Cardiovascular mortality among patients with diffuse large B-cell lymphoma: a population-based study.

We aim to investigate cardiovascular mortality risk among diffuse large B-cell lymphoma (DLBCL) patients and explore cardiovascular mortality trends in the past decades in United States. We extracted data from the Surveillance, Epidemiology, and End Results database for adult patients diagnosed with DLBCL between 1975 and 2019. Standardized mortality ratio, joinpoint regression analysis, and competing risk model were analyzed. Overall, 49,918 patients were enrolled, of whom 4167 (8.3%) cardiovascular deaths were observed, which was 1.22 times the number expected (95%CI, 1.19-1.26). During 1985-2019, the incidence-based cardiovascular mortality rate increased by 0.98% per year (95%CI, 0.58-1.39%), with statistically significant increases in age groups younger than 75 years. The cumulative mortality from cardiovascular disease increased by age but never exceeded that from DLBCL. Older age, male sex, earlier year of diagnosis, lower tumor stage at diagnosis, chemotherapy, radiotherapy, and surgery were all poor prognostic factors for cardiovascular mortality.

Constrained deconvolution from wavefront sensing using the frozen flow hypothesis and complex wavelet regularization.

Deconvolution from wavefront sensing (DWFS) is a high-performance image restoration technique designed to compensate for atmospheric turbulence-induced wavefront distortions. It uses simultaneously recorded short-exposure images of the object and high cadence wavefront sensor (WFS) data to estimate both the wavefronts and the object. Conventional DWFS takes no account of the temporal correlations in WFS data, which limits the reconstruction of high-spatial frequency components of wavefront distortion and then the recovery of the object. This paper takes the frozen flow hypothesis (FFH) to model the temporal evolution of atmospheric turbulence. Under this assumption, a joint estimation is performed in a Bayesian framework to simultaneously estimate the object and the turbulence phases with strict constraints imposed by WFS data and the FFH. It takes into account the temporal correlations in WFS data as well as the available a priori knowledge about the object and turbulence phases. Taking advantage of the sparse analysis prior of the object in the wavelet domain, a sparse regularization of the object based on the 2D dual-tree complex wavelet transform is proposed. Numerical experiments show that the proposed method is robust and effective for high-resolution image restoration in different seeing conditions.

Meta-analysis of the relationship between interleukin-6 levels and the prognosis and severity of acute coronary syndrome.

This study aimed to explore the relationship between plasma interleukin 6 (IL-6) levels, adverse cardiovascular events, and the severity of acute coronary syndrome (ACS). A literature review was performed of studies regarding IL-6 and ACS extracted from databases including EMBASE, Cqvip, MEDLINE, Web of Knowledge, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang data. The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the literature. The literature was screened, its quality was evaluated, and relevant data were extracted for performing meta-analysis using RevMan software (version 5.3). A total of 524 studies were included in the initial survey. After several rounds of screening and analysis, six studies met the inclusion criteria and underwent meta-analysis using a fixed-effect model. Patients were divided into non-severe and severe groups based on the concentration of high-sensitivity C-reactive protein. Meta-analysis of the relationship between IL-6 and the severity of ACS showed that the plasma IL-6 level of patients in the severe group was significantly higher than that of patients in the non-severe group (p<0.00001). Additionally, patients with experience of major adverse cardiovascular events had significantly higher plasma IL-6 levels than did patients without experience of such events (p<0.00001). In summary, patients with ACS and high IL-6 levels tended to be in a critical condition, with a higher risk of adverse cardiovascular events and worse prognosis. Thus, IL-6 levels could indicate whether patients with ACS may have adverse cardiovascular events and determine the severity of ACS.

Meta-analysis of the relationship between interleukin-6 levels and the prognosis and severity of acute coronary syndrome

This study aimed to explore the relationship between plasma interleukin 6 (IL-6) levels, adverse cardiovascular events, and the severity of acute coronary syndrome (ACS). A literature review was performed of studies regarding IL-6 and ACS extracted from databases including EMBASE, Cqvip, MEDLINE, Web of Knowledge, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang data. The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the literature. The literature was screened, its quality was evaluated, and relevant data were extracted for performing meta-analysis using RevMan software (version 5.3). A total of 524 studies were included in the initial survey. After several rounds of screening and analysis, six studies met the inclusion criteria and underwent meta-analysis using a fixed-effect model. Patients were divided into non-severe and severe groups based on the concentration of high-sensitivity C-reactive protein. Meta-analysis of the relationship between IL-6 and the severity of ACS showed that the plasma IL-6 level of patients in the severe group was significantly higher than that of patients in the non-severe group (p<0.00001). Additionally, patients with experience of major adverse cardiovascular events had significantly higher plasma IL-6 levels than did patients without experience of such events (p<0.00001). In summary, patients with ACS and high IL-6 levels tended to be in a critical condition, with a higher risk of adverse cardiovascular events and worse prognosis. Thus, IL-6 levels could indicate whether patients with ACS may have adverse cardiovascular events and determine the severity of ACS.

InGaAsP/InP Nanocavity for Single-Photon Source at 1.55-µm Telecommunication Band.

A new structure of 1.55-µm pillar cavity is proposed. Consisting of InP-air-aperture and InGaAsP layers, this cavity can be fabricated by using a monolithic process, which was difficult for previous 1.55-µm pillar cavities. Owing to the air apertures and tapered distributed Bragg reflectors, such a pillar cavity with nanometer-scaled diameters can give a quality factor of 104-105 at 1.55 µm. Capable of weakly and strongly coupling a single quantum dot with an optical mode, this nanocavity could be a prospective candidate for quantum-dot single-photon sources at 1.55-µm telecommunication band.

New insights into diversification of hyper-heuristics.

There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realized by perturbing the incumbent solutions to escape from local optima. In this paper, we report our attempt toward providing a new diversification mechanism, which is based on the concept of instance perturbation. In contrast to existing approaches, the proposed mechanism achieves the diversification by perturbing the instance under solving, rather than the solutions. To tackle the challenge of incorporating instance perturbation into hyper-heuristics, we also design a new hyper-heuristic framework HIP-HOP (recursive acronym of HIP-HOP is an instance perturbation-based hyper-heuristic optimization procedure), which employs a grammar guided high level strategy to manipulate the low level heuristics. With the expressive power of the grammar, the constraints, such as the feasibility of the output solution could be easily satisfied. Numerical results and statistical tests over both the Ising spin glass problem and the p -median problem instances show that HIP-HOP is able to achieve promising performances. Furthermore, runtime distribution analysis reveals that, although being relatively slow at the beginning, HIP-HOP is able to achieve competitive solutions once given sufficient time.

Hyper-heuristics with low level parameter adaptation.

Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.