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A laboratory study is presented of the rotary-kiln pre-reduction and oxygen bath-smelting process for pellets comprising vanadium-titanium magnetite concentrate (from Panzhihua region, Sichuan province, China), coal and calcium hydroxide flux. The chemical and mineralogical compositions of the iron particles and slag are investigated using X-ray diffraction, scanning electron microscopy and energy-dispersive X-ray analyses. The influence of the slag basicity, smelting temperature, smelting time and pulverised coal addition on the slag-iron separation is determined. The results indicate that the optimum smelting parameters are smelting temperature above 1 450 degrees C, slag basicity above 0.8, smelting time 30-40 min and coal addition 20-22%. The sulphur content of the iron particles achieves 0.23 and 0.21% at 1 450 and 1 500 degrees C, respectively, at a slag basicity of 1.1, and the titanium dioxide content reaches above 38%., A laboratory study is presented of the rotary-kiln pre-reduction and oxygen bath-smelting process for pellets comprising vanadium-titanium magnetite concentrate (from Panzhihua region, Sichuan province, China), coal and calcium hydroxide flux. The chemical and mineralogical compositions of the iron particles and slag are investigated using X-ray diffraction, scanning electron microscopy and energy-dispersive X-ray analyses. The influence of the slag basicity, smelting temperature, smelting time and pulverised coal addition on the slag-iron separation is determined. The results indicate that the optimum smelting parameters are smelting temperature above 1 450 degrees C, slag basicity above 0.8, smelting time 30-40 min and coal addition 20-22%. The sulphur content of the iron particles achieves 0.23 and 0.21% at 1 450 and 1 500 degrees C, respectively, at a slag basicity of 1.1, and the titanium dioxide content reaches above 38%.

Yingyi Zhang,1 Libin Ren,1 Min Ren,2 Hua Yang,1 Kunmeng Li,1 Hongliang Cong,1 Zhigang Guo3 1Department of Cardiology, Tianjin Chest Hospital, Tianjin, 300222, People’s Republic of China; 2Tianjin Cardiovascular Institute, Tianjin, 300222, People’s Republic of China; 3Department of Cardiac Surgery, Tianjin Chest Hospital, Tianjin, 300222, People’s Republic of ChinaCorrespondence: Hongliang CongDepartment of Cardiology, Tianjin Chest Hospital, No. 261 of Taierzhuang South Road, Jinnan District, Tianjin, 300222, People’s Republic of ChinaTel +86 13702102871Email chlcong@163.comZhigang GuoDepartment of Cardiac Surgery, Tianjin Chest Hospital, No. 261 of Taierzhuang South Road, Jinnan District, Tianjin, 300222, People’s Republic of ChinaTel +86 13821002900Email zgguohosp@163.comObjective: This study aims to investigate the correlation between triglyceride–glucose index (TyG) and the risk of cardiovascular disease (CVD).Methods: A total of 102,061 permanent residents of Tianjin, China, aged 35– 75 years were surveyed. A questionnaire, physical examination, and blood tests for biochemical markers were conducted for all subjects. The risk of CVD was judged based on the results, identifying the population with a high risk of CVD. TyG was calculated for all subjects who were then grouped into TyG quartiles. The correlation between TyG and the detection rate of subjects with a high risk of CVD was analyzed using the chi-square test and Pearson’s correlation analysis. The cut-off points and the magnitude of the predictive effect of TyG in determining a high risk of CVD were identified by calculating the TyG through analysis of the receiver operator characteristic (ROC) curve.Results: The surveyed population consisted of 39,598 males (38.8%) and 62,463 females (61.2%). The average age was 55.84 ± 10.27 years. A statistically significant difference in the incidence of a high CVD risk between subjects in the four groups divided by the TyG levels was identified (p

With the rapid development of mobile devices, smartphones have gradually become an indispensable part of people's lives. Meanwhile, biometric authentication has been corroborated to be an effective method for establishing a person's identity with high confidence. Hence, recently, biometric technologies for smartphones have also become increasingly sophisticated and popular. But it is noteworthy that the application potential of palmprints for smartphones is seriously underestimated. Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence, and have high user acceptance. However, currently, studies specializing in palmprint verification for smartphones are still quite sporadic, especially when compared to face- or fingerprint-oriented ones. In this paper, aiming to fill the aforementioned research gap, we conducted a thorough study of palmprint verification on smartphones and our contributions are twofold. First, to facilitate the study of palmprint verification on smartphones, we established an annotated palmprint dataset named MPD, which was collected by multi-brand smartphones in two separate sessions with various backgrounds and illumination conditions. As the largest dataset in this field, MPD contains 16,000 palm images collected from 200 subjects. Second, we built a DCNN-based palmprint verification system named DeepMPV+ for smartphones. In DeepMPV+, two key steps, ROI extraction and ROI matching, are both formulated as learning problems and then solved naturally by modern DCNN models. The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments. To make our results fully reproducible, the labeled dataset and the relevant source codes have been made publicly available at https://cslinzhang.github.io/MobilePalmPrint/.

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations extracted from an open source eye-tracking corpus (OSEC). Moreover, we propose strategies to make eye fixation duration more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.

Cryo-electron tomography combined with subtomogram averaging (StA) has yielded high-resolution structures of macromolecules in their native context. However, high-resolution StA is not commonplace due to beam-induced sample drift, images with poor signal-to-noise ratios (SNR), challenges in CTF correction, and limited particle number. Here we address these issues by collecting tilt series with a higher electron dose at the zero-degree tilt. Particles of interest are then located within reconstructed tomograms, processed by conventional StA, and then re-extracted from the high-dose images in 2D. Single particle analysis tools are then applied to refine the 2D particle alignment and generate a reconstruction. Use of our hybrid StA (hStA) workflow improved the resolution for tobacco mosaic virus from 7.2 to 4.4 Å and for the ion channel RyR1 in crowded native membranes from 12.9 to 9.1 Å. These resolution gains make hStA a promising approach for other StA projects aimed at achieving subnanometer resolution.

Cryo electron tomography (cryo-ET) combined with subtomogram averaging (StA) enables structural determination of macromolecules in their native context. A few structures were reported by StA at resolution higher than 4.5 Å, however all of these are from viral structural proteins or vesicle coats. Reaching high resolution for a broader range of samples is uncommon due to beam-induced sample drift, poor signal-to-noise ratio (SNR) of images, challenges in CTF correction, limited number of particles. Here we propose a strategy to address these issues, which consists of a tomographic data collection scheme and a processing workflow. Tilt series are collected with higher electron dose at zero-degree tilt in order to increase SNR. Next, after performing StA conventionally, we extract 2D projections of the particles of interest from the higher SNR images and use the single particle analysis tools to refine the particle alignment and generate a reconstruction. We benchmarked our proposed hybrid StA (hStA) workflow and improved the resolution for tobacco mosaic virus from 7.2 to 5.2 Å and the resolution for the ion channel RyR1 in crowded native membranes from 12.9 to 9.1 Å. We demonstrate that hStA can improve the resolution obtained by conventional StA and promises to be a useful tool for StA projects aiming at subnanometer resolution or higher.