TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding

ICD coding is designed to assign the disease codes to electronic health records (EHRs) upon discharge, which is crucial for billing and clinical statistics. In an attempt to improve the effectiveness and efficiency of manual coding, many methods have been proposed to automatically predict ICD codes from clinical notes. However, most previous works ignore the decisive information contained in structured medical data in EHRs, which is hard to be captured from the noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal Attention Network (TreeMAN) to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features via the attention mechanism. Tree-based features are constructed according to decision trees learned from structured multimodal medical data, which capture the decisive information about ICD coding. We can apply the same multi-label classifier from previous text models to the multimodal representations to predict ICD codes. Experiments on two MIMIC datasets show that our method outperforms prior state-of-the-art ICD coding approaches. The code is available at https://github.com/liu-zichen/TreeMAN

Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies

Protein content (PC), an important trait in soybean (Glycine max) breeding, is controlled by multiple genes with relatively small effects. To identify the quantitative trait nucleotides (QTNs) controlling PC, we conducted a multi-locus genome-wide association study (GWAS) for PC in 144 four-way recombinant inbred lines (FW-RILs). All the FW-RILs were phenotyped for PC in 20 environments, including four locations over 4 years with different experimental treatments. Meanwhile, all the FW-RILs were genotyped using SoySNP660k BeadChip, producing genotype data for 109,676 non-redundant single-nucleotide polymorphisms. A total of 129 significant QTNs were identified by five multi-locus GWAS methods. Based on the 22 common QTNs detected by multiple GWAS methods or in multiple environments, pathway analysis identified 8 potential candidate genes that are likely to be involved in protein synthesis and metabolism in soybean seeds. Using superior allele information for 22 common QTNs in 22 elite and 7 inferior lines, we found higher superior allele percentages in the elite lines and lower percentages in the inferior lines. These findings will contribute to the discovery of the polygenic networks controlling PC in soybean, increase our understanding of the genetic foundation and regulation of PC, and be useful for molecular breeding of high-protein soybean varieties

Long-term exposure to air pollution and lung function among children in China: Association and effect modification

BackgroundChildren are vulnerable to the respiratory effects of air pollution, and their lung function has been associated with long-term exposure to low air pollution level in developed countries. However, the impact of contemporary air pollution level in developing countries as a result of recent efforts to improve air quality on children's lung function is less understood.MethodsWe obtained a cross-sectional sample of 617 schoolchildren living in three differently polluted areas in Anhui province, China. 2-year average concentrations of air pollutants at the year of spirometry and the previous year (2017–2018) obtained from district-level air monitoring stations were used to characterize long-term exposure. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and forced expiratory flow between 25 and 75% of FVC (FEF25−75) were determined under strict quality control. Multivariable regression was employed to evaluate the associations between air pollution level and lung function parameters, overall and by demographic characteristics, lifestyle, and vitamin D that was determined by liquid chromatography tandem mass spectrometry.ResultsMean concentration of fine particulate matter was 44.7 μg/m3, which is slightly above the interim target 1 standard of the World Health Organization. After adjusting for confounders, FVC, FEV1, and FEF25−75 showed inverse trends with increasing air pollution levels, with children in high exposure group exhibiting 87.9 [95% confidence interval (CI): 9.5, 166.4] mL decrement in FEV1 and 195.3 (95% CI: 30.5, 360.1) mL/s decrement in FEF25−75 compared with those in low exposure group. Additionally, the above negative associations were more pronounced among those who were younger, girls, not exposed to secondhand smoke, non-overweight, physically inactive, or vitamin D deficient.ConclusionsOur study suggests that long-term exposure to relatively high air pollution was associated with impaired lung function in children. More stringent pollution control measures and intervention strategies accounting for effect modification are needed for vulnerable populations in China and other developing countries

Smithian platform-bearing gondolellid conodonts from Yiwagou Section, northwestern China and implications for their geographic distribution in the Early Triassic

Abundant platform-bearing gondolellid conodonts, including Scythogondolella mosheri (Kozur and Mostler), Sc. phryna Orchard and Zonneveld, and Sc. cf. milleri (Müller), have been discovered from the Yiwagou Section of Tewo, together with Novispathodus waageni waageni (Sweet) and Nv. w. eowaageni Zhao and Orchard. This is the first report of Smithian platform-bearing gondolellids from the Paleo-Tethys region. In addition, Eurygnathodus costatus Staesche, E. hamadai(Koike), Parafurnishius xuanhanensis Yang et al., and the genera Pachycladina Staesche, Parachirognathus Clark, and Hadrodontina Staesche have also been recovered from Dienerian to Smithian strata at Yiwagou Section. Three conodont zones are established, in ascending order: Eurygnathodus costatus-E. hamadai Assemblage Zone, Novispathodus waageni-Scythogondolella mosheri Assemblage Zone, and the Pachycladina-Parachirognathus Assemblage Zone. The platform-bearing gondolellids were globally distributed just after the end-Permian mass extinction, but the formerly abundant Clarkina Kozur disappeared in the late Griesbachian. Platform-bearing gondolellids dramatically decreased to a minimum of diversity and extent in the Dienerian before recovering in the Smithian. Scythogondolella Kozur, probably a thermophilic and eurythermic genus, lived in all latitudes at this time whereas other genera did not cope with Smithian high temperatures and so became restricted to the high-latitude regions. However, the maximum temperature in the late Smithian likely caused the extinction of almost all platform-bearing gondolellids. Finally, the group returned to equatorial regions and achieved global distribution again in the cooler conditions of the late Spathian. We conclude that temperature (and to a lesser extent oxygen levels) exerted a strong control on the geographical distribution and evolution of platform-bearing gondolellids in the Early Triassic

Enhanced photocurrent in ferroelectric Bi_0.5Na_0.5TiO₃ materials via ferro-pyro-phototronic effect

The photocurrent generated by ferroelectric materials via the photovoltaic effect has latent applications in opto-electronic devices due to the introduction of a polarization-induced charge carrier separation and transport, which is absent in semiconducting p-n junctions. However, the precise mechanism of photocurrent production in photoelectric devices by a range of ferroelectric materials remains a topic of continued interest. Here we report a significant enhancement in the photocurrent of a ferroelectric Bi0.5Na0.5TiO3-based photoelectric device by ferro-pyro-phototronic effect. When compared with the generated photocurrent under illumination alone, the peak and platform photocurrents induced by the simultaneous use of both light and heating can be increased by 131% and 57%, respectively. The increase in photocurrent is found to increase with temperature, where the mechanism of enhancement is associated with the increase in pyroelectric coefficient, photoexcited carrier concentration and carrier mobility at higher operating temperatures. These factors also lead to a rapid response time of 84.2 ms. This work provides guidelines for photocurrent promotion in ferroelectric devices, pushing forward the potential applications of ferroelectric materials for self-powered photodetectors.</p

Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine

All the supervised classification methods need sufficient and efficient samples, which are commonly labeled by visual inspection. In this study, to resolve the issues of insufficient training samples and time-consuming, a novel method for detailed and automated LULC classification by LC_Type1 of MCD12Q1 IGBP schemes in the GEE cloud platform was proposed based on the RF and CART classifiers. The results present that the validation overall accuracy of the RF classifier is higher than the CART, 87.24% in Australia, and 85.18% in the USA, respectively. The automated classification results of the RF classifier are more concentrated than CART, which the RF classifier is more suitable for this automated method. Moreover, the proposed method can accomplish accurate, detailed, and automated LULC classification based on the GEE which is making satellite imagery computing an efficient, flexible, and fast process. The workflow provides a reliable method for detailed, automated, and remotely LULC classification

Spatio-Temporal 3-D Residual Networks for Simultaneous Detection and Depth Estimation of CFRP Subsurface Defects in Lock-In Thermography

Nondestructive thermography is a high-speed, low-cost, and safe solution for subsurface defects detection of carbon fiber reinforced polymer (CFRP) materials, providing essential quality control in aerospace, automobile, and sports industries. In this article, we build a reflective lock-in thermography system and construct a dataset that contains real-captured thermal image sequences of CFRP samples with various simulated internal defects under different excitation frequencies. Then, we present a novel 3-D convolutional neural network (CNN) model incorporating a combination of spatial and temporal convolutional filters and batch-size independent group normalization (GN) as a unified framework to process thermal image sequences captured by lock-in thermography for simultaneous subsurface defect detection and depth estimation. Finally, we define a multitask loss function to perform end-to-end training of both defect detection and depth estimation tasks based on the real-captured infrared sequences. Comparative experiments are carried out on CFRP specimens with artificial defects of various sizes/shapes and at different depths. Qualitative and quantitative results illustrate that our 3-D CNN model is capable of predicting accurate locations and depths of subsurface defects and performs favorably against the hand-crafted and CNN-based methods in lock-in thermography for individual defect detection and depth estimation tasks. The captured dataset and the source codes will be made publicly available