Magnetoresistance in La- and Ca-doped YBa2Cu3O7–δ

We studied the microstructures, electronic, and magnetic properties on La-doped and La- and Ca-codoped YBa2Cu3O7−δ (YBCO). The superconducting transition temperature remains unchanged up to 10% for La-doped YBCO. The competition between electrons and holons was assumed according to the variation of Tc0 in La and Ca codopings in YBCO. The magnetoresistance (MR) effect is about 8%, which is observed obviously near the critical temperature and is independent of the content of La in La-doped YBCO. MR increases up to about 40% with the incorporation of Ca in La-doped YBCO. We present here possible explanations for the magnetoresistance effect in polycrystalline samples based on the microstructure and the increase of oxygen vacancies at grain-boundary interface. © 2006 American Institute of Physicspublished_or_final_versio

Detrended fluctuation analysis for fractals and multifractals in higher dimensions

One-dimensional detrended fluctuation analysis (1D DFA) and multifractal detrended fluctuation analysis (1D MF-DFA) are widely used in the scaling analysis of fractal and multifractal time series because of being accurate and easy to implement. In this paper we generalize the one-dimensional DFA and MF-DFA to higher-dimensional versions. The generalization works well when tested with synthetic surfaces including fractional Brownian surfaces and multifractal surfaces. The two-dimensional MF-DFA is also adopted to analyze two images from nature and experiment and nice scaling laws are unraveled.Comment: 7 Revtex pages inluding 11 eps figure

Emergence of long memory in stock volatility from a modified Mike-Farmer model

The Mike-Farmer (MF) model was constructed empirically based on the continuous double auction mechanism in an order-driven market, which can successfully reproduce the cubic law of returns and the diffusive behavior of stock prices at the transaction level. However, the volatility (defined by absolute return) in the MF model does not show sound long memory. We propose a modified version of the MF model by including a new ingredient, that is, long memory in the aggressiveness (quantified by the relative prices) of incoming orders, which is an important stylized fact identified by analyzing the order flows of 23 liquid Chinese stocks. Long memory emerges in the volatility synthesized from the modified MF model with the DFA scaling exponent close to 0.76, and the cubic law of returns and the diffusive behavior of prices are also produced at the same time. We also find that the long memory of order signs has no impact on the long memory property of volatility, and the memory effect of order aggressiveness has little impact on the diffusiveness of stock prices.Comment: 6 pages, 6 figures and 1 tabl

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Multiple-element exposure and metabolic syndrome in Chinese adults: A case-control study based on the Beijing population health cohort

Background Metabolic syndrome (MetS) patients have a considerably increased risk for noncommunicable diseases, which poses a serious burden on public health. The effects of different elements on MetS have received increasing attention in the field of noncommunicable diseases over the past decade. These elements can exert adverse or favourable effects on human health by synergistic or antagonistic actions. Nevertheless, few studies have explored the relationship between multiple-element exposure and MetS. Method A total of 2095 MetS patients and 2039 controls free of major cardiovascular disease at baseline and follow-up visits were frequency matched for age (±5 years) and sex. The internal exposure levels of 15 elements in serum were investigated. Logistic regression models were employed to estimate odds ratios (ORs) of MetS for element concentrations categorized according to quartiles in the controls. Result Magnesium (Mg), selenium (Se), barium (Ba) and mercury (Hg) were significantly associated with MetS in the multi-element exposure model. The ORs for the extreme quartiles of Mg, Se, Ba, and Hg were 0.29 (95% CI: 0.23–0.37, P-trend < 0.001), 0.52 (95% CI: 0.42–0.65, P-trend < 0.001), 1.86 (95% CI: 1.51–2.28, P-trend < 0.001), and 2.61 (95% CI: 2.11–3.22, P-trend < 0.001), respectively. Ba may be antagonistic to Mg and Se in the human body. Conclusions Our study suggested that MetS was negatively associated with Mg and Se and positively associated with Ba and Hg. There were significant dose-response relationships between Mg, Se, Ba and Hg and the prevalence of MetS, suggesting that multiple elements may be involved in MetS

Adipocytes Activate Mitochondrial Fatty Acid Oxidation and Autophagy to Promote Tumor Growth in Colon Cancer

Obesity has been associated with increased incidence and mortality of a wide variety of human cancers including colorectal cancer. However, the molecular mechanism by which adipocytes regulate the metabolism of colon cancer cells remains elusive. In this study, we showed that adipocytes isolated from adipose tissues of colon cancer patients have an important role in modulating cellular metabolism to support tumor growth and survival. Abundant adipocytes were found in close association with invasive tumor cells in colon cancer patients. Co-culture of adipocytes with colon cancer cells led to a transfer of free fatty acids that released from the adipocytes to the cancer cells. Uptake of fatty acids allowed the cancer cells to survive nutrient deprivation conditions by upregulating mitochondrial fatty acid β-oxidation. Mechanistically, co-culture of adipocytes or treating cells with fatty acids induced autophagy in colon cancer cells as a result of AMPK activation. Inhibition of autophagy attenuated the ability of cancer cells to utilize fatty acids and blocked the growth-promoting effect of adipocytes. In addition, we found that adipocytes stimulated the expression of genes associated with cancer stem cells and downregulated genes associated with intestinal epithelial cell differentiation in primary colon cancer cells and mouse tumor organoids. Importantly, the presence of adipocytes promoted the growth of xenograft tumors in vivo. Taken together, our results show that adipocytes in the tumor microenvironment serve as an energy provider and a metabolic regulator to promote the growth and survival of colon cancer cells

A wave-resolving modeling study of rip current variability, rip hazard, and swimmer escape strategies on an embayed beach

Drownings due to rip currents are a major threat to beach safety. In this study a high-resolution Boussinesq model with a modified wave-resolving Lagrangian tracking module has been applied to a 2 km long embayed beach, Dadonghai of Sanya, Hainan Island, with the purpose of studying rip current variability, real-time rip hazard identification, and the optimal swimmer escape strategies. The beach stage evolves periodically at the study site and plays an important role in the long-term modulation of the occurrence and strength of rip currents according to the modeling. A series of tests are designed and confirm that rip current strength is closely related to wave properties and tidal levels. Spectral analysis of output time series at specific points shows that the modeled rip currents fluctuate on the orders of 1 and 10 min, which suggests the effects of wave-group-forced infragravity (IG) and very-low-frequency (VLF) motions. Rip hazard levels are defined by combining rip strength and its duration. An attempt to use the GPU-accelerated FUNWAVE-TVD (Total Variation Diminishing version of the Fully Nonlinear Boussinesq Wave Model) embedded with the spectral wave model WAM6-GPU (GPU version of the third-generation spectral wave model WAM Cycle 6) exhibits its capability to evaluate rip hazard levels in real time. One of the differences of the present study from previous works is that the random, wave-resolving tracking of virtual swimmers is performed with 1 m resolution to study beach safety strategies. The results demonstrate that multiple factors contribute to the survival of swimmers caught in the rip currents, including surf-zone bathymetry, rip strength, fine-scale flow patterns, the bather's position, and swimming ability. For weak-to-moderate rip currents and longshore currents, swim onshore consistently seems to be the most successful strategy across all the scenarios in this study. Higher surf-zone exit rates along Dadonghai beach are not favorable for stay afloat action, which puts swimmers at a higher risk of being expelled to deeper water. The effects of wave randomness of incoming wave trains and assignment of wave-following coefficients on Lagrangian tracking are also discussed.</p

Field-Induced Transition in the S=1 Antiferromagnetic Chain with Single-Ion Anisotropy in a Transverse Magnetic Field

The field-induced transition in one-dimensional S=1 Heisenberg antiferromagnet with single-ion anisotropy in the presence of a transverse magnetic field is obtained on the basis of the Schwinger boson mean-field theory. The behaviors of the specific heat and susceptibility as functions of temperature as well as the applied transverse field are explored, which are found to be different from the results obtained under a longitudinal field. The anomalies of the specific heat at low temperatures, which might be an indicative of a field-induced transition from a Luttinger liquid phase to an ordered phase, are explicitly uncovered under the transverse field. A schematic phase diagram is proposed. The theoretical results are compared with experimental observations.Comment: Revtex, 7 figure

Integral Sliding Mode Control for Markovian Jump T-S Fuzzy Descriptor Systems Based on the Super-Twisting Algorithm

This paper investigates integral sliding mode control problems for Markovian jump T-S fuzzy descriptor systems via the super-twisting algorithm. A new integral sliding surface which is continuous is constructed and an integral sliding mode control scheme based on a variable gain super-twisting algorithm is presented to guarantee the well-posedness of the state trajectories between two consecutive switchings. The stability of the sliding motion is analyzed by considering the descriptor redundancy and the properties of fuzzy membership functions. It is shown that the proposed variable gain super-twisting algorithm is an extension of the classical single-input case to the multi-input case. Finally, a bio-economic system is numerically simulated to verify the merits of the method proposed

Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial

Background : By the early 1980s, tuberculosis treatment was shortened from 24 to 6 months, maintaining relapse rates of 1-2%. Subsequent trials attempting shorter durations have failed, with 4-month arms consistently having relapse rates of 15-20%. One trial shortened treatment only among those without baseline cavity on chest x-ray and whose month 2 sputum culture converted to negative. The 4-month arm relapse rate decreased to 7% but was still significantly worse than the 6-month arm (1.6%, P<0.01).  We hypothesize that PET/CT characteristics at baseline, PET/CT changes at one month, and markers of residual bacterial load will identify patients with tuberculosis who can be cured with 4 months (16 weeks) of standard treatment.Methods: This is a prospective, multicenter, randomized, phase 2b, noninferiority clinical trial of pulmonary tuberculosis participants. Those eligible start standard of care treatment. PET/CT scans are done at weeks 0, 4, and 16 or 24. Participants who do not meet early treatment completion criteria (baseline radiologic severity, radiologic response at one month, and GeneXpert-detectable bacilli at four months) are placed in Arm A (24 weeks of standard therapy). Those who meet the early treatment completion criteria are randomized at week 16 to continue treatment to week 24 (Arm B) or complete treatment at week 16 (Arm C). The primary endpoint compares the treatment success rate at 18 months between Arms B and C.Discussion: Multiple biomarkers have been assessed to predict TB treatment outcomes. This study uses PET/CT scans and GeneXpert (Xpert) cycle threshold to risk stratify participants. PET/CT scans are not applicable to global public health but could be used in clinical trials to stratify participants and possibly become a surrogate endpoint. If the Predict TB trial is successful, other immunological biomarkers or transcriptional signatures that correlate with treatment outcome may be identified. TRIAL REGISTRATION: NCT02821832