Association of Surfactant-Associated Protein D Gene Polymorphisms with the Risk of COPD: a Meta-Analysis

The relationship between surfactant-associated protein D polymorphisms and chronic obstructive pulmonary disease risk remains controversial. This article is the first to systematically evaluate this relationship. A comprehensive worldwide search was conducted for relevant literature on surfactant-associated protein D gene mutations and chronic obstructive pulmonary disease risk prediction. Study quality was evaluated using the Newcastle-Ottawa scale. After four genetic models (the allele, additive, recessive, and dominant models) were identified, odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were applied in this meta-analysis. The meta-analysis included 659 individuals in the case group and 597 in the control group. In the Asian population, none of the four genetic models revealed any significant association between rs2243639 genotype and the risk of chronic obstructive pulmonary disease. In Caucasians, however, the recessive model exhibited significant risk associated with rs2243639. Furthermore, there was a significant association between rs721917 genotype and the risk of chronic obstructive pulmonary disease in the Asian population. In contrast, none of the four gene models revealed any significant risk associated with this gene in the Caucasian population. This meta-analysis suggests that rs2243639 is not related to the risk of chronic obstructive pulmonary disease in the Asian population but is related to this risk in the Caucasian population. Regarding rs721917, the T allele may increase the risk of chronic obstructive pulmonary disease in the Asian population

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms are designed for online time series forecasting, with some exploiting cross-variable dependency while others assume independence among variables. Given every data assumption has its own pros and cons in online time series modeling, we propose \textbf{On}line \textbf{e}nsembling \textbf{Net}work (OneNet). It dynamically updates and combines two models, with one focusing on modeling the dependency across the time dimension and the other on cross-variate dependency. Our method incorporates a reinforcement learning-based approach into the traditional online convex programming framework, allowing for the linear combination of the two models with dynamically adjusted weights. OneNet addresses the main shortcoming of classical online learning methods that tend to be slow in adapting to the concept drift. Empirical results show that OneNet reduces online forecasting error by more than $\mathbf{50\%}$ compared to the State-Of-The-Art (SOTA) method. The code is available at \url{https://github.com/yfzhang114/OneNet}.Comment: 32 pages, 11 figures, 37th Conference on Neural Information Processing Systems (NeurIPS 2023

GRB 120422A: A Low-luminosity Gamma-ray Burst Driven by Central Engine

GRB 120422A is a low-luminosity Gamma-ray burst (GRB) associated with a bright supernova, which distinguishes itself by its relatively short T90 ~ 5 s and an energetic X-ray tail. We analyze the Swift BAT and XRT data and discuss the physical implications. We show that the early steep decline in the X-ray light curve can be interpreted as the curvature tail of a late emission episode around 58-86 s, with a curved instantaneous spectrum at the end of the emission episode. Together with the main activity in the first ~ 20 s and the weak emission from 40 s to 60 s, the prompt emission is variable, which points towards a central engine origin, in contrast to the shock breakout origin as invoked to interpret some other nearby low-luminosity supernova GRBs. The curvature effect interpretation and interpreting the early shallow decay as the coasting external forward shock emission in a wind medium both give a constraint on the bulk Lorentz factor \Gamma to be around several. Comparing the properties of GRB 120422A and other supernova GRBs, we found that the main criterion to distinguish engine-driven GRBs from the shock breakout GRBs is the time-averaged luminosity, with a separation line of about ~ 10^48 erg s^-1.Comment: ApJ accepted version; 6 pages, 1 table, 5 figures; minor changes; references update

Desain Interior Rumah Sakit Jiwa Dr.Radjiman Wediodiningrat Lawang Malang

Kesehatan manusia merupakan kesehatan secara holistic baik jiwa maupun raga. Kesehatan mental merupakan unsur vital bagi produktifitas manusia. Di era globalisasi seperti ini tingkatan stres masyarakat meningkat, sehingga dibutuhkan fasilitas penunjang perawatan kesehatan jiwa. Rumah Sakit Jiwa merupakan satu satunya rujukan utama bagi perawatan jiwa masyarakat. Dewasa ini, pengembangan peran serta lingkungan fisik terhadap kesehatan mental pasien cukup pesat. Sehingga dapat menjadi literature dalam Perencanaan re-desain interior Rumah Sakit Jiwa Lawang Malang. Perencanaan re-desain interior RSJ berdasarkan observasi obyek desain, wawancara pihak petugas medis rumah sakit, maupun wawancara dari pihak keluarga pasien. Hasil yang diperoleh berupa konsep perancangan Rumah Sakit Jiwa dengan konsep terapeutik. Oleh karena itu, dapat disusun sebuah konsep dan meredesain desain interior Rumah Sakit Jiwa Dr. Radjiman Wediodiningrat Lawang Malang dengan konsep terapeutik yang mengedepankan Kenyamanan pasien dari aspek sirkulasi dan desain ruangan terapi dari Rumah Sakit Jiwa Dr. Radjiman Wediodiningrat Lawang Malang

Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection

BACKGROUND: Acupuncture has been practiced in China for thousands of years as part of the Traditional Chinese Medicine (TCM) and has gradually accepted in western countries as an alternative or complementary treatment. However, the underlying mechanism of acupuncture, especially whether there exists any difference between varies acupoints, remains largely unknown, which hinders its widespread use. RESULTS: In this study, we develop a novel Linear Programming based Feature Selection method (LPFS) to understand the mechanism of acupuncture effect, at molecular level, by revealing the metabolite biomarkers for acupuncture treatment. Specifically, we generate and investigate the high-throughput metabolic profiles of acupuncture treatment at several acupoints in human. To select the subsets of metabolites that best characterize the acupuncture effect for each meridian point, an optimization model is proposed to identify biomarkers from high-dimensional metabolic data from case and control samples. Importantly, we use nearest centroid as the prototype to simultaneously minimize the number of selected features and the leave-one-out cross validation error of classifier. We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR). We find that our LPFS method tends to reveal a small set of metabolites with small standard deviation and large shifts, which exactly serves our requirement for good biomarker. Biologically, several metabolite biomarkers for acupuncture treatment are revealed and serve as the candidates for further mechanism investigation. Also biomakers derived from five meridian points, Zusanli (ST36), Liangmen (ST21), Juliao (ST3), Yanglingquan (GB34), and Weizhong (BL40), are compared for their similarity and difference, which provide evidence for the specificity of acupoints. CONCLUSIONS: Our result demonstrates that metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture. Comparing with other existing methods, LPFS shows better performance to select a small set of key molecules. In addition, LPFS is a general methodology and can be applied to other high-dimensional data analysis, for example cancer genomics