Mowing aggravates the adverse effects of nitrogen addition on soil acid neutralizing capacity in a meadow steppe.

Soil acidification induced by reactive nitrogen (N) inputs is a major environmental issue in grasslands, as it lowers the acid neutralizing capacity (ANC). The specific impacts of different N compound forms on ANC remain unclear. Grassland management practices like mowing and grazing can remove a considerable amount of soil N and other nutrients, potentially mitigating soil acidification by removing N from the ecosystem or aggravating it by removing base cations. However, empirical evidence regarding the joint effects of adding different forms of N compounds and mowing on ANC changes in different-sized soil aggregates is still lacking. This study aimed to address this knowledge gap by examining the effects of three N compounds (urea, ammonium nitrate, and ammonium sulfate) combined with mowing (mown vs. unmown) on soil ANC in different soil aggregate sizes (>2000 µm, 250-2000 µm, and <250 µm) through a 6-year field experiment in Inner Mongolia grasslands. We found that the average decline in soil ANC caused by ammonium sulfate (AS) addition (-78.9%) was much greater than that by urea (-25.0%) and ammonium nitrate (AN) (-52.1%) as compared to control. This decline was attributed to increased proton (H+) release from nitrification and the leaching of exchangeable Ca2+ and Mg2+. Mowing aggravated the adverse effects of urea and AN on ANC, primarily due to the reduction in soil organic matter (SOM) contents and the removal of exchangeable Ca2+, K+, and Na + via plant biomass harvest. This pattern was consistent across all aggregate fractions. The lack of variation in soil ANC among different soil aggregate fractions is likely due to the contrasting trend in the distribution of exchangeable Ca2+ and Mg2+. Specifically, the concentration of exchangeable Ca2+ increased with increasing aggregate size, while the opposite was true for that of exchangeable Mg2+. These findings underscore the importance of considering the forms of N compounds when assessing the declines of ANC induced by N inputs, which also calls for an urgent need to reduce N emissions to ensure the sustainable development of the meadow ecosystems.

Apoptosis, rather than neurogenesis, induces significant hippocampal-dependent learning and memory impairment in chronic low Cd2+ exposure.

Cadmium (Cd), a ubiquitous toxic heavy metal, with the intractable trait of low degradation, can induce multiple organ damage. Whereas, far less is known about its neurotoxicity and the specific mechanism in the chronic low Cd exposure. To investigate the chronic neurotoxicity of Cd2+ , we traced its effects for up to 30 months in mice which were exposed to Cd2+ by drinking the mimicking Cd-polluted water. We found the toxicity of chronic Cd exposure was a process associated with the transition from autophagy to apoptosis, and the switch of autophagy-apoptosis was Cd dose-dependent with the threshold of [Cd2+ ] 0.04 mg/L. Furthermore, JNK was found to be a hub molecule orchestrated the switch of autophagy-apoptosis by interacting with Sirt1 and p53. At last, the hippocampus-dependent learning and memory was damaged by continuous neuron apoptosis rather than deficit of neurogenesis. Therefore, elucidation of the effect, process, and potential molecular mechanism of the chronic low Cd2+ exposure is important for controlling of the environmental-pollutant Cd.

Klp2 and Ase1 synergize to maintain meiotic spindle stability during metaphase I.

The spindle apparatus segregates bi-oriented sister chromatids during mitosis but mono-oriented homologous chromosomes during meiosis I. It has remained unclear if similar molecular mechanisms operate to regulate spindle dynamics during mitosis and meiosis I. Here, we employed live-cell microscopy to compare the spindle dynamics of mitosis and meiosis I in fission yeast cells and demonstrated that the conserved kinesin-14 motor Klp2 plays a specific role in maintaining metaphase spindle length during meiosis I but not during mitosis. Moreover, the maintenance of metaphase spindle stability during meiosis I requires the synergism between Klp2 and the conserved microtubule cross-linker Ase1, as the absence of both proteins causes exacerbated defects in metaphase spindle stability. The synergism is not necessary for regulating mitotic spindle dynamics. Hence, our work reveals a new molecular mechanism underlying meiotic spindle dynamics and provides insights into understanding differential regulation of meiotic and mitotic events.

Correction to: Chaperone-substrate interactions monitored via a robust TEM-1 ß-lactamase fragment complementation assay.

In the original publication of the article, under the "Acknowledgement" section, the Grant No. 31611011097 should read as No. 31661143021.

Chaperone-substrate interactions monitored via a robust TEM-1 ß-lactamase fragment complementation assay.

OBJECTIVE: To investigate the application of the TEM-1 ß-lactamase protein fragment complementation assay (PCA) in detecting weak and unstable protein-protein interactions as typically observed during chaperone-assisted protein folding in the periplasm of Escherichia coli. RESULTS: The TEM-1 ß-lactamase PCA system effectively captured the interactions of three pairs of chaperones and substrates. Moreover, the strength of the interactions can be quantitatively analyzed by comparing different levels of penicillin resistance, and the assay can be performed under 0.5% butanol, a stress condition thought to be physiologically relevant. CONCLUSIONS: The ß-lactamase PCA system faithfully reports chaperone-substrate interactions in the bacterial cell envelope, and therefore this system has the potential to map the complex protein homeostasis network under a fluctuating environment.

Vests with Radiative Cooling Materials to Improve Thermal Comfort of Outdoor Workers: An Experimental Study.

This study focuses on improving human thermal comfort in a high-temperature outdoor environment using vests with a radiative cooling coating. The effects of coating thickness on the radiative cooling performance were first evaluated, and an optimal thickness of 160 µm was achieved. Then, six subjects were recruited to evaluate the thermal comfort in two scenarios: wearing the vest with radiative cooling coatings, and wearing the standard vest. Compared with the standard vest, the coated vest decreases the maximum temperature at the vest inner surface and the outer surface by 5.54 °C and 4.37 °C, respectively. The results show that thermal comfort is improved by wearing radiative cooling vests. With an increase of wet bulb globe temperature (WBGT), the improving effects tend to decline. A significant improvement in human thermal comfort is observed at a WBGT of 26 °C. Specifically, the percentage of thermal sensation vote (TSV) wearing the cooling vest in the range of 0 to 1 increases from 29.2% to 66.7% compared with that of the untreated vest. At the same time, the average value of thermal comfort vote (TCV) increases from -0.5 to 0.2.

The adsorption properties and mechanisms of magnetic carbon-silicon composites in situ prepared from coal gasification fine slag.

A novel magnetic carbon-silicon composite (Fe-HH-CGFS) was prepared from solid waste coal gasification fine slag (CGFS) by a two-step acid leaching and one-step chemical co-precipitation process, which was optimized using a 3-factor, 3-level Box-Behnken design and then analyzed for correlation. Fe-HH-CGFS was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer-Emmett-Teller (BET), thermal gravimetric analysis (TGA), and vibrating sample magnetometer (VSM) measurements. The results demonstrated that Fe-HH-CGFS had a reverse spinel structure with an average particle size of 5.14 nm, exhibiting a microporous/mesoporous structure with a specific surface area (SSA) of 196.84 m2 g-1 and pore volume of 0.346 cm3 g-1. Furthermore, Fe-HH-CGFS could achieve 97.59% removal efficiency of rhodamine B (RhB) under the optimal conditions: an initial concentration of RhB of 100 mg L-1, an adsorption time of 60 min, and a dosage of Fe-HH-CGFS of 1.0 g L-1. The pseudo-second-order model and the Langmuir isotherm satisfactorily described the adsorption behavior. The results indicated that the RhB removal process was a single-molecule layer endothermic adsorption, which is dominated by chemical adsorption reactions. This work is expected to provide an alternative route for the high-value utilization of CGFS and offer a valuable insight for the recycling of other solid wastes, aligning with the green development concept of "treating wastes with wastes".

Coal gasification crude slag based complex flocculants by two-step acid leaching process: synthesis, flocculation and mechanisms.

Coal gasification crude slag (CGCS) is the side-product of the coal gasification process, and its effective utilization has attracted great attention. A novel flocculant of poly-aluminum-ferric-acetate-chloride (PAFAC) was synthesized based on the recovery of CGCS by a two-step acid leaching process, namely HCl-acid leaching and HAc-acid leaching, which was optimized by an acid leaching liquor volume ratio of HCl to HAc of 3 : 2, polymerization pH of 3.5, and reaction temperature and time of 70 °C and 3.0 h, respectively. The performance of PAFAC was further evaluated by kaolin simulated wastewater, domestic sewage, river water, and aquaculture wastewater. The results revealed that PAFAC was feasible for the removal of turbidity, chemical oxygen demand (COD) and total phosphorus (TP). Moreover, PAFAC was characterized by X-ray Diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray fluorescence spectrometry (XRF) and scanning electron microscopy (SEM), which proved that PAFAC was a kind of amorphous polyionic composite. Additionally, the acid leaching kinetics and flocculation mechanisms were further investigated. It was found that the acid leaching process was followed by the unreacted shrinkage core model, and the flocculation process was dominated by charge neutralization, adsorption bridging and precipitation net trapping. The work is expected to develop a new method for the safe disposal of CGCS and provide a novel way for the preparation of Fe-Al composite flocculants, especially, offering a potential strategy for the promotion of the additional value of the coal chemical industry.

Exploring Temporal Community Structure via Network Embedding.

Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.

HB-DSBM: Modeling the Dynamic Complex Networks From Community Level to Node Level.

A variety of methods have been proposed for modeling and mining dynamic complex networks, in which the topological structure varies with time. As the most popular and successful network model, the stochastic block model (SBM) has been extended and applied to community detection, link prediction, anomaly detection, and evolution analysis of dynamic networks. However, all current models based on the SBM for modeling dynamic networks are designed at the community level, assuming that nodes in each community have the same dynamic behavior, which usually results in poor performance on temporal community detection and loses the modeling of node abnormal behavior. To solve the above-mentioned problem, this article proposes a hierarchical Bayesian dynamic SBM (HB-DSBM) for modeling the node-level and community-level dynamic behavior in a dynamic network synchronously. Based on the SBM, we introduce a hierarchical Dirichlet generative mechanism to associate the global community evolution with the microscopic transition behavior of nodes near-perfectly and generate the observed links across the dynamic networks. Meanwhile, an effective variational inference algorithm is developed and we can easy to infer the communities and dynamic behaviors of the nodes. Furthermore, with the two-level evolution behaviors, it can identify nodes or communities with abnormal behavior. Experiments on simulated and real-world networks demonstrate that HB-DSBM has achieved state-of-the-art performance on community detection and evolution. In addition, abnormal evolutionary behavior and events on dynamic networks can be effectively identified by our model.