Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning

We present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60 mm diameter agar-plate and analyzes these time-lapsed holograms using deep neural networks for rapid detection of bacterial growth and classification of the corresponding species. The performance of our system was demonstrated by rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples. These results were confirmed against gold-standard culture-based results, shortening the detection time of bacterial growth by >12 h as compared to the Environmental Protection Agency (EPA)-approved analytical methods. Our experiments further confirmed that this method successfully detects 90% of bacterial colonies within 7-10 h (and >95% within 12 h) with a precision of 99.2-100%, and correctly identifies their species in 7.6-12 h with 80% accuracy. Using pre-incubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L within 9 h of total test time. This computational bacteria detection and classification platform is highly cost-effective (~$0.6 per test) and high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing analytical methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time, also automating the identification of colonies, without labeling or the need for an expert.Comment: 24 pages, 6 figure

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Phosphate Removal Mechanisms in Aqueous Solutions by Three Different Fe-Modified Biochars

Iron-modified biochar can be used as an environmentally friendly adsorbent to remove the phosphate in wastewater because of its low cost. In this study, Fe-containing materials, such as zero-valent iron (ZVI), goethite, and magnetite, were successfully loaded on biochar. The phosphate adsorption mechanisms of the three Fe-modified biochars were studied and compared. Different characterization methods, including scanning electron microscopy/energy-dispersive spectrometry (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), were used to study the physicochemical properties of the biochars. The dosage, adsorption time, pH, ionic strength, solution concentration of phosphate, and regeneration evaluations were carried out. Among the three Fe-modified biochars, biochar modified by goethite (GBC) is more suitable for phosphate removal in acidic conditions, especially when the pH = 2, while biochar modified by ZVI (ZBC) exhibits the fastest adsorption rate. The maximum phosphate adsorption capacities, calculated by the Langmuir–Freundlich isothermal model, are 19.66 mg g−1, 12.33 mg g−1, and 2.88 mg g−1 for ZBC, GBC, and CSBC (biochar modified by magnetite), respectively. However, ZBC has a poor capacity for reuse. The dominant mechanism for ZBC is surface precipitation, while for GBC and CSBC, the major mechanisms are ligand exchange and electrostatic attraction. The results of our study can enhance the understanding of phosphate removal mechanisms by Fe-modified biochar and can contribute to the application of Fe-modified biochar for phosphate removal in water

Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg∙ha−1) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N0 and N25 were significantly lower than those of high-N treatments (N50, N100, N150, and N200) for all three years. The variables of CC × Height had a significant linear relationship with dry matter, with R2 values above 0.87. The good performance of the CC × Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC × Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50–100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

A Machine‐Learning‐Based Bibliometric Analysis of Cell Membrane‐Coated Nanoparticles in Biomedical Applications over the Past Eleven Years

Abstract Cell membrane encapsulation is a growing concept in nanomedicine, for it achieves the purpose of camouflage nanoparticles, realizing the convenience for drug delivery, bio‐imaging, and detoxification. Cell membranes are constructed by bilayer lipid phospholipid layers, which have unique properties in cellular uptake mechanism, targeting ability, immunomodulation, and regeneration. Current medical applications of cell membranes include cancers, inflammations, regenerations, and so on. In this article, a general bibliometric overview is conducted of cell membrane‐coated nanoparticles covering 11 years of evolution in order to provide researchers in the field with a comprehensive view of the relevant achievements and trends. The authors analyze the data from Web of Science Core Collection database, and extract the annual publications and citations, most productive countries/regions, most influential scholars, the collaborations of journals and institutions. The authors also divided cell membranes into several subgroups to further understand the application of different cell membranes in medical scenarios. This study summarizes the current research overview in cell membrane‐coated nanoparticles and intuitively provides a direction for future research

C8orf76 Modulates Ferroptosis in Liver Cancer via Transcriptionally Up-Regulating SLC7A11

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. Chromosome 8 open reading frame 76 (C8orf76), a novel gene located in the nucleus, is highly expressed in many tumor types. However, the specific mechanisms and functions of C8orf76 in HCC remain unclear. Here, we reported for the first time that C8orf76 gene expression levels were frequently upregulated in liver cancer and significantly correlated with HCC development. C8orf76 downregulation induced G1-S arrest and inhibited cell proliferation. Intriguingly, C8orf76 deficiency could accelerate erastin or sorafenib-induced ferroptosis through increasing lipid reactive oxygen species (ROS) levels. Moreover, although C8orf76 overexpression did not affect tumorigenesis under normal conditions, it increased resistance to lipid disturbance and ferroptosis triggered by erastin or sorafenib, which further facilitated HCC cell growth and tumor progression. Mechanistically, C8orf76 bound to the promoter region of the solute carrier family 7 member 11 (SLC7A11) gene and upregulated SLC7A11 transcriptionally. SLC7A11-dependent cystine import led to sufficient GSH synthesis and lipid peroxidation inhibition, thus accelerating tumor growth. Our study indicated that C8orf76 could be a novel marker for HCC diagnosis. In addition, a better comprehensive understanding of the potential role of C8orf76 in HCC helped us develop novel therapeutic strategies for this intractable cancer