Evaluating how lodging affects maize yield estimation based on UAV observations

Timely and accurate pre-harvest estimates of maize yield are vital for agricultural management. Although many remote sensing approaches have been developed to estimate maize yields, few have been tested under lodging conditions. Thus, the feasibility of existing approaches under lodging conditions and the influence of lodging on maize yield estimates both remain unclear. To address this situation, this study develops a lodging index to quantify the degree of lodging. The index is based on RGB and multispectral images obtained from a low-altitude unmanned aerial vehicle and proves to be an important predictor variable in a random forest regression (RFR) model for accurately estimating maize yield after lodging. The results show that (1) the lodging index accurately describes the degree of lodging of each maize plot, (2) the yield-estimation model that incorporates the lodging index provides slightly more accurate yield estimates than without the lodging index at three important growth stages of maize (tasseling, milking, denting), and (3) the RFR model with lodging index applied at the denting (R5) stage yields the best performance of the three growth stages, with R2 = 0.859, a root mean square error (RMSE) of 1086.412 kg/ha, and a relative RMSE of 13.1%. This study thus provides valuable insight into the precise estimation of crop yield and demonstra\tes that incorporating a lodging stress-related variable into the model leads to accurate and robust estimates of crop grain yield

Reproductive toxicity of low level bisphenol A exposures in a two-generation zebrafish assay: Evidence of male-specific effects

Bisphenol A (BPA), a high-volume chemical used to make polycarbonate plastic and epoxy resins, is a ubiquitous contaminant in environment and human body. To investigate the reproductive effects of long-term exposure to low concentrations of BPA, a two-generation study was conducted using the aquatic model species of zebrafish. Our findings revealed that exposure to 1 nM (0.228 μg/L) BPA for continuous two generations resulted in female-biased sex ratio in both F1 and F2 adult population, decreased sperm density, and decreased sperm quality as measured by motility, velocity, ATP content and lipid peroxidation in F1 and F2 males. Females were less sensitive to BPA exposures than males as no adverse effects were found in female gonads or gametes. Delayed hatching at 48 hpf and increased malformation and mortality were found in the offspring from BPA exposed F2, but not F1 parents. Most importantly, the adverse effect on larval development and survival from BPA exposed F2 parents was paternal-specific, resulting mainly from BPA exposed males. Subsequent transcription analysis of F2 male gonads revealed dysregulated mitochondrial biogenesis and significant activation of non-canonical Wnt/planar cell polarity and Wnt/Calcium signaling pathways. Gene expression analysis of larvae from BPA exposed F2 parents showed significant reduced expression of DNA methyltransferases such as dnmt1, dnmt3, and dnmt5. In conclusion, low level BPA exposures for continuous two generations not only affects sex ratio and sperm quantity/quality in F1 and F2 adults, reproductive success in offspring from F2 parents, but also perturbs various molecular pathways potentially contributing to these BPA induced male-specific reproductive defects.This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/aquatic-toxicology/Keywords: Wnt signaling, BPA, Zebrafish, Chronic exposure, Sperm, Mitochondrial biogenesisKeywords: Wnt signaling, BPA, Zebrafish, Chronic exposure, Sperm, Mitochondrial biogenesi

Assessing the Quality of Reports about Randomized Controlled Trials of Acupuncture Treatment on Diabetic Peripheral Neuropathy

BACKGROUND: To evaluate the reports' qualities which are about randomized controlled trials (RCTs) of acupuncture treatment on Diabetic Peripheral Neuropathy (DPN). METHODOLOGY/PRINCIPAL FINDINGS: Eight databases including The Cochrane Library(1993-Sept.,2011), PubMed (1980-Sept., 2011), EMbase (1980-Sept.,2011), SCI Expanded (1998-Sept.,2011), China Biomedicine Database Disc (CBMdisc, 1978-Sept., 2011), China National Knowledge Infrastructure (CNKI, 1979-Sept., 2011 ), VIP (a full text issues database of China, 1989-Sept., 2011), Wan Fang (another full text issues database of China 1998-Sept., 2011) were searched systematically. Hand search for further references was conducted. Language was limited to Chinese and English. We identified 75 RCTs that used acupuncture as an intervention and assessed the quality of these reports with the Consolidated Standards for Reporting of Trials statement 2010 (CONSORT2010) and Standards for Reporting Interventions Controlled Trials of Acupuncture 2010(STRICTA2010). 24 articles (32%) applied the method of random allocation of sequences. No article gave the description of the mechanism of allocation concealment, no experiment applied the method of blinding. Only one article (1.47%) could be identified directly from its title as about the Randomized Controlled Trials, and only 4 articles gave description of the experimental design. No article mentioned the number of cases lost or eliminated. During one experiment, acupuncture syncope led to temporal interruption of the therapy. Two articles (2.94%) recorded the number of needles, and 8 articles (11.76%) mentioned the depth of needle insertion. None of articles reported the base of calculation of sample size, or has any analysis about the metaphase of an experiment or an explanation of its interruption. One (1.47%) mentioned intentional analysis (ITT). CONCLUSIONS/SIGNIFICANCE: The quality of the reports on RCTs of acupuncture for Diabetic Peripheral Neuropathy is moderate to low. The CONSORT2010 and STRICTA2010 should be used to standardize the reporting of RCTs of acupuncture in future

High-Pitch, Low-Voltage and Low-Iodine-Concentration CT Angiography of Aorta: Assessment of Image Quality and Radiation Dose with Iterative Reconstruction

Objective: To assess the image quality of aorta obtained by dual-source computed tomography angiography (DSCTA), performed with high pitch, low tube voltage, and low iodine concentration contrast medium (CM) with images reconstructed using iterative reconstruction (IR). Methods: One hundred patients randomly allocated to receive one of two types of CM underwent DSCTA with the electrocardiogram-triggered Flash protocol. In the low-iodine group, 50 patients received CM containing 270 mg I/mL and were scanned at low tube voltage (100 kVp). In the high-iodine CM group, 50 patients received CM containing 370 mg I/mL and were scanned at the tube voltage (120 kVp). The filtered back projection (FBP) algorithm was used for reconstruction in both groups. In addition, the IR algorithm was used in the low-iodine group. Image quality of the aorta was analyzed subjectively by a 3-point grading scale and objectively by measuring the CT attenuation in terms of the signal- and contrast-to-noise ratios (SNR and CNR, respectively). Radiation and CM doses were compared.Results: The CT attenuation, subjective image quality assessment, SNR, and CNR of various aortic regions of interest did not differ significantly between two groups. In the low-iodine group, images reconstructed by FBP and IR demonstrated significant differences in image noise, SNR, and CNR (p<0.05). The low-iodine group resulted in 34.3% less radiation (4.4 ± 0.5 mSv) than the high-iodine group (6.7 ± 0.6 mSv), and 27.3% less iodine weight (20.36 ± 2.65 g) than the high-iodine group (28 ± 1.98 g). Observers exhibited excellent agreement on the aortic image quality scores (κ = 0.904). Conclusions: CT images of aorta could be obtained within 2 s by using a DSCT Flash protocol with low tube voltage, IR, and low-iodine-concentration CM. Appropriate contrast enhancement was achieved while maintaining good image quality and decreasing the radiation and iodine doses

Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-based MRC models (for example, restrictions on word count), such models cannot effectively integrate significant features, such as syntax relations, semantic connections, and long-distance semantics between sentences, leading to the inability of the available models to better understand the intrinsic connections between text and questions to be answered based on it. In this paper, a multi-granularity syntax guidance (MgSG) module that consists of a &ldquo;graph with dependence&rdquo; module and a &ldquo;graph with entity&rdquo; module is proposed. MgSG selects both sentence and word granularities to guide the text model to decipher the text. In particular, syntactic constraints are used to guide the text model while exploiting the global nature of graph neural networks to enhance the model&rsquo;s ability to construct long-range semantics. Simultaneously, named entities play an important role in text and answers and focusing on entities can improve the model&rsquo;s understanding of the text&rsquo;s major idea. Ultimately, fusing multiple embedding representations to form a representation yields the semantics of the context and the questions. Experiments demonstrate that the performance of the proposed method on the Stanford Question Answering Dataset is better when compared with the traditional BERT baseline model. The experimental results illustrate that our proposed &ldquo;MgSG&rdquo; module effectively utilizes the graph structure to learn the internal features of sentences, solve the problem of long-distance semantics, while effectively improving the performance of PrLM in machine reading comprehension

SPWM Smoothing Control Method Based on Wind Power Grid Connection

Energy is the foundation of economic development and technological development. Facing the present situation of non-renewable energy decline, wind power generation has been developed rapidly. However, the problem of unstable output voltage of wind power generation due to unstable wind speed needs to be solved, and traditional solutions cannot make the generated electric energy meet the national standards for grid-connected and off-grid operation. In this paper, the electric energy generated by wind turbine is rectified by bridge rectifier circuit and using large capacity capacitor filtering to generate DC with flat waveform. Then, using SPWM inversion technology, the normal rotation wave with the same frequency as the power grid is used as the modulation wave and according to the required voltage amplitude, and the triangle wave with appropriate duty ratio is calculated by SPWM smoothing control theory as carrier wave. Subsequently, accurate filtering is carried out and grid-connected and off-grid operation is carried out through the self-aligning device. Finally, with the help of MATLAB simulation platform, the wind turbine is simulated to work under different wind conditions, and whether the generated electric energy meets the grid-connected and off-grid operation standards is judged, which further determines the reliability and authenticity of the theory

Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-based MRC models (for example, restrictions on word count), such models cannot effectively integrate significant features, such as syntax relations, semantic connections, and long-distance semantics between sentences, leading to the inability of the available models to better understand the intrinsic connections between text and questions to be answered based on it. In this paper, a multi-granularity syntax guidance (MgSG) module that consists of a “graph with dependence” module and a “graph with entity” module is proposed. MgSG selects both sentence and word granularities to guide the text model to decipher the text. In particular, syntactic constraints are used to guide the text model while exploiting the global nature of graph neural networks to enhance the model’s ability to construct long-range semantics. Simultaneously, named entities play an important role in text and answers and focusing on entities can improve the model’s understanding of the text’s major idea. Ultimately, fusing multiple embedding representations to form a representation yields the semantics of the context and the questions. Experiments demonstrate that the performance of the proposed method on the Stanford Question Answering Dataset is better when compared with the traditional BERT baseline model. The experimental results illustrate that our proposed “MgSG” module effectively utilizes the graph structure to learn the internal features of sentences, solve the problem of long-distance semantics, while effectively improving the performance of PrLM in machine reading comprehension