Approximating Partial Likelihood Estimators via Optimal Subsampling

With the growing availability of large-scale biomedical data, it is often time-consuming or infeasible to directly perform traditional statistical analysis with relatively limited computing resources at hand. We propose a fast and stable subsampling method to effectively approximate the full data maximum partial likelihood estimator in Cox's model, which reduces the computational burden when analyzing massive survival data. We establish consistency and asymptotic normality of a general subsample-based estimator. The optimal subsampling probabilities with explicit expressions are determined via minimizing the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator. We propose a two-step subsampling algorithm for practical implementation, which has a significant reduction in computing time compared to the full data method. The asymptotic properties of the resulting two-step subsample-based estimator is established. In addition, a subsampling-based Breslow-type estimator for the cumulative baseline hazard function and a subsample estimated survival function are presented. Extensive experiments are conducted to assess the proposed subsampling strategy. Finally, we provide an illustrative example about large-scale lymphoma cancer dataset from the Surveillance, Epidemiology,and End Results Program

miR-181a increases FoxO1 acetylation and promotes granulosa cell apoptosis via SIRT1 downregulation.

Oxidative stress impairs follicular development by inducing granulosa cell (GC) apoptosis, which involves enhancement of the transcriptional activity of the pro-apoptotic factor Forkhead box O1 (FoxO1). However, the mechanism by which oxidative stress promotes FoxO1 activity is still unclear. Here, we found that miR-181a was upregulated in hydrogen peroxide (

Fetal-maternal interactions during pregnancy: a ‘three-in-one’ perspective

A successful human pregnancy requires the maternal immune system to recognize and tolerate the semi-allogeneic fetus, allowing for appropriate trophoblasts invasion and protecting the fetus from invading pathogens. Therefore, maternal immunity is critical for the establishment and maintenance of pregnancy, especially at the maternal-fetal interface. Anatomically, the maternal-fetal interface has both maternally- and fetally- derived cells, including fetal originated trophoblasts and maternal derived immune cells and stromal cells. Besides, a commensal microbiota in the uterus was supposed to aid the unique immunity in pregnancy. The appropriate crosstalk between fetal derived and maternal originated cells and uterine microbiota are critical for normal pregnancy. Dysfunctional maternal-fetal interactions might be associated with the development of pregnancy complications. This review elaborates the latest knowledge on the interactions between trophoblasts and decidual immune cells, highlighting their critical roles in maternal-fetal tolerance and pregnancy development. We also characterize the role of commensal bacteria in promoting pregnancy progression. Furthermore, this review may provide new thought on future basic research and the development of clinical applications for pregnancy complications

Efficacy of roxithromycin with gamma globulin in children with mycoplasma pneumonia and its effect on immunity

Purpose: To determine the efficacy of roxithromycin plus gamma globulin in the treatment of children with mycoplasma pneumonia (MPP) and its effect on immune function.Methods: From January 2019 to January 2021, 100 children with MPP assessed for eligibility in Qingdao Women and Children's Hospital, Shandong Province, China, were recruited and randomized (1:1) to receive either gamma globulin (control group) or roxithromycin plus gamma globulin (study group). Levels of tumor necrosis factor (TNF)-α, immunoglobulin (Ig)A, IgM, and IgG were evaluated. Clinical indices, including fever reduction, cough disappearance, duration of hospital stay, etc were also assessed.Results: The study group had a significantly higher clinical efficacy (88 %) than the control group (68 %) (p < 0.05). After treatment, patients in the study group showed lower levels of tumor necrosis factor (TNF)-α than those in the control group (p < 0.05). The eligible patients given roxithromycin plus gamma globulin showed significantly higher levels of immunoglobulin (Ig)A, IgM, and IgG versus those given gamma globulin alone (p < 0.05). Patients in the study group had a shorter time lapse before fever reduction, cough disappearance, lung sign disappearance, and duration of hospital stay than those in the control group (p < 0.05).Conclusion: Roxithromycin plus gamma globulin demonstrate significant benefits in the treatment of children with MPP by mitigating inflammatory response, enhancing immune function, and also significantly alleviating clinical symptoms. Thus, the combination treatment shows good potentials for use in clinical practice

Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD), runs test (RT), and relevance vector machine (RVM). First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF) components and residual (RES) component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels) by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy

Different Angiogenic Potentials of Mesenchymal Stem Cells Derived from Umbilical Artery, Umbilical Vein, and Wharton's Jelly

Human mesenchymal stem cells derived from the umbilical cord (UC) are a favorable source for allogeneic cell therapy. Here, we successfully isolated the stem cells derived from three different compartments of the human UC, including perivascular stem cells derived from umbilical arteries (UCA-PSCs), perivascular stem cells derived from umbilical vein (UCV-PSCs), and mesenchymal stem cells derived from Wharton’s jelly (WJ-MSCs). These cells had the similar phenotype and differentiation potential toward adipocytes, osteoblasts, and neuron-like cells. However, UCA-PSCs and UCV-PSCs had more CD146+ cells than WJ-MSCs (P<0.05). Tube formation assay in vitro showed the largest number of tube-like structures and branch points in UCA-PSCs among the three stem cells. Additionally, the total tube length in UCA-PSCs and UCV-PSCs was significantly longer than in WJ-MSCs (P<0.01). Microarray, qRT-PCR, and Western blot analysis showed that UCA-PSCs had the highest expression of the Notch ligand Jagged1 (JAG1), which is crucial for blood vessel maturation. Knockdown of Jagged1 significantly impaired the angiogenesis in UCA-PSCs. In summary, UCA-PSCs are promising cell populations for clinical use in ischemic diseases

Mst1-Mediated Phosphorylation of Nur77 Improves the Endometrial Receptivity in Human and Mice

Background: Successful embryo implantation requires the attachment of a blastocyst to the receptive endometrial epithelium, which was disturbed in the women with recurrent implantation failure (RIF). Endometrial β3-integrin was the most important adhesion molecule contributing to endometrial receptivity in both humans and mice. Nur77 has been proven indispensable for fertility in mice, here we explore the role of Nur77 on embryo-epithelial adhesion and potential treatment to embryo implantation failure. Methods: The expression and location of Mst1 and Nur77 in endometrium from fertile women and RIF patients were examined by IHC, qRT-PCR and Western blotting. In vitro kinase assay following with LC-MS/MS were used to identify the phosphorylation site of Nur77 activated by Mst1. The phosphorylated Nur77 was detected by phos-tag SDS-PAGE assay and specific antibody against phospho-Nur77-Thr366. The effect of embryo-epithelium interaction was determined in the BeWo spheroid or mouse embryo adhesion assay, and delayed implantation mouse model. RNA-seq was used to explore the mechanism by which Nur77 derived peptide promotes endometrial receptivity. Findings: Endometrial Mammalian sterile 20 (STE20)-like kinase 1 (Mst1) expression level was decreased in the women with RIF than that in the fertile control group, while Mst1 activation in the epithelial cells promoted trophoblast-uterine epithelium adhesion. The effect of Nur77 mediated trophoblast-uterine epithelium adhesion was facilitated by active Mst1. Mechanistically, mst1 promotes the transcription activity of Nur77 by phosphorylating Nur77 at threonine 366 (T366), and consequently increased downstream target β3-integrin expression. Furthermore, a Nur77-derived peptide containing phosphorylated T366 markedly promoted mouse embryo attachment to Ishikawa cells ([4 (2-4)] vs [3 (2-4)]) and increased the embryo implantation rate (4 vs 1.4) in a delayed implantation mouse model by regulating integrin signalling. Finally, it is observed that the endometrial phospho-Nur77 (T366) level is decreased by 80% in the women with RIF. Interpretation: In addition to uncovering a potential regulatory mechanism of Mst1/Nur77/β3-integrin signal axis involved in the regulation of embryo-epithelium interaction, our finding provides a novel marker of endometrial receptivity and a potential therapeutic agent for embryo implantation failure. Funding: National Key Research and Development Program of China (2018YFC1004400), the National Natural Science Foundation of China (82171653, 82271698, 82030040, 81971387 and 30900727), and National Institutes of Health grants (R01HL103869)

Pengaruh sense of school belonging terhadap student's misbehavior

Penelitian ini bertujuan untuk mengetahu pengaruh sense of school belonging terhadap student’s misbehavior. Penelitian ini merupakan penelitian korelasional dengan menggunakan teknnik pengumpulan data berupa skala likert yaitu skala sense of school belonging dan skala student’s misbehavior masing masing terdiri dari 30 aitem yang sudah melalui uji coba. Skala sense of school belonging memiliki reabilitas sebesar 0,899 sedangkan skala student’s misbehavior memiliki reabilitas sebesar 0,924. Subjek penelitian berjumlah 144 siswa dari jumlah populasi sebesar 576 siswa. Pengambilan data menggunakan simple random sampling. Hasil penelitian menujukkan bahwa terdapat pengaruh sense of school belonging terhadap student’s misbehavior dengan nilai signifikansi 0,000 < 0,05. Dalam table model summary pada analisis regresi linier sederhana, sense of school belonging memberikan pengaruh sebesar 17,7% terhadap student’s misbehavior. Pada table correlation, terdapat nilai koerfisien korelasi sebesar -0,420 yang berarti semakin tinggi sense of school belonging maka semakin rendah student’s misbehavior yang dilakukan oleh siswa

An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795