Whole Brain Multiparametric Mapping in Two Minutes Using a Dual-Flip-Angle Stack-of-Stars Blipped Multi-Gradient-Echo Acquisition.

A new MRI technique is presented for three-dimensional fast simultaneous whole brain mapping of myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+). Phantom and human (N = 9) datasets were acquired using a dual-flip-angle blipped multi-gradient-echo (DFA-mGRE) sequence with a stack-of-stars (SOS) trajectory. Images were reconstructed using a subspace-based algorithm with a locally low-rank constraint. A novel joint-sparsity-constrained multicomponent T2*-T1 spectrum estimation (JMSE) algorithm is proposed to correct for the T1 saturation effect and B1+/B1- inhomogeneities in the quantification of MWF. A tissue-prior-based B1+ estimation algorithm was adapted for B1 correction in the mapping of T1 and PD. In the phantom study, measurements obtained at an acceleration factor (R) of 12 using prospectively under-sampled SOS showed good consistency (R2 > 0.997) with Cartesian reference for R2*/T1app/M0app. In the in vivo study, results of retrospectively under-sampled SOS with R = 6, 12, 18, showed good quality (structure similarity index measure > 0.95) compared with those of fully-sampled SOS. Besides, results of prospectively under-sampled SOS with R = 12 showed good consistency (intraclass correlation coefficient > 0.91) with Cartesian reference for T1/PD/B1+/MWF/QSM/R2*, and good reproducibility (coefficient of variation < 7.0%) in the test-retest analysis for T1/PD/B1+/MWF/R2*. This study has demonstrated the feasibility of simultaneous whole brain multiparametric mapping with a two-minute scan using the DFA-mGRE SOS sequence, which may overcome a major obstacle for neurological applications of multiparametric MRI.

Fluorine-Containing Ionogels with Stretchable, Solvent-Resistant, Wide Temperature Tolerance, and Transparent Properties for Ionic Conductors.

Stretchable ionogels, as soft ion-conducting materials, have generated significant interest. However, the integration of multiple functions into a single ionogel, including temperature tolerance, self-adhesiveness, and stability in diverse environments, remains a challenge. In this study, a new class of fluorine-containing ionogels was synthesized through photo-initiated copolymerization of fluorinated hexafluorobutyl methacrylate and butyl acrylate in a fluorinated ionic liquid 1-butyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide. The resulting ionogels demonstrate good stretchability with a fracture strain of ~1300%. Owing to the advantages of the fluorinated network and the ionic liquid, the ionogels show excellent stability in air and vacuum, as well as in various solvent media such as water, sodium chloride solution, and hexane. Additionally, the ionogels display impressive wide temperature tolerance, functioning effectively within a wide temperature range from -60 to 350 °C. Moreover, due to their adhesive properties, the ionogels can be easily attached to various substrates, including plastic, rubber, steel, and glass. Sensors made of these ionogels reliably respond to repetitive tensile-release motion and finger bending in both air and underwater. These findings suggest that the developed ionogels hold great promise for application in wearable devices.

Adaptive-weighted deep multi-view clustering with uniform scale representation.

Multi-view clustering has attracted growing attention owing to its powerful capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally fail to distinguish the unequal importance of multiple views to the clustering task and overlook the scale uniformity of learned latent representation among different views, resulting in blurry physical meaning and suboptimal model performance. To address these issues, in this paper, we propose a joint learning framework, termed Adaptive-weighted deep Multi-view Clustering with Uniform scale representation (AMCU). Specifically, to achieve more reasonable multi-view fusion, we introduce an adaptive weighting strategy, which imposes simplex constraints on heterogeneous views for measuring their varying degrees of contribution to consensus prediction. Such a simple yet effective strategy shows its clear physical meaning for the multi-view clustering task. Furthermore, a novel regularizer is incorporated to learn multiple latent representations sharing approximately the same scale, so that the objective for calculating clustering loss cannot be sensitive to the views and thus the entire model training process can be guaranteed to be more stable as well. Through comprehensive experiments on eight popular real-world datasets, we demonstrate that our proposal performs better than several state-of-the-art single-view and multi-view competitors.

Grouped Multilayer Practical Byzantine Fault Tolerance Algorithm: A Practical Byzantine Fault Tolerance Consensus Algorithm Optimized for Digital Asset Trading Scenarios.

Based on the practical Byzantine fault tolerance algorithm (PBFT), a grouped multilayer PBFT consensus algorithm (GM-PBFT) is proposed to be applied to digital asset transactions in view of the problems with excessive communication complexity and low consensus efficiency found in the current consensus mechanism for digital asset transactions. Firstly, the transaction nodes are grouped by type, and each group can handle different types of consensus requests at the same time, which improves the consensus efficiency as well as the accuracy of digital asset transactions. Second, the group develops techniques like validation, auditing, and re-election to enhance Byzantine fault tolerance by thwarting malicious node attacks. This supervisory mechanism is implemented through the Raft consensus algorithm. Finally, the consensus is stratified for the nodes in the group, and the consensus nodes in the upper layer recursively send consensus requests to the lower layer until the consensus request reaches the end layer to ensure the consistency of the block ledger in the group. Based on the results of the experiment, the approach may significantly outperform the PBFT consensus algorithm when it comes to accuracy, efficiency, and preserving the security and reliability of transactions in large-scale network node digital transaction situations.

Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1) Is a Novel Therapeutic Target in Liposarcoma: A Tissue Microarray Study.

BACKGROUND: Liposarcoma is the most commonly diagnosed subtype of soft tissue sarcoma. As these tumors often arise near vital organs and neurovascular structures, complete resection can be challenging; consequently, recurrence rates are high. Additionally, available chemotherapeutic agents have shown limited benefit and substantial toxicities. There is, therefore, a clear and unmet need for novel therapeutics for liposarcoma. Discoidin domain receptor tyrosine kinase 1 (DDR1) is involved in adhesion, proliferation, differentiation, migration, and metastasis in several cancers. However, the expression and clinical importance of DDR1 in liposarcoma are unknown. QUESTIONS/PURPOSES: The purposes of this study were to assess (1) the expression, (2) the association between DDR1 and survival, and (3) the functional roles of DDR1 in liposarcoma. METHODS: The correlation between DDR1 expression in tumor tissues and clinicopathological features and survival was assessed via immunohistochemical staining of a liposarcoma tissue microarray. It contained 53 samples from 42 patients with liposarcoma and 11 patients with lipoma. The association between DDR1 and survival in liposarcoma was analyzed by Kaplan-Meier plots and log-rank tests. The DDR1 knockout liposarcoma cell lines were generated by CRISPR-Cas9 technology. The DDR1-specific and highly selective DDR1 inhibitor 7RH was applied to determine the impact of DDR1 expression on liposarcoma cell growth and proliferation. In addition, the effect of DDR1 inhibition on liposarcoma growth was further accessed in a three-dimensional cell culture model to mimic DDR1 effects in vivo. RESULTS: The results demonstrate elevated expression of DDR1 in all liposarcoma subtypes relative to benign lipomas. Specifically, high DDR1 expression was seen in 55% (23 of 42) of liposarcomas and no benign lipomas. However, DDR1 expression was not found to be associated with poor survival in patients with liposarcoma. DDR1 knockout or treatment of 7RH showed decreased liposarcoma cell growth and proliferation. CONCLUSION: DDR1 is aberrantly expressed in liposarcoma, and it contributes to several markers of oncogenesis in these tumors. CLINICAL RELEVANCE: This work supports DDR1 as a promising therapeutic target in liposarcoma.

Semisupervised Progressive Representation Learning for Deep Multiview Clustering.

Multiview clustering has become a research hotspot in recent years due to its excellent capability of heterogeneous data fusion. Although a great deal of related works has appeared one after another, most of them generally overlook the potentials of prior knowledge utilization and progressive sample learning, resulting in unsatisfactory clustering performance in real-world applications. To deal with the aforementioned drawbacks, in this article, we propose a semisupervised progressive representation learning approach for deep multiview clustering (namely, SPDMC). Specifically, to make full use of the discriminative information contained in prior knowledge, we design a flexible and unified regularization, which models the sample pairwise relationship by enforcing the learned view-specific representation of must-link (ML) samples (cannot-link (CL) samples) to be similar (dissimilar) with cosine similarity. Moreover, we introduce the self-paced learning (SPL) paradigm and take good care of two characteristics in terms of both complexity and diversity when progressively learning multiview representations, such that the complementarity across multiple views can be squeezed thoroughly. Through comprehensive experiments on eight widely used image datasets, we prove that the proposed approach can perform better than the state-of-the-art opponents.

STEB: A secure service trading ecosystem based on blockchain.

A service can be an intangible commodity in which no physical goods are transferred from the seller to the buyer. However, traditional trading platforms have many limitations in trading services due to dishonest buyers and brokers. In this paper, we propose a service trading ecosystem based on blockchain, named STEB, which combines blockchain, smart contract, encryption, and digital authentication techniques for service trading. In addition, a dual-chain architecture, which contains two types of blockchains, namely TraChain and SerChain, and a hierarchical encryption scheme of the data on the chain, are proposed to ensure the integrity of transaction data and fine-grained privacy protection of users. Furthermore, we describe a new set of smart contracts to ensure safe transactions for the entire service trading. Security analysis and simulation results confirm that the proposed STEB can achieve more efficient contract execution and enhance service transaction privacy.

Optimization of PBFT Algorithm Based on QoS-Aware Trust Service Evaluation.

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme.

Cyclin-dependent kinase 12 (CDK12) in chordoma: prognostic and therapeutic value.

PURPOSE: To determine the cyclin-dependent kinase 12 (CDK12) expression in chordoma patient tissues and cell lines, its correlation with oncologic outcomes, and its function in chordoma cell proliferation. METHODS: A chordoma tissue microarray was constructed from fifty-six patient specimens and examined by immunohistochemistry to measure CDK12 expression and its correlation to patient clinical characteristics and survival. CDK12 expression in chordoma cell lines and patient tissues was evaluated via western blot. CDK12 specific small interfering RNA (siRNA) was applied to determine whether its inhibition attenuated chordoma cell growth and proliferation. RESULTS: CDK12 was expressed in the majority of chordoma specimens, with notably higher expression in patients with recurrent or metastatic disease. High CDK12 expression was an independent prognostic predictor for shorter overall and progression-free survival in chordoma by univariate and multivariate analysis. Western blot analysis revealed that CDK12 was also highly expressed in chordoma cell lines, with CDK12 specific small interfering RNA (siRNA) mediated knockdown decreasing proliferation and inducing apoptosis. Mechanistically, inhibition of CDK12 decreased phosphorylation of RNA polymerase II (RNAP II) and the anti-apoptotic proteins Survivin and Mcl-1. CONCLUSION: High expression of CDK12 is an independent predictor of poor prognosis in chordoma. Inhibition of CDK12 significantly decreased chordoma cell proliferation and induced apoptosis. Our results support CDK12 as a novel prognostic biomarker and therapeutic target in chordoma.

Myc is a prognostic biomarker and potential therapeutic target in osteosarcoma.

BACKGROUND: Over the past four decades, outcomes for osteosarcoma patients have plateaued as there have been few emerging therapies showing clinical results. Thus, the identification of novel biomarkers and therapeutic strategies are urgently needed to address these primary obstacles in patient care. Although the Myc-oncogene has known roles in oncogenesis and cancer cell growth, its expression and function in osteosarcoma are largely unknown. METHODS: Expression of Myc was determined by Western blotting of osteosarcoma cell lines and patient tissues, and by immunohistochemistry of a unique osteosarcoma tissue microarray (TMA) constructed from 70 patient samples with extensive follow-up data. Myc specific siRNA and inhibitor 10058-F4 were applied to examine the effect of Myc inhibition on osteosarcoma cell proliferation. The clonogenicity and migration activity was determined by clonogenic and wound-healing assays. A mimic in vivo assay, three-dimensional (3D) cell culture model, was performed to further validate the effect of Myc inhibition on osteosarcoma cell tumorigenic markers. RESULTS: Myc was significantly overexpressed in human osteosarcoma cell lines compared with normal human osteoblasts, and also highly expressed in fresh osteosarcoma tissues. Higher Myc expression correlated significantly with metastasis and poor prognosis. Through the addition of Myc specific siRNA and inhibitor, we significantly reduced Myc protein expression, resulting in decreased osteosarcoma cell proliferation. Inhibition of Myc also suppressed the migration, clonogenicity, and spheroid growth of osteosarcoma cells. CONCLUSION: Our results support Myc as an emerging prognostic biomarker and therapeutic target in osteosarcoma therapy.

Discovery of a Novel Benzenesulfonamide Analogue That Inhibits Proliferation and Metastasis Against Ovarian Cancer OVCAR-8 Cells.

BACKGROUND: Ovarian cancer has been a salient public health concern in the world. It is necessary to develop novel antitumor drugs to treat ovarian cancer. PURPOSE: This study investigated the synthesis, antiproliferation ability, antitumor mechanisms in vitro and in vivo of a novel benzenesulfonamide derivative. METHODS: The novel benzenesulfonamide-1,2,3-triazole hybrid 7c was synthesized from 4-fluorobenzenesulfonyl chloride, prop-2-yn-1-amine and 1-(azidomethyl)-3-phenoxybenzene. The structure of this benzenesulfonamide-1,2,3-triazole hybrid 7c was confirmed by 13C NMR, and 1H NMR. Compound 7c was evaluated for its antitumor effects in vitro and in vivo against ovarian cancer OVCAR-8 cells. RESULTS: We discovered that the benzenesulfonamide hybrid 7c potently inhibited cell proliferation against ovarian cancer. Especially, it inhibited cell proliferation with an IC50 value of 0.54µM against OVCAR-8 cells. It could inhibit migration and invasion against OVCAR-8 cells in a concentration-dependent and time-dependent manner. In addition, compound 7c affected the Wnt/ß-catenin/GSK3ß pathway against ovarian cancer OVCAR-8 cells. In vivo study suggested that compound 7c inhibited tumor growth remarkably without obvious toxicity. CONCLUSION: In conclusion, benzenesulfonamide hybrid 7c could be a lead compound for further antitumor drug discovery to treat ovarian cancer.

ATR and p-ATR are emerging prognostic biomarkers and DNA damage response targets in ovarian cancer.

BACKGROUND: Although ataxia-telangiectasia and Rad3 related (ATR) has an established role in the DNA damage response of various cancers, its clinical and prognostic significance in ovarian cancer remains largely unknown. The aims of this study were to assess the expression, function, and clinical prognostic relationship of ATR and phospho-ATR ser428 (p-ATR) in ovarian cancer. METHODS: We confirmed ATR and p-ATR expression by immunohistochemistry (IHC) in a unique ovarian cancer tissue microarray constructed of paired primary, recurrent, and metastatic tumor tissues from 26 individual patients. ATR-specific small interfering RNA (siRNA) and ATR inhibitor VE-822 were applied to determine the effects of ATR inhibition on ovarian cancer cell proliferation, apoptosis, and DNA damage. ATR expression and the associated proteins of the ATR/Chk1 pathway in ovarian cancer cell lines were evaluated by Western blotting. The clonogenicity was also examined using clonogenic assays. A three dimensional (3D) cell culture model was performed to mimic the in vivo ovarian cancer environment to further validate the effects of ATR inhibition on ovarian cancer cells. RESULTS: We show recurrent ovarian cancer tissues express higher levels of ATR and p-ATR than their patient-matched primary tumor counterparts. Additionally, higher expression of p-ATR correlates with decreased survival in ovarian cancer patients. Treatment of ovarian cancer cells with ATR specific siRNA or ATR inhibitor VE-822 led to significant apoptosis and inhibition of cellular proliferation, with reduced phosphorylation of Chk1 (p-Chk1), Cdc25c (p-Cdc25c), Cdc2 (p-Cdc2), and increased expression of cleaved PARP and γH2AX. Inhibition of ATR also suppressed clonogenicity and spheroid growth of ovarian cancer cells. CONCLUSION: Our results support the ATR and p-ATR pathway as a prognostic biomarker, and targeting the ATR machinery is an emerging therapeutic approach in the treatment of ovarian cancer.

PDZ and LIM domain protein 4 suppresses the growth and invasion of ovarian cancer cells via inactivation of STAT3 signaling.

AIMS: PDZ and LIM domain protein 4 (PDLIM4) is frequently repressed in cancer tissues. However, the expression and role of PDLIM4 in ovarian cancer has not been addressed. MAIN METHODS: In this study, we examined the expression and prognostic significance of PDLIM4 in ovarian cancer. The function of PDLIM4 in ovarian cancer cell growth, invasion, and tumorigenesis was further explored. KEY FINDINGS: PDLIM4 is downregulated in ovarian cancer compared to adjacent normal ovarian tissues. Downregulation of PDLIM4 is correlated with advanced tumor stage and lymph node metastasis. Low PDLIM4 expression is significantly associated with shorter overall survival in patients with ovarian cancer (P = 0.0136). Biologically, PDLIM4 overexpression suppresses the proliferation, colony formation, migration, and invasion of both CAOV3 and SKOV3 ovarian cancer cells, compared to empty vector-transfected cells. Consistently, in vivo data show that PDLIM4 overexpression inhibits the growth of SKOV3 xenograft tumors. Mechanistic investigation reveals that overexpression of PDLIM4 blocks the phosphorylation of STAT3 and represses STAT3-dependent transcriptional activation. Moreover, ectopic expression of PDLIM4 downregulates the expression of CCND1 and MMP9 in ovarian cancer cells. Rescue experiments demonstrate that overexpression of constitutively active STAT3 reverses PDLIM4-induced anticancer effects on ovarian cancer cells. SIGNIFICANCE: Overall, PDLIM4 downregulation is associated with aggressive tumor features and poor prognosis in ovarian cancer patients. PDLIM4 suppresses ovarian cancer cell growth and invasion by inhibiting STAT3 signaling. This study provides a potential therapeutic target for ovarian cancer.

Exosomes promote pre-metastatic niche formation in ovarian cancer.

Ovarian cancer is one of the most common gynecological malignancies. Upon initial diagnosis, the majority of patients present with widespread metastatic growth within the peritoneal cavity. This metastatic growth occurs in stages, with the formation of a pre-metastatic niche occurring prior to macroscopic tumor cell invasion. Exosomes released by the primary ovarian tumor are small extracellular vesicles which prepare the distant tumor microenvironment for accelerated metastatic invasion. They regulate intercellular communication between tumor cells and normal stroma, cancer-associated fibroblasts, and local immune cells within the tumor microenvironment. In this review, we highlight the emerging roles of ovarian cancer exosomes as coordinators of pre-metastatic niche formation, biomarkers amenable to liquid biopsy, and targets of chemotherapy.

[Study on the technique of Rehmannia glutinosa virus-free].

The stem tip from germ-free stem segment of Rehmannia glutinosa cultured in test tube can be induced into virus-free seedling. The experiment showed that the proper disinfectant for stem segments of Rehmannia glutinosa was 0.05% HgCl2. The seedings from stem segments grew better with MS in the concentration of agar 0.7% and pH7. The stem tips could be directly induced to seedlings by using MS + 6-BA(0.05 mg/L). The MS media for seedlings virus-free culture are 1/4 macro-elements + 1/2 micro-elements and using edible sugar instead of sucrose, so the cost of media could be decreased to 48.7%.