“Fu” and “Zhou”—A preliminary study on “language worship” and its symbolization

The article aims to, based on the study of “Spells” (or “Fuzhou”, 符咒, including the magic figures and incantations), find out the relationship of “Fu” (符, talisman1), “Zhou” (咒, incantations)” and “language worship” (including written language and oral language). There is an in-depth probe into “language worship”, and the clarification of the term “Fu” and “Fushu” (the use of Fu), “Zhou” and “Zhoushu” (the use of Zhou), no matter in a narrow sense or a broad one. In addition, the differentiation of language, “language worship” and “Spells” has been achieved via symbols and their symbolization. The final conclusion of such study shows that language worship is the process of language symbolization, and spells, in essence, is the symbol of language

Development of a Peanut Canopy Measurement System Using a Ground-Based LiDAR Sensor

Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression as well as incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to scan rows of peanut plants in the field, and a data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and an RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted once each month from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53 ms. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and shape/density were evaluated. Euler number, entropy, cluster count, and mean number of connected objects were extracted from the image and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. A high correlation was also observed between the LiDAR and ground-truth measurements for plant height. This approach should be useful for phenotyping peanut germplasm for canopy architecture

Isolation and Characterization of New 24 Microsatellite DNA Markers for Golden Cuttlefish (Sepia esculenta)

Twenty-four microsatellite DNA markers were isolated and characterized for golden cuttlefish (Sepia esculenta) from a (GT)13—enriched genomic library. Loci were tested in 48 individuals from Jiaozhou bay of China. The numbers of alleles per locus ranged from two to 25 with an average of 10.3. The observed and expected heterozygosities ranged from 0.063 to 0.896 and from 0.137 to 0.953, with averages of 0.519 and 0.633, respectively. Six loci significantly deviated from Hardy-Weinberg equilibrium after Bonferroni’s correction and no significant linkage disequilibrium between loci pairs was detected. These microsatellite markers would be useful for analyzing the population genetic structure to make conservation and management decisions for S. esculenta

Investigation of the clinical features and therapeutic methods for the management of inflammatory lacrimal punctum diseases

Purpose: To establish if there are different classes of inflammatory lacrimal punctum diseases (ILPDs) and to examine the various strategies by which they can be managed therapeutically.Methods: Two hundred and fifty nine (259) patients with inflammatory punctum lacrimal disease were identified and used as subjects for this study. Each patient was carefully examined for evidence of morphology of lacrimal punctum which was confirmed mainly by lacrimal duct flushing and probing. Appropriate therapeutic managements were adopted for patients with other inflammatory conditions besides ILPD. The clinical effects of the various therapeutic strategies were documented. .Results: Eighty-seven (87) patients out of the 259 (32.53 %) suffered from acute or chronic conjunctivitis while 66 patients (5.61 %) suffered from inflammatory lacrimal passage diseases. Patients with both conjunctivitis and lacrimal passage inflammation, patients with dry-eye symptoms, patients with just one of the conditions, and patients with mere evidence of superior punctalacrimalis represented 13.15, 14.19, 14.53, and 33.91 %, respectively. Mere evidence of inferior punctalacrimalis, and presence of acute inflammation were seen in 48.76 and 13.49 % of the 259 patients, respectively, while those with chronic inflammation lasting for 2.97 ± 0.13 years, comprised 86.51 %. Antibiotic eye drops were used for acute inflammation, while chronic inflammation was treated with antibiotic eye drops, lacrimal punctum expansion, pus elimination, and punctum-sparing canaliculotomy. Both therapeutic methods produced satisfactory curative effects.Conclusion: The results show that satisfactory therapy of lacrimal punctum inflammation can be achieved if the right therapeutic agents and procedures are adopted based on clinical characteristics of the ILPD manifesting in the patient.Keywords: Lacrimal punctum, Inflammatory disease, Conjunctivitis, Dry-eye symptom

Identification of New Glutamate Decarboxylases from \u3cem\u3eStreptomyces\u3c/em\u3e for Efficient Production of γ-Aminobutyric Acid in Engineered \u3cem\u3eEscherichia coli\u3c/em\u3e

Background Gamma (γ)-Aminobutyric acid (GABA) as a bioactive compound is used extensively in functional foods, pharmaceuticals and agro-industry. It can be biosynthesized via decarboxylation of monosodium glutamate (MSG) or L-glutamic acid (L-Glu) by glutamate decarboxylase (GAD; EC4.1.1.15). GADs have been identified from a variety of microbial sources, such as Escherichia coli and lactic acid bacteria. However, no GADs from Streptomyces have been characterized. The present study is aimed to identify new GADs from Streptomyces strains and establish an efficient bioproduction platform for GABA in E. coli using these enzymes. Results By sequencing and analyzing the genomes of three Streptomycesstrains, three putative GADs were discovered, including StGAD from Streptomyces toxytricini NRRL 15443, SsGAD from Streptomyces sp.MJ654-NF4 and ScGAD from Streptomyces chromofuscus ATCC 49982. The corresponding genes were cloned from these strains and heterologously expressed in E. coli BL21(DE3). The purified GAD proteins showed a similar molecular mass to GadB from E. coliBL21(DE3). The optimal reaction temperature is 37 °C for all three enzymes, while the optimum pH values for StGAD, SsGAD and ScGAD are 5.2, 3.8 and 4.2, respectively. The kinetic parameters including Vmax, Km, kcat and kcat/Km values were investigated and calculated through in vitro reactions. SsGAD and ScGAD showed high biocatalytic efficiency with kcat/Km values of 0.62 and 1.21 mM− 1·s− 1, respectively. In addition, engineered E. coli strains harboring StGAD, SsGAD and ScGAD were used as whole-cell biocatalysts for production of GABA from L-Glu. E. coli/SsGAD showed the highest capability of GABA production. The cells were repeatedly used for 10 times, with an accumulated yield of 2.771 kg/L and an average molar conversion rate of 67% within 20 h. Conclusions Three new GADs have been functionally characterized from Streptomyces, among which two showed higher catalytic efficiency than previously reported GADs. Engineered E. coli harboring SsGAD provides a promising cost-effective bioconversion system for industrial production of GABA

Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment

Video quality assessment (VQA) has attracted growing attention in recent years. While the great expense of annotating large-scale VQA datasets has become the main obstacle for current deep-learning methods. To surmount the constraint of insufficient training data, in this paper, we first consider the complete range of video distribution diversity (\ie content, distortion, motion) and employ diverse pretrained models (\eg architecture, pretext task, pre-training dataset) to benefit quality representation. An Adaptive Diverse Quality-aware feature Acquisition (Ada-DQA) framework is proposed to capture desired quality-related features generated by these frozen pretrained models. By leveraging the Quality-aware Acquisition Module (QAM), the framework is able to extract more essential and relevant features to represent quality. Finally, the learned quality representation is utilized as supplementary supervisory information, along with the supervision of the labeled quality score, to guide the training of a relatively lightweight VQA model in a knowledge distillation manner, which largely reduces the computational cost during inference. Experimental results on three mainstream no-reference VQA benchmarks clearly show the superior performance of Ada-DQA in comparison with current state-of-the-art approaches without using extra training data of VQA.Comment: 10 pages, 5 figures, to appear in ACM MM 202