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Simple Summary: In intelligent agriculture, non-contact, automatic body size measurement is widely used for livestock. However, the common measurement methods based on the whole point cloud are complex and prone to errors because of the positions of the cattle. To measure the body sizes of livestock more accurately, a cattle body measuring system is proposed. The system includes a new algorithm called dynamic unbalanced octree grouping (DUOS), based on PointNet++, and an efficient method of body size measurement based on segmentation results. The network divides the cow into the following seven parts: body length, withers height, hip height, thoracic circumference, abdominal circumference, and cannon circumference. Compared with some of the other models, the system has higher segmentation accuracy and lower measurement errors. The system can be widely applied in the non-contact body measurement of livestock; in addition, it can increase efficiency and reduce costs. It also has broad prospects in the intelligent livestock industry. The common non-contact, automatic body size measurement methods based on the whole livestock point cloud are complex and prone to errors. Therefore, a cattle body measuring system is proposed. The system includes a new algorithm called dynamic unbalanced octree grouping (DUOS), based on PointNet++, and an efficient method of body size measurement based on segmentation results. This system is suitable for livestock body feature sampling. The network divides the cow into seven parts, including the body and legs. Moreover, the key points of body size are located in the different parts. It combines density measurement, point cloud slicing, contour extraction, point cloud repair, etc. A total of 137 items of cattle data are collected. Compared with some of the other models, the DUOS algorithm improves the accuracy of the segmentation task and mean intersection by 0.53% and 1.21%, respectively. Moreover, compared with the manual measurement results, the relative errors of the experimental measurement results are as follows: withers height, 1.18%; hip height, 1.34%; body length, 2.52%; thoracic circumference, 2.12%; abdominal circumference, 2.26%; and cannon circumference, 2.78%. In summary, the model is proven to have a good segmentation effect on cattle bodies and is suitable for cattle body size measurement. [ABSTRACT FROM AUTHOR]

Comprehensive Summary Single‐nucleotide variants (SNVs) are crucial in disease development, but their accurate detection is challenging due to their low abundance and interference from wild‐type targets. Although nucleic acid analogs like peptide nucleic acids (PNAs) have been used for SNV detection, they often lack programmable sensitivity and specificity due to poorly calculated thermodynamics and kinetics. Here, we present a computational method for calculating the stacking energy of PNA and DNA hybrids, leveraging nearest neighbor parameters. Validation against experimental data from 16 sequences under varied hybridization conditions yielded good agreement using Bland‐Altman analysis, with all data points falling within the confidence interval. Our findings indicate that PNA‐DNA hybridization is thermodynamically more stable and exhibits kinetics 140‐fold faster than DNA‐DNA hybridization for identical sequences. Utilizing this computational framework, we designed PNA toehold probes, which were screened via simulations and experiments. This combined approach facilitated the identification of highly sensitive and specific PNA toehold probes for single point mutation detection via strand displacement reaction. Our results demonstrate the successful application of PNA toehold probes for detecting point mutations with high sensitivity and specificity, achieving a selective amplification of approximately 200‐fold for variants with a variant allele frequency (VAF) of 0.5% using quantitative polymerase chain reaction. [ABSTRACT FROM AUTHOR]

Cattle detection and counting is one of the most important topics in the development of modern agriculture and animal husbandry. The traditional manual monitoring methods are inefficient and constrained by factors such as site. To solve the above problems, a SCS-YOLOv5 cattle detection and counting model for complex breeding scenarios is proposed. The original SPPF module is replaced in the YOLOv5 backbone network with a CSP structured SPPFCSPC. A CA (Coordinate Attention) mechanism is added to the neck network, as well as the SC (Standard Convolution) of the Neck network is replaced with a light convolution GSConv and Slim Neck is introduced, and training strategies such as multi-scale training are also employed. The experimental results show that the proposed method enhances the feature extraction ability and feature fusion ability, balances the localization accuracy and detection speed, and improves the use effect in real farming scenarios. The Precision of the improved network model is improved from 93.2% to 95.5%, mAP@0.5 is improved from 94.5% to 95.2%, the RMSE is reduced by about 0.03, and the FPS reaches 88. Compared with other mainstream algorithms, the comprehensive performance of SCS-YOLOv5 s is in a leading position, with fewer missed and false detections, and the strong robustness and generalization ability of this model are proved on multi-category public datasets. Applying the improvement ideas in this paper to YOLOv8 s also yields an increase in accuracy. The improved method in this study can greatly improve the accuracy of cattle detection and counting in complex environments, and has good real-time performance, so as to provide technical support for large-scale cattle breeding. [ABSTRACT FROM AUTHOR]

The detection and identification of individual cattle plays an integral role in precision feeding and insurance claims, among others. Most current research is based on high-performance computing devices, which limits the application of deep learning techniques to mobile terminals. To this end, in this paper, we propose a channel-pruned YOLOv5 network-based method for cattle face detection on mobile terminals, referred to as NS-YOLO. First, the original model is sparsely trained and a sparse regularization penalty term is applied to the BN layers, then the corresponding mask values are labeled according to different weight thresholds, and the channels are pruned with global thresholds. Second, the detection precision is recovered by fine-tuning the model. Finally, the NCNN forward inference framework is used to quantize the model and an Android-based cattle face detection application is developed. Experimental results show that the model size, number of parameters and FLOPs are reduced by 86.10%, 88.19% and 63.25%, respectively, and the inference time is reduced by 35.53% compared to the original model, while mAP0.5 is reduced by only 1.6%. In particular, the 16-bit quantized model reduces the model size by 93.97% and the inference time by 34.97% compared to the original model on the mobile side. The results show that the proposed method can be easily deployed in resource-constrained mobile devices and has great advantages in practical engineering applications. [ABSTRACT FROM AUTHOR]

As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features. [ABSTRACT FROM AUTHOR]

In this work, we propose a hairpin probe-mediated exponential amplification reaction (HEAR) strategy that combines DNA strand displacement with a "who triggers, who gets generated" mode, providing excellent single-base discrimination and a reduced background signal. The detection limit is 19 aM, which is reduced by 3 orders of magnitude compared to traditional exponential amplification approaches. This one-pot strategy also exhibits a wide dynamic range, high specificity and short detection time. It is expected to become a powerful tool for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Sixteen substituted 1-hydroxy-3-methylxanthones were synthesized in one step. The yields ranged from 33 to 76%. Then, the antitumor, antioxidant, anti-tyrosinase, anti-pancreatic lipase, and antifungal activities of compounds 1–16 were evaluated. Compounds 10–12 and 14 inhibited tyrosinase and pancreatic lipase activity to a certain extent, respectively. Compound 16 exhibited obvious cytotoxicity against fifteen cancer cells, moderate antioxidant activity, and moderate inhibitory activity against Candida albicans. In particular, compound 16 exhibited strong inhibitory activity against A-549 and A549/Taxol cells. These results demonstrated that compounds 10–12, 14, and 16 are promising leads for further structural modification. [ABSTRACT FROM AUTHOR]

Non-contact identification of cattle is the basis for cattle management, insurance claims, and traceability of milk sources and meat products. In order to lower the threshold of deep learning technology in practical application and improve the recognition accuracy, this paper uses the localization and classification paradigm, fills the image and proposes the LKA (Locate Key Area) module, which further improves the identification accuracy of cattle without using any labeling information. To avoid the impact of different collection devices, we use three brands of mobile phones and two cameras to collect data. Due to the fact that CNN features do not have scale invariance, the image is filled as a square, and the filling value is discussed. Finally, (0,0,0) is selected for filling. In order to reduce the labeling of new data, the face, torso, and body of the cattle are first detected, and then automatically trimmed to build a classification dataset. In order to further improve the accuracy of classification, LKA module is proposed. This module locates key areas based on the results of class activation map and uses shallow features for monitoring. Thereby completing fine-grained feature extraction without supervision information. Finally, the features of different parts are adjusted for fusion recognition. Experiments were conducted on the head, torso, and whole of cattle. Based on the ResNet50 network architecture, after filling the image into a square, the recognition accuracy has been improved by 0.30%, 0.21%, and 0.43%, respectively. After further adding LKA modules, the accuracy rates were improved by 0.59%, 0.59%, and 0.45%, respectively. In the fusion experiment, ResNet50_LKA and MobileNetV3_LKA achieves 100% accuracy. Experiments have realized dataset construction and fine-grained feature extraction without labeled information, and achieved high recognition accuracy, proving the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Let {(ξni, ηni), 1 ≤ i ≤ n, n ≥ 1} be a triangular array of independent bivariate elliptical random vectors with the same distribution function as (S1,ρnS1+1−ρn2S2),ρn∈(0,1), where (S1, S2) is a bivariate spherical random vector. For the distribution function of radius S12+S22 belonging to the max-domain of attraction of the Weibull distribution, the limiting distribution of maximum of this triangular array is known as the convergence rate of ρn to 1 is given. In this paper, under the refinement of the rate of convergence of ρn to 1 and the second-order regular variation of the distributional tail of radius, precise second-order distributional expansions of the normalized maxima of bivariate elliptical triangular arrays are established. [ABSTRACT FROM AUTHOR]

The biologic studies of human neural crest stem cells (hNCSCs) are extremely challenging due to the limited source of hNCSCs as well as ethical and technical issues surrounding isolation of early human embryonic tissues. On the other hand, vast majority of studies on MycN have been conducted in human tumor cells, thus, the role of MycN in normal human neural crest development is completely unknown. In the present study, we determined the role of MycN in hNCSCs isolated from in vitro-differentiating human embryonic stem cells (hESCs). For the first time, we show that suppression of MycN in hNCSCs inhibits cell growth and cell cycle progression. Knockdown of MycN in hNCSCs increases the expression of Cdkn1a, Cdkn2a and Cdkn2b, which encodes the cyclin-dependent kinases p21CIP1, p16 INK4a and p15INK4b. In addition, MycN is involved in the regulation of human sympathetic neurogenesis, as knockdown of MycN enhances the expression of key transcription factors involved in sympathetic neuron differentiation, including Phox2a, Phox2b, Mash1, Hand2 and Gata3. We propose that unlimited source of hNCSCs provides an invaluable platform for the studies of human neural crest development and diseases. [ABSTRACT FROM AUTHOR]

A G6PD genotyping method for two common mutations (c.1376G>T and c.1388G>A) based on multi-code magnetic beads and conjugated strand displacement reaction. [Display omitted] • UDG mediated PCR was constructed to obtain toehold probe for G6PD genotyping. • Genotype c.1376G > T and c.1388G > A sites of G6PD using multi-code magnetic beads. • Compatibility with flow cytometry analysis, fluorescent microscopic image analysis, and spectrofluorimetry. Small variations, even single-nucleotide variants in nucleic acid sequences may exert significant influence in many diseases. The reliable detection and quantification of DNA can provide a variety of information that has significant clinical value, including disease risk assessment, screening, diagnosis, prognosis, the selection of therapies and monitoring. Due to the fact coupling efficiency of dNTPs and purification methodology can limit the chemical synthesis of long stranded probes. We demonstrated a PCR-based method to construct a long-stranded detection probe with high levels of selectivity via a strand displacement reaction with ssDNA target generates by asymmetric PCR. This detection probe was enzymatically generated from a double-stranded DNA duplex, containing a single-stranded active toehold domain. This approach was successfully implemented to genotype human glucose-6-phosphate dehydrogenase (G6PD) deficiency, focusing upon the c.1376G > T and c.1388G > A variants. [ABSTRACT FROM AUTHOR]

A series of aralkyl diamine derivatives were designed, synthesized, and evaluated for their triple reuptake inhibitory abilities. Compounds 18c (5-HT, NE, DA, IC 50 = 389, 69, 238 nM), 36a (5-HT, NE, DA, IC 50 = 378, 477, 247 nM), and 36d (5-HT, NE, DA, IC 50 = 501, 206, 357 nM) showed in vivo activities in the rat forced swim test at 5, 10, and 20 mg/kg PO. 36a was identified as the most promising candidate in this study. Specifically, 36a exhibited high selectivity for monoamine transporters over a number of CNS-related targets. Furthermore, 36a showed a good pharmacokinetic properties and acceptable safety profile in preclinical studies. [ABSTRACT FROM AUTHOR]

• Proposed a cattle face recognition model based on a two-branch CNN. • Constructed face image datasets of dairy cows and beef cows. • Reduced the influence of cow face posture on recognition accuracy. • The network model is small and the recognition accuracy is high. Due to changes in cattle posture and different shooting angles, some features of collected cattle face images are missing, which leads to a decline in the accuracy of cattle face recognition. This paper proposes a cattle face recognition model based on a two-branch convolutional neural network (TB-CNN). The collected two cattle face images from different angles are input to the convolutional neural network of different channels for feature extraction, the features of the two channels are feature fused, and the global average pooling layer is combined with the classifier to identify the individual cattle. The squeeze-and-excitation block (SE) is embedded in the feature extraction network in the network model to improve the network feature extraction capability. The global average pooling layer is used to replace the fully connected layer, which improves the network classification capability and reduces the number of network parameters. The experimental results show that the recognition rate of the cattle face recognition model based on the TB-CNN is 99.85% on the Simmental beef cattle face image dataset, 99.81% on the Holstein cow face image dataset, and 99.71% on the beef cattle and cow mixed dataset. The cattle face recognition model proposed in this paper has good robustness and generalization ability, which can effectively reduce the influence of cattle face angle changes on the cattle face recognition rate and improve the accuracy of cattle face recognition. [ABSTRACT FROM AUTHOR]

Nucleic acid hybridization plays a critical role in medical diagnostics and nanotechnology, but its selectivity and robustness remain to be improved. Here, focusing on double-stranded nucleic acid-based hybridization, we present a series of related strategies. Above all, two simple strategies for enriching toehold-included double-stranded nucleic acids have been proposed. On this basis, two universal hybridization methods with higher selectivity than typical toehold exchange reaction and a long target detection method using short probes to extend the detectable length range are realized. We also provide a double-stranded nucleic acids-catalyzed cycle amplification reaction to improve sensitivity, which has superior interference resistance and excellent discrimination for single-base mismatches. Besides, double-stranded nucleic acids with forked toeholds are used as essential elements to construct a series of logic gates that can evaluate different input combinations. Given the unique advantages of double-stranded nucleic acids, we expect the current work to advance the application of double-stranded nucleic acid-based hybridization in medical diagnostics and nanotechnology. [Display omitted] • Two methods for enriching double-stranded nucleic acids that possess toeholds. • Two universal hybridization methods and a long targets detection method were provided. • A double-stranded nucleic acids-catalyzed cycle amplification reaction for improving sensitivity. • Double-stranded nucleic acids for robust logic computation. [ABSTRACT FROM AUTHOR]

Toughening the petroleum-based epoxy resin blends with bio-based modifiers without compromising their modulus, mechanical strength, and other properties is still a big challenge in view of the sustainability. In this study, a bio-based liquid crystal epoxy resin (THMT-EP) with an s-triazine ring structure was utilized to modify a petroleum-based bisphenol A epoxy resin (E51) with 4,4′-diaminodiphenylsulfone (DDS) as a curing agent, and the blended systems were evaluated for their thermal stability, mechanical properties, and flame retardancy. The results showed that the impact strength of the blended system initially increased and then decreased with the increase in THMT-EP content, and it reached the a maximum value of 26.5 kJ/m2 when the THMT-EP content was 5%, which was 31.2% higher than that of E51/DDS. Notably, the flexural strength, modulus, and glass transition temperature of the blended system were all simultaneously improved with the addition of THMT-EP. At the same time, the addition of THMT-EP enhanced the flame retardancy of the system by increasing the char yield at 700 °C and decreasing the peak heat release rate and total heat release rate. This work paves the way for a more sustainable improvement in the comprehensive performance of epoxy resin. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed cooperative learning (DCL) from adaptive neural network (NN) control for a group of strict-feedback systems, where the structure of all strict-feedback systems is identical. In order to achieve DCL easily, the system transformation method is employed for the strict-feedback systems. For an agent, only one radial basis function NN is used to approximate the lumped uncertainty in control design. Then the output tracking performance of all strict-feedback systems is guaranteed. What's more, we prove that weights of all NNs in a multi-agent system converge to a small neighborhood around their common optimal value if the topology of the multi-agent system is connected and undirected. Thus, the approximation domain of all NNs is enlarged. Further, the previous learned NNs are used to improve the control performance. Finally, we provide two examples to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The emergence of DNA nanotechnology has enabled the systematic design of diverse bionic dissipative behaviors under the precise control of nucleic acid nanodevices. Nevertheless, when compared to the dissipation observed in robust living systems, it is highly desirable to enhance the anti-interference for artificial DNA dissipation to withstand perturbations and facilitate repairs within the complex biological environments. In this study, we introduce strategically designed "trash cans" to facilitate kinetic control over interferences, transforming the stochastic binding of individual components within a homogeneous solution into a competitive binding process. This approach effectively eliminates incorrect binding and the accumulation of systemic interferences while ensuring a consistent pattern of energy fluctuation from response to silence. Remarkably, even in the presence of numerous interferences differing by only one base, we successfully achieve complete system reset through multiple cycles, effectively restoring the energy level to a minimum. The system was able to operate stably without any adverse effect under conditions of irregular interference, high-abundance interference, and even multiplex interferences including DNA and RNA crosstalk. This work not only provides an effective paradigm for constructing robust DNA dissipation systems but also greatly broadens the potential of DNA dissipation for applications in high-precision molecular recognition and complex biological reaction networks. [Display omitted] • By eliminating cumulative errors we built an anti-interference DNA dissipation system first time. • Affinity trash bring the dissipation network closer to living organisms to multiple complex stimuli. • Be able to stably work in high abundance, irregular, multiple interference, and RNA and DNA crosstalk. [ABSTRACT FROM AUTHOR]

Predicting water quality data is an important measure for ecological environment protection in watersheds. Aiming at the problem that existing prediction algorithms rarely analyze the characteristics of future changes in water quality indicators, this paper proposes an out-of-sample prediction model for water quality parameters based on the dual-attention mechanism. The model adopts the Encoder-Decoder architecture to realize the prediction of data series, and combines the dual attention of dimension and time step to improve the prediction performance of out-of-sample data. The model is used to predict the water quality parameters of a multi-parameter river, analyze the trend of the out-of-sample data, and compare the prediction results with the traditional LSTM network and Encoder-Decoder LSTM network, the prediction accuracies of the water quality indicators are improved, and the prediction accuracy of the out-of-sample data of the water quality indicators reaches 80%. This will be of great significance to the comprehensive management of river waters and the high-quality development of ecological environment. • The model achieved out-of-sample prediction of water quality parameters and analyzed the future trend of the data. • Seq2Seq codec structure has been adopted to improve out-of-sample prediction performance. • A dual-attention mechanism has been created to assign weights in both time step and dimension simultaneously. [ABSTRACT FROM AUTHOR]

• Autonomous data partitioning algorithm are filtered by Hammersley sequence. • General trapezoidal T2FSs can be approximated by symmetrical trapezoidal T2FSs. • Consequent part is a tensor, and it is unfolded for consequent part parameters learning. In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system. [ABSTRACT FROM AUTHOR]

Detecting low‐frequency DNA mutations hotspots cluster is critical for cancer diagnosis but remains challenging. Quantitative PCR (qPCR) is constrained by sensitivity, and allele‐specific PCR is restricted by throughput. Here we develop a long blocker displacement amplification (LBDA) coupled with qPCR for ultrasensitive and multiplexed variants detection. By designing long blocker oligos to perfectly match wildtype sequences while mispairing with mutants, long blockers enable 14–44 nt enrichment regions which is 2‐fold longer than normal BDA in the experiments. For wild template with a specific nucleotide, LBDA can detect different mutation types down to 0.5 % variant allele frequency (VAF) in one reaction, with median enrichment fold of 1,000 on 21 mutant DNA templates compared to the wild type. We applied LBDA‐qPCR to detect KRAS and NRAS mutation hotspots, utilizing a single plex assay capable of covering 81 mutations and tested in synthetic templates and colorectal cancer tissue samples. Moreover, the mutation types were verified through Sanger sequencing, demonstrating concordance with results obtained from next generation sequencing. Overall, LBDA‐qPCR provides a simple yet ultrasensitive approach for multiplexed detection of low VAF mutations hotspots, presenting a powerful tool for cancer diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

Detecting low‐frequency DNA mutations hotspots cluster is critical for cancer diagnosis but remains challenging. Quantitative PCR (qPCR) is constrained by sensitivity, and allele‐specific PCR is restricted by throughput. Here we develop a long blocker displacement amplification (LBDA) coupled with qPCR for ultrasensitive and multiplexed variants detection. By designing long blocker oligos to perfectly match wildtype sequences while mispairing with mutants, long blockers enable 14–44 nt enrichment regions which is 2‐fold longer than normal BDA in the experiments. For wild template with a specific nucleotide, LBDA can detect different mutation types down to 0.5 % variant allele frequency (VAF) in one reaction, with median enrichment fold of 1,000 on 21 mutant DNA templates compared to the wild type. We applied LBDA‐qPCR to detect KRAS and NRAS mutation hotspots, utilizing a single plex assay capable of covering 81 mutations and tested in synthetic templates and colorectal cancer tissue samples. Moreover, the mutation types were verified through Sanger sequencing, demonstrating concordance with results obtained from next generation sequencing. Overall, LBDA‐qPCR provides a simple yet ultrasensitive approach for multiplexed detection of low VAF mutations hotspots, presenting a powerful tool for cancer diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

Resonance absorption mechanism-based metasurface absorbers can realize perfect optical absorption. Further, all-dielectric metasurface absorbers have more extensive applicability than metasurface absorbers that contain metal components. However, the absorption peaks of the all-dielectric metasurface absorbers reported to date are very sharp. In this work, we propose a broadband optical absorption all-dielectric metasurface, where a unit cell of this metasurface is composed of two coupled subwavelength semiconductor resonators arrayed in the direction of the wave vector and embedded in a low-index material. The results indicate that the peak absorption for more than 99% is achieved across a 60 nm bandwidth in the short-wavelength infrared region. This absorption bandwidth is three times that of a metasurface based on the conventional design scheme that consists of only a single layer of semiconductor resonators. Additionally, the coupled semiconductor resonator-based all-dielectric metasurface shows robust perfect absorption properties when the geometrical and material parameters—including the diameter, height, permittivity, and loss tangent of the resonator and the vertical and horizontal distances between the two centers of the coupled resonators—are varied over a wide range. With the convenience of use of existing semiconductor technologies in micro/nano-processing of the surface, this proposed broadband absorption all-dielectric metasurface offers a path toward realizing potential applications in numerous optical devices. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (KOA) stands as a degenerative ailment with a substantial and escalating prevalence. The practice of traditional Chinese non-pharmacological therapy has become a prevalent complementary and adjunctive approach. A mounting body of evidence suggests its efficacy in addressing KOA. Recent investigations have delved into its underlying mechanism, yielding some headway. Consequently, this comprehensive analysis seeks to encapsulate the clinical application and molecular mechanism of traditional Chinese non-pharmacological therapy in KOA treatment. The review reveals that various therapies, such as acupuncture, electroacupuncture, warm needle acupuncture, tuina, and acupotomy, primarily target localized knee components like cartilage, subchondral bone, and synovium. Moreover, their impact extends to the central nervous system and intestinal flora. More perfect experimental design and more comprehensive research remain a promising avenue in the future. [ABSTRACT FROM AUTHOR]

The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) is one of the most devastating diseases affecting the pig industry globally. Due to the emergence of novel strains, no effective vaccines are available for prevention and control. Investigating the pathogenic mechanisms of PEDV may provide insights for creating clinical interventions. This study constructed and expressed eukaryotic expression vectors containing PEDV proteins (except NSP11) with a 3' HA tag in Vero cells. The subcellular localization of PEDV proteins was examined using endogenous protein antibodies to investigate their involvement in the viral life cycle, including endocytosis, intracellular trafficking, genome replication, energy metabolism, budding, and release. We systematically analyzed the potential roles of all PEDV viral proteins in the virus life cycle. We found that the endosome sorting complex required for transport (ESCRT) machinery may be involved in the replication and budding processes of PEDV. Our study provides insight into the molecular mechanisms underlying PEDV infection. The global swine industry has suffered immense losses due to the spread of PEDV. Currently, there are no effective vaccines available for clinical protection. Exploring the pathogenic mechanisms of PEDV may provide valuable insights for clinical interventions. This study investigated the involvement of viral proteins in various stages of the PEDV lifecycle in the state of viral infection and identified several previously unreported interactions between viral and host proteins. These findings contribute to a better understanding of the pathogenic mechanisms underlying PEDV infection and may serve as a basis for further research and development of therapeutic strategies. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) is one of the most devastating diseases affecting the pig industry globally. Due to the emergence of novel strains, no effective vaccines are available for prevention and control. Investigating the pathogenic mechanisms of PEDV may provide insights for creating clinical interventions. This study constructed and expressed eukaryotic expression vectors containing PEDV proteins (except NSP11) with a 3' HA tag in Vero cells. The subcellular localization of PEDV proteins was examined using endogenous protein antibodies to investigate their involvement in the viral life cycle, including endocytosis, intracellular trafficking, genome replication, energy metabolism, budding, and release. We systematically analyzed the potential roles of all PEDV viral proteins in the virus life cycle. We found that the endosome sorting complex required for transport (ESCRT) machinery may be involved in the replication and budding processes of PEDV. Our study provides insight into the molecular mechanisms underlying PEDV infection. The global swine industry has suffered immense losses due to the spread of PEDV. Currently, there are no effective vaccines available for clinical protection. Exploring the pathogenic mechanisms of PEDV may provide valuable insights for clinical interventions. This study investigated the involvement of viral proteins in various stages of the PEDV lifecycle in the state of viral infection and identified several previously unreported interactions between viral and host proteins. These findings contribute to a better understanding of the pathogenic mechanisms underlying PEDV infection and may serve as a basis for further research and development of therapeutic strategies. [ABSTRACT FROM AUTHOR]

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes. [ABSTRACT FROM AUTHOR]

Sensitive and specific detection of single nucleotide variants (SNVs) has great potential for disease diagnosis and prognosis. Hybridization probes based on DNA strand displacement have shown high SNV discrimination and programmability. However, it remains a challenge to balance the specificity and sensitivity of hybridization reactions due to the limited adjustability of probe sequences. Herein, we inserted bulge-loops in probes to fine-tune the thermodynamic changes of the entropy-driven catalytic reaction (EDC), significantly improving the selectivity (the DF of CAC>CGC mutation was promoted from 17.66832 ± 2.25913–72.54 ± 8.21) and responsiveness of SNV identification. In addition, oligonucleotides with chromophores (Tags) were used to impart microspheres with different fluorescence characteristics, to construct a set of convenient and flexible barcodes. Coupled with the bulge-loops tuned EDC probes and the Tag-encoded barcodes, the designed biosensor achieved higher specificity (DF=194.86 ± 62.11) and lower signal leakage. Most importantly, it allowed simultaneous detection of low-abundance five-plexed EGFR mutations in the same system. This work provided insights into a rational design of DNA cascade reaction networks and advanced the construction of customizable multiplexed detection systems in the suspension array technology. • We insert bulge-loops into EDC probes to achieve superior specificity while maintaining satisfactory yield. • We propose a flexible and stable suspension array composed of ten optical barcodes. • As-proposed probes with barcodes reaches a detection limit of down to 0.05% CAC > CGC mutation. • Constructed Multiplexed detection system simultaneously detected five 1% EGFR mutations from 1 femtomolar gene sample. [ABSTRACT FROM AUTHOR]

Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming. [ABSTRACT FROM AUTHOR]

Highlights: [•] To detect object from complex background, illumination variations and texture. [•] Object recognition from mobile device that utilizes texture and color features. [•] A simple victor distance matching classifier. [•] The proposed method would adopt the few characteristic value. [•] Euclidean distance is also used to represent object similarity. [Copyright &y& Elsevier]

• Novel bio-based poly (aryl ether ketone) from FDCA and guaiacal with the highest biomass content. • The optimum conditions for the reaction using orthogonal experiments. • Excellent thermal properties, mechanical properties, thermoforming solution processablity. • Study of mechanism of polymerization by analyzing the molecular simulations and the properties of the resins. To reduce the dependence on non-renewable resources and to address environmental issues, bio-based high-performance poly(aryl ether ketone), PGFEKK, is prepared using two bio-based monomers derived from guaiacol and 2,5-furandicarboxylic acid. With a biomass content of 64.9%, it is the highest biomass content for poly(aryl ether ketone)s reported to date. In this report, the reaction of the bio-based monomers 6-(4-hydroxy-3-methoxyphenyl) pyridazin-3(2H)-one (GSPZ) and furan-2,5-diylbis((4-fluorophenyl)methanone) (BFBF) is carried out via base-catalyzed nucleophilic substitution. The optimum conditions for the reaction are studied using orthogonal experiments and PGFEKK is obtained with a molecular weight up to 21.5 kg/mol, a glass transition temperature of 212 °C, a 10% weight loss temperature of 452 °C, and a tensile strength of 84 MPa. In addition, PGFEKK has a melt viscosity of only 207 MPa at 329 °C and dissolves in most solvents at room temperature. Such a high-performance bio-based poly(aryl ether ketone) has never been reported before. Furthermore, to study the mechanism of how two bio-based monomers affected the polymerization process and material properties, three other polymers are prepared using two petroleum-based monomers, DHPZ and BFBB, instead of GSPZ and BFBF, by analyzing the molecular simulations and the properties of the materials. [ABSTRACT FROM AUTHOR]

Seven benzophenone compounds were synthesized in one or two steps, then their antitumor activity was evaluated. The total yields ranged from 9% to 44%. Compounds 3c–5c exhibited obvious antitumor activity. Among them, compounds 3c and 4c exhibited excellent and broad-spectrum antitumor activity. Compound 3c exhibited much stronger inhibitory activities against fourteen cancer cells than cisplatin. In particular, compound 3c exhibited stronger cytotoxicity against hepatocarcinoma SMMC-7721 cells than Taxol, with a half maximal inhibitory concentration (IC50) of approximately 0.111 μM. These results demonstrated that compounds 3c, 4c and 5c were very promising antitumor leads for further structural modification. [ABSTRACT FROM AUTHOR]

Abstract: Polo-like kinase 1 (PLK1), a variety of serine/threonine-protein kinase, has been reported to play important roles in malignant transformation. The purpose of this study was to investigate the clinicopathological significance of PLK1 expression in malignant glioma. A semi-quantitative RT-PCR assay was performed to detect the expression of PLK1 mRNA in 68 cases of glioma tissues and corresponding non-cancerous brain tissues. Additionally, the correlation of PLK1 mRNA expression with clinicopathological factors or prognosis of glioma patients was statistically analyzed. Multivariate analysis of prognostic factors was performed using the Cox proportional hazard model. Small interfering RNA was used to knockdown PLK1 expression in a glioma cell line and analyze the effects of PLK1 inhibition on growth, cell cycle, apoptosis and chemo- or radiosensitivity of glioma cells. Results showed that the expression of PLK1 mRNA was significantly higher in glioma tissues than in corresponding normal brain tissues. The expression of PLK1 mRNA was closely correlated with WHO grade, KPS and tumor recurrence of glioma patients (P =0.022, 0.030 and 0.041, respectively). Meanwhile, the disease-free and overall survival rates of patients with high PLK1 mRNA expression were obviously lower than those of patients with low PLK1 mRNA expression. Multivariate analysis showed that high PLK1 mRNA expression was a poor prognostic factor for glioma patients (P =0.028). The expression of PLK1 mRNA and protein was significantly down-regulated in stably transfected U251-S cells. PLK1 down-regulation could inhibit growth, induce cell arrest in G2/M phase of cell cycle and apoptosis enhancement in glioma cells. Further, PLK1 down-regulation could enhance the sensitivity of glioma cells to cisplatin or irradiation. Thus, the status of PLK1 mRNA expression might be an independent prognostic factor for glioma patients and targeting PLK1 could be a novel strategy for chemo- or radiosensitization of human malignant gliomas. [Copyright &y& Elsevier]

In this paper, we study the joint limit distributions of point processes of exceedances and partial sums of multivariate Gaussian sequences and show that the point processes and partial sums are asymptotically independent under some mild conditions. As a result, for a sequence of standardized stationary Gaussian vectors, we obtain that the point process of exceedances formed by the sequence (centered at the sample mean) converges in distribution to a Poisson process and it is asymptotically independent of the partial sums. The asymptotic joint limit distributions of order statistics and partial sums are also investigated under different conditions. [ABSTRACT FROM AUTHOR]

Abstract: A facile method to prepare PtRu/MWCNT nanocomposite with excellent electrocatalytic properties was reported in here. Highly dispersed bimetallic PtRu alloy catalysts have been supported on the ultrasonically treated MWCNTs by a rapid ultrasound induced HCHO reduction technique. The microstructure and nature of the resulting PtRu/MWCNT nanocomposite were characterized by Raman spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). The results demonstrated that the highly dispersed bimetallic PtRu alloy could be formed on the MWCNTs surface within 5min at ambient temperature. The obtained PtRu/MWCNT electrode exhibits excellent electrocatalytic activity to methanol. The peak current of the 500th scan of PtRu-1 and E-TEK catalyst is about 75% and 70%, respectively, of that of the first scan. By contrast, for PtRu/MWCNT nanocomposite, after 500-cycle scans, its peak current still remains about 85%. [Copyright &y& Elsevier]

ylonitrile/ N-[4-(aminosulfonyl)phenyl]acrylamide (AN/ASPAA) copolymers were synthesized and used as a host of lithium ion conducting electrolytes. The composition, molecular weight and molecular weight distribution of AN/ASPAA copolymers were determined, and the influence of copolymer composition on the glass temperature of AN/ASPAA copolymers and the ion conductivity of electrolytes were investigated. The molecular weights of AN/ASPAA copolymers were lower than those of AN and ASPAA homopolymers due to the cross-termination reaction. The glass temperatures of AN/ASPAA copolymers increased as the molar fraction of ASPAA units in copolymers increased. The lithium ion conductivities of the polymer electrolytes increased initially as the molar fraction of ASPAA units in copolymers increased, and a maximum conductivity was achieved when the molar fraction of ASPAA in the copolymer was 16.8%. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) is the most abundant post-transcriptional modification in RNA and has important implications in physiological processes and tumor development. However, sensitive and specific quantification of locus-specific m6A modification levels remains a challenging task. In the present work, a novel m6A-sensitive DNAzyme was utilized to directly detect m6A by coupling with a three-way junction-mediated isothermal exponential CRISPR amplification reaction for the first time. This method was built on the fact that the binding arm of the DNAzyme bound to the specific site and its core structure catalyzed the selective cleavage of unmodified adenine instead of methylated adenines. Subsequently, the intact RNA was identified by the proximity effect of the three-way junction. Enormous amounts of single-stranded DNA products were generated through a combination of SDA and EXPAR for signal amplification. The specific real-time curve of products was recorded through detecting the fluorescence intensity triggered by CRISPR Cas12a. As a result, methylation target of abundance down to 1% was successfully identified. In addition, this strategy could be used for the analysis of cell RNA extracts. Combined with an electrochemical sensor for quantitative detection of RNA methylation, we demonstrated the generality of as-proposed strategy. We envision the present method would provide a new platform for the analysis of m6A in RNA and promote its application in clinical diseases. • DNAzyme and the three-way junction were firstly used to specifically identify m6A RNA. • The integration of EXPAR and CRISPR Cas12a assisted exponential amplification realized sensitive detection of m6A modifications down to 1% abundance. • This strategy was easily elaborated into multiple reactions for different detection purposes. [ABSTRACT FROM AUTHOR]

An exfoliated layered double hydroxides/poly(methyl methacrylate) (LDHs/PMMA) nanocomposite was prepared by in situ solution polymerization of methyl methacrylate (MMA) in the presence of 4-vinylbenzenesulfonate intercalated LDHs(MgAl-VBS LDHs). MgAl-VBS LDHs was prepared by the ion exchange method, and the structure and composition of the MgAl-VBS LDHs were determined by X-ray diffraction (XRD), infrared spectroscopy and elemental analysis. XRD and transmission electron microscopy (TEM) were employed to examine the structure of LDHs/PMMA nanocomposite. It was indicated that the LDHs layers were well exfoliated and dispersed in the PMMA matrix. The grafting of PMMA onto LDHs was confirmed by the extraction result and the weight fraction of grafted PMMA increased as the weight fraction of LDHs in the nanocomposites increased. [ABSTRACT FROM AUTHOR]

Oleic acid (OA)-modified SiO2(OA-m-SiO2) nanoparticles were prepared using surface modification method. Infrared spectroscopy (IR) was used to investigate the structure of the OA-m-SiO2 nanoparticles, and the result showed that OA attached onto the surface of SiO2 nanoparticles through esterification. Effect of OA concentration on the dispersion stability of OA-m-SiO2 in heptane was also studied, and the result indicated that OA-m-SiO2 nanoparticles were dispersed in heptane more stably than the unmodified ones. OA-m-SiO2 nanoparticles can also be dispersed in polypropylene (PP) matrix in nano-scale. The effect of OA-m-SiO2 on crystallization of PP was studied by means of DSC. It was found that the introduction of OA-m-SiO2 resulted in significant increase in the crystallization temperature, crystallization degree and crystallization rate of PP, and OA-m-SiO2 could effectively induce the formation of β-crystal PP. Effect of OA-m-SiO2 content on mechanical properties of PP/OA-m-SiO2 nanocomposites was also studied. The results show that OA-m-SiO2 can significantly improve the mechanical properties of PP. [ABSTRACT FROM AUTHOR]

The effect of N,N,N′,N′-tetraalkyl terephthalamide (TATA) on the non-isothermal crystallization and melting characteristics of polypropylene (PP) was studied. The addition of TATA can lead to the formation of β-crystal PP. With the increase in TATA concentration the degree of crystallinity for β-crystal PP increased significantly, and that for α-crystal PP decreased, which indicated that TATA effectively induced the formation of β-crystal PP. WAXD also revealed the existence of β-crystal PP after the introduction of TATA into PP. PP containing TATA crystallized at a temperature range of 5–10°C higher than that of pure PP, and the half-crystallization time (t1/2) and Avrami exponent (n) of PP at the same cooling rate were decreased by the addition of TATA, indicating that TATA influenced the crystallization rate and crystallization growth mode of PP. The rate constant of crystallization of PP containing TATA (Zc) was larger than that of pure PP, which further indicated that the crystallization of PP was accelerated by the addition of TATA. [ABSTRACT FROM AUTHOR]

Abstract: Acrylate polymer/silica nanocomposite particles were prepared through miniemulsion polymerization by using methyl methacrylate/butyl acrylate mixture containing the well-dispersed nano-sized silica particles coupling treated with 3-(trimethoxysilyl)propyl methacrylate (MPS). The encapsulation efficiency of silica particles was determined through the elution and hydrofluoride acid etching experiments, and the size distribution and the morphology of the composite latex particles were characterized by dynamic light scattering and transmission electron microscopy. The coupling treatment of silica with MPS can improve the encapsulation efficiency of silica and the degree of grafting of polymer onto silica. When 0.10g MPS/g silica was used to modify silica, the encapsulation efficiency of silica was greater than 95%, and the degree of grafting of acrylate polymer onto silica was about 60%. Although the average size and the size distribution index of the composite latex particles increased as the weight fraction of silica increased, the stable latex containing the ‘guava-like’ composite particles was obtained. The grafting of polymer onto silica particles improved the dispersion of silica particles in the solvents for acrylate polymer and in the polymer matrix. [Copyright &y& Elsevier]

Nonenzymatic self-assembly circuit utilizing hairpin substrates has been developed to be a powerful tool for information transduction, amplification and computation. However, the sensitivity, stability and application of this circuit are impeded by the presence of leakage which refers to undesired triggering in the absence of input. Herein, we proposed a movable toehold principle to suppress leakage and accelerate the catalytic reaction through removing partial hairpin toehold responsible for the leakage and transferring it to the catalyst. With m ovable t oehold, catalytic hairpin assembly (called mtCHA) exhibited an excellent signal-to-background ratio of over 100, high robustness and improved specificity. In more complex circuit, including proximity recognition, signal amplification of small molecules (such as ATP), logic network, autocatalysis circuit and two-layer cascade circuit, mtCHA also demonstrated satisfactory performance. Our findings suggest that mtCHA holds great potential for broader applications, and the approach of repurposing harmful fragments into beneficial candidates can provide valuable insights for other chemical systems. [ABSTRACT FROM AUTHOR]

In this study, an ultrasensitive electrochemical miRNA biosensor based on toehold mediated strand displacement reaction circuits (SDRCs) and molecular beacon mediated isothermal circular strand displacement polymerization reaction (ICSDPR) has been proposed. During the SDRCs module, the cascade strand displacement reaction induces the recycling of the target let 7a and generation of a large amount of strand A (SA). The SA recognition opens the hairpin capture probe immobilized on the gold electrode, thus, varying the distance between the redox molecules and electrode surface. The primer mediated ICSDPR is observed to further generate a large amount of SA, thus, leading to a reduction in the signal. Considering these merits, the proposed method is observed to exhibit a log-linear linearity from 10 aM to 100 pM and ultrahigh sensitivity towards let 7a down to 6.2 aM, with a capability of distinguishing the let 7a family members, thereby, providing a new electrochemical route for early cancer screening. Isothermal amplification combined SDRCs and ICSDPR for sensitive and selective let 7a detection. Image 1 • A multiple amplification strategy based on SDRCs and ICSDPR was designed. • The SDRCs and ICSDPR process exhibited good selectivity and amplified the signal efficiently. • The method exhibited high sensitivity with the LOD of 6.2 aM for miRNA let 7a. • The proposed strategy achieved extensive prospect in complex matrix and real samples of cells. [ABSTRACT FROM AUTHOR]

Sensitive and specific miRNA detection is essential for the early cancer diagnosis. In this work, we design a fluorescent microRNA biosensor based on exponential amplification reaction (EXPAR) and nicking endonuclease-powered three-dimensional (3-D) bipedal DNA walkers (BDW). Target microRNA initiates EXPAR with the help of polymerase and nicking endonuclease to generate the large number of BDW in solution. The newly generated BDW can be continuously assembled onto polystyrene microsphere track co-modified with fluorescence-labeled DNA strand. Thus, in the presence of nicking endonuclease, the walking machine is activated to produce enhanced fluorescent signal in the supernatant. Besides, we prove that BDW holds the faster walking speed than single-legged DNA walker (SDW) based on comparative study. Under optimal conditions, the proposed amplification method owns a wide linear range from 10 fM to 5 nM with a detection limit down to 5.2 fM. The reaction time of the assay takes about 70 min. The combination of enzyme-assisted EXPAR in solution and enzyme-powered BDW on particle significantly increases the signal amplification efficiency and improves the detection sensitivity. Therefore, our method has enormous potential for the application of BDW-related biosensors. EXPAR-BDW strategy based on exponential amplification reaction-triggered three-dimensional bipedal DNA walkers. Image 1 • A strategy for detection microRNA was constructed based on exponential amplification reaction and 3-D bipedal DNA walkers. • The proposed assay shows high sensitivity towards target microRNA 21 with a detection limit of 5.2 fM. • This method is simple in nucleic acid design and can be completed in short measurement time. [ABSTRACT FROM AUTHOR]

During the damage and defects related to rolling contact fatigue (RCF), together with wear occurring at the rail surface, the microscopic characteristics of the rail material change with the accumulation of wheel cycles. To find the relationship between rail material characteristics and RCF damage under wheel-rail contact situations, a series of twin-disc rolling-sliding tests, based on real wheel-rail contact situations at a radius of 80 mm rail profile, was established for a heavy-haul railway in China. The microscale characteristics of the rail disc contact surface was researched, including crack, wear, roughness, and hardness of the U75V rail with different load cycles. The results show that the damage mechanism of RCF and wear alternately dominates the rail disc surface at the microscale, taking a certain cycle as a boundary which makes the RCF crack number and wear rate have opposite evolutionary trends. The above factors also affect the wear at the surface rail discs, alternating between fatigue and adhesion. Meanwhile, increasing the surface micro-hardness leads to a decrease in the relative wear rate and an increase in the roughness, length and number of RCF cracks. The plastic deformation layers, including the severely deformed layer (SDL) and the transition layer (TL), almost maintain the same thickness and the dynamic equilibrium increases during the cycles. Two kinds of cracks are initiated and propagated at the rail disc surface, one at the SDL and the other at the junction of SDL and TL. The number of each kind of crack depends on the SDL thickness. • The RCF and wear mechanisms dominated the rail surface alternately by a certain cycle. • Increasing the microhardness makes wear decreasing followed by an increase in roughness and RCF cracks. • The adhesive and fatigue wear happened alternately during the cycles. • The fracture mode of along or through the crystal depends on plastic deformation layer. • The thickness of the severely deformed and transition layers changes on opposite while keeps the entire thickness constant. [ABSTRACT FROM AUTHOR]

This study utilized air quality data on six pollutants (PM 2.5 , PM 10 , O 3 , CO, NO 2 , and SO 2 ) obtained by monitoring for one year (from Jan 1 to Dec 31, 2016) in 338 Chinese cities at or above the prefectural level, and introduced the comprehensive air quality index (CAQI). Specifically, the CAQI was used to measure the comprehensive status of ambient air quality, after which the spatiotemporal distribution of the CAQI and its spatial correlation and clustering were investigated at the month and year levels, respectively. The CAQI values were generally high nationwide, with remarkable spatiotemporal variations. Additionally, cities with higher (or lower) CAQI values were concentrated in north (or south) China, while those with higher CAQI values were also observed in west and east China. The CAQI values exhibited a U-shaped trend from January to December, with the highest values being observed in winter and spring and the lowest during summer. Moreover, particulate matter (PM 2.5 and PM 10 ) is the major pollutant during most of the year, with PM 2.5 being prevalent in east and central-south China and PM 10 in northwest and north China; CAQI values are highly dependent on particulate concentrations. During summer, O 3 becomes a major pollutant and contributes greatly to CAQI, with the highest O 3 being observed in the Bohai Rim, the middle and lower reaches of the Yellow River, and the eastern coast of China. Finally, there was significant spatial autocorrelation and clustering of the CAQI, with spatial hot spots of CAQI being observed in southwest Xinjiang province, where air pollution issues have not received a great deal of attention, as well in Beijing-Tianjin-Hebei (BTH) and its surrounding areas, while cold spots for CAQI are mainly in south and northeast China. [ABSTRACT FROM AUTHOR]

Genetic signaling and redox homeostasis are required for proper growth of blood vessels. Here, we report a novel function of peroxiredoxin1 (Prdx1) in vascular development in zebrafish. Knockdown of prdx1 impairs the growth of intersegmental vessel and caudal vein plexus (CVP), and reduces the expression of vascular markers, thus suggesting a role for prdx1 in vasculature and indicating that the antioxidant function of prdx1 is important. We found that H2O2-treated embryos also have CVP defects and observed synergistic effects when prdx1 knockdown was combined with H2O2 treatment. Moreover, N-acetyl-cysteine treatment rescues the vascular defects in prdx1 morphants. These results suggest that oxidative stress disturbs vascularization. Furthermore, we show that the regulation of prdx1 is mediated by Notch and BMP signals. [ABSTRACT FROM AUTHOR]

Pt-doped α -Fe 2 O 3 mesoporous microspheres for acetone detection with high sensitivity and selectivity. [Display omitted] • A simple solvothermal method was used to synthesize Pt-doped α -Fe 2 O 3 mesoporous microspheres. • The sensor based on Pt-doped α -Fe 2 O 3 presents superior sensing properties to 100 ppm of acetone with response of 105 at a low operating temperature of 175 °C. • The gas sensor manifests satisfied humidity resistance, stability and low detection limit as low as 20 ppb toward acetone. • A simulated detection of human breath components was carried out, and the sensor achieved effective identification of acetone gas. In this study, Pt-doped α -Fe 2 O 3 mesoporous microspheres were prepared by a simple solvothermal method and used for the detection of acetone. XRD, SEM, TEM, Raman, FT-IR, XPS, UV–vis and O 2 -TPD were utilized to characterize the structure and chemical properties of the samples. Different components were prepared by controlling the amount of the amount of Pt, and the corresponding sensing performance detection was carried out. The excellent sensing performance was obtained in the sample of 2 at% Pt-doped α -Fe 2 O 3 (Pt 2% -Fe). At a lower operating temperature (175 °C), the Pt 2% -Fe sensor response of 100 ppm acetone reaches 105, which is 2.5 times of pure α -Fe 2 O 3. Even the concentration reduces to 20 ppb acetone, the response can still reach 1.24 at 175 °C. Meanwhile, the favorable sensing performance can still be maintained under high humidity (90 % RH). The addition of Pt increases the specific surface area and reduces the band gap of the sensitive material. The excellent acetone sensing performance indicated that doping Pt effectively increases the adsorption capacity (catalytic properties) of the surface for the sensitive material and the sensitivity to the target gas (alter in the depletion layer). Hence, this high response, low detection limit and good moisture resistance sensor has a significant potential in the detection of acetone in human breath. [ABSTRACT FROM AUTHOR]

Digestive system cancers account for nearly half of all cancers around the world and have a high mortality rate. Cell culture and animal models represent cornerstones of digestive cancer research. However, their ability to enable cancer precision medicine is limited. Cell culture models cannot retain the genetic and phenotypic heterogeneity of tumors and lack tumor microenvironment (TME). Patient-derived xenograft mouse models are not suitable for immune-oncology research. While humanized mouse models are time- and cost-consuming. Suitable preclinical models, which can facilitate the understanding of mechanisms of tumor progression and develop new therapeutic strategies, are in high demand. This review article summarizes the recent progress on the establishment of TME by using tumor organoid models and microfluidic systems. The main challenges regarding the translation of organoid models from bench to bedside are discussed. The integration of organoids and a microfluidic platform is the emerging trend in drug screening and precision medicine. A future prospective on this field is also provided. [ABSTRACT FROM AUTHOR]

The electrochemical biosensor is developed based on CeO 2 @Ag HNFs coupling with a target-feedback rolling-cleavage signal amplifier. The CeO 2 @Ag HNFs possessing unique three-dimensional layered structure were firstly synthesized without complicated reaction conditions such as heating and vacuum. To further improve detection sensitivity of the electrochemical biosensor, we designed a novel DNA cascading amplifier based on target-driven catalytic hairpin assembly and DNAzyme-assisted rolling cleavage. Benefited from the excellent peroxidase activity of CeO 2 @Ag HNFs and cascading amplification process, the biosensor exhibited a detection limit down to 0.89 fM with persuasive selectivity, high stability, and excellent reproducibility even in serum. [Display omitted] • The CeO 2 @Ag HNFs processing excellent electrocatalytic activity were firstly synthesized. • A novel DNA cascading amplifier was designed combining target-driven branched catalytic hairpin assembly and DNAzyme-assisted rolling cleavage. • This electrochemical biosensor achieved a satisfactory result in analysis of serum samples. • The biosensor has a promising application in miRNA associated fundamental research and clinical diagnosis. Currently, developing an effective and easy-to-operate signal amplification assay to detect the trace-amount miRNAs in serum remains a significant challenge. Herein, an ultrasensitive CeO 2 @Ag hybrid nanoflower (CeO 2 @Ag HNF)-labeled electrochemical biosensor was developed for sensing miRNA, based on a target-feedback rolling-cleavage (TFRC) signal amplifier. CeO 2 @Ag HNFs possessing a unique three-dimensional layered structure were synthesized without any complex reaction conditions, such as heating and vacuum. The bimetallic nanoflowers provided a large surface area and excellent CAT-like activity, thereby enhancing electrochemical performance. Based on the principle of branched catalytic hairpin assembly, target miRNA could continuously trigger the assembly of branched junctions with ends composed of DNAzyme. The activated DNAzyme was able to cleave the hairpin substrate efficiently and release to capture more CeO 2 @Ag HNFs label. The process combining target-driven branched catalytic hairpin assembly and DNAzyme-assisted rolling cleavage were defined as TFRC signal amplification. This sensitive electrochemical biosensor exhibited good linear relationship of 1 fM − 1 nM with a detection limit down to 0.89 fM. The proposed method is expected to have a promising application in the miRNA-associated fundamental research and clinical diagnosis. [ABSTRACT FROM AUTHOR]