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Digital image processing is widely used in the non-destructive diagnosis of plant nutrition. Previous plant nitrogen diagnostic studies have mostly focused on characteristics of the rice canopy or leaves at some specific points in time, with the long sampling intervals unable to provide detailed and specific “dynamic features.” According to plant growth mechanisms, the dynamic changing rate in leaf shape and color differ between different nitrogen supplements. Therefore, the objective of this study was to diagnose nitrogen stress levels by analyzing the dynamic characteristics of rice leaves. Scanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB. Newly developed shape characteristics such as etiolation area (EA) and etiolation degree (ED), in addition to shape (area, perimeter) and color characteristics (green, normalized red index, etc.), were used to quantify the process of leaf change. These characteristics allowed sensitive indices to be established for further model validation. Our results indicate that the changing rates in dynamic characteristics, in particular the shape characteristics of the first incomplete leaf (FIL) and the characteristics of the 3rd leaf (leaf color and etiolation indices), expressed obvious distinctions among different nitrogen treatments. Consequently, we achieved acceptable diagnostic accuracy (training accuracy 77.3%, validation accuracy 64.4%) by using the FIL at six days after leaf emergence, and the new shape characteristics developed in this article (ED and EA) also showed good performance in nitrogen diagnosis. Based on the aforementioned results, dynamic analysis is valuable not only in further studies but also in practice. [ABSTRACT FROM AUTHOR]

Background: Acute respiratory infections (ARI) are the major worldwide health problem associated with high morbidity and mortality rates. Human adenovirus (HAdV) is one of the most common pathogens associated with viral ARI, and thus calls for specific diagnosis and better understanding of the epidemiology and clinical characteristics. Methods: Total 4,130 children with ARI requiring hospitalization from 2012 to 2013 were retrospectively studied. Throat swab specimens were collected from each patient. Fluorescence Quantitative PCR was performed to detect adenovirus as well as other common ARI-related pathogens. The seven HAdV hypervariable regions (HVRs) of the hexon gene from fifty-seven HAdVs-positive samples collected in the seasonal peaks were sequenced. Phylogenetic analysis of HVRs was also conducted to confirm the molecular types and genetic variation. In addition, epidemiological features and co-infection with other human respiratory pathogens were investigated and analyzed. Results: Of 4,130 hospitalized pediatric patients tested, the positive rates of respiratory syncytial virus (RSV), Mycoplasma pneumoniae (MP), and HAdV were 13.7%, 13.2%, and 12.0%, respectively. The HAdV positive patients accounted for 7.9%, 17.2%, 17.5% and 10.7% in age groups <1, 1–3, 3–6 and 6–14 years, respectively. Eighty-four HAdV positive children were co-infected with other respiratory pathogens (84/495, 17.0%). The most common co-infection pathogens with HAdV were MP (57.1%) and Human Bocavirus (HBoV) (16.7%). The majority of HAdV infected patients were totally recovered (96.9%, 480/495); However, four (0.8%) patients, who were previously healthy and at the age of 2 years or younger died of pneumonia. Seasonal peaks of HAdV infection occurred in the summer season of 2012 and 2013; the predominant HAdV type was HAdV-3 (70%), followed by HAdV-7 (28%). These epidemiological features were different from those in Northern China. The HAdV-55 was identified and reported for the first time in Guangzhou metropolitan area. Phylogenetic analysis indicated that all the HVR sequences of the hexon gene of HAdV-3 and -7 strains have high similarity within their individual types, and these strains were also similar to those circulating in China currently, indicating the conservation of hexon genes of both HAdV-3 and HAdV-7. Conclusions: Knowledge of the epidemiological features and molecular types of HAdV, a major pathogen of pediatric ARI, as well as other co-infected respiratory pathogens circulating in Guangzhou, southern China, is vital to predict and prevent future disease outbreaks in children. This study will certainly facilitate HAdV vaccine development and treatment of HAdV infections in children. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a clonal plasma cell disorder which constitutes the second most common hematological malignancy, and remains an incurable tumor with poor survival. Recently, interleukin-17 (IL-17), produced locally in the tumor microenvironment, has been reported to play a crucial role in tumor immunity. In this study, we determined that exposure of MM cells to IL-17 had various promotive influences on different aspects of tumor progression. IL-17 significantly induced cell proliferation, inhibited cellular apoptosis, repressed cell adhesion to fibronectin and collagen I, and facilitated cell migration. Exposure to IL-17 also resulted in epithelial-mesenchymal transition (EMT), as evidenced by repression of the epithelial marker E-cadherin, and induction of the mesenchymal marker Vimentin, and EMT transcription factors Snail and Slug. Further experiments showed that IL-17 activated the oncogenic p65 transcription factor, which directly repressed the miR-192 gene via binding to the miR-192 promoter. Loss of miR-192 in MM cells can mimic the effects of IL-17, and was required for the above oncogenic effects of IL-17 on MM. Furthermore, we found that miR-192, and its homologous miR-215 directly targeted the 3′-untranslated regions of IL-17Rs, including IL-17RA and RE mRNA. By examining bone marrow specimens derived from MM patients, a negative correlation between miR-192 expression and IL-17 or IL-17RA expression was observed. Also, IL-17 was negatively correlated with E-cadherin and positively with Vimentin. Taken together, our study provides evidence that the IL-17/miR-192/IL-17Rs regulatory feedback loop is manifest in MM and might represent a promising and efficient prognostic marker and therapeutic target for MM. [ABSTRACT FROM AUTHOR]

Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2nd and 3rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel fractal coding method with the block classification scheme based on a shared domain block pool. In our method, the domain block pool is called dictionary and is constructed from fractal Julia sets. The image is encoded by searching the best matching domain block with the same BTC (Block Truncation Coding) value in the dictionary. The experimental results show that the scheme is competent both in encoding speed and in reconstruction quality. Particularly for large images, the proposed method can avoid excessive growth of the computational complexity compared with the traditional fractal coding algorithm. [ABSTRACT FROM AUTHOR]

Image encryption is an important and effective technique to protect image security. In this paper, a novel image encryption algorithm combining Julia sets and Hilbert curves is proposed. The algorithm utilizes Julia sets’ parameters to generate a random sequence as the initial keys and gets the final encryption keys by scrambling the initial keys through the Hilbert curve. The final cipher image is obtained by modulo arithmetic and diffuse operation. In this method, it needs only a few parameters for the key generation, which greatly reduces the storage space. Moreover, because of the Julia sets’ properties, such as infiniteness and chaotic characteristics, the keys have high sensitivity even to a tiny perturbation. The experimental results indicate that the algorithm has large key space, good statistical property, high sensitivity for the keys, and effective resistance to the chosen-plaintext attack. [ABSTRACT FROM AUTHOR]