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Abstract Background and aim 3D fusion model of cone-beam computed tomography (CBCT) and oral scanned data can be used for the accurate design of root canal access and guide plates in root canal therapy (RCT). However, the pose accuracy of the dental pulp and crown in data registration has not been investigated, which affects the precise implementation of clinical planning goals. We aimed to establish a novel registration method based on pulp horn mapping surface (PHMSR), to evaluate the accuracy of PHMSR versus traditional methods for crown-pulp registration of CBCT and oral scan data. Materials and methods This vitro study collected 8 groups of oral scanned and CBCT data in which the left mandibular teeth were not missing, No. 35 and No. 36 teeth were selected as the target teeth. The CBCT and scanned model were processed to generate equivalent point clouds. For the PHMSR method, the similarity between the feature directions of the pulp horn and the surface normal vectors of the crown were used to determine the mapping points in the CBCT point cloud that have a great influence on the pulp pose. The small surface with adjustable parameters is reconstructed near the mapping point of the crown, and the new matching point pairs between the point and the mapping surface are searched. The sparse iterative closest point (ICP) algorithm is used to solve the new matching point pairs. Then, in the C + + programming environment with a point cloud library (PCL), the PHMSR, the traditional sparse ICP, ICP, and coherent point drift (CPD) algorithms are used to register the point clouds under two different initial deviations. The root square mean error (RSME) of the crown, crown-pulp orientation deviation (CPOD), and position deviation (CPPD) were calculated to evaluate the registration accuracy. The significance between the groups was tested by a two-tailed paired t-test (p

The isothermal assumption that treats the gas temperature as a constant is widely adopted in the traditional research. However, the gas temperature has a noticeable effect on the optimal power and gas flow (OPGF) calculation. Moreover, the rational allocation of energy storage can enhance the flexibility and economy of the power and gas system. This paper presents a nonisothermal OPGF considering energy storage, which effectively avoids the constant assumptions on the gas temperature. A simplified partial differential equation which only has bilinear constraints is introduced to depict the temperature change of natural gas. Then, the partial differential equation is discretized by the finite difference scheme and an algebraic pipeline thermal model is developed to avoid the mathematical complexity of the energy conservation equation. The energy storage model is embedded into the OPGF model to enhance the system economy and flexibility. Coupling the DC power flow model and the nonisothermal gas flow model, the nonisothermal OPGF model considering energy storage is formulated. Comprehensive simulations illustrates that the proposed nonisothermal OPGF model considering energy storage can better evaluate the operation state of the integrated energy system. In addition, the energy storage can improve the operation flexibility of systems, thereby significantly reducing the operation costs of the integrated energy systems.

The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance.

Abstract In this work, an active protective epoxy coating with weathering resistant, corrosion-warning, and self-healing properties was developed by incorporating tannic acid (TA) loaded mesoporous silica (MSN-TA) nanocontainers. The introduction of MSN-TA nanocontainers could alleviate the coating degradation via scavenging the radicals generated during UV irradiation. Compared with the blank coating, the coating containing 5 wt.% MSN-TA nanocontainers exhibited much less degradation in surface morphology, wettability and glossiness, and maintained a good barrier property after 384 h of accelerated weathering. Once the coating was damaged, the released TA could react with the Fe3+ ions to form a chelate that endowed the coating scratch with a visible black coloration, i.e. triggering a self-warning capability to indicate the initial onset of corrosion. In addition, the generated chelate could inhibit extensive corrosion propagation, offering a significant self-healing effect demonstrated by the stabilized impedance modulus values during 28 days of immersion in NaCl solution.

Automatic and fast segmentation of retinal vessels in fundus images is a prerequisite in clinical ophthalmic diseases; however, the high model complexity and low segmentation accuracy still limit its application. This paper proposes a lightweight dual-path cascaded network (LDPC-Net) for automatic and fast vessel segmentation. We designed a dual-path cascaded network via two U-shaped structures. Firstly, we employed a structured discarding (SD) convolution module to alleviate the over-fitting problem in both codec parts. Secondly, we introduced the depthwise separable convolution (DSC) technique to reduce the parameter amount of the model. Thirdly, a residual atrous spatial pyramid pooling (ResASPP) model is constructed in the connection layer to aggregate multi-scale information effectively. Finally, we performed comparative experiments on three public datasets. Experimental results show that the proposed method achieved superior performance on the accuracy, connectivity, and parameter quantity, thus proving that it can be a promising lightweight assisted tool for ophthalmic diseases.

Purpose: Accurate retinal vessel segmentation is of great value in the auxiliary screening of various diseases. However, due to the low contrast between the ends of the branches of the fundus blood vessels and the background, and the variable morphology of the optic disc and cup in the retinal image, the task of high-precision retinal blood vessel segmentation still faces difficulties. Method: This paper proposes a multi-scale integrated context network, MIC-Net, which fully fuses the encoder-decoder features, and extracts multi-scale information. First, a hybrid stride sampling (HSS) block was designed in the encoder to minimize the loss of helpful information caused by the downsampling operation. Second, a dense hybrid dilated convolution (DHDC) was employed in the connection layer. On the premise of preserving feature resolution, it can perceive richer contextual information. Third, a squeeze-and-excitation with residual connections (SERC) was introduced in the decoder to adjust the channel attention adaptively. Finally, we utilized a multi-layer feature fusion mechanism in the skip connection part, which enables the network to consider both low-level details and high-level semantic information. Results: We evaluated the proposed method on three public datasets DRIVE, STARE and CHASE. In the experimental results, the Area under the receiver operating characteristic (ROC) and the accuracy rate (Acc) achieved high performances of 98.62%/97.02%, 98.60%/97.76% and 98.73%/97.38%, respectively. Conclusions: Experimental results show that the proposed method can obtain comparable segmentation performance compared with the state-of-the-art (SOTA) methods. Specifically, the proposed method can effectively reduce the small blood vessel segmentation error, thus proving it a promising tool for auxiliary diagnosis of ophthalmic diseases.

Background: Automatic liver segmentation is a prerequisite for hepatoma treatment; however, the low accuracy and stability hinder its clinical application. To alleviate this limitation, we deeply mine the context information of different scales and combine it with deep supervision to improve the accuracy of liver segmentation in this paper. Methods: We proposed a new network called MAD-UNet for automatic liver segmentation from CT. It is grounded in the 3D UNet and leverages multi-scale attention and deep supervision mechanisms. In the encoder, the downsampling pooling in 3D UNet is replaced by convolution to alleviate the loss of feature information. Meanwhile, the residual module is introduced to avoid gradient vanishment. Besides, we use the long-short skip connections (LSSC) to replace the ordinary skip connections to preserve more edge detail. In the decoder, the features of different scales are aggregated, and the attention module is employed to capture the spatial context information. Moreover, we utilized the deep supervision mechanism to improve the learning ability on deep and shallow information. Results: We evaluated the proposed method on three public datasets, including, LiTS17, SLiver07, and 3DIRCADb, and obtained Dice scores of 0.9727, 0.9752, and 0.9691 for liver segmentation, respectively, which outperform the other state-of-the-art (SOTA) methods. Conclusions: Both qualitative and quantitative experimental results demonstrate that the proposed method can make full use of the feature information of different stages while enhancing spatial data's learning ability, thereby achieving high liver segmentation accuracy. Thus, it proved to be a promising tool for automatic liver segmentation in clinical assistance.

Abstract In 2020, nearly 20 million peoples got cancer and nearly 10 million peoples died of cancer, indicating the cancer remains a great threat to human health and life. New therapies are still in urgent demand. We here develop a novel cancer therapy named Ferroptosis ASsassinates Tumor (FAST) by combining iron oxide nanoparticles with cancer-selective knockdown of seven key ferroptosis-resistant genes (FPN, LCN2, FTH1, FSP1, GPX4, SLC7A11, NRF2). We found that FAST had notable anti-tumor activity in a variety of cancer cells but little effect on normal cells. Especially, FAST eradicated three different types of tumors (leukemia, colon cancer, and lung metastatic melanoma) from over 50% of cancer mice, making the mice survive up to 250 days without tumor relapse. FAST also significantly inhibited and prevented the growth of spontaneous breast cancer and improved survival in mice. FAST showed high pan anti-tumor efficacy, high cancer specificity, and in vivo safety. FAST defines a new form of advanced nanomaterials, advanced combinatorial nanomaterials, by combining two kinds of nanomaterials, a chemical nanomaterial (iron oxide nanoparticles) and a biochemical nanomaterial (adeno-associated virus), which successfully turns a general iron nanomaterial into an unprecedented assassin to cancer.

Abstract Background Besides featured glucose consumption, recent studies reveal that cancer cells might prefer “addicting” specific energy substrates from the tumor microenvironment (TME); however, the underlying mechanisms remain unclear. Methods Fibroblast-specific long noncoding RNAs were screened using RNA-seq data of our NJLCC cohort, TCGA, and CCLE datasets. The expression and package of LINC01614 into exosomes were identified using flow cytometric sorting, fluorescence in situ hybridization (FISH), and quantitative reverse transcription polymerase chain reaction (RT-PCR). The transfer and functional role of LINC01614 in lung adenocarcinoma (LUAD) and CAFs were investigated using 4-thiouracil-labeled RNA transfer and gain- and loss-of-function approaches. RNA pull-down, RNA immunoprecipitation, dual-luciferase assay, gene expression microarray, and bioinformatics analysis were performed to investigate the underlying mechanisms involved. Results We demonstrate that cancer-associated fibroblasts (CAFs) in LUAD primarily enhance the glutamine metabolism of cancer cells. A CAF-specific long noncoding RNA, LINC01614, packaged by CAF-derived exosomes, mediates the enhancement of glutamine uptake in LUAD cells. Mechanistically, LINC01614 directly interacts with ANXA2 and p65 to facilitate the activation of NF-κB, which leads to the upregulation of the glutamine transporters SLC38A2 and SLC7A5 and eventually enhances the glutamine influx of cancer cells. Reciprocally, tumor-derived proinflammatory cytokines upregulate LINC01614 in CAFs, constituting a feedforward loop between CAFs and cancer cells. Blocking exosome-transmitted LINC01614 inhibits glutamine addiction and LUAD growth in vivo. Clinically, LINC01614 expression in CAFs is associated with the glutamine influx and poor prognosis of patients with LUAD. Conclusion Our study highlights the therapeutic potential of targeting a CAF-specific lncRNA to inhibit glutamine utilization and cancer progression in LUAD.

Abstract Due to low contrast and the blurred boundary between liver tissue and neighboring organs sharing similar intensity values, the problem of liver segmentation from CT images has not yet achieved satisfactory performance and remains a challenge. To alleviate these problems, we introduce deep supervision (DS) and atrous inception (AI) technologies with conditional random field (CRF) and propose three major improvements that are experimentally shown to have substantive and practical value. First, we replace the encoder's standard convolution with the residual block. Residual blocks can increase the depth of the network. Second, we provide an AI module to connect the encoder and decoder. AI allows us to obtain multi-scale features. Third, we incorporate the DS mechanism into the decoder. This helps to make full use of information of the shallow layers. In addition, we employ the Tversky loss function to balance the segmented and non-segmented regions and perform further refinement with a dense CRF. Finally, we extensively validate the proposed method on three public databases: LiTS17, 3DIRCADb, and SLiver07. Compared to the state-of-the-art methods, the proposed method achieved increased segmentation accuracy for the livers with low contrast and the fuzzy boundary between liver tissue and neighboring organs and is, therefore, more suited for automatic segmentation of these livers.

Abstract Background Circulating tumor DNA (ctDNA) has been proven as a marker for detecting minimal residual diseases following systemic therapies in mid-to-late-stage non-small-cell lung cancers (NSCLCs) by multiple studies. However, fewer studies cast light on ctDNA-based MRD monitoring in early-to-mid-stage NSCLCs that received surgical resection as the standard of care. Methods We prospectively recruited 128 patients with stage I–III NSCLCs who received curative surgical resections in our Lung Cancer Tempo-spatial Heterogeneity prospective cohort. Plasma samples were collected before the surgery, 7 days after the surgery, and every 3 months thereafter. Targeted sequencing was performed on a total of 628 plasma samples and 645 matched tumor samples using a panel covering 425 cancer-associated genes. Tissue clonal phylogeny of each patient was reconstructed and used to guide ctDNA detection. Results The results demonstrated that ctDNA was more frequently detected in patients with higher stage diseases pre- and postsurgery. Positive ctDNA detection at as early as 7 days postsurgery identified high-risk patients with recurrence (HR = 3.90, P

This work synthesized novel Fe3O4 nanoparticles loaded with 2-mercaptobenzothiazole corrosion inhibitors (Fe3O4-MBT NPs), which possessed not only photothermal conversion property but also corrosion inhibition ability. The loading percentage of MBT inhibitors in the Fe3O4-MBT NPs was 9.69 wt.%. The obtained Fe3O4-MBT NPs were incorporated into a shape memory epoxy coating to achieve passive protection and self-healing property. The passive protection property of intact Fe3O4-MBT/epoxy coating was assessed using electrochemical impedance spectroscopy (EIS) measurement during immersion in 3.5 wt.% NaCl solution. The low-frequency impedance modulus at 0.01 Hz of the composite coating containing 3 wt.% Fe3O4-MBT NPs was approximately 5 times higher than that of the blank epoxy coating after 240 h of immersion, indicating the superior barrier property of the composite coating. As for the self-healing property, the scratch width on the coating surface could narrow from 80 µm to ∼5 µm after near-infrared (NIR) light irradiation for 30 s, which was ascribed to the excellent photothermal conversion property of Fe3O4 NPs. EIS results verified that both the scratch closure triggered by the photothermal conversion of Fe3O4 NP and the release of MBT inhibitors are beneficial for the suppression of corrosion activity within the coating scratch. The Fe3O4-MBT/epoxy coating with outstanding passive protection and self-healing property holds great potential to achieve long-term corrosion protection.

A liquid biopsy is a minimally invasive or non-invasive method to analyze a range of tumor material in blood or other body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), messenger RNA (mRNA), microRNA (miRNA), and exosomes, which is a very promising technology. Among these cancer biomarkers, plasma cfDNA is the most widely used in clinical practice. Compared with a tissue biopsy of traditional cancer diagnosis, in assessing tumor heterogeneity, a liquid biopsy is more reliable because all tumor sites release cfDNA into the blood. Therefore, a cfDNA liquid biopsy is less invasive and comprehensive. Moreover, the development of next-generation sequencing technology makes cfDNA sequencing more sensitive than a tissue biopsy, with higher clinical applicability and wider application. In this publication, we aim to review the latest perspectives of cfDNA liquid biopsy clinical significance and application in cancer diagnosis, treatment, and prognosis. We introduce the sequencing techniques and challenges of cfDNA detection, analysis, and clinical applications, and discuss future research directions.

This paper proposes an improved ResU-Net framework for automatic liver CT segmentation. By employing a new loss function and data augmentation strategy, the accuracy of liver segmentation is improved, and the performance is verified on two public datasets LiTS17 and SLiver07. Firstly, to speed up the convergence of the model, the residual module is used to replace the original convolution module of U-Net. Secondly, to suppress the problem of pixel imbalance, the opposite number of Dice is proposed to replace the cross-entropy loss function, and the morphological method is introduced to weigh the pixels. Finally, to improve the generalization ability of the model, random affine transformation and random elastic deformation are employed for data augmentation. From 20 training datasets of Sliver07, 16 sets were selected as the training set, two sets were used for verification, and two sets were used for the test; meanwhile, from 131 training datasets of LiTS2017, eight sets were selected as the test set. In the experiment, four evaluation metrics, including DICE global, DICE per case, VOE, and RVD, were calculated, with the accuracies of 94.28, 94.24 ± 2.07, 10.83 ± 3.70, and -0.25 ± 2.74, respectively. Compared with U-Net and ResU-Net, the performance of the proposed method is significantly improved. The experimental results show that, although the method's complexity is high, it has a faster convergence speed and stronger generalization ability. The segmentation effect on the 2D image is significantly improved, and the scalability on 3D data is also robust. In addition, the proposed method performs well in the case of low-contrast neighboring organs, which proves the robustness of the proposed method.

Improved therapeutic strategies are needed as drug resistance limits the therapeutic efficacy of several clinically approved cancer therapeutics. Here, the authors report a ferroptosis-based therapy using a combination of iron nanoparticles with gene interference to knockdown iron metabolic genes, FPN and LCN2.

Extrinsic self-healing corrosion protective coatings have attracted substantial interest because of their ability to prevent damage propagation by releasing corrosion inhibitors and polymerizable agents from micro/nanocarriers. Various micro/nanoencapsulation technologies have been adopted to optimize the loading capacity and to control the releasing behavior of the healing agents for enhancing the self-healing functionality. This study aims to review recent advances in self-healing corrosion protective coatings based on micro/nanocarriers. This review also provides insights for the further development of extrinsic self-healing coatings by evaluating the advantages and limitations of different healing systems.

The corrosion inhibition effect of 3-amino-5-mercapto-1,2,4-triazole (AMT) on AA2024 aluminium alloy in 3.5 wt.% NaCl solution was investigated, and the corrosion inhibition mechanism was revealed. The influence of AMT concentration on the corrosion inhibition performance was evaluated by potentiodynamic polarization curve and electrochemical impedance spectroscopy (EIS). Surface analysis and surface-enhanced Raman scattering (SERS) spectra were used to study the adsorption process and corrosion inhibition mechanism of AMT on the alloy surface. Polarization curve and EIS results showed that when the AMT concentration was 1.5 g/L, the corrosion current density (icorr) was the lowest and the resistance of adsorption film (Rf) was the largest, illustrating the highest corrosion inhibition efficiency. Moreover, the adsorption kinetics process of AMT was revealed by SERS measurement, and a positive correlation between the SERS intensity and Rf values of AMT after different immersion time was achieved. It indicated that the efficient adsorption of corrosion inhibitors significantly enhanced the corrosion inhibition performance. Density functional theory (DFT) and molecular dynamics simulations were used to give further insight into the adsorption and inhibition mechanism of AMT on the aluminium alloy surface.

Cas13a has already been successfully applied to virus detection. However, as a new gene interference tool, its potential in cancer treatment was not fully explored until now. This study constructed a new Cas13a expression vector, decoy minimal promoter-Cas13a-U6-guide RNA (DMP-Cas13a-U6-gRNA [DCUg]), by controlling the Cas13a and gRNA expression with a nuclear factor κB (NF-κB)-specific promoter and U6 promoter, respectively. DCUg could specifically and effectively knock down the expression of reporter genes in the 293T and HepG2 cells. DCUg could also similarly knock down the expression of endogenous oncogenes (TERT, EZH2, and RelA) at both mRNA and protein levels in a human hepatoma cell HepG2, which led to significant apoptosis and growth inhibition. In contrast, the same transfection did not affect the target gene expression, cell apoptosis, and growth of a human normal liver cell HL7702. Finally, DCUg targeting these oncogenes was packaged into adeno-associated virus (AAV) and treated four cells (HepG2, HL7702, WEHI-3, and Hepa1-6) and tumor-bearing mice. As results, the recombinant AAV significantly inhibited the growth of three cancer cells (HepG2, Hepa1-6, and WEHI-3) in vitro and the xenografted Hepa1-6 and WEHI-3 tumors in mice. This study therefore developed a new tool for the CRISPR-Cas13a-based cancer gene therapy.

Abstract Background Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

The effective non-invasive diagnosis and prognosis are critical for cancer treatment. The plasma cell-free DNA (cfDNA) provides a good material for cancer liquid biopsy and its worth in this field is increasingly explored. Here we describe a new pipeline for effectively finding new cfDNA-based biomarkers for cancers by combining SALP-seq and machine learning. Using the pipeline, 30 cfDNA samples from 26 esophageal cancer (ESCA) patients and 4 healthy people were analyzed as an example. As a result, 103 epigenetic markers (including 54 genome-wide and 49 promoter markers) and 37 genetic markers were identified for this cancer. These markers provide new biomarkers for ESCA diagnosis, prognosis and therapy. Importantly, these markers, especially epigenetic markers, not only shed important new insights on the regulatory mechanisms of this cancer, but also could be used to classify the cfDNA samples. We therefore developed a new pipeline for effectively finding new cfDNA-based biomarkers for cancers by combining SALP-seq and machine learning. In this study, we also discovered new clinical worth of cfDNA distinct from other reported characters.

In this work, a triple-action self-healing coating was fabricated to promote the corrosion protection of carbon steel based on a shape memory polymer (SMP) epoxy coating and polyaniline/benzotriazole (PANI/BTA) nanocapsules. The PANI/BTA nanocapsules had dual functions: (1) PANI as a saline (NaCl)-responsive gatekeeper could achieve the controllable release of BTA corrosion inhibitors, which could adsorb on the metal surface to inhibit corrosion; (2) PANI can prompt the formation of Fe2O3 passive layer. Besides, the thermo-responsive SMP coating could narrow the scratch size after heating, which was beneficial to reduce the amount of corrosion inhibitors needed to fully cover the steel surface. Electrochemical impedance spectroscopy (EIS) measurements were performed to investigate the self-healing performance of the coatings containing different contents of PANI/BTA nanocapsules. Furthermore, the synergetic inhibition effects of PANI, BTA and SMP coating were demonstrated using scanning electrochemical microscopy. The results indicated that the healed 0.5 wt% PANI/BTA coating exhibited an excellent self-healing property and its self-healing efficiency was higher than 96%. The XPS results indicated that the Fe2O3 passive layer was formed in the scratched region to resist steel corrosion.

Background and Objectives: Lipidomics is a pivotal tool for investigating the pathogenesis of mental disorders. However, studies qualitatively and quantitatively analyzing peripheral lipids in adult patients with schizophrenia (SCZ) and major depressive disorder (MDD) are limited. Moreover, there are no studies comparing the lipid profiles in these patient populations. Materials and Method: Lipidomic data for plasma samples from sex- and age-matched patients with SCZ or MDD and healthy controls (HC) were obtained and analyzed by liquid chromatography-mass spectrometry (LC-MS). Results: We observed changes in lipid composition in patients with MDD and SCZ, with more significant alterations in those with SCZ. In addition, a potential diagnostic panel comprising 103 lipid species and another diagnostic panel comprising 111 lipid species could distinguish SCZ from HC (AUC = 0.953) or SCZ from MDD (AUC = 0.920) were identified, respectively. Conclusions: This study provides an increased understanding of dysfunctional lipid composition in the plasma of adult patients with SCZ or MDD, which may lay the foundation for identifying novel clinical diagnostic methods for these disorders.

Liver segmentation from CT is regarded as a prerequisite for computer-assisted clinical applications. However, automatic liver segmentation technology still faces challenges due to the variable shapes and low contrast. In this paper, a patient-specific probabilistic atlas (PA)-based method combing modified distance regularized level set for liver segmentation is proposed. Firstly, the similarities between training atlases and testing patient image are calculated, resulting in a series of weighted atlas, which are used to generate the patient-specific PA. Then, a most likely liver region (MLLR) can be determined based on the patient-specific PA. Finally, the refinement is performed by the modified distance regularized level set model, which takes advantage of both edge and region information as balloon force. We evaluated our proposed scheme based on 35 public datasets, and experimental result shows that the proposed method can be deployed for robust and precise liver segmentation, to replace the tedious and time-consuming manual method.

TALE has always had potential as a gene-editing and regulatory tool. However, with the advent of CRISPR/Cas9, an easier to use tool with the same function, TALE has recently been abandoned because of the time-consuming and low-efficiency process required for its construction. The off-target activity of CRISPR/Cas9 has been a challenge to its in vivo application. By contrast, TALE has been applied in vivo for gene editing and therapy because of its high targeting capability. To overcome the key limitation of the TALE technique, we developed a high-efficiency method for constructing custom TALEs. We created 62 new monomers and developed a new pipeline that enabled assembly of custom TALEs in just 1 day. We verified the new method by assembling nine TALEs targeting the promoters of two transcription factor genes: HNF4α and E47. The expression of the two endogenous genes in two cancer cells, HepG2 and PANC1, was activated by the constructed TALEs, which promoted differentiation of the two cancer cells. Using the new method, custom TALEs can be generated as easily and rapidly as CRISPR, thus promoting the wide application of TALE-based techniques. Keywords: TALE, construction, rapid, one day

This paper presents a new rolling circle amplification (RCA) technique using stem-loop primers (SLP). The technique enables detection of target DNA by either linear or exponential amplification (SLP-lRCA and SLP-eRCA) in both liquid and solid phases. For solid-phase detection, SLP-eRCA detects nucleic acids in four steps: (1) covalently immobilize an array of capture probes (CP) on a solid support; (2) hybridize the CP array with the DNA sample; (3) incubate the CP array with an RCA reaction containing two SLPs; (4) image the CP array. SLP-eRCA detects nucleic acids in liquid phase in one step: a real-time RCA reaction containing the DNA sample and two SLPs. Both liquid- and solid-phase detection methods employ a general rolling circle and an SLP. The other SLP is specific to the target. The technique was verified by detecting synthesized oligonucleotides and six different human papillomaviruses (HPVs), both in liquid phase and on a solid surface. The technique also detected two high-risk HPVs (HPV16 and HPV18) in cervical carcinoma cells (HeLa and SiHa) and clinical samples. This study provides proof-of-concept for the new RCA technique for nucleic acid detection, which overcomes major limitations of current RCA approaches.

Abstract Background Next-generation sequencing (NGS) is fundamental to the current biological and biomedical research. Construction of sequencing library is a key step of NGS. Therefore, various library construction methods have been explored. However, the current methods are still limited by some shortcomings. Results This study developed a new NGS library construction method, Single strand Adaptor Library Preparation (SALP), by using a novel single strand adaptor (SSA). SSA is a double-stranded oligonucleotide with a 3′ overhang of 3 random nucleotides, which can be efficiently ligated to the 3′ end of single strand DNA by T4 DNA ligase. SALP can be started with any denatured DNA fragments such as those sheared by Tn5 tagmentation, enzyme digestion and sonication. When started with Tn5-tagmented chromatin, SALP can overcome a key limitation of ATAC-seq and become a high-throughput NGS library construction method, SALP-seq, which can be used to comparatively characterize the chromatin openness state of multiple cells unbiasly. In this way, this study successfully characterized the comparative chromatin openness states of four different cell lines, including GM12878, HepG2, HeLa and 293T, with SALP-seq. Similarly, this study also successfully characterized the chromatin openness states of HepG2 cells with SALP-seq by using 105 to 500 cells. Conclusions This study developed a new NGS library construction method, SALP, by using a novel kind of single strand adaptor (SSA), which should has wide applications in the future due to its unique performance.

Abstract Background In the active shape model framework, principal component analysis (PCA) based statistical shape models (SSMs) are widely employed to incorporate high-level a priori shape knowledge of the structure to be segmented to achieve robustness. A crucial component of building SSMs is to establish shape correspondence between all training shapes, which is a very challenging task, especially in three dimensions. Methods We propose a novel mesh-to-volume registration based shape correspondence establishment method to improve the accuracy and reduce the computational cost. Specifically, we present a greedy algorithm based deformable simplex mesh that uses vector field convolution as the external energy. Furthermore, we develop an automatic shape initialization method by using a Gaussian mixture model based registration algorithm, to derive an initial shape that has high overlap with the object of interest, such that the deformable models can then evolve more locally. We apply the proposed deformable surface model to the application of femur statistical shape model construction to illustrate its accuracy and efficiency. Results Extensive experiments on ten femur CT scans show that the quality of the constructed femur shape models via the proposed method is much better than that of the classical spherical harmonics (SPHARM) method. Moreover, the proposed method achieves much higher computational efficiency than the SPHARM method. Conclusions The experimental results suggest that our method can be employed for effective statistical shape model construction.

The deactivated CRISPR/Cas9 (dCas9) is now the most widely used gene activator. However, current dCas9-based gene activators are still limited by their unsatisfactory activity. In this study, we developed a new strategy, the CRISPR-assisted trans enhancer, for activating gene expression at high efficiency by combining dCas9-VP64/sgRNA with the widely used strong CMV enhancer. In this strategy, CMV enhancer DNA was recruited to target genes in trans by two systems: dCas9-VP64/csgRNA-sCMV and dCas9-VP64-GAL4/sgRNA-UAS-CMV. The former recruited trans enhancer by annealing between two short complementary oligonucleotides at the ends of the sgRNA and trans enhancer. The latter recruited trans enhancer by binding between GAL4 fused to dCas9 and UAS sequence of trans enhancer. The trans enhancer activated gene transcription as the natural looped cis enhancer. The trans enhancer could activate both exogenous reporter genes and variant endogenous genes in various cells, with much higher activation efficiency than that of current dCas9 activators.

Leukemia is a common and lethal disease. In recent years, iron-based nanomedicines have been developed as a new ferroptosis inducer to leukemia. However, the cytotoxicity of iron nanoparticles to leukemia cells at the transcriptomic level remains unclear. This study investigated the effects of two kinds of iron nanoparticles, 2,3-Dimercaptosuccinic acid (DMSA)-coated Fe3O4 nanoparticles (FeNPs) as a reactive oxygen species (ROS) inducer and Prussian blue nanoparticles (PBNPs) as an ROS scavenger, on the transcriptomic profiles of two leukemia cells (KG1a and HL60) by RNA-Seq. As a result, 470 and 1690 differentially expressed genes (DEGs) were identified in the FeNP-treated HL60 and KG1a cells, respectively, and 2008 and 2504 DEGs were found in the PBNP-treated HL60 and KG1a cells, respectively. Among them, 14 common upregulated and 4 common downregulated DEGs were found, these genes were representative genes that play key roles in lipid metabolism (GBA and ABCA1), iron metabolism (FTL, DNM1, and TRFC), antioxidation (NQO1, GCLM, and SLC7A11), vesicle traffic (MCTP2, DNM1, STX3, and BIN2), and innate immune response (TLR6, ADGRG3, and DDX24). The gene ontology revealed that the mineral absorption pathway was significantly regulated by PBNPs in two cells, whereas the lipid metabolism and HIF-1 signaling pathways were significantly regulated by FeNPs in two cells. This study established the gene signatures of two kinds of nanoparticles in two leukemia cells, which revealed the main biological processes regulated by the two kinds of iron nanoparticles. These data shed new insights into the cytotoxicity of iron nanoparticles that differently regulate ROS in leukemia cells with variant stemness.

This paper reports the design of Ag-Al2O3-Ag heterojunctions based on Ag nanorods (AgNRs) and their applications as thermally stable and ultrasensitive substrates of surface-enhanced Raman scattering (SERS). Specifically, an ultrathin Al2O3 capping layer of 10 nm on top of AgNRs serves to slow down the surface diffusion of Ag at high temperatures. Then, an additional Ag layer on top of the capping layer creates AgNRs-Al2O3-Ag heterojunctions, which lead to giant enhancement of electromagnetic fields within the Al2O3 gap regions that could boost the SERS enhancement. As a result of this design, the SERS substrates are thermally stable up to 200 °C, which has been increased by more than 100 °C compared with bare AgNRs, and their sensitivity is about 400% that of pure AgNRs. This easy yet effective capping approach offers a pathway to fabricate ultrasensitive, thermally stable and easily prepared SERS sensors, and to extend SERS applications for high-temperature detections, such as monitoring in situ the molecule reorientation process upon annealing. Such simultaneous achievement of thermal stability and SERS sensitivity represents a great advance in the design of SERS sensors and will inspire the fabrication of novel hetero-nanostructures.

In this paper, a novel 3D vector field convolution (VFC)-based B-spline deformation model is proposed for accurate and robust cartilage segmentation. Firstly, the anisotropic diffusion method is utilized for noise reduction, and the Sinc interpolation method is employed for resampling. Then, to extract the rough cartilage, features derived from Hessian matrix are chosen to enhance the cartilage, followed by binarizing the images via an optimal thresholding method. Finally, the proposed VFC-based B-spline deformation model is used to refine the rough segmentation. In the experiments, the proposed method was evaluated and demonstrated on 46 magnetic resonance images (MRI) (including 20 hip joints and 26 knee joints), and the results were compared with three state-of-the-art cartilage segmentation methods. Both qualitative and quantitative segmentation results indicate that the proposed method can be deployed for accurate and robust cartilage segmentation. Furthermore, from the segmentation results, patient-specific 3D models of the patient’s anatomy can be derived, which then can be utilized in a wide range of clinical applications, such as 3D visualization for surgical planning and guidance.

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This paper describes an exonuclease-mediated enzyme-linked immunosorbent assay (ELISA)-like assay (EMEA) for detecting the DNA binding activity of nuclear factor κB (NF-κB). For EMEA, a special double-stranded DNA (dsDNA)-coupled plate was first prepared by immobilizing a DNA probe on an N-oxysuccinimide ester-coated plate. The immobilized DNA probe, which was internally labeled with digoxigenin (DIG)-dT, contained a NF-κB binding consensus sequence for capturing activated NF-κB in analyzed samples. For measurement, the plate was first incubated with a protein sample and then treated with exonuclease III to eliminate the probes not bound by NF-κB. Finally, the probes protected by NF-κB were colorimetrically detected by an alkaline phosphatase (AP)-conjugated anti-DIG antibody. The major advantage of EMEA is that it detects NF-κB without the need for NF-κB antibodies. EMEA may provide a general approach for assays of DNA sequence-specific transcription factors for which specific antibodies are unavailable, expensive, or of insufficient quality.

The sequence specific recognitions between DNAs and proteins play important roles in many biological functions. The use of double-stranded DNA arrays (ds-DNA arrays) for studying sequence specific recognition between DNAs and proteins is a promising method. Here we report the use of a ds-DNA probe with multi operation sites of restriction proteins in the middle sequence to investigate DNA-protein sequence-specific interactions including methylation. We arranged EcoR I site and Rsa I site on the same duplex DNA probe to fabricate ds-DNA arrays. We used the ds-DNA arrays to study DNA-restriction enzyme reactions before and after duplex DNA methylation under different probe concentration and reaction time conditions. Our results indicated that the ds-DNA arrays can be further biochemically modified and made accessible for interactions between DNAs and proteins in complex multi-step gene-regulation processes.

We present a novel method for fabricating unimole cular double-stranded DNA microarrays on solid surfaces, which were used to probe sequence-specific DNA/protein interactions. For manufacturing the unimolecular double-stranded DNA microarrays, two kinds of special single-stranded oligonucleotides, constant oligonucleotide and target oligonucleotide, were chemically synthesized. The constant oligonucleotides with internal aminated dT were used to capture and immobilize the target oligonucleotides onto the solid surface, and also to provide a primer for later enzymatic extension reactions, while target oligonucleotides took the role of harbouring DNA-binding sites of DNA-binding proteins. The variant target oligonucleotides were annealed and ligated with the constant oligonucleotides to form the new unimolecular oligonucleotides for microspotting. The prepared unimolecular oligonucleotides were microspotted on aldehyde-derivatized glass slides to make partial-dsDNA microarrays. Finally, the partial-dsDNA microarrays were converted into a unimolecular complete-dsDNA microarray by a DNA polymerase extension reaction. The efficiency and accuracy of the polymerase synthesis were demonstrated by the fluorescent-labeled dUTP incorporation in the enzymatic extension reaction and the restriction endonuclease digestion of the fabricated unimolecular complete-dsDNA microarray. The accessibility and specificity of the sequence-specific DNA-binding proteins binding to the immobilized unimolecular dsDNA probes were demonstrated by the binding of Cy3 labeled NF-?B (p50·p50) to the unimolecular dsDNA microarray. This unimolecular dsDNA microarray provides a general technique for high-throughput DNA-protein or DNA-drugs interactions.

In this study, we established a single nucleotide mutation matrix (SNMM) model based on the relative binding affinities of NF-κB p50 homodimer to a wild-type binding site (GGGACTTTCC) and its all single-nucleotide mutants detected with the double-stranded DNA microarray. We evaluated this model by scoring different groups of 10-bp DNA sequences with this model and analyzing the correlations between the scores and the relative binding affinities detected with three wet experiments, including the electrophoresis mobility shift assay (EMSA), the protein-binding microarray (PBM) and the systematic evolution of ligands by exponential enrichment-sequencing (SELEX-Seq). The results revealed that the SNMM scores were strongly correlated with the detected binding affinities. We also scored the DNA sequences with other three models, including the principal coordinate (PC) model, the position weight matrix scoring algorithm (PWMSA) model and the Match model, and analyzed the correlations between the scores and the detected binding affinities. In comparison with these models, the SNMM model achieved reliable results. We finally determined 0.747 as the optimal threshold for predicting the NF-κB DNA-binding sites with the SNMM model. The SNMM model thus provides a new alternative model for scoring the relative binding affinities of NF-κB to the 10-bp DNA sequences and predicting the NF-κB DNA-binding sites.

SELEX-Seq is now the optimal high-throughput technique for characterizing DNA-binding specificities of transcription factors. In this study, we introduced an improved EMSA-based SELEX-Seq strategy with several advantages. The improvements of this strategy included: (1) using a FAM-labeled probe to track protein-DNA complex in polyacrylamide gel for rapidly recovering the protein-bound dsDNA without relying on traditional radioactive labeling or ethidium bromide staining; (2) monitoring the specificity of SELEX selection by detecting a positive and negative sequence doped into the input DNAs used in each round with PCR amplification; (3) using nested PCR to ensure the specificity of PCR amplification of the selected DNAs after each round; (4) using the nucleotides added at the 5' end of the nested PCR primers as the split barcode to code DNAs from various rounds for multiplexing sequencing samples. The split barcode minimized selection times and thus greatly simplified the current SELEX-Seq procedure. The reliability of the strategy was demonstrated by performing a successful SELEX-Seq of a well-known transcription factor, NF-κB. Therefore, this study provided a useful SELEX-Seq strategy for characterizing DNA-binding specificities of transcription factors.

BACKGROUND:Liaoning cashmere goat is a famous goat breed for cashmere wool. In order to increase the transcriptome data and accelerate genetic improvement for this breed, we performed de novo transcriptome sequencing to generate the first expressed sequence tag dataset for the Liaoning cashmere goat, using next-generation sequencing technology. RESULTS:Transcriptome sequencing of Liaoning cashmere goat on a Roche 454 platform yielded 804,601 high-quality reads. Clustering and assembly of these reads produced a non-redundant set of 117,854 unigenes, comprising 13,194 isotigs and 104,660 singletons. Based on similarity searches with known proteins, 17,356 unigenes were assigned to 6,700 GO categories, and the terms were summarized into three main GO categories and 59 sub-categories. 3,548 and 46,778 unigenes had significant similarity to existing sequences in the KEGG and COG databases, respectively. Comparative analysis revealed that 42,254 unigenes were aligned to 17,532 different sequences in NCBI non-redundant nucleotide databases. 97,236 (82.51%) unigenes were mapped to the 30 goat chromosomes. 35,551 (30.17%) unigenes were matched to 11,438 reported goat protein-coding genes. The remaining non-matched unigenes were further compared with cattle and human reference genes, 67 putative new goat genes were discovered. Additionally, 2,781 potential simple sequence repeats were initially identified from all unigenes. CONCLUSION:The transcriptome of Liaoning cashmere goat was deep sequenced, de novo assembled, and annotated, providing abundant data to better understand the Liaoning cashmere goat transcriptome. The potential simple sequence repeats provide a material basis for future genetic linkage and quantitative trait loci analyses.