worldwide. In desert ecosystems, soils are protected from surface disturbance by BSCs, but it is unknown whether Rs is affected by disturbance of this crust layer. We measured Rs in three types of disturbed and undisturbed crusted soils (algae, lichen, and moss), as well as bare land from April to August, 2010, in Mu Us desert, northwest China. Rs was similar among undisturbed soils but increased significantly in disturbed moss and algae crusted soils. The variation of Rs in undisturbed and disturbed soil was related to soil bulk density. Disturbance also led to changes in soil organic carbon and fine particles contents, including declines of 60-70% in surface soil C and N, relative to predisturbance values. Once BSCs were disturbed, Q 10 increased. Our findings indicate that a loss of BSCs cover will lead to greater soil C loss through respiration. Given these results, understanding the disturbance sensitivity impact on Rs could be helpful to modify soil management practices which promote carbon sequestration.

[Detection of epileptic waves in EEG based on wavelet transform].

Detection of epileptic waves in EEG is particularly helpful in the interpretation of the underlying process in seizures. This study is aimed at providing a new method for automatic detection of epileptic waves through the wavelet analysis of EEGs. It mainly deals with the detection of spikes or spike-waves based on wavelet transform. Since spikes and spike-waves contain high frequency energy, they will be represented in a particular scale localized in a small time window. According to these feature waveforms of epileptic waves, a continuous processing system for epileptic waveforms detection is constructed. We apply discrete wavelet transform on EEGs. Because of the time-frequency domain localization of wavelet transforms, we can get the local maximal positions across several successive dyadic scales of wavelet transform. And these positions indicate the points of sharp transitions in EEGs. Then we calculate the distance between every two successive maximal positions in each scale. This distance stands for the period of subwave. Furthermore, the distribution of subwave periods of each scale can be worked out. Then, comparing the distribution of normal EEG's and epileptic EEG's. The difference between these two waveforms provides us the criteria for automatic detection and classification. In order to reduce the detection workload, we also compare the detection efficiency of each scale. The scale that provides highest accuracy is selected for our automatic detection system. The results presented in this study show that scale 3 provides the best detection accuracy. So, scale 3 is deemed to be the proper scale for automatic detection. This system has the following advantages: (1) Reduced the workload significantly by selecting proper scale(s) for automatic selection; (2) Enhanced the detection accuracy by selecting proper criteria and threshold; (3) Capable of continuous detection; (4) It is also fit for the detection of other biomedical signals. This system showed good performance, and the initial clinical results obtained are also encouraging.