A power-law coupled three-form dark energy model

We consider a field theory model of coupled dark energy which treats dark energy as a three-form field and dark matter as a spinor field. By assuming the effective mass of dark matter as a power-law function of the three-form field and neglecting the potential term of dark energy, we obtain three solutions of the autonomous system of evolution equations, including a de Sitter attractor, a tracking solution and an approximate solution. To understand the strength of the coupling, we confront the model with the latest Type Ia Supernova (SN Ia), Baryon Acoustic Oscillations (BAO) and Cosmic Microwave Backround (CMB) radiation observations, with the conclusion that the combination of these three databases marginalized over the present dark matter density parameter $\Omega_{m0}$ and the present three-form field $\kappa X_{0}$ gives stringent constraints on the coupling constant, $-0.017< \lambda <0.047$ ($2\sigma$ confidence level), by which we give out the model applicable parameter range.Comment: 16 pages, 5 figures, refernces added, Eur. Phys. J. C (2018

BOIN: An R Package for Designing Single-Agent and Drug-Combination Dose-Finding Trials Using Bayesian Optimal Interval Designs

This article describes the R package BOIN, which implements a recently developed methodology for designing single-agent and drug-combination dose-finding clinical trials using Bayesian optimal interval designs (Liu and Yuan 2015; Yuan, Hess, Hilsenbeck, and Gilbert 2016). The BOIN designs are novel "model-assisted" phase I trial designs that can be implemented simply and transparently, similar to the 3 + 3 design, but yield excellent performance comparable to those of more complicated, model-based designs. The BOIN package provides tools for designing, conducting, and analyzing single-agent and drug-combination dose-finding trials

Unreliable quantitation of species abundance based on high-throughput sequencing data of zooplankton communities

High-throughput sequencing (HTS) is rapidly becoming a popular and robust tool to characterize biodiversity of complex communities, especially for those dominated by microscopic species such as zooplankton. The popular use of HTS-based methods has prompted a possible method of inferring relative species abundance from sequencing data. However, these methods remain largely untested in many communities as to whether sequence data can reliably quantify relative species abundance. Here we tested the relationship between species abundance and sequence abundance in zooplankton using 2 methods: (1) spiking known amounts of indicator species into existing zooplankton communities, and (2) comparing results obtained from parallel replicates for the same natural zooplankton communities. Although we detected a general trend that low-abundance species usually corresponded to low-abundance sequence reads, further statistical analyses revealed that sequencing data could not reliably quantify relative species abundance, even for the same indicator species spiked into different zooplankton communities. The distribution of sequence reads statistically varied even between parallel replicates of the same natural zooplankton communities. Our study reveals that sequence abundance may generally qualitatively reflect species abundance as the general trend between these 2 variables exists; however, extra caution is required when using HTS-based approaches to make quantitative inferences regarding zooplankton communities

Serum MicroRNA-27b as a Screening Biomarker for Left Ventricular Hypertrophy

MicroRNA-27b (miR-27b) is frequently upregulated in pressure-overloaded hypertrophic hearts. The clinical implications of aberrant circulating miR-27b in the diagnosis and management of left ventricular hypertrophy warrant study. We investigated whether serum miR-27b is a biomarker for left ventricular hypertrophy (LVH). We used stem-loop reverse-transcription quantitative polymerase chain reaction techniques to analyze serum miR-27b levels in 200 hypertensive patients with LVH, 100 hypertensive patients without LVH, and 100 healthy volunteers. We found that serum miR-27b levels were significantly higher in the hypertensive patients with LVH than in the hypertensive patients without LVH and in the healthy volunteers. Upon receiver operating characteristic curve analysis, serum miR-27b had an area under the curve of 0.885 with 91% sensitivity and 73% specificity in distinguishing hypertensive patients with LVH from healthy volunteers (P=0.021), and an area under the curve of 0.818 with 79.1% sensitivity and 70.3% specificity in distinguishing hypertensive patients with LVH from those without LVH (P=0.036). We conclude that circulating miR-27b might serve as a specific, noninvasive biomarker in screening for LVH

Vertical Federated Learning

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy. We provide an exhaustive categorization for VFL settings and privacy-preserving protocols and comprehensively analyze the privacy attacks and defense strategies for each protocol. In the end, we propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, and effectiveness constraints. Finally, we review the most recent advances in industrial applications, highlighting open challenges and future directions for VFL

Detection and attribution of nitrogen runoff trend in China's croplands

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990–2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China

Biosensing strategies for amyloid‐like protein aggregates

Protein aggregate species play a pivotal role in the pathology of various degenerative diseases. Their dynamic changes are closely correlated with disease progression, making them promising candidates as diagnostic biomarkers. Given the prevalence of degenerative diseases, growing attention is drawn to develop pragmatic and accessible protein aggregate species detection technology. However, the performance of current detection methods is far from satisfying the requirements of extensive clinical use. In this review, we focus on the design strategies, merits, and potential shortcomings of each class of detection methods. The review is organized into three major parts: native protein sensing, seed amplification, and intricate program, which embody three different but interconnected methodologies. To the best of our knowledge, no systematic review has encompassed the entire workflow, from the molecular level to the apparatus organization. This review emphasizes the feasibility of the methods instead of theoretical detection limitations. We conclude that high selectivity does play a pivotal role, while signal compilation, multilateral profiling, and other patient-oriented strategies (i.e. less invasiveness and assay speed) are also important

Diagnostic value of urine sCD163 levels for sepsis and relevant acute kidney injury: a prospective study

BACKGROUND: Sepsis is a common syndrome in critically ill patients and easily leads to the occurrence of acute kidney injury (AKI), with high mortality rates. This study aimed to investigate the diagnostic value of urine soluble CD163 (sCD163) for identification of sepsis, severity of sepsis, and for secondary AKI, and to assess the patients’ prognosis. METHODS: We enrolled 20 cases with systemic inflammatory response syndrome (SIRS), 40 cases with sepsis (further divided into 17 sepsis cases and 23 severe sepsis cases) admitted to the intensive care unit (ICU), and 20 control cases. Results for urine sCD163 were recorded on the day of admission to the ICU, and AKI occurrence was noted. RESULTS: On the day of ICU admission, the sepsis group exhibited higher levels of urine sCD163 (74.8 ng/ml; range: 47.9-148.3 ng/ml) compared with those in the SIRS group (31.9 ng/ml; 16.8-48.0, P < 0.001). The area under the curve (AUC) was 0.83 (95% confidence interval [CI]: 0.72-0.94, P < 0.001) the sensitivity was 0.83, and the specificity was 0.75 (based on a cut-off point of 43.0 ng/ml). Moreover, the severe sepsis group appeared to have a higher level of sCD163 compared with that in the sepsis group (76.2; 47.2-167.5 ng/ml vs. 74.2; 46.2-131.6 ng/ml), but this was not significant. For 15 patients with AKI, urine sCD163 levels at AKI diagnosis were significantly higher than those of the remaining 35 sepsis patients upon ICU admission (121.0; 74.6-299.1 ng/ml vs. 61.8; 42.8-128.3 ng/ml, P = 0.049). The AUC for urine sCD163 was 0.688 (95% CI: 0.51-0.87, P = 0.049). Sepsis patients with a poor prognosis showed a higher urine sCD163 level at ICU admission (98.6; 50.3-275.6 ng/ml vs. 68.0; 44.8-114.5 ng/ml), but this was not significant. Patients with AKI with a poor prognosis had higher sCD163 levels than those in patients with a better prognosis (205.9; 38.6-766.0 ng/ml vs. 80.9; 74.9-141.0 ng/ml), but this was not significant. CONCLUSIONS: This study shows, for the first time, the potential value of urine sCD163 levels for identifying sepsis and diagnosing AKI, as well as for assessment of patients’ prognosis. TRIAL REGISTRATION: ChiCTR-ONC-1000081