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Identifying relatives across cohorts makes one of the basic routines for genomic data. As conventional such practice often requires explicit genomic data sharing, it is easily hampered by privacy or ethical constraints. In this study, using our proposed scheme for genomic encryption we developedencG-reg, a regression approach that is able to detect relatives of various degrees based on encrypted genomic data. The encryption properties ofencG-regis built on random matrix theory, which masks the original genotypic matrix but still provides controllable precision to that of direct individual-level genotype data. After having found tractable eighth-order moments for encrypted genotype, we established connection between the dimension of a random matrix and the required precision of a study.encG-regconsequently led to balanced i) false positive and false negative rates and ii) the computational cost and the degree of relatives to be searched. We validatedencG-regin 485,158 UKBiobank multi-ethnical samples, and the resolution ofencG-regwas comparable with the conventional method such as KING. In a more complex application, we launched a fine-devised multi-center collaboration across 6 research institutes in China, covering 11 cohorts of 64,091 GWAS samples. In both examples,encG-regrobustly identified and validated relatives existing across the cohorts even under various ethnical background and different genotypic qualities.

The purpose of this study was to evaluate the association between starchy vegetable consumption and subgroup consumption in the first trimester and the risk of gestational diabetes mellitus (GDM).A prospective study (Among the 1444 participants in our study, 520 were diagnosed with GDM. The adjusted RRs (95% CIs) for GDM from the lowest to the highest quartiles of total starchy vegetable consumption were 1.00 (reference), 1.29 (1.06, 1.57), 1.13 (0.93, 1.40), and 1.26 (1.02, 1.56), respectively;A higher consumption of total starchy vegetables and potatoes in the first trimester is associated with a greater risk of GDM.

Background Epidermal growth factor-like domain 7 (Egfl7), a recently identified secreted protein, was significantly increased in patients with HCC by our previous studies. However, its efficacy in the diagnosis of early HCC remains unknown. In this study, we therefore evaluate the efficacy of serum Egfl7 for early HCC diagnosis and compare it with alpha-fetoprotein (AFP). Methods Serum Egfl7 levels in testing cohort (1081 participants) and validation cohort (476 participants) were measured by a sandwich enzyme-linked immunoassay (ELISA). The cut-off value of Egfl7 was determined by Youden’s index and the efficacies of Egfl7 and AFP in diagnosing early HCC were estimated by receiver operating characteristic (ROC). Results Serum Egfl7 was significantly elevated in patients with early HCC than all non-HCC controls in whatever Testing Cohort or Validation Cohort. In the Testing Cohort, ROC curves showed the optimum cut-off value of Egfl7 was 2610 ng/mL and Egfl7 showed a significantly higher sensitivity than AFP in discriminating early HCC from healthy individuals (77.4% vs. 65.3%, P = 0.0013) but the area under ROC (AUROC) and accuracy of Egfl7 and AFP were similar (0.860 vs. 0.868, P = 0.704; 80.2% vs. 83.8%, P = 0.184). In distinguishing patients with early HCC from patients with chronic liver disease (CLD), the AUROC, sensitivity, specificity and accuracy of Egfl7 were 0.800, 75.2, 71.7 and 73.5%, which were all significantly higher than AFP (0.675, 61.8, 62.0 and 61.9% in order). Egfl7 also showed a significant higher sensitivity and accuracy than AFP (76.6% vs. 64.0%, P = 0.0031; 79.9% vs. 66.1%, P Conclusion Egfl7 is a valuable serum marker in the diagnosis of early HCC and could complement the efficacy of AFP, especially in distinguishing early HCC from CLD and identifying patients with AFP-negative early HCC.

To investigate the dairy product intake during pregnancy in Southwest China and to explore its relationship with neonatal birth body mass.A prospective study was conducted to select healthy singleton pregnant women at 8-14 weeks of gestation in a maternal and fetal health care institution in Chengdu City. Dairy product consumption during the first, second, third trimester of pregnancy were collected by 24-hour dietary recalls at 8-14 weeks, 24-28 weeks and 32-36 weeks of pregnancy, respectively, and the total milk intake and milk consumption rate were calculated. According to the dietary guidelines for Chinese pregnant women (2016), the recommended amount of milk (300 g/d) was used as the standard to calculate the compliance rate. The respondents were divided into three groups: no dairy consumption group, insufficient dairy consumption group and suitable dairy consumption group. The gestational age at delivery and neonatal birth body mass were collected by the hospital information system. Logistic regression model was used to analyze the association between milk intake during pregnancy and neonatal birth body mass.A total of 962 pregnant women were included. The average milk intake in the first, second and third trimester of pregnancy were 125.0 (0, 236.1) g/d, 208.3 (0, 284.7) g/d and 250.0 (150.0, 416.7) g/d, respectively, with the compliance rates of 12.6%, 33.2% and 48.4%, respectively. The average neonatal birth body mass was (3 225.0±399.8) g. The incidence of small for gestational age (SGA) and large for gestational age (LGA) was 8.3% and 3.9%, respectively. Compared with no dairy consumption group in the second trimester of pregnancy, the risk of SGA was lower in suitable dairy consumption group (odds ratio (Insufficient milk intake of pregnant women is a significant problem in southwest China and needs to be improved. Milk intake during pregnancy is associated with neonatal birth body mass, and increased milk intake in the second and third trimester of pregnancy may reduce the risk of SGA.

To date, factorization machines (FMs) have emerged as a powerful model in many applications. In this work, we study the training of FM with the logistic loss for binary classification, which is a nonlinear extension of the linear model with the logistic loss (i.e., logistic regression). For the training of large-scale logistic regression, Newton methods have been shown to be an effective approach, but it is difficult to apply such methods to FM because of the nonconvexity. We consider a modification of FM that is multiblock convex and propose an alternating minimization algorithm based on Newton methods. Some novel optimization techniques are introduced to reduce the running time. Our experiments demonstrate that the proposed algorithm is more efficient than stochastic gradient algorithms and coordinate descent methods. The parallelism of our method is also investigated for the acceleration in multithreading environments.

Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and offline validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches. Experimental results show significant improvements.

To offer a series of efficient methods to physiologists in appropriate selection, and application of statistical techniques.We bring about two questions as follows:What's the role of statistics in the process of a physiological research? How to make sure the results produced in a physiological research can be repeatable in practice in the long run. From the answers to these two questions, we highlight the importance of the discipline of statistics to research work, explain why it is difficult, how to choose a suitable statistical method in a specific situation, and offer the critical methods to use statistics accurately and appropriately.We abstract three core sections from the discipline of statistics:how to make the design of a study impeccable; How to strictly follow the protocol of a study, and How to draw conclusions well reasoned and strongly supported by evidence. By elaborating these sections, it is feasible to correctly use statistical methods for data analysis and results interpretation.In physiological research, conclusion can stand with time and repeatable in practice only when researchers strictly and rigorously follow the rule of scientific research.

Table S1. Clinicopathologcal characteristics of HCC patients in training cohort and validation cohort. Table S2 Correlation between CASZ1 expression and clinicopathologic characteristics of HCC patients in training cohort and validation cohort. Table S3 Univariate and multivariate analyses of risk factors associated with overall survival and disease-free survival of HCC patients in training cohort. Table S4 Univariate and multivariate analyses of risk factors associated with overall survival and disease-free survival of HCC patients in validation cohort. (DOC 282 kb)

Intrahepatic cholestasis of pregnancy (ICP) is an idiopathic liver disease while the biochemical characteristic is the elevated level of total bile acid (TBA). The present study investigated whether miR-148a mediates the induced effect of estrogen on the development of ICP and the proper mechanism: PXR/MRP3 signal pathway. mRNA expression was detected by qPCR, protein expression was detected by western blotting, the concentration of estrogen and TBA were detected by reagent kit respectively. In the cinical research, it was found that miR-148a expression was positive related with the concentration of TBA in the serum of ICP patients. In in vitro research, estradiol (500 nmol/L, 12 h) significantly upregulated miR-148a expression and LV-148a-siRNA inhibited the function of estradiol (500 nmol/L, 48 h) on TBA secretion. In addition, gene silence of miR-148a upregulated PXR expression which was inhibited by estradiol in LO2 cells. Pretreatment of rifampin (10 μmol/L), the agonist of PXR alleviated the TBA secretion induced by estradiol (500 nmol/L, 48 h). miR-148a-siRNA and PXR had a synergistic action on TBA secretion of LO2. Both of miR-148a-siRNA and rifampin (10 μmol/L) inhibited the upregulated effect of estradiol on MRP3 expression. This research has demonstrated that miR-148a may be involved in the induction of estrogen on ICP via PXR signal pathway, and MRP3 may be involved.

A new hybrid film solar cell was made with a structure of Glass/ITO/PbS/P3HT/Al. PbS film was prepared by a simple solid-solid reaction and poly(3-hexylthiophene) (P3HT) film was obtained by a spin coating method. The solar cells are photosensitive in a large spectral range (extending from near infrared to high energy side regions). Without any special treatment, the cell with an area of 0.15 cm2 has shown values of open-circuit voltage (Voc) of 85 mV and fill factor (FF) of 0.33 under an illumination intensity of 100 mW/cm2.

Lead sulfide (PbS) magic cubes were prepared by a simple hydrothermal method without any organic solvent. The product was characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM) and absorption spectrum. A solar cell, with a structure of Al/P3HT:PbS/PEDOT:PSS/ITO/Glass, was made. By a spin coating method, a hybrid film of poly(3-hexylthiophene) (P3HT) and PbS was prepared on the PEDOT:PSS layer. The solar cells are photosensitive in a large spectral range (visible and near infrared regions). The cells, with the area of 0.15 cm2 without any special treatment, have shown the values of open-circuit voltage (Voc) of 242 mV, short circuit current (Jsc) of 0.01 mA/cm2, energy conversion efficiency (η) of 0.01 % and fill factor (FF) of 0.31 under illumination intensity of 100 mW/cm2.

A probabilistic relational database is a probability distribution over a set of deterministic relational databases (namely, possible worlds). Efficient processing of updating information in probabilistic databases is required in several applications, such as sensor networking, data cleaning. As an important class of updating probabilistic databases, conditioning refines probability distribution of possible worlds, and possibly removing some of the possible worlds based on general knowledge, such as primary key constraints, functional dependencies and others. The existing methods for conditioning are exponential over the number of variables in the probabilistic database for an arbitrary constraint. In this paper, a constraint-based conditioning algorithm is proposed by only considering the variables in the given constraint without enumerating the truth values of all the variables in the formulae of tuples. Then we prove the correctness of the algorithm. The experimental study shows our proposed algorithm is more efficient comparing the work in the literatures.