Validation of LAMOST Stellar Parameters with the PASTEL Catalog

Recently the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) published its first data release (DR1), which is ranked as the largest stellar spectra dataset in the world so far. We combine the PASTEL catalog and SIMBAD radial velocities as a testing standard to validate the DR1 stellar parameters (effective temperature $T_{\mathrm{eff}}$, surface gravity $\log g$, metallicity $\mathrm{[Fe/H]}$ and radial velocity $V_{\mathrm{r}}$). Through cross-identification of the DR1 catalogs and the PASTEL catalog, we obtain a preliminary sample of 422 stars. After removal of stellar parameter measurements from problematic spectra and applying effective temperature constraints to the sample, we compare the stellar parameters from DR1 with those from PASTEL and SIMBAD to prove that the DR1 results are reliable in restricted $T_{\mathrm{eff}}$ ranges. We derive standard deviations of 110 K, 0.19 dex, 0.11 dex and 4.91 $\mathrm{km\,s^{-1}}$ , for $T_{\mathrm{eff}}$, $\log g$, $[\mathrm{Fe/H}]$ when $T_{\mathrm{eff}}<8000\,\mathrm{K}$, and for $V_{\mathrm{r}}$ when $T_{\mathrm{eff}}<10000\,\mathrm{K}$, respectively. Systematic errors are negligible except for that of $V_{\mathrm{r}}$. Besides, metallicities in DR1 are systematically higher than those in PASTEL, in the range of PASTEL $[\mathrm{Fe/H}]<-1.5$.Comment: 9 pages, 4 figure

Insulin resistance predicts progression of de novo atherosclerotic plaques in patients with coronary heart disease: a one-year follow-up study

BACKGROUND: The aim of our study was to explore and evaluate the relationship between insulin resistance and progression of coronary atherosclerotic plaques. With the great burden coronary heart disease is imposing on individuals, healthcare professionals have already embarked on determining its potential modifiable risk factors in the light of preventive medicine. Insulin resistance has been generally recognized as a novel risk factor based on epidemiological studies; however, few researches have focused on its effect on coronary atherosclerotic plaque progression. METHODS: From June 7, 2007 to December 30, 2011, 366 patients received their index coronary angiogram and were subsequently found to have coronary atherosclerotic plaques or normal angiograms were consecutively enrolled in the study by the department of cardiology at the Ruijin Hospital, which is affiliated to the Shanghai Jiaotong University School of Medicine. All patients had follow-up angiograms after the 1-year period for evaluating the progression of the coronary lesions. The modified Gensini score was adopted for assessing coronary lesions while the HOMA-IR method was utilized for determining the state of their insulin resistance. Baseline characteristics and laboratory test results were described and the binomial regression analysis was conducted to investigate the relationship between insulin resistance and coronary atherosclerotic plaque progression. RESULTS: Index and follow-up Gensini scores were similar between the higher insulin lower insulin resistant groups (9.09 ± 14.33 vs 9.44 ± 12.88, p = 0.813 and 17.21 ± 18.46 vs 14.09 ± 14.18, p =0.358). However the Gensini score assessing coronary lesion progression between both visits was significantly elevated in the higher insulin resistant group (8.13 ± 11.83 versus 4.65 ± 7.58, p = 0.019). Multivariate logistic binomial regression analysis revealed that insulin resistance (HOMA-IR > 3.4583) was an independent predictor for coronary arterial plaque progression (OR = 4.969, p = 0.011). We also divided all the participants into a diabetic (n = 136) and a non-diabetic group (n = 230), and HOMA-IR remained an independent predictor for atherosclerosis plaque progression. CONCLUSIONS: Insulin resistance is an independent predictor of atherosclerosis plaque progression in patients with coronary heart disease in both the diabetic and non-diabetic population

Unique allosteric effect driven rapid adsorption of carbon dioxide on a new ionogel [P4444][2-Op]@MCM-41 with excellent cyclic stability and loading-dependent capacity

Allosteric effect-driven rapid stepwise CO2 adsorption of pyridine-containing anion functionalized ionic liquid [P4444][2-Op] confined into mesoporous silica MCM-41.</p

CD45 Phosphatase Inhibits STAT3 Transcription Factor Activity in Myeloid Cells and Promotes Tumor-Associated Macrophage Differentiation

Recruitment of monocytic myeloid-derived suppressor cells (MDSCs) and differentiation of tumor-associated macrophages (TAMs) are the major factors contributing to tumor progression and metastasis. We demonstrated that differentiation of TAMs in tumor site from monocytic precursors was controlled by downregulation of the activity of the transcription factor STAT3. Decreased STAT3 activity was caused by hypoxia and affected all myeloid cells but was not observed in tumor cells. Upregulation of CD45 tyrosine phosphatase activity in MDSCs exposed to hypoxia in tumor site was responsible for downregulation of STAT3. This effect was mediated by the disruption of CD45 protein dimerization regulated by sialic acid. Thus, STAT3 has a unique function in the tumor environment in controlling the differentiation of MDSC into TAM, and its regulatory pathway could be a potential target for therapy

Research on source location of micro-seismic event ‎based on dynamic cluster velocity model

A new velocity model based on dynamic cluster was proposed in this paper. During the process ‎of iteration, the sensors can be formed a cluster according to the velocity similitude degree. ‎Based on the assumption that the speeds from source to each sensor in the same cluster are ‎equal, the corresponding objective function was proposed to solve the source location, which ‎didn’t include the velocity parameter. It not only avoided the error from field measurement ‎and the inversion, but also appropriated for the actual situation that the speeds from every ‎source to different sensors are different. By analyzing 24 different cases, the positioning ‎accuracy based on the velocity model proposed in this paper was verified to be preferable and ‎stable, no matter the source is within the region of the sensor’s array or not. Even for the cases ‎of different velocity variation ranges, the velocity model was still reliable.

Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation

Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses the data sharing problem, enabling privacy-preserved collaborative training to provide sufficient precious data. Great potential, therefore, arises with the confluence of them, known as multimodal federated learning. However, limitation lies in the predominant approaches as they often assume that each local dataset records samples from all modalities. In this paper, we aim to bridge this gap by proposing an Unimodal Training - Multimodal Prediction (UTMP) framework under the context of multimodal federated learning. We design HA-Fedformer, a novel transformer-based model that empowers unimodal training with only a unimodal dataset at the client and multimodal testing by aggregating multiple clients' knowledge for better accuracy. The key advantages are twofold. Firstly, to alleviate the impact of data non-IID, we develop an uncertainty-aware aggregation method for the local encoders with layer-wise Markov Chain Monte Carlo sampling. Secondly, to overcome the challenge of unaligned language sequence, we implement a cross-modal decoder aggregation to capture the hidden signal correlation between decoders trained by data from different modalities. Our experiments on popular sentiment analysis benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that HA-Fedformer significantly outperforms state-of-the-art multimodal models under the UTMP federated learning frameworks, with 15%-20% improvement on most attributes.Comment: 10 pages,5 figure

Direction convergence analysis of weighted rule for minor component extraction information criteria

The extraction of parallel minor components algorithm in directional convergence in signal feature extraction was studied.By comparing the unweighted projection approximation subspace tracking (PAST) algorithm with the weighted PAST parallel minor component extraction algorithm,the evolution method of the minor component extraction algorithm was analyzed.Theorical analysis illustrated that the weighted rule was able to guide the angle evolution between the vectors of the state matrix and minor components.Finally,Matlab simulation verifies the validity of the proposed theory