Rumen Development of Tianhua Mutton Sheep Was Better than That of Gansu Alpine Fine Wool Sheep under Grazing Conditions.

The purpose of this experiment was to investigate the differences in rumen tissue morphology, volatile fatty acid content, and rumen microflora between Tianhua mutton sheep and Gansu alpine fine wool sheep under the same grazing conditions. Twelve 30-day-old lambs were randomly selected from two different flocks in Duolong Village and grazed together for a period of 150 days. The rumen tissue was fixed with 4% paraformaldehyde and brought back to the laboratory for H&E staining, the volatile fatty acid content of the rumen contents was detected by gas chromatography, and the rumen flora structure was sequenced by full-length sequencing of the bacterial 16S rRNA gene using the PacBio sequencing platform. The acetic acid and total acid contents of the rumen contents of Tianhua mutton sheep were significantly higher than those of Gansu alpine fine wool sheep (p < 0.05). The rumen papillae height of Tianhua mutton sheep was significantly higher than that of Gansu alpine fine wool sheep (p < 0.05). The diversity and richness of the rumen flora of Tianhua mutton sheep were higher than those of Gansu alpine fine wool sheep, and Beta analysis showed that the microflora structure of the two fine wool sheep was significantly different. At the phylum level, Firmicutes and Bacteroidetes dominated the rumen flora of Tianhua mutton sheep and Gansu alpine fine wool sheep. At the genus level, the dominant strains were Christensenellaceae_R_7_group and Rikenellaceae_RC9_gut_group. LEfSe analysis showed that Prevotella was a highly abundant differential species in Tianhua mutton sheep and lachnospiraccac was a highly abundant differential species in Gansu alpine fine wool sheep. Finally, both the KEGG and COG databases showed that the enrichment of biometabolic pathways, such as replication and repair and translation, were significantly higher in Tianhua mutton sheep than in Gansu alpine fine wool sheep (p < 0.05). In general, there were some similarities between Tianhua mutton sheep and Gansu alpine fine wool sheep in the rumen tissue morphology, rumen fermentation ability, and rumen flora structure. However, Tianhua mutton sheep had a better performance in the rumen acetic acid content, rumen papillae height, and beneficial bacteria content. These differences may be one of the reasons why Tianhua mutton sheep are more suitable for growing in alpine pastoral areas than Gansu alpine fine wool sheep.

A Deep Long-Term Joint Temporal-Spectral Network for Spectrum Prediction.

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal-spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time-frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R2, respectively, which is better than all the baseline deep learning models.

A target-triggered fluorescence-SERS dual-signal nano-system for real-time imaging of intracellular telomerase activity.

Telomerase (TE) is a promising diagnostic and prognostic biomarker for many cancers. Quantification of TE activity in living cells is of great significance in biomedical and clinical research. Conventional fluorescence-based sensors for quantification of intracellular TE may suffer from problems of fast photobleaching and auto-fluorescence of some endogenous molecules, and hence are liable to produce false negative or positive results. To address this issue, a fluorescence-SERS dual-signal nano-system for real-time imaging of intracellular TE was designed by functionalizing a bimetallic Au@Ag nanostructure with 4-p-mercaptobenzoic acid (internal standard SERS tag) and a DNA hybrid complex consisted of a telomerase primer strand and its partially complimentary strand modified with Rhodamine 6G. The bimetallic Au@Ag nanostructure serves as an excellent SERS-enhancing and fluorescence-quenching substrate. Intracellular TE will trigger the extension of the primer strand and cause the shedding of Rhodamine 6G-modified complimentary strand from the nano-system through intramolecular DNA strand displacement, resulting in the recovery of the fluorescence of Rhodamine 6G and decrease in its SERS signal. Both the fluorescence of R6G and the ratio between the SERS signals of 4-p-mercaptobenzoic acid and Rhodamine 6G can be used for in situ imaging of intracellular TE. Experimental results showed that the proposed nano-system was featured with low background, excellent cell internalization efficiency, good biocompatibility, high sensitivity, good selectivity, and robustness to false positive results. It can be used to distinguish cancer cells from normal ones, identify different types of cancer cells, as well as perform absolute quantification of intracellular TE, which endows it with great potential in clinical diagnosis, target therapy and prognosis of cancer patients.

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar.

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range-time-Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

Tracing the geographical origin of tobacco at two spatial scales by stable isotope and element analyses with chemometrics.

Tobacco is a widely cultivated cash crop, but it is often smuggled and sold illegally. Unfortunately, there is currently no way to verify the origin of tobacco in China. In an effort to address this issue, we conducted a study using stable isotopes and elements from 176 tobacco samples at both provincial and municipal scales. Our findings revealed significant differences in δ13C, K, Cs, and 208/206Pb at the provincial-level, and Sr, Se, and Pb at the municipal level. We created a heat map at the municipal level, which showed a similar cluster classification to geographic grouping and provided an initial assessment of tobacco origins. Using OPLS-DA modeling, we achieved a 98.3% accuracy rate for the provincial scale and 97.6% for the municipal scale. It is worth noting that the importance of rankings of variables varied depending on the spatial scale of the evaluation. This study offers the first traceability fingerprint dataset of tobacco and has the potential to combat mislabeling and fraudulent conduct by identifying the geographical origin of tobacco.

You Only Train Once: Learning General and Distinctive 3D Local Descriptors.

Extracting distinctive, robust, and general 3D local features is essential to downstream tasks such as point cloud registration. However, existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural architectures. It remains challenging to learn robust and general local feature descriptors for surface matching. In this paper, we propose a new, simple yet effective neural network, termed SpinNet, to extract local surface descriptors which are rotation-invariant whilst sufficiently distinctive and general. A Spatial Point Transformer is first introduced to embed the input local surface into an elaborate cylindrical representation (SO(2) rotation-equivariant), further enabling end-to-end optimization of the entire framework. A Neural Feature Extractor, composed of point-based and 3D cylindrical convolutional layers, is then presented to learn representative and general geometric patterns. An invariant layer is finally used to generate rotation-invariant feature descriptors. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities.

Telomerase-initiated three-dimensional DNAzyme motor for monitoring of telomerase activity in living cells.

Telomerase (TE) is recognized as a potential biomarker for early diagnosis, monitoring and treatment of cancer. At present, most of the methods for TE detection are only applicable to in vitro assays, and unsuitable for in vivo applications. Though a few intracellular probes have been reported to have good specificity for TE, they do not involve signal amplification, which hinders their applicability in scenarios requiring high sensitivity. It is rather challenging to develop highly sensitive biosensors for intracellular TE detection due to the difficulty in design TE probes with both high specificity and compatibility with signal amplification in living cells. Herein, a highly sensitive and selective three-dimensional DNAzyme motor for monitoring of TE activity in living cells was developed by innovatively integrating TE-mediated chain replacement reaction with a three-dimensional DNA walker. Specifically, the DNAzyme motor was constructed by assembling both DNAzyme substrates and swing arms made up of a hairpin-structured DNAzyme and a telomeric primer onto gold nanoparticles. TE in cells can activate the DNAzyme motor to carry out continuous chain replacement and substrate cutting reactions, and hence realize signal amplification in living cells. The DNAzyme motor was successfully utilized to monitor the dynamic changes of TE activity in four types of cells. Due to the advantages of simple synthesis, good biocompatibility and high sensitivity and specificity for TE, the proposed DNAzyme motor is expected to have great application potential in the early diagnosis of cancer.

Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism.

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.

Ultrasensitive detection of multiple cancer biomarkers by a triple cascade amplification strategy in combination with single particle inductively coupled plasma mass spectrometry.

A versatile triple cascade amplification strategy was developed for ultrasensitive simultaneous detection of multiple cancer biomarkers using single particle inductively coupled plasma mass spectrometry (spICP-MS). The triple cascade amplification strategy consisted of an enhanced RecJf exonuclease-assisted target recycling amplification module, a hybridization chain reaction amplification module, and a signal amplification module based on DNA-templated multiple metal nanoclusters. In the enhanced RecJf exonuclease-assisted target recycling amplification module, the DNA bases at the 5' ends of aptamers for specific recognition of biomarkers were deliberately replaced by the corresponding RNA bases to enhance amplification efficiency. The signal amplification module based on DNA-templated multiple metal nanoclusters was innovatively used to amplify the signals measured by spICP-MS and at the same time effectively suppress possible background interferences. The proposed spICP-MS platform achieved satisfactory quantitative results for both carcinoembryonic antigen (CEA) and a-fetoprotein (AFP) in human serum samples with accuracy comparable to that of the commercial ELISA kits. Moreover, it has wide dynamic ranges for both CEA (0.01-100 ng/mL) and AFP (0.01-200 ng/mL). The limit of detection for CEA and AFP was 0.6 and 0.5 pg/mL, respectively. Compared with conventional biomarkers detection methods, the proposed spICP-MS platform has the advantages of operational simplicity, ultra-high sensitivity, wide dynamic range, and low background. Therefore, it is reasonable to expect that the proposed spICP-MS platform can be further developed to be a promising alternative tool for biomarker detection in fields of clinical diagnosis and biomedical research.

Effects of activated carbon, biochar, and carbon nanotubes on the heterogeneous Fenton oxidation catalyzed by pyrite for ciprofloxacin degradation.

Pyrite and engineering carbon materials have received increasing attention for their catalytic potential in Fenton reactions due to their extensive sources and low cost. However, effects of carbon materials on the degradation of pollutants by pyrite-catalyzed heterogeneous Fenton oxidation have not been fully understood. In this study, the performance of pyrite-catalyzed heterogeneous Fenton system on the degradation of ciprofloxacin (CIP) was investigated in the presence of activated carbon (AC), biochar (BC), and carbon nanotubes (CNTs). Synchronous and asynchronous experiments (adsorption and catalysis) were conducted to elucidate the roles of the carbon materials in pyrite-catalyzed Fenton reactions. The results demonstrated that all the three carbon materials accelerated the pyrite-catalyzed Fenton oxidation of CIP. Under the experimental conditions, the reaction rates, which were obtained by fitting the synchronous experimental results with the pseudo-first-order kinetic model, of pyrite/AC, pyrite/BC and pyrite/CNTs with H2O2 for the removal of CIP were 8.28, 3.40 and 3.37 times faster than that of pyrite alone. Adsorption experiments and characterization analysis showed that AC had a higher adsorption capacity than BC and CNTs for CIP, which enabled it to distinguish itself in assisting the pyrite-catalyzed Fenton oxidation. In the presence of the carbon materials, the adsorption effect should not be neglected when studying the catalytic performance of pyrite. Free radical quenching experiments and electron spin-resonance spectroscopy (ESR) were used to detect and identify free radical species in the reactions. The results showed that hydroxyl radicals (•OH) contributed significantly to the degradation of CIP. The addition of carbon materials promoted the production of •OH, which favored the degradation of CIP. The results of this study suggested that the synergistic effect of oxidation and adsorption promoted the removal of CIP in pyrite/carbon materials/H2O2 systems, and coupling pyrite and carbon materials shows great potential in treating antibiotic wastewater.

Calibration model transfer in mid-infrared process analysis with in situ attenuated total reflectance immersion probes.

Process applications of mid-infrared (MIR) spectrometry may involve replacement of the spectrometer and/or measurement probe, which generally requires a calibration transfer method to maintain the accuracy of analysis. In this study, direct standardisation (DS), piecewise direct standardisation (PDS) and spectral space transformation (SST) were compared for analysis of ternary mixtures of acetone, ethanol and ethyl acetate. Three calibration transfer examples were considered: changing the spectrometer, multiplexing two probes to a spectrometer, and changing the diameter of the attenuated total reflectance (ATR) probe (as might be required when scaling up from lab to process analysis). In each case, DS, PDS and SST improved the accuracy of prediction for the test samples, analysed on a secondary spectrometer-probe combination, using a calibration model developed on the primary system. When the probe diameter was changed, a scaling step was incorporated into SST to compensate for the change in absorbance caused by the difference in ATR crystal size. SST had some advantages over DS and PDS: DS was sensitive to the choice of standardisation samples, and PDS required optimisation of the window size parameter (which also required an extra standardisation sample). SST only required a single parameter to be chosen: the number of principal components, which can be set equal to the number of standardisation samples when a low number of standards (n < 7) are used, which is preferred to minimise the time required to transfer the calibration model.

The role of microplastics in altering arsenic fractionation and microbial community structures in arsenic-contaminated riverine sediments.

The ability of microplastics (MPs) to interact with environmental pollutants is of great concern. Riverine sediments, as sinks for multi-pollutants, have been rarely studied for MPs risk evaluation. Meanwhile, MPs generated from biodegradable plastics are questioning the safety of the promising materials. In this study, we investigated the effects of typical non-degradable polyethylene (PE) and biodegradable polylactic acid (PLA) MPs on sediment enzymes, arsenic (As) fractionation, and microbial community structures in As-contaminated riverine sediments. The results indicated that the presence of MPs (1% and 3%, w/w) led As transformed into more labile and bioavailable fractions in riverine sediments, especially under higher As and MPs levels. Analysis on microbial activities and community structures confirmed the strong potential of MPs in inhibiting microbial activities and shifting bacterial community succession patterns through enrichment of certain microbiota. Moreover, biodegradable PLA MPs presented stronger alterations in arsenic fractionation and microbial community structures than PE MPs did, which might be jointly attributed to adsorption behaviors, microbial alterations, and potential PLA degradation behaviors. The study indicated that MPs contamination increased As mobility and bioavailability, and shifted microbial communities in riverine sediments. Moreover, biodegradable MPs might lead to stronger microbial alterations and increases in As bioavailability, acting as a threat to ecological safety, which needed further exploration.

Impacts of typical engineering nanomaterials on the response of rhizobacteria communities and rice (Oryza sativa L.) growths in waterlogged antimony-contaminated soils.

The combined eco-risks of Sb (widely presented in soils, especially nearing mining areas) and the engineering nanomaterials (ENMs) (applied in agriculture and soil remediation) still remain uncovered. The current study investigated the impacts of single and combined exposure of CuO, CeO2 nanoparticles (NPs) and multi-walled carbon nanotube (MWCNTs) with Sb on rice growths and rhizosphere bacterial communities. The results showed that co-exposure of CuO NPs (0.075 wt%) with Sb (III) posed the most adverse impacts on root biomass and branches (up to 66.59% and 70.00% compared to other treatments, respectively). Treatments containing MWCNTs showed insignificant dose-dependent effects, while CeO2 NPs combined with Sb (III) showed significant synergistic stimulating effects on the fresh weights of root and shoot, by 68.30% and 73.48% (p < 0.05) compared to single Sb exposure, respectively. The rice planting increased the percentage of non-specifically sorbed Sb in soils by 1.50-14.49 than the no-planting stage. Analysis on microbial communities revealed that co-exposure of CuO NPs with Sb (III) induced the greatest adverse impacts on rhizobacteria abundances and community structures at both phylum and genus levels. Therein, significant decrease of Bacteroidetes, Acidobacteria and increase of Firmicutes abundance at the phylum level were observed. This study provided information about the risks of different ENMs released to Sb-contaminated soils under flooded condition on both crops and bacterial communities.

The effect of UV exposure on conventional and degradable microplastics adsorption for Pb (II) in sediment.

Plastic discharged into the environment would break down into microplastics (MPs). However, the possible impact of MPs on heavy metals in the aquatic sediment remains unknown. In order to evaluate the potential role of MPs as carriers of coexisting pollutants, the adsorption capacity of lead ions from sediment onto aged degradable and conventional MPs were investigated as a function of lead ions concentration, contact time, temperature, MPs dosage, aging time, and fulvic acid concentration. MPs were exposed to UV to obtain aged polyethylene (A-PE) and aged polylactic acid (A-PLA). The aging treatment increased the oxygen content, specific surface area and hydrophilicity of MPs. The adsorption capacity of A-PE for Pb(II) in sediment increased from 10.1525 to 10.4642 mg g-1 with the increasing aging time. However, the adsorption capacity of A-PLA for Pb(II) in sediment decreased from 9.3199 to 8.7231 mg g-1 with the increasing aging time. The adsorption capacity of MPs in sediment for Pb(II) was in the following order: A-PE > PLA > PE > A-PLA. Fulvic acid could promote the adsorption of Pb(II) by MPs in sediment. These results indicated that the aging process of the plastics in the environment would affect its role as a carrier of coexisting pollutants.

The combined toxicity and mechanism of multi-walled carbon nanotubes and nano zinc oxide toward the cabbage.

The natural environment is a complex system, and there is never only one kind of nanomaterial entering the environment. However, many studies only considered the plant toxicity of one kind of nanomaterial and do not consider the influence of two or more kinds of nanomaterials on plant toxicity. Multi-walled carbon nanotubes (MWCNTs) and zinc oxide nanoparticles (ZnO NPs) are two common and widely used nanomaterials in water environment, so these two kinds of nanomaterials were chosen to explore the effects of their combined toxicity on cabbage. This study investigated the toxicity of MWCNTs combined with ZnO NPs on cabbage by measuring the length of roots and stems, chlorophyll content, oxidative stress, antioxidant enzyme activity, metal element content, and root scanning electron microscopy. The toxicity of single MWCNTs toward cabbage was attributed to direct oxidative damage, while the toxicity of single ZnO NPs toward cabbage was due to the high level of zinc concentration. Moreover, ZnO NPs (10 mg/L) ameliorated MWCNTs toxicity toward cabbage by improving the activity of antioxidant enzymes. ZnO NPs (50 and 100 mg/L) because of the high content of zinc disrupted the balance of other metals in the plant and increased the toxicity of MWCNTs. In conclusion, the combined toxicity of different concentrations and types of nanomaterials should be considered for a more accurate assessment of environmental risks.

The combined toxicity and mechanism of multi-walled carbon nanotubes and nano copper oxide toward freshwater algae: Tetradesmus obliquus.

Nanoparticles (NPs) are widely used for their special physical properties and released into the natural environment. When two types of NPs exist in the same environment, the presence of one type of NP may affect the properties of the other type of NP. This study investigated the toxic effects of multi-walled carbon nanotubes (MWCNTs) and copper oxide nanoparticles (CuO NPs) on Tetradesmus obliquus. Both NPs had toxic effects on algae, and the toxic effects of MWCNTs were significantly stronger than CuO NPs which the 96-hr median effective concentration to algae were 33.8 and 169.2 mg/L, respectively. Oxidative stress and cell membrane damage were the main reasons for the toxicity of NPs to algae, and they were concentration-dependent, and the existence of CuO NPs in some groups reduced cell membrane damage caused by MWCNTs which may because that CuO NPs formed heteroaggregation with MWCNTs, reducing the contact of nanoparticles with cell membranes, then reducing physical damage. Scanning electron microscopy (SEM) and transmission electron microscope (TEM) results indicated cell damage, the heteroaggregation of MWCNTs-CuO NPs and obvious nanoparticles internalization. In some groups, the presence of CuO NPs significantly reduced reactive oxygen species (ROS) level induced by MWCNTs. However, for the highest concentration group, the ROS level was much higher than that of the two NPs alone treatment groups, which might be related to the high concentration of MWCNTs promoting the internalization of CuO NPs. MWCNTs and CuO NPs affected and interacted with each other, causing more complex toxic effects on aquatic organisms.

Label-free and sensitive microRNA detection method based on the locked nucleic acid assisted fishing amplification strategy.

Herein, a label free and sensitive miRNA detection method with enhanced practical applicability was developed based on the locked nucleic acid (LNA) assisted repeated fishing amplification strategy. The working mechanism of the proposed method is as follows: 1) a DNA probe (i.e, L-DNA) with LNA bases is immobilized onto the surface of a gold foil. The L-DNA hybridizes with the 3' terminus of the first strands of complementary deoxyribonucleic acid (cDNA) of the target miRNA in the test samples; 2) The protruding 5' terminus of the cDNA serves as a 'fishhook' to repeatedly fish the products of a hybridization chain reaction (HCR) out from a 'reaction tube'; 3) The HCR products can be unloaded from the gold foil into a 'product tube' through temperature-controlled dehybridization; 4) The concentration of the target miRNA is determined based on the fluorescence intensity generated by the addition of SYBR-Green I (SG) into the 'product tube'. The proposed platform was applied to the detection of miRNA-122 in cell lysate samples and obtained quantitative results with accuracy comparable to the quantitative reverse transcription PCR method (qRT-PCR). It is worth pointing out that the proposed platform achieved a limit of detection value of 2.9 fM for miRNA-122 by a simple but effective LNA-assisted repeated fishing amplification strategy instead of complicated enzyme-based amplification techniques. It is reasonable to expect that the proposed method provides a competitive alternative for designing practically applicable, cost-effective and label-free miRNA detection methods.

Evaluating the metabolic functional profiles of the microbial community and alfalfa (Medicago sativa) traits affected by the presence of carbon nanotubes and antimony in drained and waterlogged sediments.

Antimony (Sb) is the ubiquitous re-emerging contaminant greatly accumulated in sediments which has been revealed risky to ecological environment. However, the impacts of Sb (III/V) on microbes and plants in sediments, under different water management with presence of engineering materials are poorly understood. This study conducted sequential incubation of sediments (flooding, draining and planting) with presence of multiwall carbon nanotubes (MWCNTs) and Sb to explore the influence on microbial functional diversity, Sb accumulation and alfalfa traits. Results showed that water management and planting led to greater impacts of sediment enzyme activities and microbial community metabolic function and bioavailable Sb fractions (defined as sum of acid-soluble fraction and reducible fraction, F1 + F2). Available fractions of Sb (V) showed higher correlation to microbial metabolism (r = 0.933) than that of Sb (III) (r = -0.480) in planting stage. MWCNTs with increasing concentrations (0.011%, w/w) positively correlated to microbial community metabolic function in planting stage whereas resulted in decreasing of Sb (III/V) concentrations in alfalfa, although 0.01% MWCNT led to increase of Sb (V) and decrease of Sb (V) by 50.97% and 32.68% respectively. This study provided information for investigating combined ecological impacts of heavy metal and engineering materials under different water managing sediments.

Label-free microRNA detection through analyzing the length distribution pattern of the residual fragments of probe DNA produced during exonuclease III assisted signal amplification by mass spectrometry.

Biosensors based on various spectroscopic techniques discriminate the target microRNA (miRNA) from non-target ones with single nucleotide polymorphisms (SNPs) according to the differences in signal intensities which can be caused by other factors besides SNPs. As a result, they are liable to produce false positive results. Herein, we report an attempt to develop a false-positive resistance, sensitive and reliable mass spectrometric platform for miRNA detection. In the proposed platform, the qualitative and quantitative information of the target miRNA was obtained through analyzing mass spectral responses of the multiply charged ions of the residual fragments of the probe DNA produced during exonuclease III assisted signal amplification reaction using an advanced data analysis method. The proposed platform could achieve sensitive and accurate quantitative results for the target miRNA (e.g., miRNA-141) in complex medium with a detection limit of about 1 pM, and unambiguously identify non-target miRNAs with SNPs based on the length distribution patterns of residual fragments of probe DNA. The findings obtained in this study might open an avenue for the design of new miRNA detection methods based on mass spectrometry in combination with various nuclease assisted signal amplification strategies.

Highly Sensitive and Specific Mass Spectrometric Platform for miRNA Detection Based on the Multiple-Metal-Nanoparticle Tagging Strategy.

The multiple-metal-nanoparticle tagging strategy has generally been applied to the multiplexed detection of multiple analytes of interest such as microRNAs (miRNAs). Herein, it was used for the first time to improve both the specificity and sensitivity of a novel mass spectroscopic platform for miRNA detection. The mass spectroscopic platform was developed through the integration of the ligation reaction, hybridization chain reaction amplification, multiple-metal-nanoparticle tagging, and inductively coupled plasma mass spectrometry. The high specificity resulted from the adoption of the ligation reaction is further enhanced by the multiple-metal-nanoparticle tagging strategy. The combination of hybridization chain reaction amplification and metal nanoparticle tagging endows the proposed platform with the feature of high sensitivity. The proposed mass spectrometric platform achieved quite satisfactory quantitative results for Let-7a in real-world cell line samples with accuracy comparable to that of the real-time quantitative reverse-transcriptase polymerase chain reaction method. Its limit of detection and limit of quantification for Let-7a were experimentally determined to be about 0.5 and 10 fM, respectively. Furthermore, due to the unique way of utilizing the multiple-metal-nanoparticle tagging strategy, the proposed platform can unambiguously discriminate between the target miRNA and nontarget ones with single-nucleotide polymorphisms based on their response patterns defined by the relative mass spectral intensities among the multiple tagged metal elements and can also provide location information of the mismatched bases. Its unique advantages over conventional miRNA detection methods make the proposed platform a promising and alternative tool in the fields of clinical diagnosis and biomedical research.