PE-YOLO: Pyramid Enhancement Network for Dark Object Detection

Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge. To address this issue, we propose a pyramid enhanced network (PENet) and joint it with YOLOv3 to build a dark object detection framework named PE-YOLO. Firstly, PENet decomposes the image into four components of different resolutions using the Laplacian pyramid. Specifically we propose a detail processing module (DPM) to enhance the detail of images, which consists of context branch and edge branch. In addition, we propose a low-frequency enhancement filter (LEF) to capture low-frequency semantics and prevent high-frequency noise. PE-YOLO adopts an end-to-end joint training approach and only uses normal detection loss to simplify the training process. We conduct experiments on the low-light object detection dataset ExDark to demonstrate the effectiveness of ours. The results indicate that compared with other dark detectors and low-light enhancement models, PE-YOLO achieves the advanced results, achieving 78.0% in mAP and 53.6 in FPS, respectively, which can adapt to object detection under different low-light conditions. The code is available at https://github.com/XiangchenYin/PE-YOLO.Comment: Accepted at ICANN 202

Negative symptom dimensions and social functioning in Chinese patients with schizophrenia

ObjectiveNegative symptoms can seriously affect social functioning in patients with schizophrenia. However, the role of various components of negative symptoms in social functioning remains unclear. This study aimed to explore the associations among three different dimensions of negative symptoms (i.e., communication, emotion, and motivation) and social functioning to identify potential therapeutic targets.MethodsThis cross-sectional study enrolled 202 Chinese participants with schizophrenia. Negative symptoms were evaluated using the Negative Symptom Assessment (NSA). Social functioning was represented by the Personal and Social Performance Scale (PSP) total score and employment status. Correlation analysis was conducted to clarify the relationship between negative symptoms and the PSP total score. Regression analysis was performed to explore the determinants of the PSP total score and employment status, considering negative symptoms and possible confounders, such as demographic features, positive symptoms, cognitive symptoms, depressive symptoms, and extrapyramidal side effects.ResultsThe PSP total score was correlated with all three dimensions of negative symptoms (i.e., emotion, motivation, and communication; rs = –0.509, –0.662, and –0.657, respectively). Motivation, instead of emotion or communication, predicted both low PSP total scores and unemployment.ConclusionSocial functioning in patients with schizophrenia was significantly related to motivation. Further studies should focus on motivation and consider it as a therapeutic target to improve patients’ social functioning

High-density lipoprotein cholesterol level as an independent protective factor against aggravation of acute pancreatitis: a case–control study

Background and aimsAt present, evidence on the association between high-density lipoprotein cholesterol (HDL-C) levels and aggravation of acute pancreatitis (AP) is limited. This study aimed to investigate the relationship between the lowest HDL-C level during intensive care units (ICU) stay and AP aggravation and to determine the optimum cutoff lowest HDL-C level.MethodsPatients admitted to the ICU of the Shandong Provincial Hospital for AP from 2015 to 2021 were included. The lowest HDL-C level during ICU stay was set as the independent variable, and the progression or non-progression to severe AP (SAP) was set as the dependent variable. Univariate and multivariate analyses were performed to determine the relationship between the two variables, and receiver operating characteristic (ROC) curves were plotted to analyze the predictive ability of the lowest HDL-C level for progression to SAP.ResultsThis study included 115 patients. The difference in the lowest HDL-C level between the SAP and moderately SAP groups was significant (P < 0.05). After adjusting for covariates, the lowest HDL-C level showed a negative correlation with the occurrence of SAP, with a relative risk of 0.897 (95% confidence interval: 0.827–0.973). The area under the ROC curve for prediction of AP aggravation by the lowest HDL-C level was 0.707, and the optimum cutoff lowest HDL-C level was 0.545 mmol/L.ConclusionNo less than 0.545 mmol/L of the HDL-C level during ICU stay may be an independent protective factor for the aggravation of AP

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Whole-genome sequencing of <em>Oryza brachyantha</em> reveals mechanisms underlying <em>Oryza</em> genome evolution

The wild species of the genus Oryza contain a largely untapped reservoir of agronomically important genes for rice improvement. Here we report the 261-Mb de novo assembled genome sequence of Oryza brachyantha. Low activity of long-terminal repeat retrotransposons and massive internal deletions of ancient long-terminal repeat elements lead to the compact genome of Oryza brachyantha. We model 32,038 protein-coding genes in the Oryza brachyantha genome, of which only 70% are located in collinear positions in comparison with the rice genome. Analysing breakpoints of non-collinear genes suggests that double-strand break repair through non-homologous end joining has an important role in gene movement and erosion of collinearity in the Oryza genomes. Transition of euchromatin to heterochromatin in the rice genome is accompanied by segmental and tandem duplications, further expanded by transposable element insertions. The high-quality reference genome sequence of Oryza brachyantha provides an important resource for functional and evolutionary studies in the genus Oryza

Study on Three-Dimensional Data Acquisition of Crop Grains

International audienceThe rapid, efficient and non-destructive 3D morphological data acquisition of plants are great significance to the study of digital plant, functional structural plant model and crop phenotype. This paper discusses 3D data acquisition methods for smaller plant organs, which take maize grain as an example. Smartscan and Micro-CT scanning can be used to obtain the morphological data of the grains. The efficiency, accuracy, processing of data in two scanning ways are compared and analyzed. The results shows that the Micro-CT is more suitable for obtaining information of internal structure of maize grain. While grain morphology in SmartScan can get better visualization than Micro-CT, and the former one can also obtain image texture information. These two kinds of methods for volume measurement have good consistency except for Denghai 605. The study will provide theoretical basis for obtaining 3D data of plant organs at smaller scales

Mapping of the Greenhouse Gas Emission Potential for the Offshore Wind Power Sector in Guangdong, China

This study aims to assess the potential greenhouse gas (GHG) emissions of delivering 1 kWh from planned offshore wind farm sites to the grid in the Guangdong Province, China. In contrast to most previous studies, we avoided underestimating GHG emissions per kWh by approximately 49% by adopting a spatialized life-cycle inventory (LCI)-improved stock-driven model under the medium scenario combination. We also developed a callable spatialized LCI to model the spatial differences in the GHG emissions per kWh by cells in planned offshore wind farm sites in Guangdong. The modeling results indicate that, under the medium scenario combination, the GHG emissions per kWh will range from 4.6 to 19 gCO2eq/kWh and the cells with higher emissions are concentrated in the deep-water wind farms in the eastern ocean of the Guangdong Province. According to the mechanism by which the different scenarios affect the modeling results, increasing the unit capacity of turbines is the most effective approach for reducing the GHG emissions per kWh and decreasing the impact of natural conditions. Air density can be used as an empirical spatial variable to predict the GHG emission potential of planned wind farm sites in Guangdong. The modeling framework in this study provides a more reliable quantitative tool for decision-makers in the offshore wind sector that can be used directly for any offshore wind system with a monopile foundation and be extended to wind power systems with other foundation types, or even to the entire renewable energy and other infrastructure systems after certain modifications

Multi-scale 3D Data Acquisition of Maize

International audienceIn recent years, three-dimensional (3D) data acquisition and model reconstruction of plants have been developed as a hot topic of plant scientific researches. However, the morphological structure of plants is very complex and it is hard to describe the details. The data acquisition approaches are diverse for different parts of plants. This study introduces the data acquisition methods of different scales of maize. The grain, leaf and ear, individual plant and maize colony represents the target models for different scales. 3D data acquisition instruments are used to acquire the morphometric of each target. It is found that the organ scale is the simplest to obtain and process. The smallest grain needs high-resolution scanner to acquire the morphological details, while the plant canopy is the hardest one for point cloud process and modeling. The data and reconstructed models are oriented to digital plant, phenotyping analysis, FSPMs research, and popular science education application

Mapping of the Greenhouse Gas Emission Potential for the Offshore Wind Power Sector in Guangdong, China

This study aims to assess the potential greenhouse gas (GHG) emissions of delivering 1 kWh from planned offshore wind farm sites to the grid in the Guangdong Province, China. In contrast to most previous studies, we avoided underestimating GHG emissions per kWh by approximately 49% by adopting a spatialized life-cycle inventory (LCI)-improved stock-driven model under the medium scenario combination. We also developed a callable spatialized LCI to model the spatial differences in the GHG emissions per kWh by cells in planned offshore wind farm sites in Guangdong. The modeling results indicate that, under the medium scenario combination, the GHG emissions per kWh will range from 4.6 to 19 gCO2eq/kWh and the cells with higher emissions are concentrated in the deep-water wind farms in the eastern ocean of the Guangdong Province. According to the mechanism by which the different scenarios affect the modeling results, increasing the unit capacity of turbines is the most effective approach for reducing the GHG emissions per kWh and decreasing the impact of natural conditions. Air density can be used as an empirical spatial variable to predict the GHG emission potential of planned wind farm sites in Guangdong. The modeling framework in this study provides a more reliable quantitative tool for decision-makers in the offshore wind sector that can be used directly for any offshore wind system with a monopile foundation and be extended to wind power systems with other foundation types, or even to the entire renewable energy and other infrastructure systems after certain modifications

Geographical distribution and risk assessment of dichlorodiphenyltrichloroethane and its metabolites in Perna viridis mussels from the northern coast of the South China Sea

Mussels (Perna viridis) were collected from the northern coast of the South China Sea (NSCS) to investigate the geographical distribution and potential risk of dichlorodiphenyltrichloroethane and its metabolites (DDTs). DDTs had concentrations that ranged from 248 ng/g to 4650 ng/g lipid weight (lw), with an average of 807 +/- 932 ng/ng lw. A comparison of the levels of DDTs in mussels indicated that the NSCS is still one of the most polluted areas in the world, although a decreasing trend was observed. DDT metabolites were predominant in all samples, suggesting that historical residue was the main source of DDT pollution. However, there were new inputs of DDTs which likely associated with antifouling paints. The human health risk assessment revealed that the current concentrations of DDTs in mussels might pose little health risk for the consumers