Fiber bundle imaging resolution enhancement using deep learning

We propose a deep learning based method to estimate high-resolution images from multiple fiber bundle images. Our approach first aligns raw fiber bundle image sequences with a motion estimation neural network and then applies a 3D convolution neural network to learn a mapping from aligned fiber bundle image sequences to their ground truth images. Evaluations on lens tissue samples and a 1951 USAF resolution target suggest that our proposed method can significantly improve spatial resolution for fiber bundle imaging systems.National Institute of Biomedical Imaging and Bioengineering (NIBIB) [R21EB022378]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

The interferometry technique is corn commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 27 pi ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.China Scholarship Council (CSC) [201704910730]; National Science Foundation (NSF) [1455630]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The functions and regulatory pathways of S100A8/A9 and its receptors in cancers

Inflammation primarily influences the initiation, progression, and deterioration of many human diseases, and immune cells are the principal forces that modulate the balance of inflammation by generating cytokines and chemokines to maintain physiological homeostasis or accelerate disease development. S100A8/A9, a heterodimer protein mainly generated by neutrophils, triggers many signal transduction pathways to mediate microtubule constitution and pathogen defense, as well as intricate procedures of cancer growth, metastasis, drug resistance, and prognosis. Its paired receptors, such as receptor for advanced glycation ends (RAGEs) and toll-like receptor 4 (TLR4), also have roles and effects within tumor cells, mainly involved with mitogen-activated protein kinases (MAPKs), NF-κB, phosphoinositide 3-kinase (PI3K)/Akt, mammalian target of rapamycin (mTOR) and protein kinase C (PKC) activation. In the clinical setting, S100A8/A9 and its receptors can be used complementarily as efficient biomarkers for cancer diagnosis and treatment. This review comprehensively summarizes the biological functions of S100A8/A9 and its various receptors in tumor cells, in order to provide new insights and strategies targeting S100A8/A9 to promote novel diagnostic and therapeutic methods in cancers

CDKN1C (p57KIP2) Is a Direct Target of EZH2 and Suppressed by Multiple Epigenetic Mechanisms in Breast Cancer Cells

CDKN1C (encoding tumor suppressor p57KIP2) is a cyclin-dependent kinase (CDK) inhibitor whose family members are often transcriptionally downregulated in human cancer via promoter DNA methylation. In this study, we show that CDKN1C is repressed in breast cancer cells mainly through histone modifications. In particular, we show that CDKN1C is targeted by histone methyltransferase EZH2-mediated histone H3 lysine 27 trimethylation (H3K27me3), and can be strongly activated by inhibition of EZH2 in synergy with histone deacetylase inhibitor. Consistent with the overexpression of EZH2 in a variety of human cancers including breast cancer, CDKN1C in these cancers is downregulated, and breast tumors expressing low levels of CDKN1C are associated with a poor prognosis. We further show that assessing both EZH2 and CDKN1C expression levels as a measurement of EZH2 pathway activity provides a more predictive power of disease outcome than that achieved with EZH2 or CDKN1C alone. Taken together, our study reveals a novel epigenetic mechanism governing CDKN1C repression in breast cancer. Importantly, as a newly identified EZH2 target with prognostic value, it has implications in patient stratification for cancer therapeutic targeting EZH2-mediated gene repression

Evolution and Efficiency Assessment of Pesticide and Fertiliser Inputs to Cultivated Land in China

Excessive use of pesticides and fertilisers has been a key issue limiting sustainable agricultural development. China is a typical pesticide- and chemical-fertiliser-dependent agricultural production area. We have matched the target indicators related to sustainable agricultural development (SDG1 and SDG2) and analysed the gap between China and four developed countries in terms of fertiliser and pesticide use intensity and efficiency from 2002 to 2016. We have used an improved Logarithmic Mean Divisia Index model and cluster analysis to identify the factors and effects driving increased pesticide and fertiliser inputs in China, and we discuss the exploratory effects of different provinces in reducing pesticide and fertiliser application and increasing efficiency. The findings reveal that (1) China is a typical pesticide- and fertiliser-dependent agricultural production area. The average combined fertiliser application efficiency in China from 2002 to 2016 was only 28% of that of the Netherlands, and the country’s average combined pesticide application efficiency was only 35% of that of the USA. (2) The most important of the three main drivers of the increase in pesticide and fertiliser inputs in China is the value added of the primary industry, contributing 56% for the period 2007–2016. (3) Further analysis at the provincial level according to four types—high-intensity high-yield type, high-intensity low-yield type, low-intensity high-yield type, and low-intensity low-yield type—clarified the provinces that should be focused on at the national level in terms of pesticide and fertiliser application reduction and efficiency increase in the future

Fiber bundle image restoration using deep learning

We propose a deep learning-based restoration method to remove honeycomb patterns and improve resolution for fiber bundle (FB) images. By building and calibrating a dual-sensor imaging system, we capture FB images and corresponding ground truth data to train the network. Images without fiber bundle fixed patterns are restored from raw FB images as direct inputs, and spatial resolution is significantly enhanced for the trained sample type. We also construct the brightness mapping between the two image types for the effective use of all data, providing the ability to output images of the expected brightness. We evaluate our framework with data obtained from lens tissues and human histological specimens using both objective and subjective measures. (c) 2019 Optical Society of Americ

Cyclizing-berberine A35 induces G2/M arrest and apoptosis by activating YAP phosphorylation (Ser127)

Abstract Background A35 is a novel synthetic cyclizing-berberine recently patented as an antitumor compound. Based on its dual targeting topoisomerase (top) activity, A35 might overcome the resistance of single-target top inhibitors and has no cardiac toxicity for not targeting top2β. In this study we further explored the biological effects and mechanisms of A35. Methods Antitumor activity of A35 was evaluated by SRB and colony formation assay. G2/M phase arrest (especially M) and first damage of M-phase cells were investigated by flow cytometry, cytogenetic analysis, immunofluorescence, co-immunoprecipitation and WB. The key role of phospho-YAP (Ser127) in decreasing YAP nuclear localization, subsequent G2/M arrest and proliferation inhibition were explored by YAP1−/− cells, mutated Ser127 YAP construct (Ser127A) and TUNEL. Results G2/M arrest induced by A35 was independent of p53. M phase cells at low dose were firstly damaged and most damaged-cells accumulated in M phase, and that was a result of preferring targeting top2α by A35, as top2α is essential to push M phase into next phase, and targeting top2α induced cells arrested at M phase. A35 decreased YAP1 nuclear localization by activating YAP phosphorylation (Ser127) which subsequently regulated the transcription of YAP target genes associated with growth and cycle regulation to induce G2/M arrest and growth inhibition. Conclusions Our studies suggested the mechanism of G2/M arrest induced by A35 and a novel role of YAP1 (Ser127) in G2/M arrest. As a dual topoisomerase inhibitor characterized by no cardiac toxicity, A35 is a promising topoisomerase anticancer agent and worthy of further development in future

Development Trend and Driving Factors of Agricultural Chemical Fertilizer Efficiency in China

In China, a high input of chemical fertilizers is currently required for a relatively low increase in agricultural production, and this has resulted in prominent nonpoint source pollution and problems related to the quality of agricultural products. These phenomena threaten China’s implementation of United Nations Sustainable Development Goal 2 (SDG-2). To explore agricultural chemical fertilizer efficiency and the factors driving the growth in chemical fertilizer use in China, as based on an international comparative analysis of China’s chemical fertilizer input, the development trend in the application level and the efficiency in the use of chemical fertilizer in China were subject to time series analysis, and the factors influencing change were identified and analyzed using the Logarithmic Mean Divisia Index (LMDI). The results show that: (1) The gap in agricultural chemical fertilizer efficiency is still large when comparing China with modern agricultural countries, and excessive fertilizer input is still a major problem. (2) The continuous growth in the total amount of chemical fertilizer applied in China during the past 18 years has contributed to the increase in chemical fertilizer application intensity, which provided a cumulative contribution of 85.52%, with smaller contributions from the planting structure and crop sown area. Based on the analysis of fertilizer application, the chemical fertilizer application intensity of the main grain crops was the most significant factor, accounting for about a 40.00% cumulative contribution. (3) Since 2015, the total amount of chemical fertilizers has been reduced through gradually improving the utilization rate of chemical fertilizers, reducing the application intensity of chemical fertilizers, and implementing the fallow rotation system and other measures. Of these, the reduction in application intensity was the most effective at reducing the overall amount of applied fertilizer. To meet the target for achieving sustainable agricultural development, China must still reduce its use of chemical fertilizers by at least 21.80 million tons. Based on the results of current measures and international experience, some suggestions for reducing fertilizer usage are provided

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]