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Understanding how organisms have adapted to persist in unpredictable environments is a fundamental goal in biology. Bet hedging, an evolutionary adaptation observed from microbes to humans, facilitates reproduction and population persistence in randomly fluctuating environments. Despite its prevalence, empirical evidence in microalgae, crucial primary producers and carbon sinks, is lacking. Here, we report a bet-hedging strategy in the unicellular microalga Haematococcus pluvialis . We show that isogenic populations reversibly diversify into heterophenotypic mobile and non-mobile cells independently of environmental conditions, likely driven by stochastic gene expression. Mobile cells grow faster but are stress-sensitive, while non-mobile cells prioritise stress resistance over growth. This is due to shifts from growth-promoting activities (cell division, photosynthesis) to resilience-promoting processes (thickened cell wall, cell enlargement, aggregation, accumulation of antioxidant and energy-storing compounds). Our results provide empirical evidence for bet hedging in a microalga, indicating the potential for adaptation to current and future environmental conditions and consequently conservation of ecosystem functions. Bet hedging is an evolutionary strategy facilitating survival in randomly fluctuating environments. Here, the authors report bet hedging in the unicellular microalga Haematococcus pluvialis , undergoing reversible diversification into mobile and non-mobile cells.

The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are lacking. Advanced virtual screening techniques can significantly reduce the time and cost of novel antiviral drug identification. In this study, we used a cyanobacterial secondary metabolite library as an example and trained three models to identify compounds with potential antiviral activity using a machine learning method based on message-passing neural networks. Using this method, 364 potential antiviral compounds were screened from >2000 cyanobacterial secondary metabolites, with amides predominating (area under the receiver operating characteristic curve value: 0.98). To verify the actual effectiveness of the candidate antiviral compounds, HIV virus reverse transcriptase (HIV-1 RT) was selected as a target to evaluate their antiviral potential. Molecular docking experiments demonstrated that candidate compounds, including kororamide, mollamide E, nostopeptolide A3, anachelin-H, and kasumigamide, produced relatively robust non-covalent bonding interactions with the RNase H active site on HIV-1 RT, supporting the effectiveness of the proposed screening model. Our data demonstrate that artificial intelligence-based screening methods are effective tools for mining potential antiviral compounds, which can facilitate the exploration of various natural product libraries.

Diazotrophic cyanobacteria can fix nitrogen gas (N 2 ) but are usually scarce in nitrogen-limited coastal waters, which poses an apparent ecological paradox. One hypothesis is that high salinities (> 10 g/L NaCl) may inhibit cyanobacterial N 2 fixation. However, here we show that N 2 fixation in a unicellular coastal cyanobacterium exclusively depends on sodium ions and is inhibited at low NaCl concentrations (< 4 g/L). In the absence of Na + , cells of Cyanothece sp. ATCC 51142 (recently reclassified as Crocosphaera subtropica ) upregulate the expression of nifHDK genes and synthesise a higher amount of nitrogenase, but do not fix N 2 and do not grow. We find that the loss of N 2 -fixing ability in the absence of Na + is due to insufficient ATP supply. Additional experiments suggest that N 2 fixation in this organism is driven by sodium energetics and mixed-acid fermentation, rather than proton energetics and aerobic respiration, even though cells were cultured aerobically. Further work is needed to clarify the underlying mechanisms and whether our findings are relevant to other coastal cyanobacteria. High salinities are thought to inhibit nitrogen fixation by cyanobacteria in coastal waters. In contrast, Tang et al. show that nitrogen fixation in a coastal cyanobacterium requires sodium ions and is apparently driven by sodium energetics and mixed-acid fermentation.

Biodegradable microplastics (Bio-MPs) are a hot topic in soil research due to their potential to replace conventional microplastics. Biofertilizers are viewed as an alternative to inorganic fertilizers in agriculture due to their potential to enhance crop yields and food safety. The use of both can have direct and indirect effects on rhizosphere microorganisms. However, the influence of the coexistence of “Bio-MPs and biofertilizers” on rhizosphere microbial characteristics remains unclear. We investigated the effects of coexisting biofertilizers and Bio-MPs on the structure, function, and especially the carbon metabolic properties of crop rhizosphere bacteria, using a pot experiment in which polyethylene microplastics (PE-MPs) were used as a reference. The results showed that the existence of both microplastics (MPs) changed the physicochemical properties of the rhizosphere soil. Exposure to MPs also remarkably changed the composition and diversity of rhizosphere bacteria. The network was more complex in the Bio-MPs group. Additionally, metagenomic analyses showed that PE-MPs mainly affected microbial vitamin metabolism. Bio-MPs primarily changed the pathways related to carbon metabolism, such as causing declined carbon fixation/degradation and inhibition of methanogenesis. After partial least squares path model (PLS-PM) analysis, we observed that both materials influenced the rhizosphere environment through the bacterial communities and functions. Despite the degradability of Bio-MPs, our findings confirmed that the coexistence of biofertilizers and Bio-MPs affected the fertility of the rhizosphere. Regardless of the type of plastic, its use in soil requires an objective and scientifically grounded approach.

The economic study towards gender equality has a long history. Traditionally, people believe that higher equality between female and male employees under the same business lead to more harmonious and efficient surroundings; consequently, all workers will be encouraged to contribute and promote the firm's growth greatly. To examine this statement, this paper empirically studies the correlation between gender equality, productivity, and employment. To be specific, we study whether gender equality in the workplace can effectively promote manufacturing productivity growth, and how this growth is affected by the firms' employees and sizes. We looked into the Chilean manufacturing firms from 2001 to 2007; the data come from National Annual Industrial Survey conducted the National Statistics Institute of Chile. In order to avoid potential endogeneity and simultaneity, we used a semi-parametric method to estimate productivity. We consider four types of employees: executives and specialized production workers, identified as high-skill employees; and administrative staff and auxiliary production workers, as low-skill employees. Our statistical analysis as well as many other literatures shows that in Chile, severe gender inequality still exists nowadays. For example, the majority of the observed employees (80%) are male. We then study gender equality through two measures at the same time: female labor-force participation rate, and gender equivalence as how the female participation rate deviates from 0.5. We conduct simultaneous regression to estimate the influence exerted on firms' productivity by these two measures among each of the four types of employees – executives, specialized workers, administrative staff, and auxiliary workers. Our findings are mixed and very interesting. Among those small firms with less than 50 employees, higher female labor-force participation among high-skill employees significantly increases a firms' productivity. For larger firms with more than 50 employees, only better gender equality among the low-skill employees improves productivity. Therefore, a more equalized distributed workforce between female and male workers does significantly lead to faster productivity growth, but it depends on the size of the firm and specific types of employees. Therefore indeed, we can effectively promote the growth of a firm through different gender equality policies such as balancing welfare treatment between males and females, or publicizing our efforts to the society; but we also need to count both the firm's size and the types of employees into consideration. Our findings also provide insights into a firm's growth pattern. High-skill employees are the leading force of small firms, while big firms consistently reply on all the ordinary-level employees.

Adoption of the Internet of Things (IoT) in education brings many benefits. However, the poor implementation of access control of educational data produced by the IoT devices has brought students’ and teachers’ privacy into danger. Attackers can access educational data that they are not permitted to access and even erase the records during access. To tackle this problem, we employ blockchain technology to guarantee the integrity of access control rules and trace the records of access events. In this paper, we propose a blockchain-based access control scheme for the data produced by IoT devices. The scheme consists of three components: (1) a well-implemented data collection module that is deployed in smart classrooms, which collects and uploads data about the real-time situation inside the smart classroom to the data center; (2) a MongoDB-based data center and its control module that makes access control decisions based on the verification of the permissions of visitors, where the permissions are managed by blockchain; and (3) a customized blockchain system that stores and keeps security policy updates of the role-based access control module and records access events in a trusted way. Our analysis indicates that the proposed access control scheme guarantees the correctness of the access control process and makes the access of collected educational data auditable and responsible. Our system collectively analyzes the context of the smart classroom and is capable of detecting multiple scenarios such as absence, lateness, and gunshot. We show how the scheme preserves students’ and teachers’ privacy by carrying out extensive experimental studies. The results indicate that the proposed data management system can give correct responses as quickly as a traditional data server does while preserving privacy.

It is of great significance to understand the diffusion rate of the volatile corrosion inhibitor (VCI) in the VCI films for corrosion inhibition. The diffusion behavior of 1,2,3-Benzotriazole (BTA) in pure low density polyethylene (LDPE) and VCIs/LDPE blends was investigated using molecular dynamics (MD) simulation at 310, 328 and 353 K temperatures. The temperature dependence and diffusion property of BTA in LDPE were revealed. Subsequently, the accuracy of the MD simulations was confirmed by comparing the diffusion coefficients obtained from the MD simulations with those obtained from the experiments. The fractional free volume, interaction energy between BTA and VCIs/LDPE, activation energy of BTA and the self-diffusion behavior of LDPE on the diffusion of BTA were explored, which illustrated the microscopic diffusion mechanism of BTA in LDPE. Results showed that the diffusion coefficients of BTA increased with increasing temperature, increasing free volume and the more flexible chain of LDPE, while the increase in the interaction energy between BTA and VCIs/LDPE slowed down the diffusion of BTA. It can be concluded that the increase in the interaction energy between BTA and the system, the activation energy of BTA and the formation of H-bonds due to the addition of other VCIs led to the decrease in the diffusion coefficients of BTA. The strong molecular interactions between BTA and VCIs were the main reason for the decrease in the BTA diffusion coefficients. Adjusting the formulation of the VCI films can provide new ideas for regulating the BTA diffusion rate, which is beneficial for extending the corrosion inhibition time of BTA.

Cadmium (Cd) pollution in farmland soils severely affects agricultural production safety, thereby threatening human health. Sedum plumbizincicola is a Cd and Zn hyperaccumulator commonly used for the phytoremediation of Cd-contaminated soil. This study was aimed to improve the remediation effect of S. plumbizincicola on Cd-contaminated farmland soil and provide a theoretical basis for the enhancement of endophytic bacteria in the repair of Cd-contaminated soil with S. plumbizincicola. Four kinds of endophytic bacteria, namely Buttiauxella , Pedobacter , Aeromonas eucrenophila , and Ralstonia pickettii , were used, and soil culture experiments and pot experiments were conducted to explore the effects of endophytic bacteria on soil Cd speciation and phytoremediation efficiency of Cd-contaminated farmland soils. Under the experimental conditions, after inoculation with endophytic bacteria, the soil pH was effectively reduced, content of weak acid-extracted Cd and oxidizable Cd increased, and content of reducible Cd and residual Cd decreased. Soil Cd activity was increased, and the availability coefficient of soil Cd increased by 1.15 to 6.41 units compared with that of the control (CK 2 ). Compared with CK 2 , the biomass of S. plumbizincicola significantly increased by 23.23–55.12%; Cd content in shoots and roots of S. plumbizincicola increased by 29.63–46.01% and 11.42–84.47%, respectively; and bioconcentration factor was 2.13 to 2.72 times that of CK 2 . The Cd removal rate of S. plumbizincicola monocropping was 48.25%. When S. plumbizincicola was planted with inoculating endophytic bacteria, the Cd removal rate in the soil reached 61.18–71.49%, which was significantly higher than that of CK 2 ( p  < 0.05). The treatment with endophytic bacteria activated soil Cd, promoted the growth of S. plumbizincicola , increased its Cd content, and enhanced the phytoremediation of Cd-contaminated farmland soil. Therefore, endophytic bacteria can be used to improve the remediation efficiency of S. plumbizincicola in Cd-contaminated farmland soils.

The deterioration of a shot sleeve in squeeze casting due to thermo-mechanical fatigue often results in lowering the reliability and availability of the squeeze casting machine, thus reducing its productivity, meanwhile increasing the life-cycle maintenance cost. This paper presents an efficient Bayesian kriging meta-modeling method for spatiotemporal prediction under data uncertainty and non-normality, with the target applications for controlling the deformation, optimizing machine design, and predicting component fatigue cracking thus improving the reliability and availability of mechanical system. Spatiotemporal kriging model is established to substitute the complicated computer model by using numerically simulated data. Bayesian probabilistic approach is then developed to quantitatively evaluate the validity and predictive capacity of kriging meta-model, considering data uncertainty. The Anderson-Darling goodness-of-fit test is employed to perform the normality hypothesis test of difference values of validation data. Box–Cox transformation method is utilized to convert the non-normality data with the purpose of facilitating the overall validation assessment of meta-models with higher accuracy. Bayesian confidence measure is presented to quantify the confidence on the predictive capacity of the kriging meta-models, given the transformed data. A procedure is proposed to implement the proposed probabilistic methodology for meta-modeling and model validation with non-normality response series. The impact of data normality assumption and decision threshold parameter in quantitative model assessment is also investigated by using Bayesian inference approach. The effectiveness of the proposed methodology and procedure is demonstrated with the spatiotemporal temperature prediction in squeeze casting.

A kriging modeling method is proposed to conduct the temperature uncertainty analysis of an injection mechanism in squeeze casting. A mathematical model of temperature prediction with multi input and single output is employed to estimate the temperature spatiotemporal distributions of the injection mechanism. The kriging model applies different weights to the independent variables according to spatial location of sample points and their correlation, thus reducing the estimation variance. The predicted value of the kriging model is compared with the sample data at the corresponding position to investigate the influence of the temperature uncertainty of the injection mechanism on the injection process including friction. The results indicate that the significant error is observed at a few sample points in the early injection due to the impact of the uncertainty facts. The variance mean and standard deviation obtained by the model calibrated by experimental samples reduce largely in comparison to those obtained from the initial kriging model. This study indicates that model calibration produces more accurate prediction.