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Abstract Background MicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease associations vital for disease prevention and therapy. However, traditional laboratory methods for detecting these associations are slow, strenuous, expensive, and uncertain. Although numerous advanced algorithms have emerged, it is still a challenge to develop more effective methods to explore underlying miRNA-disease associations. Results In the study, we designed a novel approach on the basis of deep autoencoder and combined feature representation (DAE-CFR) to predict possible miRNA-disease associations. We began by creating integrated similarity matrices of miRNAs and diseases, performing a logistic function transformation, balancing positive and negative samples with k-means clustering, and constructing training samples. Then, deep autoencoder was used to extract low-dimensional feature from two kinds of feature representations for miRNAs and diseases, namely, original association information-based and similarity information-based. Next, we combined the resulting features for each miRNA-disease pair and used a logistic regression (LR) classifier to infer all unknown miRNA-disease interactions. Under five and tenfold cross-validation (CV) frameworks, DAE-CFR not only outperformed six popular algorithms and nine classifiers, but also demonstrated superior performance on an additional dataset. Furthermore, case studies on three diseases (myocardial infarction, hypertension and stroke) confirmed the validity of DAE-CFR in practice. Conclusions DAE-CFR achieved outstanding performance in predicting miRNA-disease associations and can provide evidence to inform biological experiments and clinical therapy.

Ion conductive membranes (ICMs) with highly conductive proton selectivity are of significant importance and greatly desired for energy storage devices. However, it is extremely challenging to construct fast proton-selective transport channels in ICMs. Herein, a membrane with highly conductive proton selectivity was fabricated by incorporating porous carbon sieving nanospheres with a hollow structure (HCSNs) in a polymer matrix. Due to the precise ion sieving ability of the microporous carbon shells and the fast proton transport through their accessible internal cavities, this advanced membrane presented a proton conductivity (0.084 S·cm−1) superior to those of a commercial Nafion 212 (N212) membrane (0.033 S·cm−1) and a pure polymer membrane (0.049 S·cm−1). The corresponding proton selectivity of the membrane (6.68 × 105 S·min·cm−3) was found to be enhanced by about 5.9-fold and 4.3-fold, respectively, compared with those of the N212 membrane (1.13 × 105 S·min·cm−3) and the pure membrane (1.56 × 105 S·min·cm−3). Low-field nuclear magnetic resonance (LF-NMR) clearly revealed the fast proton-selective transport channels enabled by the HCSNs in the polymeric membrane. The proposed membrane exhibited an outstanding energy efficiency (EE) of 84% and long-term stability over 1400 cycles with a 0.065% capacity decay per cycle at 120 mA·cm−2 in a typical vanadium flow battery (VFB) system.

Objectives: This study aimed to evaluate the joint effects of multiple air pollutants including PM2.5, PM10, NO2, and NOx with dementia and examined the modifying effects of genetic susceptibility.Methods: This study included 220,963 UK Biobank participants without dementia at baseline. Weighted air pollution score reflecting the joint exposure to multiple air pollutants were constructed by cross-validation analyses, and inverse-variance weighted meta-analyses were performed to create a pooled effect. The modifying effect of genetic susceptibility on air pollution score was assessed by genetic risk score and APOE ε4 genotype.Results: The HR (95% CI) of dementia for per interquartile range increase of air pollution score was 1.13 (1.07∼1.18). Compared with the lowest quartile (Q1) of air pollution score, the HR (95% CI) of Q4 was 1.26 (1.13∼1.40) (Ptrend = 2.17 × 10−5). Participants with high air pollution score and high genetic susceptibility had higher risk of dementia compared to those with low air pollution score and low genetic susceptibility.Conclusion: Our study provides evidence that joint exposure to multiple air pollutants substantially increases the risk of dementia, especially among individuals with high genetic susceptibility.

Lower-grade gliomas (LGG), characterized by heterogeneity and invasiveness, originate from the central nervous system. Although studies focusing on molecular subtyping and molecular characteristics have provided novel insights into improving the diagnosis and therapy of LGG, there is an urgent need to identify new molecular subtypes and biomarkers that are promising to improve patient survival outcomes. Here, we proposed a joint similarity network fusion (Joint-SNF) method to integrate different omics data types to construct a fused network using the Joint and Individual Variation Explained (JIVE) technique under the SNF framework. Focusing on the joint network structure, a spectral clustering method was employed to obtain subtypes of patients. Simulation studies show that the proposed Joint-SNF method outperforms the original SNF approach under various simulation scenarios. We further applied the method to a Chinese LGG data set including mRNA expression, DNA methylation and microRNA (miRNA). Three molecular subtypes were identified and showed statistically significant differences in patient survival outcomes. The five-year mortality rates of the three subtypes are 80.8%, 32.1%, and 34.4%, respectively. After adjusting for clinically relevant covariates, the death risk of patients in Cluster 1 was 5.06 times higher than patients in other clusters. The fused network attained by the proposed Joint-SNF method enhances strong similarities, thus greatly improves subtyping performance compared to the original SNF method. The findings in the real application may provide important clues for improving patient survival outcomes and for precision treatment for Chinese LGG patients. An R package to implement the method can be accessed in Github at https://github.com/Sameerer/Joint-SNF.

IntroductionChestnut (Castanea mollissima BL.) is an important woody grain, and its flower formation has a significant impact on fruit yield and quality. Some chestnut species in northern China re-flower in the late summer. On the one hand, the second flowering consumes a lot of nutrients in the tree, weakening the tree and thus affecting flowering in the following year. On the other hand, the number of female flowers on a single bearing branch during the second flowering is significantly higher than that of the first flowering, which can bear fruit in bunches. Therefore, these can be used to study the sex differentiation of chestnut.MethodsIn this study, the transcriptomes, metabolomes, and phytohormones of male and female chestnut flowers were determined during spring and late summer. We aimed to understand the developmental differences between the first and secondary flowering stages in chestnuts. We analysed the reasons why the number of female flowers is higher in the secondary flowering than in the first flowering and found ways to increase the number of female flowers or decrease the number of male flowers in chestnuts.ResultsTranscriptome analysis of male and female flowers in different developmental seasons revealed that EREBP-like mainly affected the development of secondary female flowers and HSP20 mainly affected the development of secondary male flowers. KEGG enrichment analysis showed that 147 common differentially-regulated genes were mainly enriched from circadian rhythm-plant, carotenoid biosynthesis, phenylpropanoid biosynthesis, and plant hormone signal transduction pathways. Metabolome analysis showed that the main differentially accumulated metabolites in female flowers were flavonoids and phenolic acids, whereas the main differentially accumulated metabolites in male flowers were lipids, flavonoids, and phenolic acids. These genes and their metabolites are positively correlated with secondary flower formation. Phytohormone analysis showed that abscisic and salicylic acids were negatively correlated with secondary flower formation. MYB305, a candidate gene for sex differentiation in chestnuts, promoted the synthesis of flavonoid substances and thus increased the number of female flowers.DiscussionWe constructed a regulatory network for secondary flower development in chestnuts, which provides a theoretical basis for the reproductive development mechanism of chestnuts. This study has important practical implications for improving chestnut yield and quality.

Abstract Background The choice of the perioperative crystalloid is a key component of the fluid management and must take into account the liver function and the appearing metabolic disorders to avoid increase the liver extra metabolism. The aim of this study is to analyze the effect of acetate Ringer’s solution or lactate Ringer’s solution in biliary atresia patients. Methods We included 68 infant patients aged between 21 ~ 65 d, ASA physical status II or III, who underwent elective Kasai hepatoportoenterostomy, received either AR and LR for intravenous fluid resuscitation according to their group allocation. Lactate concentration, serum electrolytes and pH were noteded before skin incision (T1), end of surgery (T2) and postoperative 12 h. We also recorded the time of operation, stay of hospital, loss of blood and urinary, total volume of infusion of crystalloid. Results Lactate level was significantly higher in Group LR than in Group AR patients at T2 (0.76 ± 0.13 versus 0.57 ± 0.22, P = 0.03). Compared with T3, sodium and chlorine were significantly higher in two groups at T2 (145.2 ± 3.1 versus 143.4 ± 3.4 and 104.6 ± 3.7 versus 105.2 ± 2.1). No significant differences were noted in potassium, HCO3 − and calcium. There was no statistically significant difference in pH. No glycopenia was recorded in two groups. No significant difference was noted in administration of vasoactive drug (0.7% versus 1%). Conclusions Resuscitation with AR and LR was associated with similar clinical improvement in infants with biliary atresia. Use of AR reduced the level of lactate comparison with LR.

Abstract Background Weifuchun (WFC), a Chinese herbal prescription consisting of Red Ginseng, Isodon amethystoides and Fructus Aurantii, is commonly used in China to treat a variety of chronic stomach disorders. The aim of the paper was to determine the effect of WFC on intestinal microbiota changes in precancerous lesions of gastric cancer (PLGC) patients. Methods PLGC patients of H. pylori negative were randomly divided into two groups and received either WFC tablets for a dose of 1.44 g three times a day or vitacoenzyme (Vit) tablets for a dose of 0.8 g three times a day. All patients were treated for 6 months consecutively. Gastroscopy and histopathology were used to assess the histopathological changes in gastric tissues before and after treatment. 16S rRNA gene sequencing was carried out to assess the effects WFC on intestinal microbiota changes in PLGC patients. Receiver Operating Characteristics (ROC) analysis was used to assess the sensitivity and specificity of different intestinal microbiota in distinguishing between PLGC patients and healthy control group. Results Gastroscopy and histopathological results indicated that WFC could improve the pathological condition of PLGC patients, especially in the case of atrophy or intestinal metaplasia. The results of 16S rRNA gene sequencing indicated that WFC could regulate microbial diversity, microbial composition, and abundance of the intestinal microbiota of PLGC patients. Following WFC treatment, the relative abundance of Parabacteroides decreased in WFC group when compared with the Vit group. ROC analysis found that the Parabacteroides could effectively distinguish PLGC patients from healthy individuals with sensitivity of 0.79 and specificity of 0.8. Conclusions WFC could slow down the progression of PLGC by regulating intestinal microbiota abundance. Trial registration NCT03814629. Name of registry: Randomized Clinical Trial: Weifuchun Treatment on Precancerous Lesions of Gastric Cancer. Registered 3 August 2018-Retrospectively registered, https://register.clinicaltrials.gov/ NCT03814629.

This study was designed to compare the effects of Jinkui Shenqi and Wuzi Yanzong pill on sperm motility and sperm DNA fragmentation rate in patients with asthenospermia. 130 cases were randomly divided into an observation and control group (n=65). The control group was treated with the Wuzi Yanzong pill while the observation group with the Jinkui Shenqi pill. The sperm motility parameters rate (PR), semen concentration, sperm motility, DFI and α-glucosidase, fructose, seminal plasma zinc (Zn), acid phosphatase (ACP) in seminal plasma biochemistry and other indexes of were observed. The biochemical indexes of seminal plasma of α-glucosidase, fructose, Zn, ACP in two groups were significantly (p

As a new machine learning technique, federated learning has received more attention in recent years, which enables decentralized model training across data silos or edge intelligent devices in the Internet of Things without exchanging local raw data. All kinds of algorithms are proposed to solve the challenges in federated learning. However, most of these methods are based on stochastic gradient descent, which undergoes slow convergence and unstable performance during the training stage. In this paper, we propose a differential adaptive federated optimization method, which incorporates an adaptive learning rate and the gradient difference into the iteration rule of the global model. We further adopt the first-order moment estimation to compute the approximate value of the differential term so as to avoid amplifying the random noise from the input data sample. The theoretical convergence guarantee is established for our proposed method in a stochastic non-convex setting under full client participation and partial client participation cases. Experiments for the image classification task are performed on two standard datasets by training a neural network model, and experiment results on different baselines demonstrate the effectiveness of our proposed method.

Hepatocellular carcinoma (HCC) is a leading malignant liver tumor with high mortality and morbidity. Patients at the same stage can be defined as different molecular subtypes associated with specific genomic disorders and clinical features. Thus, identifying subtypes is essential to realize efficient treatment and improve survival outcomes of HCC patients. Here, we applied a regularized multiple kernel learning with locality preserving projections method to integrate mRNA, miRNA and DNA methylation data of HCC patients to identify subtypes. We identified two HCC subtypes significantly correlated with the overall survival. The patient 3-years mortality rates in the high-risk and low-risk group was 51.0% and 23.5%, respectively. The high-risk group HCC patients were 3.37 times higher in death risk compared to the low-risk group after adjusting for clinically relevant covariates. A total of 196 differentially expressed mRNAs, 2,151 differentially methylated genes and 58 differentially expressed miRNAs were identified between the two subtypes. Additionally, pathway activity analysis showed that the activities of six pathways between the two subtypes were significantly different. Immune cell infiltration analysis revealed that the abundance of nine immune cells differed significantly between the two subtypes. We further applied the weighted gene co-expression network analysis to identify gene modules that may affect patients prognosis. Among the identified modules, the key module genes significantly associated with prognosis were found to be involved in multiple biological processes and pathways, revealing the mechanism underlying the progression of HCC. Hub gene analysis showed that the expression levels of CDK1, CDCA8, TACC3, and NCAPG were significantly associated with HCC prognosis. Our findings may bring novel insights into the subtypes of HCC and promote the realization of precision medicine.

Blood flesh is a key fruit trait in peaches (Prunus persica) and can be attributed to the accumulation of anthocyanins. The roles of long non-coding RNAs (lncRNAs) have been highlighted by multiple studies in regulating fruit ripening, anthocyanin accumulation, and abiotic stress responses in many flowering plants. Such regulatory functions of lncRNAs in Prunus persica, nonetheless, have not been reported. In this research, we sequenced and analyzed the complete transcriptome of C3-20 (a blood-fleshed peach) fruit at four developmental stages. Analyses of the correlated genes and differentially expressed lncRNA target genes helped to forecast lncRNAs’ possible functions. The RNA-seq data were generated using high-throughput sequencing. In total, 17,456 putative lncRNAs, including 4,800 intergenic lncRNAs, 2,199 antisense lncRNAs, and 10,439 intronic lncRNAs were discovered, of which 4,871 differentially expressed lncRNAs (DE-lncRNAs) were annotated in the fruit developmental processes. The target genes of these DE-lncRNAs and their regulatory relationship identifying 21,795 cis-regulated and 18,271 trans-regulated targets of the DE-lncRNAs were in a similar way predicted by us. The enriched GO terms for the target genes included anthocyanin biosynthesis. Flavonoid biosynthesis and plant hormone signal transduction were also included in the enriched KEGG pathways. Co-expression network construction demonstrated that the highly expressed genes might co-regulate multiple other genes associated with auxin signal transduction and take effect in equal pathways. We discovered that lncRNAs, including LNC_000987, LNC_000693, LNC_001323, LNC_003610, LNC_001263, and LNC_003380, correlated with fruit that ripened and could take part in ethylene biosynthesis and metabolism and the ABA signaling pathway. Several essential transcription factors, such as ERFs, WRKY70, NAC56, and NAC72, may in a similar way regulate fruit ripening. Three DE-lncRNAs, XLOC_011933, XLOC_001865, and XLOC_042291, are involved in UV-B-induced anthocyanin biosynthesis and positively regulating UVR8 and COP10, were identified and characterized. Our discovery and characterization of XLOC_011933, XLOC_001865, and XLOC_042291 provide a more precise understanding and preliminarily establishes a theoretical framework for UV-B-induced flesh anthocyanin biosynthesis. This phenomenon might encourage more in-depth investigations to study the molecular mechanisms underlying peach flesh coloring.

Heart failure with preserved ejection fraction (HFpEF) is associated with multiple etiologic and pathophysiologic factors. HFpEF leads to significant cardiovascular morbidity and mortality. There are various reasons that fail to identify effective therapeutic interventions for HFpEF, primarily due to its clinical heterogeneity causing significant difficulties in determining physiologic and prognostic implications for this syndrome. Thus, identifying clinical subtypes using multi-omics data has great implications for efficient treatment and prognosis of HFpEF patients. Here we proposed to integrate mRNA, DNA methylation and microRNA (miRNA) expression data of HFpEF with a similarity network fusion (SNF) method following a network enhancement (ne-SNF) denoising technique to form a fused network. A spectral clustering method was then used to obtain clusters of patient subtypes. Experiments on HFpEF datasets demonstrated that ne-SNF significantly outperforms single data subtype analysis and other integrated methods. The identified subgroups were shown to have statistically significant differences in survival. Two HFpEF subtypes were defined: a high-risk group (16.8%) and a low-risk group (83.2%). The 5-year mortality rates were 63.3% and 33.0% for the high- and low-risk group, respectively. After adjusting for the effects of clinical covariates, HFpEF patients in the high-risk group were 2.43 times more likely to die than the low-risk group. A total of 157 differentially expressed (DE) mRNAs, 2199 abnormal methylations and 121 DE miRNAs were identified between two subtypes. They were also enriched in many HFpEF-related biological processes or pathways. The ne-SNF method provides a novel pipeline for subtype identification in integrated analysis of multi-omics data.

Long non-coding RNAs (lncRNAs) play significant roles in the disease process. Understanding the pathological mechanisms of lncRNAs during the course of various diseases will help clinicians prevent and treat diseases. With the emergence of high-throughput techniques, many biological experiments have been developed to study lncRNA-disease associations. Because experimental methods are costly, slow, and laborious, a growing number of computational models have emerged. Here, we present a new approach using network consistency projection and bi-random walk (NCP-BiRW) to infer hidden lncRNA-disease associations. First, integrated similarity networks for lncRNAs and diseases were constructed by merging similarity information. Subsequently, network consistency projection was applied to calculate space projection scores for lncRNAs and diseases, which were then introduced into a bi-random walk method for association prediction. To test model performance, we employed 5- and 10-fold cross-validation, with the area under the receiver operating characteristic curve as the evaluation indicator. The computational results showed that our method outperformed the other five advanced algorithms. In addition, the novel method was applied to another dataset in the Mammalian ncRNA-Disease Repository (MNDR) database and showed excellent performance. Finally, case studies were carried out on atherosclerosis and leukemia to confirm the effectiveness of our method in practice. In conclusion, we could infer lncRNA-disease associations using the NCP-BiRW model, which may benefit biomedical studies in the future.

Abstract Background Lipid infiltration and inflammatory response run through the occurrence of atherosclerosis. Differentiation into macrophages and foam cell formation are the key steps of AS. Aim of this study was that the differential gene expression between foam cells and macrophages was analyzed to search the key links of foam cell generation, so as to explore the pathogenesis of atherosclerosis and provide targets for the early screening and prevention of coronary artery disease (CAD). Methods The gene expression profiles of GSE9874 were downloaded from Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9874 ) on GPL96 [HG-U133A] Affymetrix Human Genome U133. A total of 22,383 genes were analyzed for differentially expression genes (DEGs) by Bayes package. GO enrichment analysis and KEGG pathway analysis for DEGs were performed using KOBAS 3.0 software (Peking University, Beijing, China). STRING software (STRING 10.0; European Molecular Biology Laboratory, Heidelberg, Germany) was used to analyze the protein-protein interaction (PPI) of DEGs. Results A total of 167 DEGs between macrophages and foam cells were identified. Compared with macrophages, 102 genes were significantly upregulated and 65 genes were significantly downregulated (P 1) in foam cells. DEGs were mainly enrich in ‘sterol biosynthetic and metabolic process’, ‘cholesterol metabolic and biosynthetic process’ by GO enrichment analysis. The results of KEGG pathway analysis showed all differential genes are involved in biological processes through 143 KEGG pathways. A PPI network of the DEGs was constructed and 10 outstanding genes of the PPI network was identified by using Cytoscape, which include HMGCR, SREBF2, LDLR, HMGCS1, FDFT1, LPL, DHCR24, SQLE, ABCA1 and FDPS. Conclusion: Lipid metabolism related genes and molecular pathways were the key to the transformation of macrophages into foam cells. Therefore, lipid metabolism disorder is the key to turn macrophages into foam cells, which plays a major role in CAD.

Heart failure with preserved ejection fraction (HFpEF) has become a major health issue because of its high mortality, high heterogeneity, and poor prognosis. Using genomic data to classify patients into different risk groups is a promising method to facilitate the identification of high-risk groups for further precision treatment. Here, we applied six machine learning models, namely kernel partial least squares with the genetic algorithm (GA-KPLS), the least absolute shrinkage and selection operator (LASSO), random forest, ridge regression, support vector machine, and the conventional logistic regression model, to predict HFpEF risk and to identify subgroups at high risk of death based on gene expression data. The model performance was evaluated using various criteria. Our analysis was focused on 149 HFpEF patients from the Framingham Heart Study cohort who were classified into good-outcome and poor-outcome groups based on their 3-year survival outcome. The results showed that the GA-KPLS model exhibited the best performance in predicting patient risk. We further identified 116 differentially expressed genes (DEGs) between the two groups, thus providing novel therapeutic targets for HFpEF. Additionally, the DEGs were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to HFpEF. The GA-KPLS-based HFpEF model is a powerful method for risk stratification of 3-year mortality in HFpEF patients.

To investigate whether D-dimer level could predict pulmonary embolism (PE) severity and in-hospital death, a total of 272 patients with PE were divided into a survival group (n = 249) and a death group (n = 23). Comparisons of patient characteristics between the 2 groups were performed using Mann-Whitney U test. Significant variables in univariate analysis were entered into multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of D-dimer level alone or together with the simplified Pulmonary Embolism Severity Index (sPESI) for in-hospital death. Results showed that patients in the death group were significantly more likely to have hypotension (P = 0.008), tachycardia (P = 0.000), elevated D-dimer level (P = 0.003), and a higher sPESI (P = 0.002) than those in the survival group. Multivariable logistic regression analysis showed that D-dimer level was an independent predictor of in-hospital death (OR = 1.07; 95% CI, 1.003-1.143; P = 0.041). ROC curve analysis showed that when D-dimer level was 3.175 ng/ml, predicted death sensitivity and specificity were 0.913 and 0.357, respectively; and when combined with sPESI, specificity (0.838) and area under the curve (0.740) were increased. Thus, D-dimer level is associated with in-hospital death due to PE; and the combination with sPESI can improve the prediction level.

The prefrontal cortex (PFC) constitutes a large part of the human central nervous system and is essential for the normal social affection and executive function of humans and other primates. Despite ongoing research in this region, the development of interactions between PFC genes over the lifespan is still unknown. To investigate the conversion of PFC gene interaction networks and further identify hub genes, we obtained time-series gene expression data of human PFC tissues from the Gene Expression Omnibus (GEO) database. A statistical model, loggle, was used to construct time-varying networks and several common network attributes were used to explore the development of PFC gene networks with age. Network similarity analysis showed that the development of human PFC is divided into three stages, namely, fast development period, deceleration to stationary period, and recession period. We identified some genes related to PFC development at these different stages, including genes involved in neuronal differentiation or synapse formation, genes involved in nerve impulse transmission, and genes involved in the development of myelin around neurons. Some of these genes are consistent with findings in previous reports. At the same time, we explored the development of several known KEGG pathways in PFC and corresponding hub genes. This study clarified the development trajectory of the interaction between PFC genes, and proposed a set of candidate genes related to PFC development, which helps further study of human brain development at the genomic level supplemental to regular anatomical analyses. The analytical process used in this study, involving the loggle model, similarity analysis, and central analysis, provides a comprehensive strategy to gain novel insights into the evolution and development of brain networks in other organisms.

Genome-wide association studies focusing on a single phenotype have been broadly conducted to identify genetic variants associated with a complex disease. The commonly applied single variant analysis is limited by failing to consider the complex interactions between variants, which motivated the development of association analyses focusing on genes or gene sets. Moreover, when multiple correlated phenotypes are available, methods based on a multi-trait analysis can improve the association power. However, most currently available multi-trait analyses are single variant-based analyses; thus have limited power when disease variants function as a group in a gene or a gene set. In this work, we propose a genome-wide gene-based multi-trait analysis method by considering genes as testing units. For a given phenotype, we adopt a rapid and powerful kernel-based testing method which can evaluate the joint effect of multiple variants within a gene. The joint effect, either linear or nonlinear, is captured through kernel functions. Given a series of candidate kernel functions, we propose an omnibus test strategy to integrate the test results based on different candidate kernels. A p-value combination method is then applied to integrate dependent p-values to assess the association between a gene and multiple correlated phenotypes. Simulation studies show a reasonable type I error control and an excellent power of the proposed method compared to its counterparts. We further show the utility of the method by applying it to two data sets: the Human Liver Cohort and the Alzheimer Disease Neuroimaging Initiative data set, and novel genes are identified. Our method has broad applications in other fields in which the interest is to evaluate the joint effect (linear or nonlinear) of a set of variants.

Abstract Stimuli-responsive polymers have received growing attention in recent years owing to their wide applications in diverse fields. A novel stimuli-responsive polymer, based on oligo(ethylene glycol) diacrylate (OEGDA) and methacrylic acid (MAA), P(OEGDA-MAA), is prepared by precipitation polymerization and is shown to have a LCST-type VPTT (volume phase transition temperature) at 33 °C in water and a UCST-type VPTT at 43 °C in ethanol, all at concentration of 1 mg/mL. Both VPTTs are strongly concentration and pH dependent, providing an easy way to tune the phase transition temperature. The polymer is characterized with regard to its composition and its morphology in water and in ethanol at different concentration. The two transitions are studied and interpreted based on the results. This work provides a novel way for the preparation of a new type of stimuli-responsive polymer with great potential for different applications, particularly those in biomedical areas because PEG-based stimuli-responsive polymers are known to be nontoxic and non-immunogenic.

Abstract Longitudinal genetic data provide more information regarding genetic effects over time compared with cross-sectional data. Coupled with next-generation sequencing technologies, it becomes reality to identify important genes containing both rare and common variants in a longitudinal design. In this work, we adopted a weighted sum statistic (WSS) to collapse multiple variants in a gene region to form a gene score. When multiple genes in a pathway were considered together, a penalized longitudinal model under the quadratic inference function (QIF) framework was applied for efficient gene selection. We evaluated the estimation accuracy and model selection performance under different model settings, then applied the method to a real dataset from the Genetic Analysis Workshop 18 (GAW18). Compared with the unpenalized QIF method, the penalized QIF (pQIF) method achieved better estimation accuracy and higher selection efficiency. The pQIF remained optimal even when the working correlation structure was mis-specified. The real data analysis identified one important gene, angiotensin II receptor type 1 (AGTR1), in the Ca2+/AT-IIR/α-AR signaling pathway. The estimated effect implied that AGTR1 may have a protective effect for hypertension. Our pQIF method provides a general tool for longitudinal sequencing studies involving large numbers of genetic variants.

Congenital heart defects (CHD) is one of the most common birth defects in China. Many studies have examined risk factors for CHD, but their predictive abilities have not been evaluated. In particular, few studies have attempted to predict risks of CHD from, necessarily unbalanced, population-based cross-sectional data. Therefore, we developed and validated machine learning models for predicting, before and during pregnancy, women's risks of bearing children with CHD. We compared the results of these models in a large-scale, comprehensive population-based retrospective cross-sectional epidemiological survey of birth defects in six counties in Shanxi Province, China, covering 2006 to 2008. This contained 78 cases of CHD among 33831 live births. We constructed nine synthetic variables to use in the models: maternal age, annual per capita income, family history, maternal history of illness, nutrition and folic acid deficiency, maternal illness in pregnancy, medication use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. The machine learning algorithms Weighted Support Vector Machine (WSVM) and Weighted Random Forest (WRF) were trained on, and a logistic regression (Logit) was fitted to, two-thirds of the data. Their predictive abilities were then tested in the remaining data. True positive rate (TPR), true negative rate (TNR), accuracy (ACC), area under the curves (AUC), G-means, and Weighted accuracy (WTacc) were used to compare the classification performance of the models. Median values, from repeating the data partitioning 1000 times, were used in all comparisons. The TPR and TNR of the three classifiers were above 0.65 and 0.93, respectively, better than any reported in the literature. TPR, wtACC, AUC and G were highest for WSVM, showing that it performed best. All three models are precise enough to identify groups at high risk of CHD. They should all be considered for future investigations of other birth defects and diseases.