AUTOMATED POLYP DETECTION IN COLONOSCOPY VIDEOS USING IMAGE ENHANCEMENT AND SALIENCY DETECTION ALGORITHM

Colonoscopy has proven to be an active diagnostic tool that examines the lower half of the digestive system’s anomalies. This paper confers a Computer-Aided Detection (CAD) method for polyps from colonoscopy images that helps to diagnose the early stage of Colorectal Cancer (CRC). The proposed method consists primarily of image enhancement, followed by the creation of a saliency map, feature extraction using the Histogram of Oriented-Gradients (HOG) feature extractor, and classification using the Support Vector Machine (SVM). We present an efficient image enhancement algorithm for highlighting clinically significant features in colonoscopy images. The proposed enhancement approach can improve the overall contrast and brightness by minimizing the effects of inconsistent illumination conditions. Detailed experiments have been conducted using the publicly available colonoscopy databases CVC ClinicDB, CVC ColonDB and the ETIS Larib. The performance measures are found to be in terms of precision (91.69%), recall (81.53%), F1-score (86.31%) and F2-score (89.45%) for the CVC ColonDB database and precision (90.29%), recall (61.73%), F1-score (73.32%) and F2-score (82.64%) for the ETIS Larib database. Comparison with the futuristic method shows that the proposed approach surpasses the existing one in terms of precision, F1-score, and F2-score. The proposed enhancement with saliency-based selection significantly reduced the number of search windows, resulting in an efficient polyp detection algorithm.

The Emergence of a Ciprofloxacin-Resistant Non-Groupable Neisseria meningitidis Strain of the Clonal Complex ST-175 in the Russian Federation

Relevance. Unencapsulated strains of meningococcus (NmNG) very rarely cause invasive meningococcal disease. A new ciprofloxacin-resistant strain NmNG ST-175 cc175, which has recently caused several cases of invasive meningococcal infection in Europe, has been discovered in the Russian Federation.Aim. To compare the new Russian strains of NmNG ST-175 with the already characterized NmNG ST-175 and to analyze the genetic markers associated with antibiotic resistance.Materials and methods. The nucleotide sequences of NmNG ST-175 strains of more than 2 million base pairs were exported from the PubMLST database. Comparison was carried out for 1605 core genome loci using the N. meningitidis cgMLST v1.0 ". Genetic relationships of 127 NmNG ST-175 strains, including 8 Russian carrier strains, were visualized using the SplitsTree software (version 4.16.2).Results. Of the eight Russian strains, six found themselves in a cluster with German and Swedish isolates that caused invasive meningococcal infection. Two more Russian strains were closest to the isolates of the cluster, including carrier isolates from England. Seven strains showed resistance to ciprofloxacin and possessed the gyrA-187 and gyrA-152 alleles, which, based on the phylogenetic analysis of the alleles, belonged to the genetic branches of Neisseria cinerea and Nm.Conclusion. Protein vaccines alone could potentially provide protection against ST-175 NmNG. It seems promising to study the antigenic characteristics of Russian Nm strains, including NmNG ST-175, to assess the potential vaccination coverage with existing protein vaccines, the possibility of their registration on the territory of the Russian Federation, as well as the development of domestic vaccines.

DESIGN OF AN INTELLIGENT IRRIGATION SYSTEM FOR A JUJUBE ORCHARD BASED ON IoT

This research aimed to develop an information acquisition and intelligent irrigation decision system based on the agricultural Internet of Things (IoT). The system consists of a field control terminal and a remote client, realizing control, real-time display, alarm, and other functions. The user may apply the upper and lower limit of soil relative water content as the irrigation decision threshold. The system automatically controls irrigation according to the threshold. In the calculation and analysis part of the system, the programming software Keil 5 was used for data collection and monitoring, database comparison, calculation and analysis, irrigation decision, and other functions.

Screening and Preliminary Identification of Soil Potassium and Phosphorus-hydrolyzing Bacteria WP1-3

In order to screen the strains with Potassium and phosphorus-releasing bacteria from the soil, the potassium-dissolving medium was used for preliminary screening, and the transparent circle method was used for re-screening. The content of soluble potassium was determined by sodium tetraphenylborate method, and the content of phosphorus was determined by anticolorimetric method of molybdenum and antimony. The strains were identified by morphological, biochemical and molecular biology. The results showed that ten strains with potassium-solubilizing function were obtained by preliminary screening of potassium solubilizing medium. The re-screening by transparent circle method showed that WP1-3 strain had the strongest potassium-solubilizing ability, with D/d value of 1.33. WP1-3 strains of decomposition of soluble potassium content is 22.918 mg/L, decompose inorganic phosphorus and organic phosphorus ability is 4.443 mg/L and 22.408 mg/L, respectively. Identifying morphological and biochemical identification showed that WP1-3 strains was gram-negative bacteria, not produce spore, the nitrogen content of WP1-3 strain increased after detection and culture for 3d and 7d were 10.825 and 38.521 mg/L. The 16SrDNA size was 1351bp, the ribosomal database comparison showed that WP1-3 strain was 99.8% similar to Rhizobium. Therefore, the WP1-3 strain belongs to the rhizobia genus.