Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers.

Lower-extremity peripheral artery disease (LE-PAD) is a prevalent circulatory disorder with risks of critical limb ischemia and amputation. This study aimed to develop a prediction model for a novel LE-PAD subtype to predict the severity of the disease and guide personalized interventions. Additionally, LE-PAD pathogenesis involves altered immune microenvironment, we examined the immune differences to elucidate LE-PAD pathogenesis. A total of 460 patients with LE-PAD were enrolled and clustered using unsupervised machine learning algorithms (UMLAs). Logistic regression analyses were performed to screen and identify predictive factors for the novel subtype of LE-PAD and a prediction model was built. We performed a comparative analysis regarding neutrophil levels in different subgroups of patients and an immune cell infiltration analysis to explore the associations between neutrophil levels and LE-PAD. Through hematoxylin and eosin (H&E) staining of lower-extremity arteries, neutrophil infiltration in patients with and without LE-PAD was compared. We found that UMLAs can helped in constructing a prediction model for patients with novel LE-PAD subtypes which enabled risk stratification for patients with LE-PAD using routinely available clinical data to assist clinical decision-making and improve personalized management for patients with LE-PAD. Additionally, the results indicated the critical role of neutrophil infiltration in LE-PAD pathogenesis.

Novel roles of an intragenic G-quadruplex in controlling microRNA expression and cardiac function.

Simultaneous dysregulation of multiple microRNAs (miRs) affects various pathological pathways related to cardiac failure. In addition to being potential cardiac disease-specific markers, miR-23b/27b/24-1 were reported to be responsible for conferring cardiac pathophysiological processes. In this study, we identified a conserved guanine-rich RNA motif within the miR-23b/27b/24-1 cluster that can form an RNA G-quadruplex (rG4) in vitro and in cells. Disruption of this intragenic rG4 significantly increased the production of all three miRs. Conversely, a G4-binding ligand tetrandrine (TET) stabilized the rG4 and suppressed miRs production in human and rodent cardiomyocytes. Our further study showed that the rG4 prevented Drosha-DGCR8 binding and processing of the pri-miR, suppressing the biogenesis of all three miRs. Moreover, CRISPR/Cas9-mediated G4 deletion in the rat genome aberrantly elevated all three miRs in the heart in vivo, leading to cardiac contractile dysfunction. Importantly, loss of the G4 resulted in reduced targets for the aforementioned miRs critical for normal heart function and defects in the L-type Ca2+ channel-ryanodine receptor (LCC-RyR) coupling in cardiomyocytes. Our results reveal a novel mechanism for G4-dependent regulation of miR biogenesis, which is essential for maintaining normal heart function.

Harpertrioate A, an A,B,D-seco-Limonoid with Promising Biological Activity against Alzheimer's Disease from Twigs of Harrisonia perforata (Blanco) Merr.

Harpertrioate A (1), an A,B,D-seco-limonoid with a rearranged ring B incorporating exocyclic C-30, was isolated from the EtOAc extract of Harrisonia perforata twigs. Its structure, including absolute configurations, was determined on the basis of spectroscopic data and X-ray crystallography. This compound exhibited biological activities against Alzheimer's disease by reducing Aß42 and Aß40 production and shifting APP processing toward nonamyloidogenic pathway. The effect of 1 on the Aß production was comparable to that of gemfibrozil.

DeepLofargram: A deep learning based fluctuating dim frequency line detection and recovery.

This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, long enough time integration can always enhance the detection characteristic. But this does not hold for irregularly fluctuating lines. Deep learning has been shown to perform very well for sophisticated visual inference tasks. With the composition of multiple processing layers, very complex high level representations that amplify the important aspects of input while suppressing irrelevant variations can be learned. Hence, DeepLofargram is proposed, composed of a deep convolutional neural network and its visualization counterpart. Plugging into specifically designed multi-task loss, an end-to-end training jointly learns to detect and recover the spatial location of potential lines. Leveraging on this deep architecture, performance limits of low SNR can be achieved as low as -24 dB on average and -26 dB for some. This is far beyond the perception of human vision and significantly improves the state-of-the-art.

Arbitrary-order superdirectivity of circular sensor arrays.

Superdirectivity can yield large directivity using a small array aperture, but it suffers from high sensitivity to random errors. In a previously proposed model, robust Nth-order superdirectivity was obtained by combining low-order superdirectivity sub-solutions with orders from 0 to N. However, the orders N were exclusively positive integers, which may lead to an over-improved robustness with an excessively degraded directivity. This paper presents an arbitrary-order superdirectivity model of circular sensor arrays, which can achieve a more flexible compromise between directivity and robustness than the previous model. A coefficient η is weighted at the (N + 1)th-order entry of the optimal weighting vector and the new weighting vector will consist of the 0th- to Nth-order and the weighted (N + 1)th-order entries. The beampattern, the directivity factor, and the error sensitivity function can then be expressed as functions of the coefficient η. Moreover, broadband superdirective beampatterns with a desired directivity factor or error sensitivity function can be readily synthesized using the closed-form solutions derived from the arbitrary-order superdirectivity model. Simulations and experimental results show that the proposed model can easily synthesize superdirective beampatterns with arbitrary orders, and it is more flexible than the previous integer-order model. It is also demonstrated that the performance of the proposed method is similar to that of the optimization-based method, but it uses a simple closed-form weighting vector rather than a numerical algorithm.

Passive broadband source localization based on a Riemannian distance with a short vertical array in the deep ocean.

A passive broadband source localization approach in the deep ocean is proposed based on the coherent matched-field processing combined with a Riemannian distance. The spatial coherence between hydrophones over frequency bandwidth is utilized for eliminating the unknown source spectrum. Three Riemannian distances are introduced for the measurement of the similarity between the data and replica matrices. The experimental results indicate that the proposed processors with a short array can effectively suppress the sidelobe and improve the localization performance. The source localization influenced by the factors such as the aperture, number of hydrophones, and desired bandwidth is also demonstrated.

Long-term ambient noise statistics in the northeast South China Sea.

This paper reports on the long-term statistics of ambient noise (50-2000 Hz) in the northeast South China Sea. The data were collected from July 2016 to March 2018. The long-term statistics, seasonal, and diel variations of ambient noise are analyzed. There are significant seasonal variations from 500 to 2000 Hz. The ambient noise level at 1000 Hz is 6-10 dB higher in winter than in summer. There is only a small difference between the day and night comparison (less than 1.6 dB). The results are significant to evaluate and improve the performance of underwater acoustic systems in this area.

A performance study of acoustic interference structure applications on source depth estimation in deep water.

A recent publication by Duan et al. [J. Acoust. Soc. Am. 142(3), EL245-EL250 (2017)] analyzed the interference pattern observed in the plot of acoustic intensity versus source-receiver range and frequency in deep water. This paper extends this work by applying an equivalent acoustic intensity surface versus the vertical angle of arrivals and frequency to depth estimation of broadband sources. A matched-interference-structure (MIS) method that processes the whole acoustic intensity surface is proposed and formulated as a filtration of the surface where the binominal filter weights ±1 for each surface grid point and presumed source depth are based on model-based interference structures. For comparison, the conventional method of matching multipath delays is reformulated and applied to process the surface. Because the numerical prediction of interference structures can be costly, analytical expressions are derived to improve the efficiency. Monte Carlo simulations are carried out to compare the performance and experimental data of broadband random sequences and pulse sources are used to illustrate the methods. The MIS method has a higher resolution and can work under a lower signal-to-noise ratio in the case of weak source depth fluctuation but is not as robust to the large source depth fluctuation as the other method.

12ß-Acetyloxyperforatin, a New Limonoid from Harrisonia Perforata.

A new A, D-seco limonoid, named 12-acetyloxyperforatin (1), along with three known ones, were isolated from the leaves of Harrisonia perforata. Their structures were elucidated on the basis of spectroscopic analysis, including extensive NMR techniques and computational modelling. These compounds showed no inhibitory activity against the 11ß-HSD1 enzyme.

Structural Revision of Macropodumine A and Structure of 2-Deoxymacropodumine A, Daphniphyllum Alkaloids with 11-Membered Macrolactone Rings.

An unusual Daphniphyllum alkaloid, 2-deoxymacropodumine A (1), possessing an 11-membered macrolactone ring, was obtained from an extract of the stems of Daphniphyllum angustifolium. The structure of 1 was elucidated by 1D and 2D NMR spectroscopic methods and chemical calculations. Based on a comparison of the experimental and calculated NMR data, the structure of macropodumine A (2'), an analogue of 1, was also revised.

Particle filter for multipath time delay tracking from correlation functions in deep water.

This paper presents a particle filtering-based approach for tracking multipath time delays from correlation function, such as autocorrelation, cross-correlation, and matched-filter output. The proposed approach exploits the continuous evolution with time of the correlations between multipath arrivals masked by the background noise to track time delays. The prominent feature of this approach is tracking the signal-related peaks (single points) instead of correlation pulses adopted in conventional approaches. To do so, the correlation function with only local peaks is introduced in the model of the measurement equation. This allows no assumption on the reference signal used to match the correlation pulse and no a priori knowledge of the covariance of the background noise. The time-evolving marginal posterior probability densities are also extracted by filtering to reveal the uncertainty of the time delays in every step of tracking. The approach is performed on both simulated data in reliable acoustic path propagation and experimental data collected during two deep water experiments; the results demonstrate significant advantages of the proposed method over a conventional state-space approach, the multiple hypothesis tracking, and a modified peak amplitude detection method.

Biodegradable polymer drug-eluting stents versus second-generation drug-eluting stents in patients with and without diabetes mellitus: a single-center study.

BACKGROUND: To improve outcomes in patients with diabetes mellitus (DM) undergoing percutaneous coronary intervention remain an unmet clinical need. The study aimed to evaluate the efficacy and safety of G2-DESs and BP-DESs in patients with and without DM in a single center in China. METHODS: A total of 7666 consecutive patients who exclusively had G2-DES or BP-DES implantation throughout 2013 in our center were studied. The primary efficacy endpoint was any target lesion revascularization (TLR), whereas the primary safety endpoint was a composite of death or myocardial infarction (MI) at 2-year follow-up. RESULTS: G2-DESs had a similar occurrence of death, non-fatal MI, TLR, stroke, and stent thrombosis compared with BP-DESs in patients with DM (all P > 0.05). The incidence of TVR and TLR was lower for G2-DESs than for BP-DESs in patients without DM (3.2% vs. 5.1%, P = 0.002; 2.2% vs. 4.5%, P < 0.001, respectively). Kaplan-Meier analysis also showed better TVR- and TLR-free survival rates for G2-DESs than for BP-DESs in patients without DM. Multivariate analysis showed that a BP-DES was an independent risk factor for TLR (hazard ratio 1.963, 95% confidence interval 1.390-2.772, P < 0.001) in patients without DM, which was not predictive of other components of major adverse cardiac events (P > 0.05). CONCLUSIONS: G2-DESs have better efficacy, represented by a reduced risk of TLR, and similar safety compared with BP-DESs in patients without DM. G2-DESs have similar efficacy and safety compared with BP-DESs in patients with DM at 2-year follow-up.

Matrix-Regularized Multiple Kernel Learning via (r,p) Norms.

This paper examines a matrix-regularized multiple kernel learning (MKL) technique based on a notion of (r,p) norms. For the problem of learning a linear combination in the support vector machine-based framework, model complexity is typically controlled using various regularization strategies on the combined kernel weights. Recent research has developed a generalized ℓp-norm MKL framework with tunable variable p(p≥1) to support controlled intrinsic sparsity. Unfortunately, this ``1-D'' vector ℓp-norm hardly exploits potentially useful information on how the base kernels ``interact.'' To allow for higher order kernel-pair relationships, we extend the ``1-D'' vector ℓp-MKL to the ``2-D'' matrix (r,p) norms (1 ≤ r,p < ∞). We develop a new formulation and an efficient optimization strategy for (r,p)-MKL with guaranteed convergence. A theoretical analysis and experiments on seven UCI data sets shed light on the superiority of (r,p)-MKL over ℓp-MKL in various scenarios.

Comparison of Efficacy and Safety between First- and Second-Generation Drug-Eluting Stents in Patients with Acute Coronary Syndrome.

BACKGROUND: It remains undetermined whether second-generation drug-eluting stents (G2-DESs) outperform first-generation DESs (G1-DESs) in patients with acute coronary syndrome (ACS). We aimed to compare the efficacy and safety of G1-DES and G2-DES in ACS patients in a high-volume cardiovascular center. METHODS: In 2013, 10,724 consecutive patients underwent percutaneous coronary intervention in our institution. We included 4037 patients with ACS who underwent exclusively G1-DES or G2-DES implantation (n = 364 and n = 3673, respectively). We used propensity score matching to minimize the imbalance between the G1-DES and G2-DES groups and followed patients for 2 years. The efficacy endpoints were major adverse cardiac events (MACEs) and its components including target vessel-related myocardial infarction (TV-MI), target vessel revascularization/target lesion revascularization (TVR/TLR), and cardiac death. The safety endpoint was stent thrombosis. Continuous variables were compared by Mann-Whitney U-test, and categorical variables were compared using Pearson's Chi-square or Fisher's exact test. Kaplan-Meier curves were constructed to compare the event-free survival rates, and multivariate Cox proportional hazards regression analysis was used to assess whether stent type was an independent risk factor for the efficacy and safety endpoints. RESULTS: At the 2-year follow-up, the results for MACE and it components, as well as stent thrombosis, were similar for G1-DES and G2-DES (MACE, 5.2% vs. 4.3%, χ2 = 0.514, P = 0.474; TV-MI, 0.8% vs. 0.4%, P = 0.407; TVR, 4.9% vs. 3.7%, χ2 = 0.939, P = 0.333; TLR, 3.8% vs. 2.5%, χ2 = 1.610, P = 0.205; cardiac death, 0.3% vs. 0.5%, P = 0.670; and stent thrombosis, 0.5% vs. 0.4%, P > 0.999). Kaplan-Meier analysis indicated similar event-free survival rates between G1-DES and G2-DES after propensity score matching (all: log-rank P > 0.05). Multivariate analysis demonstrated that stent type was not an independent risk factor for the efficacy and safety endpoints (MACE, hazard ratio [HR] = 0.805, 95% confidence interval [CI]: 0.455-1.424, P = 0.456; TV-MI, HR = 0.500, 95% CI: 0.101-2.475, P = 0.395; TVR, HR = 0.732, 95% CI: 0.403-1.330, P = 0.306; TLR, HR = 0.629, 95% CI: 0.313-1.264, P = 0.193; cardiac death, HR = 1.991, 95% CI: 0.223-17.814, P = 0.538; and stent thrombosis, HR = 0.746, 95% CI: 0.125-4.467, P = 0.749). CONCLUSION: G1-DES and G2-DES have similar efficacy and safety profiles in ACS patients at the 2-year follow-up.

Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

Abietane Diterpernoids from the Roots of Euphorbia ebracteolata.

A new ent-abietane diterpernoid, named ebracteolata D (1), along with 11 known analogues, was isolated from the roots of Euphorbia ebracteolata Hayata. The structure of 1 was elucidated on the basis of spectroscopic analysis and molecular modeling. Cytotoxicity of compounds 1-12 was evaluated as well as the effect on the NF-κB pathway. Among them, compound 12, jolkinolide B, displayed broad inhibitory effects against proliferation of tumor cell lines. Mechanistic studies indicated that the compound 12 can inhibit TNF-α induced NF-κB activation, thereby inducing tumor cell apoptosis.

Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization.

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.

On the Impact of Regularization Variation on Localized Multiple Kernel Learning.

This brief analyzes the effects of regularization variations in the localized kernel weights on the hypothesis generated by localized multiple kernel learning (LMKL) algorithms. Recent research on LMKL includes imposing different regularizations on the localized kernel weights and has led to varying formulations and solution strategies. Following the stability analysis theory as presented by Bousquet and Elisseeff, we give stability bounds based on the norm of the variation of localized kernel weights for three LMKL methods cast in the support vector machine classification framework, including vector -norm LMKL, matrix-regularized -norm LMKL, and samplewise -norm LMKL. Further comparison of these bounds helps to qualitatively reveal the performance differences produced by these regularization methods, that is, matrix-regularized LMKL achieves superior performance, followed by vector -norm LMKL and samplewise -norm LMKL. Finally, a set of experimental results on ten benchmark machine learning UCI data sets is reported and shown to empirically support our theoretical analysis.

Usefulness of the SYNTAX score II to validate 2-year outcomes in patients with complex coronary artery disease undergoing percutaneous coronary intervention: A large single-center study.

OBJECTIVE: This study aimed to assess the prognostic ability of synergy between percutaneous coronary intervention with Taxus and cardiac surgery score II (SS-II) in a large cohort of patients with complex coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI) in clinical practice. BACKGROUND: Few studies have explored the usefulness of SS-II in nonrandomized clinical patients with complex CAD undergoing temporary PCI. METHODS: We prospectively enrolled 4398 consecutive patients undergoing three-vessel and/or unprotected left main PCI in a single center from January 2013 to December 2013. Patients were stratified according to SS-II for PCI tertiles as follows: SS-II ≤ 20 (n = 1474); SS-II 20-26 (n = 1462); and SS-II > 26 (n = 1462). The predictive ability for 2-year mortality was compared between angiographic scores and scores combining both angiographic and clinical variables. RESULTS: Mortality was significantly higher in the upper tertile than in the intermediate or lower tertiles during the 2-year follow-up (2.7% vs 1.7% vs 0.5%, respectively; P < 0.001). Multivariate analysis showed that SS-II was an independent predictor of 2-year mortality (hazard ratio: 1.66, 95% confidence interval: 1.03-2.68; P = 0.04). After adjusting for multivariable factors, SS-II had better prediction of 2-year mortality than baseline SS (C-index: SS-II = 0.740 vs baseline SS = 0.620; P < 0.001). CONCLUSIONS: As a risk score combining both anatomical and clinical variables, SS-II demonstrated superiority compared with the purely angiographic SS to predict 2-year mortality in a clinical population of patients with severe CAD undergoing temporary PCI.

Effect of sex difference in clinical presentation (stable coronary artery disease vs unstable angina pectoris or non-ST-elevation myocardial infarction vs ST-elevation myocardial infarction) on 2-year outcomes in patients undergoing percutaneous coronary intervention.

OBJECTIVE: To determine whether there is a difference in 2-year prognosis among patients across the spectrum of coronary artery disease undergoing percutaneous coronary intervention (PCI). METHODS: We analyzed all consecutive patients undergoing PCI at a single center from 1/1-12/31/2013. Clinical presentations were compared between sexes according to baseline clinical, angiographic, and procedural characteristics and 2-year (mean 730 ± 30-day) outcomes. RESULTS: We grouped 10 724 consecutive patients based on sex and clinical presentation. Among patients with ST-elevation myocardial infarction (STEMI), rates of all-cause death (6.7% vs 1.4%) and cardiac death (3.8% vs 1.1%) were significantly higher in women than in men (P < 0.05), but these rates did not differ between men and women with stable coronary artery disease (SCAD) and non-ST-elevation acute coronary syndrome ((NSTE-ACS). Incidence of major bleeding was greater than in men only in those women presenting with ACS. After multivariable adjustment, female sex was not an independent predictor of outcomes in STEMI (hazard ratio [HR] for all-cause death: 1.33, 95% confidence interval [CI]:0.52-3.38; P = 0.55; HR for cardiac death: 0.69, 95%CI: 0.23-2.09, P = 0.51], but was still an independent predictor of bleeding in STEMI (HR: 3.53, 95%CI: 1.26-9.91, P = 0.017). CONCLUSION: Among STEMI patients, women had worse 2-year mortality after PCI therapy, but female sex was not an independent predictor of mortality after adjustment for baseline characteristics. In STEMI patients, women were at higher bleeding risk than men after PCI, even after multivariable adjustment.