Parity and Risk of Low Birth Weight Infant in Full Term Pregnancy

Latar belakang: Berat badan lahir rendah meningkatkan morbiditas dan mortalitas pada bayi baru lahir. Hasil Riskesdas 2010 dan 2013 menunjukkan penurunan angka prevalensi berat badan lahir rendah dari 11,1% menjadi 10,2%. Tujuan penelitian ini adalah mengidentifikasi faktor risiko yang berkaitan dengan kejadian berat badan lahir rendah pada kehamilan cukup bulan. Metode: Penelitian potong lintang di dua rumah sakit di Jakarta dengan menggunakan data sekunder. Data rekam medik wanita yang melahirkan pada periode 1 Januari sampai 31 Desember 2011 dipilih secara purposif. Berat badan lahir rendah adalah berat badan kurang dari 2500g pada bayi baru lahir. Analisis data dilakukan dengan menggunakan regresi logistik. Hasil: Pada analisis ini didapatkan 2242 subyek yang memenuhi kriteria, dari 4191 subyek. Proporsi berat badan lahir rendah adalah 9,5%. Jika dibandingkan dengan primipara, wanita nullipara memiliki risiko melahirkan bayi dengan berat badan lahir rendah 46% lebih tinggi [adjusted odds ratio (ORa) = 1.46; P=0.030]. Selanjutnya, jika dibandingkan dengan bayi laki-laki, bayi perempuan memiliki risiko 42% lebih tinggi mengalami berat lahir rendah (ORa = 1.42; P=0.017) Kesimpulan: Bayi berat badan lahir rendah pada kehamilan cukup bulan lebih sering ditemukan pada wanita nullipara dan bayi perempuan. Kata kunci: paritas, jenis kelamin bayi, berat badan lahir rendahBackground: Low birth weight infants tend to increase the occurence of early infant mortality and morbidity. The survey in Indonesia suggested that the prevalence of low birth weight declined from 11.1% in 2010 to 10.2% in 2013. This study aims to identify the risk factors of low birth weight infant in full term pregnancy. Methods: This was a cross-sectional study using secondary data from two hospitals in Jakarta. The data was obtained from medical records of pregnant women who gave birth during the period of January 1 to December 31, 2011. Multivariat logistic regression model with stepwise method was used to analyze the risks of low birth weight. Results: The sample size in this study was 4191 subjects. Out of them 2242 subjects met the inclusion criteria. The proportion of low birth weight was 9.5%. Compared with primipara, nullipara had 46 % increased risk to have LBW infant (ORa = 1.46; P=0.030), meanwhile primipara and nullipara did not have significant difference for having LBW infants (ORa = 0.90; P=0.614). In term of sex of infants, female infant had 42% higher risk of having LBW infant compared with male infant (ORa = 1.42; P=0.017). Conclusion : Low birth weight infants in full term pregnancies are more common in nullipara and most of the LBW infants are femal

Small-Scale Spatial Heterogeneity of Photosynthetic Fluorescence Associated with Biological Soil Crust Succession in the Tengger Desert, China

In dryland regions, biological soil crusts (BSCs) have numerous important ecosystem functions. Crust species and functions are, however, highly spatially heterogeneous and remain poorly understood at a range of scales. In this study, chlorophyll fluorescence imaging was used to quantify millimeter-scale patterns in the distribution and activity of photosynthetic organisms in BSCs of different successional stages (including cyanobacterial, lichen, moss three main successional stages and three intermixed transitional stages) from the Tengger Desert, China. Chlorophyll fluorescence images derived from the Imaging PAM (Pulse Amplitude Modulation) showed that with the succession from cyanobacterial to lichen and to moss crusts, crust photosynthetic efficiency (including the maximum and effective photosynthetic efficiency, respectively) and fluorescence coverage increased significantly (P 0.05) between cyanobacterial and moss crusts, and showed a unimodal pattern of Fv/Fm values. In contrast, photosynthetic heterogeneity was significantly higher in lichen, cyanobacteria-moss and lichen-moss crusts (P < 0.05), with a bimodal pattern of Fv/Fm values. Point pattern analysis showed that the distribution pattern of chlorophyll fluorescence varied at different spatial scales and also among the different crust types. These new results provide a detailed (millimeter-scale) insight into crust photosynthetic mechanisms and spatial distribution patterns associated with their community types. Collectively, this information provides an improved theoretical basis for crust maintenance and management in dryland regions

Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models

With ever increasing parameters and computation, vision-language pre-trained (VLP) models exhibit prohibitive expenditure in downstream task adaption. Recent endeavors mainly focus on parameter efficient transfer learning (PETL) for VLP models by only updating a small number of parameters. However, excessive computational overhead still plagues the application of VLPs. In this paper, we aim at parameter and computation efficient transfer learning (PCETL) for VLP models. In particular, PCETL not only needs to limit the number of trainable parameters in VLP models, but also to reduce the computational redundancy during inference, thus enabling a more efficient transfer. To approach this target, we propose a novel dynamic architecture skipping (DAS) approach towards effective PCETL. Instead of directly optimizing the intrinsic architectures of VLP models, DAS first observes the significances of their modules to downstream tasks via a reinforcement learning (RL) based process, and then skips the redundant ones with lightweight networks, i.e., adapters, according to the obtained rewards. In this case, the VLP model can well maintain the scale of trainable parameters while speeding up its inference on downstream tasks. To validate DAS, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of VL tasks. The experimental results not only show the great advantages of DAS in reducing computational complexity, e.g. -11.97% FLOPs of METER on VQA2.0, but also confirm its competitiveness against existing PETL methods in terms of parameter scale and performance. Our source code is given in our appendix

DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL

Deep reinforcement learning (DRL) is becoming increasingly popular in implementing traffic signal control (TSC). However, most existing DRL methods employ fixed control strategies, making traffic signal phase duration less flexible. Additionally, the trend of using more complex DRL models makes real-life deployment more challenging. To address these two challenges, we firstly propose a two-stage DRL framework, named DynamicLight, which uses Max Queue-Length to select the proper phase and employs a deep Q-learning network to determine the duration of the corresponding phase. Based on the design of DynamicLight, we also introduce two variants: (1) DynamicLight-Lite, which addresses the first challenge by using only 19 parameters to achieve dynamic phase duration settings; and (2) DynamicLight-Cycle, which tackles the second challenge by actuating a set of phases in a fixed cyclical order to implement flexible phase duration in the respective cyclical phase structure. Numerical experiments are conducted using both real-world and synthetic datasets, covering four most commonly adopted traffic signal intersections in real life. Experimental results show that: (1) DynamicLight can learn satisfactorily on determining the phase duration and achieve a new state-of-the-art, with improvement up to 6% compared to the baselines in terms of adjusted average travel time; (2) DynamicLight-Lite matches or outperforms most baseline methods with only 19 parameters; and (3) DynamicLight-Cycle demonstrates high performance for current TSC systems without remarkable modification in an actual deployment. Our code is released at Github.Comment: 9 pages, 5figure