The effect of beta-blockers on mortality in patients with heart failure and atrial fibrillation: A meta-analysis of observational cohort and randomized controlled studies

Background: Beta-blockers (BB) are the cornerstone of therapy for heart failure (HF); however, theeffects of these drugs on the prognosis of patients with concomitant atrial fibrillation (AF) remaincontroversial. The objective of this meta-analysis was to evaluate the efficacy of BB on mortality in HFcoexisting with AF.Methods: A systematic search of PubMed, Embase and the Cochrane Library databases wasconducted. Observational cohort studies and randomized controlled trials reporting outcomes ofmortality or HF hospitalizations for patients with HF and AF, being assigned to BB treatment.A non-BB group was also included.Results: A total of 8 clinical studies (5 randomized controlled trials and 3 observational cohort studies)involving 34197 patients were included in the analysis. The pooled analysis demonstrated that BBtreatment was associated with a 22% reduction in relative risk of all-cause mortality in patients withHF and AF (RR: 0.78; 95% CI 0.71–0.86; p < 0.00001; I2 = 27%). The pooled analysis of 5 studiesreported the outcome of HF hospitalization (2774 patients) which showed that BB therapy was not associatedwith a reduction of HF hospitalizations (RR: 0.94; 95% CI 0.79–1.11; p = 0.46; I2 = 38%).Conclusions: Meta-analysis suggests the potential mortality benefit of BB in patients with HF and AF.It was concluded herein that it is premature to deny patients with AF and HF to receive BB therapyconsidering current evidence

Three-Dimensional Analysis of Wakefields Generated by Flat Electron Beams in Planar Dielectric-Loaded Structures

An electron bunch passing through dielectric-lined waveguide generates $\check{C}$erenkov radiation that can result in high-peak axial electric field suitable for acceleration of a subsequent bunch. Axial field beyond Gigavolt-per-meter are attainable in structures with sub-mm sizes depending on the achievement of suitable electron bunch parameters. A promising configuration consists of using planar dielectric structure driven by flat electron bunches. In this paper we present a three-dimensional analysis of wakefields produced by flat beams in planar dielectric structures thereby extending the work of Reference [A. Tremaine, J. Rosenzweig, and P. Schoessow, Phys. Rev. E 56, No. 6, 7204 (1997)] on the topic. We especially provide closed-form expressions for the normal frequencies and field amplitudes of the excited modes and benchmark these analytical results with finite-difference time-domain particle-in-cell numerical simulations. Finally, we implement a semi-analytical algorithm into a popular particle tracking program thereby enabling start-to-end high-fidelity modeling of linear accelerators based on dielectric-lined planar waveguides.Comment: 12 pages, 2 tables, 10 figure

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Poly[[μ-1,4-bis­(imidazol-1-ylmeth­yl)benzene]bis­(μ4-cyclo­hexane-1,4-dicarboxyl­ato)dinickel(II)]

The structure of the polymeric title compound, [Ni2(C8H10O4)2(C14H14N4)]n, features a five-coordinate NiII centre defined by four carboxyl­ate O atoms from two different cyclo­hexane-1,4-dicarboxyl­ate (chdc) ligands and an N atom from one end of a 1,4-bis­(imidazol-1-ylmeth­yl)benzene (1,4-bix) mol­ecule. The NO4 coordination geometry is distorted square-pyramidal with the N atom in the apical position. Each end of the chdc ligand links pairs of NiII atoms into a paddle-wheel assembly, i.e. Ni2(O2CR′)4. These are connected into rows owing to the bridging nature of the chdc ligands, and the rows are connected into a two-dimensional grid via the 1,4-bix ligands. The 1,4-bix ligand, which is disposed about a centre of inversion, is disorderd. Two positions of equal occupancy were discerned for the –H2C(C6H4)CH2– residue

Reaction sintered Fe–Sialon ceramic composite: Processing, characterization and high temperature erosion wear behavior

AbstractFe–Sialon ceramic matrix composite has been newly developed from ferro-silicon alloy and commercial-grade industrial alumina powders by reaction sintering under a nitrogen atmosphere. The phase composition, mechanical properties and impact erosion wear behavior were investigated. The solid particle erosion tests have been conducted at elevated temperatures ranging from 25°C to 1200°C. Sharp SiC particles between 325 and 830μm in diameter were employed as impact abrasives. The results showed that Fe–Sialon ceramic consisted of β-Sialon and Fe3Si phases. The Z value of the as-formed β-Sialon varied from 0 to 3.2 with increasing the alumina content in the starting powders. The bending strength and Rockwell hardness gradually increased with raising the alumina addition. The erosion rate of Fe–Sialon ceramic is highly dependent on the testing temperature. The minor erosion took place at room temperature or 1200°C, while the major erosion occurred at 600–1000°C. Fe–Sialon composites showed better erosion wear resistance than the control material of alumina ceramic at 1200°C, although having much lower density and slightly lower bending strength

Targeted p53 on Small-Molecules-Induced Ferroptosis in Cancers

Ferroptosis is a type of programmed cell death characterized by the accumulation of lipid reactive oxygen species (L-ROS) driven by the oxidative degeneration of lipids in an iron-dependent manner. The mechanism by which lipid oxidative degradation drives ROS-ferroptosis involves metabolic dysfunctions that result in impaired intracellular metabolic processes and ROS production. Recent studies have found that p53 acts as a positive regulator of ferroptosis by promoting ROS production. p53 directly regulates the metabolic versatility of cells by favoring mitochondrial respiration, leading to ROS-mediated ferroptosis. In mild stress, p53 protects cell survival via eliminating ROS; additionally, in human colorectal cancer, p53 antagonizes ferroptosis by formation of the DPP4–p53 complex. In short, the mechanisms of p53-mediated ROS production underlying cellular response are poorly understood. In the context of recent research results, the indistinct roles of p53 on ROS-mediated ferroptosis are scrutinized to understand the mechanism underlying p53-mediated tumor suppression

A Deep Recurrent Survival Model for Unbiased Ranking

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data. Unbiased ranking methods typically rely on causality models and debias the user feedback through inverse propensity weighting. While practical, these methods still suffer from two major problems. First, when infer a user click, the impact of the contextual information, such as documents that have been examined, is often ignored. Second, only the position bias is considered but other issues resulted from user browsing behaviors are overlooked. In this paper, we propose an end-to-end Deep Recurrent Survival Ranking (DRSR), a unified framework to jointly model user's various behaviors, to (i) consider the rich contextual information in the ranking list; and (ii) address the hidden issues underlying user behaviors, i.e., to mine observe pattern in queries without any click (non-click queries), and to model tracking logs which cannot truly reflect the user browsing intents (untrusted observation). Specifically, we adopt a recurrent neural network to model the contextual information and estimates the conditional likelihood of user feedback at each position. We then incorporate survival analysis techniques with the probability chain rule to mathematically recover the unbiased joint probability of one user's various behaviors. DRSR can be easily incorporated with both point-wise and pair-wise learning objectives. The extensive experiments over two large-scale industrial datasets demonstrate the significant performance gains of our model comparing with the state-of-the-arts

Influence of interfering anions on Cu2+ and Zn2+ ions removal on chestnut outer shell-derived hydrochars in aqueous solution

Hydrothermal carbonization method was used to produce different hydrochars from chestnut outer shell at various temperatures while resolving the environmental issues of agricultural bio-waste. Hydrochars were adopted as adsorbents to remove heavy metal ions (copper and zinc ions) from aqueous solution. Hydrochar samples were characterized by Scanning Electron Microscope (SEM), Fourier Transform Infrared (FTIR), and Brunauer-Emmett-Teller (BET) nitrogen adsorption-desorption isotherm. An increase in the hydrothermal temperature from 160 °C to 220 °C results in higher BET surface area (18.81 m2 g-1) and the porosity of the samples. The resultant hydrochar at 220 °C exhibited a more excellent adsorption performance (8.13 mg g-1 for copper nitrate) than the other two hydrochars at low hydrothermal temperature. The current study addressed the influence of interfering anions of nitrates, sulfates and chlorides on the adsorption performance. The result shows that the hydrochar possesses larger removal efficiency for heavy metal nitrates that that of chlorides and sulfates