Aptamer-mediated double strand displacement amplification with microchip electrophoresis for ultrasensitive detection of Salmonella typhimurium.

Rapid and quantitative detection of foodborne bacteria is of great significance to public health. In this work, an aptamer-mediated double strand displacement amplification (SDA) strategy was first explored to couple with microchip electrophoresis (MCE) for rapid and ultrasensitive detection of Salmonella typhimurium (S. Typhimurium). In double-SDA, a bacteria-identified probe consisting of the aptamer (Apt) and trigger sequence (Tr) was ingeniously designed. The aptamer showed high affinity to the S. Typhimurium, releasing the Tr sequence from the probe. The released Tr hybridized with template C1 chain, initiating the first SDA to produce numerous output strands (OS). The second SDA process was induced with the hybridization of the liberated OS and template C2 sequence, generating a large number of reporter strands (RS), which were separated and quantified through MCE. Cascade signal amplification and rapid separation of nucleic acids could be realized by the proposed double-SDA method with MCE, achieving the limit of detection for S. typhimurium down to 6 CFU/mL under the optimal conditions. Based on the elaborate design of the probes, the double-SDA assisted MCE strategy achieved better amplification performance, showing high separation efficiency and simple operation, which has satisfactory expectation for bacterial disease diagnosis.

Isothermal strand displacement polymerase reaction (ISDPR)-assisted microchip electrophoresis for highly sensitive detection of cancer associated microRNAs.

More and more studies have found that microRNAs (miRNAs) are markers of cancer, and detection of miRNAs is beneficial for early diagnosis and prognosis of cancer. In this paper, the isothermal strand displacement polymerase reaction (ISDPR), which is an enzyme-assisted nucleic acid amplification method, was studied to combine with microchip electrophoresis (MCE) for a simultaneously detection of two cancer related miRNAs named microRNA-21 (miR-21) and microRNA-221 (miR-221). In the ISDPR amplification, two different DNA hairpins (HPs) were specifically designed, so that miR-21 and miR-221 could respectively bind to HPs and started ISDPR amplification to generate two different products which were ultimately detected by MCE. The optimal conditions of ISDPR were carefully investigated, and the limits of detection (LOD) of miR-21 and miR-221 were as low as 0.35 fM and 0.25 fM (S/N = 3) respectively under these conditions. The human lung tumor cells and serum samples were analyzed by this ISDPR-MCE method and satisfactory results were obtained, which means that this method is of high sensitivity, high efficiency, low reagent consumption and simple operation in miRNAs detection.

Epidermal microorganisms contributed to the toxic mechanism of nZVI and TCEP in earthworms by robbing metal elements and nutrients.

Disrupting effects of pollutants on symbiotic microbiota have been regarded as an important mechanism of host toxicity, with most current research focusing on the intestinal microbiota. In fact, the epidermal microbiota, which participates in the nutrient exchange between hosts and environments, could play a crucial role in host toxicity via community changes. To compare the contributions of intestinal and epidermal symbiotic microorganisms to host toxicity, this study designed single and combined scenarios of soil contamination [nano zero-valent iron (nZVI) and tris (2-chloroethyl) phosphate (TCEP)], and revealed the coupling mechanisms between intestinal/epidermal symbiotic bacterial communities and earthworm toxicological endpoints. Microbiome analysis showed that 15% of intestinal microbes were highly correlated with host endpoints, compared to 45% of epidermal microbes showing a similar correlation. Functional comparisons revealed that key species on the epidermis were mainly heterotrophic microbes with genetic abilities to utilize metal elements and carbohydrate nutrients. Further verifications demonstrated that when facing the co-contamination of nZVI and TCEP, certain symbiotic microorganisms became dominant and consumed zinc, copper, and manganese along with saccharides and amino acids, which may be responsible for the nutritional deficiencies in the host earthworms. The findings can enrich the understanding of the coupling relationship between symbiotic microorganisms and host toxicity, highlighting the importance of epidermal microorganisms in host resistance to environmental pollution.

Research on the village layout optimization in China's developed areas based on daily life circles.

Public service facilities are the basic material carrier of social services. It is of great significance for the operation of social justice and the improvement of residents' wellbeing. In the process of rapid urbanization, the demand for traffic convenience and diversity of public service facilities in rural areas has been further improved. Since the sudden outbreak of the COVID-19 epidemic, it is more necessary to meet the public service needs of human daily life within a certain space. The huge gap between urban and rural infrastructures, human living conditions, and supporting public services such as education, health, and culture has become a key bottleneck constraining the integration of urban and rural areas. It is appropriate to focus on the requirements for the construction of daily life circles and prioritize the satisfaction of villagers' increasing demand for public services in regions characterized by high levels of urban-rural integration. The behavior preference of public service facilities significantly affects the choice of residential areas. The existing research on village layout optimization focused on the spatial pattern, landscape morphology, influencing factors, and other contents. To some extent, the preference for equalization of public services in the context of urban-rural integration has been ignored. Uneven distribution and low utilization of public service facilities in villages resulted in an inability to adapt spatially and functionally in response to the renewal of urban-rural relations. Taking a highly developed urban and rural integration area named Wuxi City of Jiangsu Province in China as an example, this paper combines the current situation of public service facility allocation and the transport situation of residents in rural areas to build a life-circle system of different levels. On this basis, combined with the optimization goal of public service facilities and the constraints of agglomeration coverage, we try to determine the village layout optimization scheme under the construction of both the daily life circle and location-allocation model. The study can effectively adjust the allocation of public resources in rural areas and solve the problems of irrational village layout leading to long travel distances. It can also serve as a reference for improving the situation of lagging rural public service functions and promoting urban-rural equalization.

FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment.

To better address the difficulties in designing green fruit recognition techniques in machine vision systems, a new fruit detection model is proposed. This model is an optimization of the FCOS (full convolution one-stage object detection) algorithm, incorporating LSC (level scales, spaces, channels) attention blocks in the network structure, and named FCOS-LSC. The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions, lighting conditions, and capture angles. Specifically, the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information. The feature pyramid network (FPN) is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way. Next, the attention mechanisms are added to each of the 3 dimensions of scale, space (including the height and width of the feature map), and channel of the generated multiscale feature map to improve the feature perception capability of the network. Finally, the classification and regression subnetworks of the model are applied to predict the fruit category and bounding box. In the classification branch, a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection. The proposed FCOS-LSC model has 38.65M parameters, 38.72G floating point operations, and mean average precision of 63.0% and 75.2% for detecting green apples and green persimmons, respectively. In summary, FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural equipment. Correspondingly, FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

In Situ Investigation of Intercellular Signal Transduction Based on Detection of Extracellular pH and ROS by Scanning Electrochemical Microscopy.

Intercellular signal transduction plays an important role in the regulation of biological activities. Herein, a Transwell chamber-based two-layer device combined with scanning electrochemical microscopy (SECM) technology has been proposed for in situ investigation of intercellular signal transduction. The cells in the device were cultured on two layers: the lower layer was for signaling cells, and the upper layer was for signal-receiving cells. The extracellular pH (pHe) and ROS (reactive oxygen species, ROSe) were in situ monitored by SECM potentiometric mode and SECM-MPSW (multipotential step waveform), respectively. When the signaling cells, including MCF-7, HeLa, and HFF cells, were electrically stimulated, the ROS release of the signal-receiving cells was promoted. By detecting the pH at the cell surface, it was found that more H+ generated by the signaling cells and two cell layers at a shorter distance could both cause the signal-receiving cells to release more ROS, revealing that H+ is one of the signaling molecules of intercellular communication. This SECM-based in situ monitoring strategy provides an effective way to investigate intercellular signal transduction and explore the corresponding mechanism.

An accurate green fruits detection method based on optimized YOLOX-m.

Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network's ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables.

Does ambient air quality standard contribute to green innovation of enterprises in China? Implications for environmental protection and public health.

In the post-COVID-19 era, environmental pollution has been a serious threat to public health. Enterprises are in urgent need of enhancing green technology innovation as the main source of pollutant emissions, and it is necessary for governments to support green innovation of enterprises to reduce pollutant emissions and promote public health. In this context, this paper investigates whether the Ambient Air Quality Standard (AAQS) implemented in 2012 in China contributes to green innovation of enterprises, to provide implications for environmental protection and public health. By using panel data of Chinese A-share listed companies from 2008 to 2020, this study adopts the difference-in-difference model to analyze the policy impact of environmental regulation on green innovation of enterprises and its internal mechanism. The results show that AAQS has significantly improved the green innovation of enterprises. Furthermore, AAQS affects the green innovation of enterprises by virtue of two mechanism paths: compliance cost effect and innovation offset effect. On the one hand, AAQS leads to an increase in production costs of enterprises, thus inhibiting green innovation activities of enterprises. On the other hand, AAQS encourages enterprises to increase R&D investment in green technology, thus enhancing their green innovation. In addition, the impact of AAQS on firms' green innovation has heterogeneous characteristics. Our findings not only enrich the studies of environmental regulation and green innovation of enterprises but also provide policymakers in China and other developing countries with implications for environmental protection and public health improvement.

Whether Green Finance Improves Green Innovation of Listed Companies-Evidence from China.

Facing the intensification of global carbon emissions and the increasingly severe pressure of environmental pollution, listed companies urgently need to promote green innovation, achieve green transformation, and alleviate environmental problems. Green finance policy has played a significant role as a financial strategy for environmental governance in affecting green innovation level over the years. In this context, taking the green finance reform and innovation pilot zone (GFRIPZ) implemented in 2017 in China as a quasi-natural experiment, this paper analyzes the impact of green finance policy on green innovation level of listed companies by the difference-in-difference model. Based on the data of Chinese A-share listed companies from 2008 to 2020, the results of empirical analysis show that green finance significantly promotes green innovation of listed companies. The effect is profound on green utility model patents, but less pronounced on green invention patents. Among all these pilot zones, the policy effects of GFRIPZ ranked in descending order are Zhejiang, Guangdong, Jiangxi, Guizhou, and Xinjiang. In addition, green finance has a more significant impact on heavy-polluting industries, large and state-owned enterprises, and listed companies located in the eastern region. Furthermore, the effects of industry heterogeneity ranked in descending order are energy, manufacturing, processing, and engineering industry, while it is not obvious in the service industry. Mechanism analysis suggests that the effect is driven by a reduction in the cost of debt financing and an increase in the long-term debt ratio. The findings provide implications for policymakers to promote the level of green innovation and environmental governance. Therefore, policymakers should support the long-term creative development of green invention patents by reducing the cost of debt financing and increasing the long-term debt ratio and consider the heterogeneous characteristics of listed companies when formulating green finance policies.

Exploring the trend in religious diversity: Based on the geographical perspective.

The formation and development of religious diversity is a manifestation of the free expression of human thought, belief, and practice, as well as a historical premise and ideological condition for the gradual recognition and integration of modern religions into modern political values. This study examines the spatial characteristics of the development of the global religious diversity index (RDI) and the evolution trend through a geographical perspective by the LISA space-time transition and convergence test. The results show that: (1) At the temporal level, RDI showed a fast and then slow increase after WWII, with an increase of 61.11%. (2) At the spatial level, Latin America has seen the most significant increase in RDI, followed by Europe, North America and the Caribbean, while Asia has a slight decrease. (3) At the country level, most countries with the highest levels of RDI are located in North America and the Caribbean, Sub-Saharan Africa, and most of these countries have a history of being colonized. RDI was mainly influenced by factors such as the missionary effect in the colonial period, precipitation, GDP per capita, and genetic diversity. (4) The evolution of the spatial structure of global RDI has a certain path-dependent, but this trend is gradually weakening. In addition to countries' own development, RDI is also influenced by spillover effects from the neighboring countries. (5) There is a significant σ convergence and absolute ß convergence in the global RDI, and most of the continental units have club convergence, i.e., the internal differences in RDI levels at the global and regional levels are gradually narrowing, and there is a spillover effect of higher RDI levels to the surrounding lower regions, and this diffusion or influence allows the lower regions to catch up in the gap of RDI.

Fast Location and Recognition of Green Apple Based on RGB-D Image.

In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.

The Impact of Ecological Civilization Construction on Environment and Public Health-Evidence from the Implementation of Ecological Civilization Demonstration Area in China.

Faced with an increasingly tight resource supply, serious environmental pollution and degrading ecosystems, human beings are eager to reduce environmental pollution and promote public health. In this context, this paper takes the ecological civilization demonstration area (ECDA) established in China as a quasi-natural experiment to test whether ecological civilization construction (ECC) is an effective solution for the reduction of environmental pollution and improvement of public health. Based on the panel data of 31 provinces in China from 2009 to 2020, the study analyzes the impact of ECC on environmental quality and public health by employing a difference-in-difference model. The results show that ECDA has restrained environmental pollution and reduced the morbidity and mortality, which indicates that ECC effectively promotes environmental quality and public health. The effect of ECC is more pronounced in economically developed regions. In addition, ECC improves environmental quality through scale effects, structural effects, technology effects, and ecological conservation effects, while the positive effects of ECC on public health are driven by scale effects and ecological conservation effects only. Therefore, policymakers should support low-carbon production, promote the upgrade of industrial structures, and encourage enterprises to develop green technologies. Ecological protection projects such as afforestation and greening are necessary. Governments should initiate ecological civilization construction in economically developed regions and then gradually promote the policies in relatively poor areas.

Whether the Agricultural Insurance Policy Achieves Green Income Growth-Evidence from the Implementation of China's Total Cost Insurance Pilot Program.

With the rise and popularization of the concept of green sustainable development, green income growth of agricultural insurance policies has attracted wide attention. Whether green income growth can be achieved has become an important criterion for measuring an agricultural insurance policy. In this context, this paper attempts to test whether the agricultural insurance policy achieves green income growth. Based on the panel data of 31 provinces (the research sample of this paper selects 31 provincial-level units (province for short) in China, including 22 provinces, 5 autonomous regions and 4 municipalities directly under the central government. Hong Kong Special Administrative Region, Macau Special Administrative Region and Taiwan Province are not included in the research sample) from 2009 to 2020 in China, this paper empirically evaluates the triple-effect of total cost insurance pilot program (TCI) on farmers' income, environment and public health by employing a difference-in-difference model (DID). The results show that TCI increases farmers' income, but deteriorates the environment and residents' health without achieving green income growth. In the analysis of heterogeneity, compared with central and western regions, farmers' income is more likely to increase in the eastern regions. However, environmental pollution is more severe, and residents' health deteriorates more, in eastern regions. In addition, the positive effect of TCI on farmers' income and the deterioration of residents' health is more obvious in areas with a higher degree of damage, while the negative effect of TCI on the environment is more obvious in areas with a lower degree of damage. Furthermore, the mechanism analysis shows that TCI not only promotes the increase in farmers' income through insurance density, but also affects the environment and residents' health through straw burning. Therefore, the government should raise the subsidy standard for farmers to use straw-processing equipment and also to implement differentiated subsidies in regions with different levels of economic development and areas with different degrees of damage.

Polar-Net: Green fruit instance segmentation in complex orchard environment.

High-quality orchard picking has become a new trend, and achieving the picking of homogeneous fruit is a huge challenge for picking robots. Based on the premise of improving picking efficiency of homo-chromatic fruit in complex environments, this paper proposes a novel homo-chromatic fruit segmentation model under Polar-Net. The model uses Densely Connected Convolutional Networks (DenseNet) as the backbone network, Feature Pyramid Network (FPN) and Cross Feature Network (CFN) to achieve feature extraction and feature discrimination for images of different scales, regions of interest are drawn with the help of Region Proposal Network (RPN), and regression is performed between the features of different layers. In the result prediction part, polar coordinate modeling is performed based on the extracted image features, and the instance segmentation problem is reduced to predict the instance contour for instance center classification and dense distance regression. Experimental results demonstrate that the method effectively improves the segmentation accuracy of homo-chromatic objects and has the characteristics of simplicity and efficiency. The new method has improved the accuracy of segmentation of homo-chromatic objects for picking robots and also provides a reference for segmentation of other fruit and vegetables.

SE-COTR: A Novel Fruit Segmentation Model for Green Apples Application in Complex Orchard.

Because of the unstructured characteristics of natural orchards, the efficient detection and segmentation applications of green fruits remain an essential challenge for intelligent agriculture. Therefore, an innovative fruit segmentation method based on deep learning, termed SE-COTR (segmentation based on coordinate transformer), is proposed to achieve accurate and real-time segmentation of green apples. The lightweight network MobileNetV2 is used as the backbone, combined with the constructed coordinate attention-based coordinate transformer module to enhance the focus on effective features. In addition, joint pyramid upsampling module is optimized for integrating multiscale features, making the model suitable for the detection and segmentation of target fruits with different sizes. Finally, in combination with the outputs of the function heads, the dynamic convolution operation is applied to predict the instance mask. In complex orchard environment with variable conditions, SE-COTR achieves a mean average precision of 61.6% with low complexity for green apple fruit segmentation at severe occlusion and different fruit scales. Especially, the segmentation accuracy for small target fruits reaches 43.3%, which is obviously better than other advanced segmentation models and realizes good recognition results. The proposed method effectively solves the problem of low accuracy and overly complex fruit segmentation models with the same color as the background and can be built in portable mobile devices to undertake accurate and efficient agricultural works in complex orchard.

Study on the Stability, Evolution of Physicochemical Properties, and Postsynthesis of Metal-Organic Frameworks in Bubbled Aqueous Ozone Solution.

Metal-organic frameworks (MOFs) are highly promising in many areas. Their application and postsynthesis under strong oxidative environments are emerging. However, the stability, physicochemical property evolution, and possible postmodification and postsynthesis of MOFs in strong oxidative solutions are largely unknown. In this paper, the behaviors of a series of MOFs in bubbled aqueous ozone (O3) solutions are studied. The chosen MOFs are categorized into trimesic type including MIL-101(Fe) and MIL-96(Al); terephthalic type including MOF-74(Co), UiO-66(Zr), MIL-53(Al), and MIL-101(Cr); and imidazole type including ZIF-67(Co) and ZIF-8(Zn), based on the ligand structure. The intrinsic stability and evolution of the physicochemical properties of these MOFs during aqueous O3 treatment are elucidated using structural, morphological, textural, thermal, and spectroscopic analyses. Several stable, metastable, and instable MOFs are identified. The critical parameters that determine the stability and capability for postsynthesis of these MOFs in aqueous O3 solutions are discussed. The stability follows the general order of trimesic-type > terephthalic-type ≫ imidazole-type MOFs because of the distinct antioxidation capability of the ligands. The effects of the ligand, metal cation, and their coordination number on stability are discussed. MIL-100(Fe), MIL-96(Al), and MOF-74(Co) are stable in aqueous O3. UiO-66(Zr), MIL-53(Al), and MIL-101(Cr) are metastable that their porosity, particle size, and crystallinity can be postmodified. ZIF-67(Co) and ZIF-8(Zn) are instable and can be gradually and completely disassembled. Their particle size and morphology and surface groups can be tuned by controlling the treatment time. Postsynthesis of metal hydroxides from ZIF-67(Co) and gradual release of dissolved zinc ion from ZIF-8(Zn) are achievable. The stable MIL-96(Al) shows promising performance in catalytic ozonation for degrading 4-nitrophenol, and the α-Co(OH)2 derived from treating ZIF-67(Co) shows highly promising performance in the electrocatalytic oxygen evolution reaction (OER).

Study on Defective T Junction-Mediated Strand Displacement Amplification and Its Application in Microchip Electrophoretic Detection of Longer Bacterial 16S rDNA.

Current strand displacement amplification (SDA)-based nucleic acid sensing methods generally rely on a ssDNA template that involves complementary bases to the endonuclease recognition sequence, which has the limitation of detecting only short nucleic acids. Herein, a new SDA method in which the defective T junction structure is first used to support SDA (dT-SDA) was proposed and applied in longer DNA detection. In dT-SDA, an auxiliary probe and a primer were designed to specifically identify the target gene, following the formation of a stable defective T junction structure through proximity hybridization, and the formation of defective T junctions could further trigger cascade SDA cycling to produce numerous ssDNA products. The quantity of these ssDNA products was detected through microchip electrophoresis (MCE) and could be transformed to the concentration of the target gene. Moreover, the applicability of this developed strategy in detecting long genomic DNA was verified by detecting bacterial 16S rDNA. This proposed dT-SDA strategy consumes less time and has satisfactory sensitivity, which has great potential for effective bacterial screening and infection diagnosis.

Simultaneous electrochemical determination of nuc and mecA genes for identification of methicillin-resistant Staphylococcus aureus using N-doped porous carbon and DNA-modified MOF.

The detection of Staphylococcus aureus specific gene in combination with the mecA gene is vitally important for accurate identification of methicillin-resistant Staphylococcus aureus (MRSA). A homogeneous electrochemical DNA sensor was fabricated for simultaneous detection of mecA and nuc gene in MRSA. Metal-organic framework (type UiO-66-NH2) was applied as nanocarrier. Two electroactive dyes, methylene blue (MB) and epirubicin (EP), were encapsulated in UiO-66-NH2, respectively, and were locked by the hybrid double-stranded DNA. Based on the target-response electroactive dye release strategy, once target DNA exists, it completely hybridizes with displacement DNA (DEP and DMB). So DEP and DMB is displaced from the MOF surface, causing the release of electroactive dyes. Co-Zn bimetallic zeolitic imidazolate framework-derived N-doped porous carbon serves for electrode modification to improve electrocatalytic performance and sensitivity. The differential pulse voltammetry peak currents of MB and EP were accurately detected at - 0.14 V and - 0.53 V versus the Ag/AgCl reference electrode, respectively. Under the optimal conditions, the detection limits of mecA gene and nuc gene were 3.7 fM and 1.6 fM, respectively. Combining the effective application of MOFs and the homogeneous detection strategy, the sensor exhibited satisfactory performance for MRSA identification in real samples. The recovery was 92.6-103%, and the relative standard deviation was less than 5%. Besides, MRSA and SA can also be distinguished. This sensor has great potential in practical applications.

Red Yeast Improves the Potential Safe Utilization of Solid Waste (Phosphogypsum and Titanogypsum) Through Bioleaching.

Phosphogypsum (PG) and titanium gypsum (TG), as a by-product (solid waste) in phosphate fertilizer and titanium dioxide industry, are causing serious environmental hazards. The resource/harmless application of PG and TG is the development trend in the future. The biological function of red yeast (Rho: Rhodotorula mucilaginosa) can effectively reduce the concentration of pollutants in the environment and has the potential of biological flotation/purification of mineral solid waste. In this study, the bioremediation mechanism and safe utilization efficiency of Rho for different contents of PG and TG were explored by using its biological flotation function. The X-ray fluorescence spectrometry (XRF) results showed that F was the main toxic element in PG and TG, and Pb and Cd did not reach the detection limit. The processing capacity of Rho for PG (>10 g/ml) is higher than that of TG (<5 g/ml). After bioleaching by Rho, the proportion of F in PG and TG solid decreased by 61.45-63.79% and 49.45-59.19%, respectively. The results of three-dimensional fluorescence, extracellular polymeric substance (EPS) extraction, X-ray diffraction (XRD), and scanning electron microscopy (SEM) confirmed that Rho could accelerate the release of harmful elements (F) in PG and TG. SEM showed that Rho cells and secretions adhered and wrapped on PG/TG, causing PG/TG decomposition and fragmentation. In addition, the adsorption of EPS and the formation of Ca5(PO4)3F are two main ways for Rho to remove F. Furthermore, under the condition of high concentration bioleaching, Rho can accelerate the release and utilization of P in PG, which is not only for the re-precipitation of Ca5(PO4)3F but also conducive to the reproduction and utilization of microorganisms. Meanwhile, the purification/safe reuse of PG by Rho is easier than that of TG. Therefore, the toxicity of PG and TG bioleaching by Rho can be greatly reduced, suggesting the huge potential of Rho in soil improvement and remediation.

Ultrasensitive microchip electrophoretic detection of the mecA gene in methicillin-resistant Staphylococcus aureus (MRSA) based on isothermal strand-displacement polymerase reaction.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the main pathogens involved in hospital and community infection. To rapidly and sensitively detect the mecA gene, which is relevant to methicillin-resistant strains, microchip electrophoresis (MCE) integrated with isothermal strand-displacement polymerase reaction (ISDPR) was developed. In the ISDPR signal recycle amplification, the target DNA opened the DNA hairpin structure by specifically binding with the hairpin probe (HP), and then the primer hybridized with the probe and released the target DNA in the presence of Klenow Fragment exo- (KF exo-) polymerase. The released target DNA hybridized with the next HP and then was displaced by the primer again, consequently achieving target recycling and amplification. The amplified products of the HP-cDNA duplex were separated rapidly from other DNAs by MCE. Under optimal conditions, the limit of detection of the target DNA was as low as 12.3 pM (S/N = 3). The proposed ISDPR with MCE method was also successfully applied to detect methicillin-resistant S. aureus, and the experimental results showed that it had some advantages such as being label free, ultrasensitive, rapid and well separated.