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Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods., Comment: 12 pages, 58 figures

Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policymakers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility- related feature mappings automatically. Specifically, HDNN adopts an elegant end-to-end learning paradigm for volatility forecasting, which consists of feature embedding and regression components. The feature embedding component explores the volatility-related temporal information from GAF images via the elaborate CNN in an underlying temporal embedding space. Then, the regression component takes these embedding vectors as input for volatility forecasting tasks. Finally, we examine the feasibility of HDNN on four synthetic GBM datasets and five realworld Stock Index datasets in terms of five regression metrics. The results demonstrate that HDNN has better performance in most cases than the baseline forecasting models of GARCH, EGACH, SVR, and NN. It confirms that the volatility-related temporal features extracted by HDNN indeed improve the forecasting ability. Furthermore, the Friedman test verifies that HDNN is statistically superior to the compared forecasting models.

Environmental stress greatly decreases crop yield. The application of noninvasive techniques is one of the most practical and feasible ways of monitoring the health condition of plants under stress. However, it remains largely unsolved. A chemical fluorescent probe can be applied as a typical nondestructive method, but it has not been applied in living plants for stress detection to date. The abscisic acid (ABA) receptor plays a central role in conferring tolerance to environmental stresses and is an excellent target for developing fluorescent probes. Herein, we developed a fluorescence molecular imaging technology to monitor live plant stress by visualizing the protein expression level of the ABA receptor PYR1. A computer-aided designed indicator dye, flubactin, exhibited an 8-fold enhancement in fluorescence intensity upon interaction with PYR1.

Torreya dapanshanica X.F.Jin, Y.F.Lu & Zi L.Chen, a new species endemic to central Zhejiang, East China, is described and illustrated. This new species is most similar to T. jiulongshanensis (Z.Y.Li, Z.C.Tang & N.Kang) C.C.Pan, J.L.Liu & X.F.Jin, but differs in having leaves with an acuminate apex (vs. leaves with an acute apex), broadly ovoid-globose or globose seeds (vs. obovoid to narrowly obovoid seeds), slightly emarginate at the apex and obtuse-rounded at the base (vs. both acute at the apex and base), testa with irregular shallow grooves (vs. testa smooth or sometimes slightly concave). The diagnostic characters are critically compared and an IUCN assessment for the risk to the new species is estimated.

BackgroundTumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear.MethodsTCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan–Meier survival curve, Cox regression analysis and Harrell’s concordance index analysis were used to determine the prognostic significance.ResultsIn this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers:STAC3,LGALS9,TREM2,FCER1G, andPILRA. Among them,FCER1Gwas abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels ofFCER1Gare related to T cell suppression. Moreover, combination ofFCER1Gand macrophage biomarkerCD68can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group.ConclusionsHigh expression ofFCER1Gin ccRCC is closely related to TAMs infiltration and suppression of T cell activation and proliferation. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC.

The exosomal long noncoding RNAs (lncRNAs) have been reported to have cardioprotective effects on ischemia-reperfusion (I/R) injury by hindering ferroptosis, but the role of lncRNA Mir9-3 host gene (Mir9-3hg) in cardiac I/R injury remains unclear. Exosomes were extracted from mouse bone marrow mesenchymal stem cells (BMSCs) and identified by detecting the exosome specific marker levels, and the results showed that Mir9-3hg was highly expressed in BMSCs-Exo. Hypoxia/reoxygenation (H/R)-treated HL-1 mouse cardiomyocytes were incubated with exosomes extracted from BMSCs transfected with Mir9-3hg siRNA. BMSCs-Exo incubation observably facilitated cell proliferation and glutathione (GSH) content, and reduced iron ion concentration, reactive oxygen species (ROS) level and ferroptosis marker protein levels in H/R-treated cells, while interfering Mir9-3hg reversed these effects. RNA binding protein immunoprecipitation assay was found that Mir9-3hg bound with pumilio RNA binding family member 2 (Pum2) protein and downregulated Pum2 expression. Silence of Pum2 reversed the effects of Mir9-3hg inhibition on cell functions. Chromatin immunoprecipitation assay was revealed that Pum2 bound with peroxiredoxin 6 (PRDX6) promoter and restrained PRDX6 expression. Silence of PRDX6 reversed the improved effects of Pum2 downregulation on cell functions. Additionally, BMSCs-Exo treatment ameliorated cardiac function in I/R-treated mice by inhibiting cardiomyocyte ferroptosis. In conclusion, BMSCs-Exo treatment attenuates I/R-induced cardiac injury by inhibiting cardiomyocyte ferroptosis through modulating the Pum2/PRDX6 axis, thereby ameliorating cardiac function. Background and Aims The exosomal long noncoding RNAs (lncRNAs) have been reported to have cardioprotective effects on ischemia-reperfusion (I/R) injury by hindering ferroptosis, but the role of lncRNA Mir9-3 host gene (Mir9-3hg) in cardiac I/R injury remains unclear. Methods and Results Exosomes were extracted from mouse bone marrow mesenchymal stem cells (BMSCs) and identified by detecting the exosome specific marker levels, and the results showed that Mir9-3hg was highly expressed in BMSCs-Exo. Hypoxia/reoxygenation (H/R)-treated HL-1 mouse cardiomyocytes were incubated with exosomes extracted from BMSCs transfected with Mir9-3hg siRNA. BMSCs-Exo incubation observably facilitated cell proliferation, increased glutathione (GSH) content, and reduced iron ion concentration, reactive oxygen species (ROS) level and ferroptosis marker protein levels in H/R-treated cells, while interfering Mir9-3hg reversed these effects. RNA binding protein immunoprecipitation assay was found that Mir9-3hg bound with pumilio RNA binding family member 2 (Pum2) protein and downregulated Pum2 expression. Silence of Pum2 reversed the effects of Mir9-3hg inhibition on cell functions. Chromatin immunoprecipitation assay was revealed that Pum2 bound with peroxiredoxin 6 (PRDX6) promoter and restrained PRDX6 expression. Silence of PRDX6 reversed the improved effects of Pum2 downregulation on cell functions. Additionally, BMSCs-Exo treatment ameliorated cardiac function in I/R-treated mice by inhibiting cardiomyocyte ferroptosis. Conclusions BMSCs-Exo treatment attenuates I/R-induced cardiac injury by inhibiting cardiomyocyte ferroptosis through modulating the Pum2/PRDX6 axis, thereby ameliorating cardiac function.

Tumor growth, metastasis and therapeutic response are believed to be regulated by the tumor and its microenvironment (TME) in advanced renal cell carcinoma (RCC). However, the mechanisms underlying genomic, transcriptomic and epigenetic alternations in RCC progression have not been completely defined. In this study, single-cell RNA-sequencing (scRNA-seq) data were obtained from eight tissue samples of RCC patients, including two matched pairs of primary and metastatic sites (lymph nodes), along with Hi-C, transposable accessible chromatin by high-throughput (ATAC-seq) and RNA-sequencing (RNA-seq) between RCC (Caki-1) and human renal tubular epithelial cell line (HK-2). The identified target was verified in clinical tissue samples (microarray of 407 RCC patients, TMA-30 and TMA-2020), whose function was further validated by in vitro and in vivo experiments through knockdown or overexpression. We profiled transcriptomes of 30514 malignant cells, and 14762 non-malignant cells. Comprehensive multi-omics analysis revealed that malignant cells and TME played a key role in RCC. The expression programs of stromal cells and immune cells were consistent among the samples, whereas malignant cells expressed distinct programs associated with hypoxia, cell cycle, epithelial differentiation, and two different metastasis patterns. Comparison of the hierarchical structure showed that SERPINE2 was related to these NNMF expression programs, and at the same time targeted the switched compartment. SERPINE2 was highly expressed in RCC tissues and lowly expressed in para-tumor tissues or HK-2 cell line. SERPINE2 knockdown markedly suppressed RCC cell growth and invasion, while SERPINE2 overexpression dramatically promoted RCC cell metastasis both in vitro and in vivo. In addition, SERPINE2 could activate the epithelial-mesenchymal transition pathway. The above findings demonstrated that the role of distinct expression patterns of malignant cells and TME played a distinct role in RCC progression. SERPINE2 was identified as a potential therapeutic target for inhibiting metastasis in advanced RCC.

Objective To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging. Methods Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers. Results Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively. Conclusions Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images.

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned with the development of computational models to train artificial intelligence systems. DL models are characterized by automatically extracting high-level features from the input data to learn the relationship between matching datasets. Thus, its implementation offers an advantage over common ML methods that often require the practitioner to have some domain knowledge of the input data to select the best latent representation. As a result of this advantage, DL has been successfully applied within the medical imaging field to address problems, such as disease classification and tumor segmentation for which it is difficult or impossible to determine which image features are relevant. Therefore, taking into consideration the positive impact of DL on the medical imaging field, this article reviews the key concepts associated with its evolution and implementation. The sections of this review summarize the milestones related to the development of the DL field, followed by a description of the elements of deep neural network and an overview of its application within the medical imaging field. Subsequently, the key steps necessary to implement a supervised DL application are defined, and associated limitations are discussed.

Although expression of interleukin (IL)-34 is upregulated in active ulcerative colitis (UC), the molecular function and underlying mechanism are largely unclear.To investigate the function of IL-34 in acute colitis, in a wound healing model and in colitis-associated cancer in IL-34-deficient mice.Colitis was induced by administration of dextran sodium sulfate (DSS), and carcinogenesis was induced by azoxymethane (AOM). Whether the impact of IL-34 on colitis was dependent on macrophages was validated by depletion of macrophages in a murine model. The association between IL-34 expression and epithelial proliferation was studied in patients with active UC.IL-34 deficiency aggravated murine colitis in acute colitis and in wound healing phase. The effect of IL-34 on experimental colitis was not dependent on macrophage differentiation and polarization. IL-34-deficient mice developed more tumors than wild-type mice following administration of AOM and DSS. No significant difference was shown in degree of cellular differentiation in tumors between wild-type and IL-34-deficient mice. IL-34 was dramatically increased in the active UC patients as previously reported. More importantly, expression of IL-34 was positively correlated with epithelial cell proliferation in patients with UC.IL-34 deficiency exacerbates colonic inflammation and accelerates colitis-associated carcinogenesis in mice. It might be served as a potential therapeutic target in UC.

In this work, we propose an unsupervised multiple parametric-margin support vector clustering (MPMSVC) for noisy clustering tasks. The main idea of MPMSVC is to find a parametric-margin center hyperplane for each cluster in a manner that gathers the within-cluster instances around the corresponding center hyperplane, and keeps the between-cluster instances far away. Specifically, our MPMSVC owns the following attractive merits: i) The primal of MPMSVC is enhanced in the least squares sense, which enjoys an effective learning procedure. ii) The utilization of the linear $L_{1}$ -norm loss makes MPMSVC be more robust to noisy clustering tasks. iii) An efficient iterative algorithm is presented to optimize the non-smooth problem in MPMSVC, which only involves a set of linear equations. Also, the convergence of the proposed algorithm is guaranteed theoretically. iv) The nonlinear extension is further derived via kernel technique to deal with more complex clustering tasks. Finally, the feasibility and effectiveness of MPMSVC is validated by extensive experiments on both synthetic and real-world datasets.

Investment casting is widely used to manufacture a variety of metal components through wax patterns fabricated by low-pressure injection molding of wax. With the high labor cost in the precision casting industry, ensuring the quality of wax patterns during the production process is crucial to manufacturers. Accordingly, an intelligent monitoring system was developed for silicone rubber mold applied in injection molding of wax patterns. The cooling time of the injection molded part obtained by the intelligent monitoring system is very close to that obtained by the on-site data acquisition system. The relationship between the temperature of the injection molded part and the cooling time after the injection molding of wax can be monitored remotely. The developed intelligent monitoring system has the functions of identifying six process parameters, including injection temperature, coolant inlet temperature, coolant inlet coolant flow rate, injection pressure, injection time, and mold temperature. The developed intelligent monitoring system is very practical and provides good application prospects in the batch production process of wax patterns.

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

AIM: To evaluate the safety and efficacy of scleral-fixated 3-looped haptics intraocular lens (IOL) implantation for surgical management of microspherophakia. METHODS: A retrospective case series include 10 microspherophakic patients (15 eyes) who underwent lens removal plus a modified surgical treatment of scleral-fixated 3-looped haptics IOL implantation. The primary outcomes involved visual acuity, intraocular pressure (IOP). Secondary outcomes were spherical equivalent (SE), anterior chamber depth (ACD), corneal endothelial cell density and postoperative complications. RESULTS: After a postoperative follow-up of 17.60±15.44mo, improved visual outcomes can be observed. The uncorrected distance visual acuity (UCVA) logMAR improved from 1.54±0.59 preoperatively to 0.51±0.35 postoperatively (P=0.001), and best corrected visual acuity (BCVA) logMAR improved from 0.97±0.91 preoperatively to 0.24±0.23 postoperatively (P=0.003). Moreover, the SE decreased from -9.58±7.47D preoperatively to -0.65±2.21 D postoperatively (P

Based on literature survey, examination of type specimens and fieldwork, seven names of Carex are synonymized in the present paper: viz. Carex hypoblephara reduced to a synonym of C. glossostigma; C. dayunshanensis and C. wuyishanensis to synonyms of C. graciliflora; C. dolichogyne to a synonym of C. truncatigluma; C. kwangtoushanica to a synonym of C. tatsiensis; and C. martini to a synonym of C. rhynchophora. The holotype of Carex fokienensis is identified and confirmed at P. The putative endemic species Carex macrosandra (basionym: C. lanceolata var. macrosandra) is synonymized to C. lanceolata, whereas C. cavaleriensis, considered a synonym of the former, is here recognized as a distinct species. A lectotype is designated for C. lanceolata var. macrosandra.

To observe the clinical effect and safety of Shanhaidan Granules (SHDG) combined with tadalafil tablets (TT) in the treatment of ED.In this open multi-center case-control clinical trial, we enrolled 247 ED patients according to the designed criteria, and treated them orally with SHDG at 10 g per time tid (n = 74), TT at 5 mg per time bid (n = 52), or SHDG + TT at the above doses (n = 121), all for 8 weeks. Before and after medication, we recorded the IIEF-6, erection hardness scores (EHS), traditional Chinese medicine syndromes (TCMS) scores, penile cavernous blood flow parameters and adverse reactions, and compared them between the 3 groups of patients.After 8 weeks of treatment, all the patients showed significantly increased IIEF-6, EHS and TCMS scores in comparison with the baseline (P0.05). The total effectiveness rates in the SHDG, TT and SHDG + TT groups were 60.8%, 67.3% and 69.4% respectively based on the IIEF-6 scores, remarkably higher in the TT and SHDG + TT groups than in the SHDG group (P0.05), and 40.5%, 32.7% and 63.6% respectively according to the TCMS scores, markedly higher in the SHDG and SHDG + TT groups than in the TT group (P0.05). Single-center data manifested significantly increased peak systolic velocity (PSV) of the penile artery in the SHDG + TT and TT groups (P0.05). The improvement values of relevant parameters were remarkably higher in the SHDG + TT group than in the TT and SHDG groups, so were IIEF-6 scores in the TT than in the SHDG group, and TCM syndromes in the SHDG than in the TT group. No medication-related adverse events were found in any of patients after treatment, except for some mild side effects including muscle soreness and gastrointestinal reactions in a few cases, all soon relieved, none with abnormalities in blood and urine routine tests or hepatic and renal function indicators.Shanhaidan Granules combined with tadalafil can significantly improve the erectile function and reduce TCM syndromes in ED patients, and therefore can be applied effectively and safely in clinical practice./.

The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

Background The benefit of targeted therapy for renal cell carcinoma (RCC) is largely crippled by drug resistance. Rapid disease progression and poor prognosis occur in patients with drug resistance. New treatments demand prompt exploration for clinical therapies. Ubiquitin-specific peptidase 39 (USP39) serves as the pro-tumor factor in several previous studies of other malignant tumors. To investigate the function and mechanism of USP39 in promoting malignant proliferation and angiogenesis of RCC. Methods We applied ONCOMINE database to analyze the correlation between USP39 expression level and the clinical characteristics of RCC. USP39 knockdown or overexpression plasmids were transfected into 786-O and ACHN cells. The HUVEC received cell supernatants of 786-O and ACHN cells with knockdown or overexpression USP39.The effect of USP39 on RCC was evaluated by MTT assay, cell cycle analysis, colony formation assay and tubule formation assay. The interaction between USP39 and VEGF-A alternative splicing was assessed by affinity purification and mass spectrometry, co-immunoprecipitation and Western blot assays. Results The mRNA expression level of USP39 in RCC was significantly higher than that in normal renal tissue (P (101–565) fragment directly mediated its binding to SRSF1 and SRPK1, and promoted the phosphorylation of SRSF1 to regulate VEGF-A alternative splicing. USP39 knockdown upregulated the expression of VEGF-A165b, and USP39 overexpression downregulated the expression of VEGF-A165b significantly (both P Conclusion USP39 acted as a pro-tumor factor by motivating the malignant biological processes of RCC, probably through inhibiting VEGF-A165b alternative splicing and regulating SRSF1 and SRPK1. USP39 may prove to be a potential therapeutic target for RCC. Graphic abstract

In this paper, we improve the projection twin support vector machine (PTSVM) to a novel nonparallel classifier, termed as ν-PTSVM. Specifically, our ν-PTSVM aims to seek an optimal projection for each class such that, in each projection direction, instances of their own class are clustered around their class center while keep instances of the other class at least one distance away from such center. Different from PTSVM, our ν-PTSVM enjoys the following characteristics: (i) ν-PTSVM is equipped by a more theoretically sound parameter ν, which can be used to control the bounds of fraction of both support vectors and margin-error instances. (ii) By reformulating the least-square loss of within-class instances in primal problems of ν-PTSVM, its dual problems no longer involve the time-costly matrix inversion. (iii) ν-PTSVM behaves consistent between its linear and nonlinear cases. Namely, the kernel trick can be applied directly to ν-PTSVM for its nonlinear extension. Experimental evaluations on both synthetic and real-world datasets demonstrate the feasibility and effectiveness of the proposed approach.

The recently proposed two-dimensional locality preserving projection (2DLPP) is an excellent matrix-based dimensionality reduction method. However, the formulation of 2DLPP may encounter several drawbacks in real-world applications, such as sensitiveness to outliers, non-orthogonal projections, and singularity problem. To alleviate these issues, in this paper, we propose a novel robust two-dimensional locality preserving projection (2DRLPP) for noisy image recognition. The proposed 2DRLPP preserves the local manifold structure of two-dimensional image space under the robust L 1 -norm criterion. Different from the existing 2DLPP, our 2DRLPP enjoys several incomparable advantages: (i) 2DRLPP extracts projections from matrix-based image space via the L 1 -norm locality preserving criterion rather than the L 2 -norm one, and hence it is more robust to outliers. (ii) The orthogonality constraint is further imposed on 2DRLPP to ensure its orthogonal projections. (iii) The introduced regularization term can not only control the model complexity but also guarantee the stability of solution. (iv) A simple but efficient iterative algorithm is presented to solve the corresponding L 1 -norm minimization problem, whose convergence is guaranteed theoretically. Extensive experimental results on three noisy face image datasets confirm the feasibility and robustness of the proposed approach.

An ICE-like transcription factor mediates jasmonate-regulated cold tolerance in the rubber tree (Hevea brasiliensis), and confers cold tolerance in transgenic Arabidopsis. The rubber tree (Hevea brasiliensis) is susceptible to low temperatures, and understanding the mechanisms regulating cold stress is of great potential value for enhancing tolerance to this environmental variable. In this study, we find that treatment with exogenous methyl jasmonate (MeJA) could significantly enhance Hevea brasiliensis cold tolerance. In addition, yeast two-hybrid and bimolecular fluorescence complementation (BiFC) experiments show that JASMONATE ZIM-DOMAIN(JAZ) proteins, HbJAZ1 and HbJAZ12, key repressors of JA signaling pathway, interact with HbICE2, a novel ICE (Inducer of CBF Expression)-like protein. HbICE2 was nuclear-localised and bound to the MYC recognition (MYCR) sequence. The transcriptional activation activity of HbICE2 in yeast cells was dependent on the N-terminus, and overexpression of HbICE2 in Arabidopsis resulted in elevated tolerance to chilling stress. Furthermore, dual-luciferase transient assay reveals that HbJAZ1 and HbJAZ12 proteins inhibit the transcriptional function of HbICE2. The expression of C-repeat-binding factor (CBF) signalling pathway genes including HbCBF1, HbCBF2 and HbCOR47 were up-regulated by MeJA. Taken together, our data suggest that the new ICE-like transcription factor HbICE2 is involved in jasmonate-regulated cold tolerance in Hevea brasiliensis.

Background The benefit of targeted therapy for renal cell carcinoma (RCC) is largely crippled by drug resistance. Rapid disease progression and poor prognosis occur in patients with drug resistance. New treatments demand prompt exploration for clinical therapies. Ubiquitin-specific peptidase 39 (USP39) serves as the pro-tumor factor in several previous studies of other malignant tumors. To investigate the function and mechanism of USP39 in promoting malignant proliferation and angiogenesis of RCC. Methods We applied ONCOMINE database to analyze the correlation between USP39 expression level and the clinical characteristics of RCC. USP39 knockdown or overexpression plasmids were transfected into 786-O and ACHN cells. The HUVEC received cell supernatants of 786-O and ACHN cells with knockdown or overexpression USP39.The effect of USP39 on RCC was evaluated by MTT assay, cell cycle analysis, colony formation assay and tubule formation assay. The interaction between USP39 and VEGF-A alternative splicing was assessed by affinity purification and mass spectrometry, co-immunoprecipitation and Western blot assays. Results The mRNA expression level of USP39 in RCC was significantly higher than that in normal renal tissue (P

Additional file 1: Figure S1. The markers labeledcell types plot. Figure S2. A Expressed ATG genes per single cell in SSBprand leukocytes. B, C inferCNV results of NPC and IPC derived fetal renalcells. Figure S3. ASIX2 expression in Monocle 3 cluster of NPCs. B IL1R1 expression in Monocle 3cluster of SSBpod and pods. (IL1R1 is the marker of SSBpod). C CENPKexpression in Monocle 3 cluster of IPC and ICs. (CENPK is the marker of IPC). Figure S4. UMAP plot of different samples in all weeks.

Generalized eigenvalue proximal support vector machine (GEPSVM) and its improvement IGEPSVM are excellent nonparallel classification methods due to their excellent generalization. However, all of them adopt the square L2-norm metric to implement their empirical risk or penalty, which is sensitive to noise and outliers. Moreover, in many real-world learning tasks, it is a significant challenge for GEPSVMs when the data appears highly correlated. To alleviate the above issues, in this paper, we propose a novel trace lasso regularized robust nonparallel proximal support vector machine (RNPSVM) for noisy classification. Compared with GEPSVMs, our RNPSVM enjoys the following advantages. First, the empirical risk of RNPSVM is implemented by the robust L1-norm metric with a maximum margin criterion. Namely, it aims to maximize the L1-norm inter-class distance dispersion while minimizing the L1-norm intra-class distance dispersion simultaneously. Second, to capture the sparsity and the underlying correlation of data, a trace lasso (adaptive norm-based training data) is further introduced to regularize RNPSVM. Third, an iterative algorithm is designed to solve the maximization optimization problem of RNPSVM, whose convergence is guaranteed theoretically. The extensive experimental results on both synthetic and real-world noisy datasets demonstrate the effectiveness of RNPSVM.

In the past decade, synthetic oxidized 5-MedC nucleosides and their derivatives have become essential tools for epigenetic research. The low efficacy of both conventional and newly reported BOP methods on C4-amination of these specific oxidized 5-MedU substrates urged us to systematically investigate how the nature of onium salt-based coupling reagents affects the C4-amination of pyrimidine nucleobases and lead us to the findings that different onium coupling reagents result in the formation of distinctive activation intermediates and PyAOP is much more potent than BOP in both activation and aminolysis steps. Direct amination without the need of ribose protection, ultrafast activation, tolerance to aqueous N-nucleophiles, and excellent yields for diverse oxidized 5MedC derivatives are the advantages of this PyAOP-based C4-amination method.

The recently proposed multi-weight vector projection support vector machine (EMVSVM) is an excellent multi-projections classifier. However, the formulation of MVSVM is based on the L2-norm criterion, which makes it prone to be affected by outliers. To alleviate this issue, in this paper, we propose a robust L1-norm MVSVM method, termed as MVSVM L 1 . Specifically, our MVSVM L 1 aims to seek a pair of multiple projections such that, for each class, it maximizes the ratio of the L1-norm between-class dispersion and the L1-norm within-class dispersion. To optimize such L1-norm ratio problem, a simple but efficient iterative algorithm is further presented. The convergence of the algorithm is also analyzed theoretically. Extensive experimental results on both synthetic and real-world datasets confirm the feasibility and effectiveness of the proposed MVSVM L 1 .

In nucleoside/nucleotide chemistry, the regioselective cleavage of 5′-O-TBS groups of persilylated nucleosides is a desired approach for structural functionalization at the 5′-position. However, efficient and practical methods for this purpose are still limited. In our research, we found that homogeneous methanolic phosphomolybdic acid (PMA) efficiently catalyzes the regioselective deprotection of 5′-O-TBS groups of a diversity of persilylated nucleoside substrates and can be applied in practical synthesis at scales of up to 15 g. 31P NMR results indicated that an anion cluster forms and the Lewis acidity of homogeneous PMA is organic-solvent dependent. The efficacy and pronounced regioselectivity of methanolic PMA occurs as a result of a lowering of the Lewis acid strength upon solvation of the molybdophosphate anions.

As an archetype C-nucleoside, pyrazofurin possesses broad-spectrum antiviral and antitumor activities. However, the presence of the acidic enol in the nucleobase of pyrazofurin poses a huge challenge to the conventional NTP synthetic methods. On the basis of a selective phosphorylation method and the P(V)-N activation strategy, the first chemical synthesis of pyrazofurin 5′-triphosphate (PTP) was accomplished, which will greatly facilitate the investigation on the interactions of viral RNA polymerases and PTP, an important cellular metabolite of pyrazofurin.

Control of morphology in the synthesis of Rh nanocrystals can be used to precisely tailor the electronic surface structure; this in turn directly influences their performance in catalysis applications. Many works bring attention to the development of Rh nanostructures with low-index surfaces, but limited effort has been placed on the study of high-index and surface-defect-enriched nanocrystals as they are not favored by thermodynamics due to the involvement of high-energy surfaces and increased surface-to-volume ratios. In this work, we demonstrate an aqueous synthesis of concave Rh nanotetrahedra (CTDs) serving as efficient catalysts for energy conversion reactions. CTDs are surface-defect-rich structures that form through a slow growth rate and follow the four-step model of metallic nanoparticle growth. By tuning the surfactant concentration, the morphology of Rh CTDs evolved into highly excavated nanotetrahedra (HETDs) and twinned nanoparticles (TWs). Unlike the CTD surfaces with abundant adatoms and v...

In this paper, by introducing the statistics of training data into support vector regression (SVR), we propose a minimum deviation distribution regression (MDR). Rather than just minimizing the structural risk, MDR also minimizes both the regression deviation mean and the regression deviation variance, which is able to deal with the different distribution of boundary data and noises. The formulation of minimizing the first and second order statistics in MDR leads to a strongly convex quadratic programming problem (QPP). An efficient dual coordinate descend algorithm is adopted for small sample problem, and an average stochastic gradient algorithm for large scale one. Both theoretical analysis and experimental results illustrate the efficiency and effectiveness of the proposed method.

In this paper, we propose a novel absolute value inequalities discriminant analysis (AVIDA) criterion for supervised dimensionality reduction. Compared with the conventional linear discriminant analysis (LDA), the main characteristics of our AVIDA are robustness and sparseness. By reformulating the generalized eigenvalue problem in LDA to a related SVM-type "concave-convex" problem based on absolute value inequalities loss, our AVIDA is not only more robust to outliers and noises, but also avoids the SSS problem. Moreover, the additional L1-norm regularization term in the objective makes sure sparse discriminant vectors are obtained. A successive linear algorithm is employed to solve the proposed optimization problem, where a series of linear programs are solved. The superiority of our AVIDA is supported by experimental results on artificial examples as well as benchmark image databases.

Reduction of the time and cost during the research and development stage of a new product is an important issue. Rapid tooling techniques can reduce the time to market compared to conventional machining approaches. In general, reduction in cooling time plays an important role on cycle time in manufacturing time. Wax injection mold fabricated from aluminum-filled epoxy resin can be employed for small-batch productions of wax patterns. However, the cooling time is much longer compared to metallic wax injection molds due to poorer thermal conductivity. In this study, three kinds of cooling-channel layouts were employed to fabricate wax injection molds for low-pressure wax injection molding using rapid prototyping and rapid tooling techniques. The effects of three kinds of wax injection molds on the cooling time during the low-pressure wax injection molding were investigated. It was found that the reduction in cooling time about 87% can be obtained when a wax injection mold with conformal cooling channels is compared to a conventional wax injection mold fabricated by Al-filled epoxy resin. The manufacturing cost reduction for a wax injection mold with high cooling efficiency about 63% can be obtained using the method proposed in this work.

Partial label learning (PLL) is a multi-class weakly supervised learning problem where each training instance is associated with a set of candidate labels but only one label is the ground truth. The main challenge of PLL is how to deal with the label ambiguities. Among various disambiguation techniques, large margin (LM)-based algorithms attract much attention due to their powerful discriminative performance. However, existing LM-based algorithms either neglect some potential candidate labels in constructing the margin or introduce auxiliary estimation of class capacities which is generally inaccurate. As a result, their generalization performances are deteriorated. To address the above-mentioned drawbacks, motivated by the optimistic superset loss, we propose an LM Partial LAbel machiNE (LM-PLANE) by extending multi-class support vector machines (SVM) to PLL. Compared with existing LM-based disambiguation algorithms, LM-PLANE considers the margin of all potential candidate labels without auxiliary estimation of class capacities. Furthermore, an efficient cutting plane (CP) method is developed to train LM-PLANE in the dual space. Theoretical insights into the effectiveness and convergence of our CP method are also presented. Extensive experiments on various PLL tasks demonstrate the superiority of LM-PLANE over existing LM based and other representative PLL algorithms in terms of classification accuracy.

This paper presents a method to install a vision-based system on 3D printers for the purpose of detection and classification of anomalies during the printing process. Recently, 3D printing has becoming popular in manufacturing industry. Hence, quality monitoring is introduced to inspect the printing process to ensure minimal defects occurred and reduce wastage of printing materials if defects occur. This project is performed using Polylactic Acid (PLA) material as the printing material. The printing process is segregated into several checkpoints to capture the top surface of the product. Several popular image processing techniques were reviewed and employed in this paper. Images taken using Raspberry Pi are processed and classified into defective and non-defective groups using Python OpenCV. Hu moments analysis is carried out to extract the information on binary features of captured images and template images. Several shape distance formulae and also combination of different Hu moments factors is employed to enhance the rate of successful detection. The detection results are presented in detection classification matrices, while detection success rate and false alarm rate are calculated.

Nanocnide zhejiangensis X.F. Jin & Y.F. Lu, a new species of Urticaceae from Zhejiang, east China, is described with illustrations. The new species is morphologically similar to Nanocnide japonica in having staminate inflorescence longer than leaves, but differs by having glabrous stems, petioles, peduncles and abaxial leaf surfaces, glabrous perianth lobes of staminate flowers, dorsally glabrous perianth lobes of pistillate flowers, and acuminate or solitary spinose–setaceous at the apex. Analysis based on ITS, atpF–H, atpB–rbcL and trnL–F sequences also demonstrate that Nanocnide japonica is the closest extant relative to the new species.

In this article, a new configuration of ultra-wideband (UWB) monopole antenna employs two slot-type split ring resonators (SRRs) with band-notched filters in 5-6 GHz WiMAX/WLAN bands. The UWB antenna consists of the octagonal, heptagonal, and triangular patches. A 50 microstrip feedline is employed to stimulate the monopole antenna. With adjusting the size and the position of two slot-type SRRs on the ground plane, the UWB antenna produce a single band-notched and decrease interference with the WiMAX/WLAN bands. The results for VSWR, far-field H- and E-plane radiation patterns and gain of the proposed antennas are presented and discussed. The radiation area of the design antenna is 24 × 17.4 mm2. The measured results show a good agreement with the simulated results. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2595–2598, 2016

Multi-label learning paradigm, which aims at dealing with data associated with potential multiple labels, has attracted a great deal of attention in machine intelligent community. In this paper, we propose a novel multi-label twin support vector machine (MLTSVM) for multi-label classification. MLTSVM determines multiple nonparallel hyperplanes to capture the multi-label information embedded in data, which is a useful promotion of twin support vector machine (TWSVM) for multi-label classification. To speed up the training procedure, an efficient successive overrelaxation (SOR) algorithm is developed for solving the involved quadratic programming problems (QPPs) in MLTSVM. Extensive experimental results on both synthetic and real-world multi-label datasets confirm the feasibility and effectiveness of the proposed MLTSVM.

In this paper, we propose a robust smooth pinball loss nonparallel support vector machine (SpinNSVM) for binary classification. We first define a smooth pinball loss function, which is insensitive to the feature noise of samples, especially for the samples located nearby the boundary, and is able to capture the distribution of samples well simultaneously. Due to its differentiability, SpinNSVM model is formulated with two convex optimization problems. Furthermore, an efficient dual coordinate descent algorithm is adopted to solve the dual formulations of SpinNSVM. Numerical experiments are provided to demonstrate the effectiveness and the efficiency of the proposed method.

A P(V)-N activation method based on nucleoside phosphoropiperidate/DCI system has been developed for improved synthesis of diverse UDP-furanoses. The reaction conditions including temperature, amount of activator, and reaction time were optimized to alleviate the degradation of UDP-furanoses to cyclic phosphates. In addition, an efficient and facile phosphoramidite route was employed for the preparation of furanosyl-1-phosphates.

The recently proposed projection twin support vector machine (PTSVM) is an excellent nonparallel classifier. However, PTSVM employs the least-squares loss function to measure its within-class empirical risk, resulting in several drawbacks, such as non-sparseness for decision, sensitivity to outliers, expensive matrix inversion, and inconsistency in the linear and nonlinear models. To alleviate these issues, in this paper, we propose a novel nonparallel sparse projection support vector machine (NPrSVM). Different from the original PTSVM that squeezes the projected values of within-class instances to its own class center, NPrSVM aims to cluster them as much as possible within an insensitive tube. Specifically, our NPrSVM owns the following attractive merits: (i) Benefiting from the L 1 -norm symmetric Hinge loss function, NPrSVM not only enjoys sparseness for decision but also improves robustness to outliers. (ii) The elegant formulation of dual problems in NPrSVM no longer involves the matrix inversion during the training procedure. This greatly saves the computing time compared to PTSVM. (iii) While the nonlinear formulation of PTSVM is not the direct extension of linear PTSVM, the linear and nonlinear versions of our NPrSVM are consistent. (iv) An efficient dual coordinate descent algorithm is further designed for NPrSVM to handle large-scale classification. Finally, the feasibility and effectiveness of NPrSVM are validated by extensive experiments on both synthetic and real-world datasets.

Many existing works have studied the learning on imbalanced data, however, it is still very challenging to handle high-dimensional imbalanced data. One key challenge of learning on imbalanced data is that most learning models usually have a bias towards the majority and its performance will deteriorate in the presence of underrepresented data and severe class distribution skews. One solution is to synthesize the minority data to balance the class distribution, but it may lead to more overlapping, especially in the high-dimensional setting. To alleviate the above challenges, in this paper, we present a novel Rectified Encoder Network (REN) for high-dimensional imbalanced learning tasks. The main contribution is that: (1) To deal with high-dimensionality, REN encodes high-dimensional imbalanced data into low dimensional latent codes as a latent representation. (2) To obtain a discriminative representation, we introduce a Rectifier to match the latent codes with our proposed Predefined Codes, which disentangles the overlapping among classes. (3) During rectification, in the Predefined Latent Distribution, we can efficiently identify and generate informative samples to maintain the balance of class distribution, so that the minority classes will not be neglected. The experimental results on several high-dimensional and image imbalanced data sets indicate that our REN obtains good representation code for classification and visualize the reason why REN gets better performance in high-dimensional imbalanced learning.

To identify the variation in soil bacterial community diversity brought by the invasion of Phyllostachys edulis into Pinus massoniana forest, we collected mixed soil samples from three types of forests, including a pure Ph. edulis forest, a mixed Ph. edulis and conifers (P. massoniana) fore-st, and a mixed forest of evergreen broadleaves and conifers. Samples were analyzed by high-throughput sequencing for measuring the soil bacterial community diversity and structure. The results showed that the bacterial communities comprised of 511 genera, 160 families, 134 orders, 88 classes, and 39 phyla. The proportion of Acidobateria in the pure Ph. edulis forest was significantly lower and the proportions of Actinobacteria, Bacteroidetes, TM7, and Chlamydiae were significantly higher than that in other forests. Meanwhile, various genera showed significant differences in proportions in both the mixed forests when compared with their corresponding proportions in the pure Ph. edulis forest. There were 130 non-dominant genera presented alone in each of the two mixed forests, at proportions between 0.005% and 0.1%. The pure Ph. edulis forest had the lowestAgr; diversity, while that of the mixed Ph. edulis and evergreen broadleaf forest was intermediate, and that of the mixed evergreen broadleaf and coniferous forest was the highest. The index ofAgr; diversity followed evergreen coniferous mixed forestbamboo needle mixed forestpure bamboo forest, and the diffe-rence between the mixed Ph. edulis and evergreen broadleaf forest and the mixed evergreen broadleaf and coniferous forest was insignificant. The PCoA results revealed that the invasion of Ph. edulis affected the population diversity and community structure of soil bacteria. There was a significant correlation between the percentage of non-dominant bacterial phyla in the soil (less than 0.1% of the proportion) and the soil environmental gradient such as water-soluble organic nitrogen and nitrate. Water-soluble organic nitrogen and nitrate had strong effects on the non-dominant bacterial population in the soil following the invasion of Ph. edulis into the P. massoniana forest. These findings would serve as important references for further related studies.为揭示毛竹入侵邻域林分过程中对土壤细菌群落多样性变化的影响,以马尾松林为研究对象,采集纯毛竹林、竹针混交林和常绿针叶(马尾松)混交林3类样地的混合土样,基于高通量测序技术分析土壤细菌群落多样性和结构变化.结果表明: 研究期间共得到细菌类群39门88纲134目160科511属;在门的分类水平上,与竹针混交林、常绿针叶混交林相比,纯毛竹林酸杆菌门所占比例显著较低,而放线菌门、拟杆菌门、TM7和衣原体门所占比例较高;在属的分类水平上,相对于纯毛竹林,竹针混交林和常绿针叶混交林均有多属表现出所占比例显著上升或下降,单独出现在竹针混交林或常绿针叶混交林且所占比例在0.005%～0.1%的非优势属有130属;α多样性指数均表现为常绿针叶混交林竹针混交林纯竹林,且纯竹林与两者均有显著差异,而常绿针叶混交林和竹针混交林之间差异不显著.PCoA分析表明,毛竹入侵对土壤细菌的种群多样性和群落结构产生了影响;所占比例小于0.1%的非优势菌群门分类水平的百分比特征与土壤环境梯度(水溶性有机氮和硝态氮)之间有显著相关性,两者对毛竹入侵马尾松林后土壤细菌群落的非优势种群影响巨大,具有重要的参考价值.

To explore clinical effects of ultrasound-guided minimal traverse-cross technique repair for acute closed Achilles tendon ruptures.From January 2015 to March 2017, 20 patients with acute closed Achilles tendon rupture were treated by minimal traverse-cross technique repair with ultrasound guided. Among them, including 13 males and 7 females, aged from 28 to 49 years old with an average of(31.3 ±4.5) years old. All patients were single side injury. Fifteen patients on the left side and 5 patients were on the right side. The time from injury to operation ranged from 1 to 5 days with an average of (2.5±0.7) days. Operative time, postoperative complications were observed, and AOFAS score before and after operation at 12 months were compared.All patients were followed up for 12 to 27 months with an average of(15.2±4.9) months. Operative time ranged from 33 to 65 min with an average of(43.7±5.6) min. Incision of one patient were continued oozing and improved after changing dressings, other patients were healed at stage I. No sural nerve irritation symptoms and palindromic rapture of heel tendon occurred. AOFAS score was improved from 65.2±7.4 before operation to 97.7±4.7 after operation at 12 months (t=22.5,Ultrasound-guided minimal traverse-cross technique repair for acute closed Achilles tendon ruptures, which promise minimal incision, protect sural nerve, ensure quality of tendon anastomosis and fixation, and is a ideal method for repairing acute closed Achilles tendon ruptures.

We propose a Laplacian unit-hyperplane classifier (LUHC) for PU learning.LUHC employs both geometrical and discriminant properties of PU information.The QPP in LUHC is rather small, resulting in its faster training speed.A parameter ? is introduced to control the upper bounds on the fractions of positive examples with margin errors.Experimental results show its efficiency and superiority. In machine learning and data mining, learning from positive and unlabeled examples (PU learning) has attracted a great deal of attention, and the corresponding classifiers are required because of its applications in many practical areas. For PU learning, we propose a novel classifier called Laplacian unit-hyperplane classifier (LUHC), which determines a decision unit-hyperplane by solving a quadratic programming problem (QPP). The advantages of our LUHC are as follows: (1) Both geometrical and discriminant properties of the examples are exploited, resulting in better classification performance. (2) The size of QPP to be solved is small since it depends only on the number of the positive examples, resulting in faster training speed. (3) A meaningful parameter ? is introduced to control the upper bounds on the fractions of positive examples with margin errors. Preliminary experiments on both synthetic and real data sets show high level of agreement with aforementioned hypothesis, suggesting that our LUHC is superior to biased support vector machine, spy-expectation maximization, and naive Bayes in both classification ability and computation efficiency.

Objective: To discuss the efficacy of levofloxacin combined with chemotherapy in the treatment of retreated smear positive pulmonary tuberculosis and its influence on t-cell subgroup levels in peripheral blood of patients. Methods: A total of 85 cases with TB were divided into control group (n=42) and study group (n=43) based on therapeutic methods, and patients in control group were given 3HRZE/6HRE conventional chemotherapy treatment, while patients in study group were added with levofloxacin treatment, on the basis of control group. Finally the influence of these two therapeutic methods on t-cell subgroup levels in peripheral blood of patients was compared. Results: (1) After treatment, t-cell subgroup levels in peripheral blood of patients in study group were significantly higher than that in control group; CD4+/CD8+ level difference of patients in both groups had no statistical significance before and after treatment; (2) Adverse reaction occurrence rate of patients in control group during the therapeutic process was 14.29% (6/42), while adverse reaction occurrence rate of patients in study group during the therapeutic process was 11.63% (5/43), adverse reaction occurrence rate difference of patients during the therapeutic process in both groups had no statistical significance. Conclusion: Efficacy of levofloxacin combined with chemotherapy in the treatment of retreated smear positive pulmonary tuberculosis was obvious, and it could improve the organism immune function through improving t-cell subgroup levels in peripheral blood of patients, which was deserved clinical popularization.