Drought stress detection technique for wheat crop using machine learning.

The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

Inferring Agronomical Insights for Wheat Canopy Using Image-Based Curve Fit K-Means Segmentation Algorithm and Statistical Analysis.

Phenomics and chlorophyll fluorescence can help us to understand the various stresses a plant may undergo. In this research work, we observe the image-based morphological changes in the wheat canopy. These changes are monitored by capturing the maximum area of wheat canopy image that has maximum photosynthetic activity (chlorophyll fluorescence signals). The proposed algorithm presented here has three stages: (i) first, derivation of dynamic threshold value by curve fitting of data to eliminate the pixels of low-intensity value, (ii) second, extraction and segmentation of thresholded region by application of histogram-based K-means algorithm iteratively (this scheme of the algorithm is referred to as the curve fit K-means (CfitK-means) algorithm); and (iii) third, computation of 23 grey level cooccurrence matrix (GLCM) texture features (traits) from the wheat images has been done. These features help to do statistical analysis and infer agronomical insights. The analysis consists of correlation, factor, and agglomerative clustering to identify water stress indicators. A public repository of wheat canopy images was used that had normal and water stress response chlorophyll fluorescence images. The analysis of the feature dataset shows that all 23 features are proved fruitful in studying the changes in the shape and structure of wheat canopy due to water stress. The best segmentation algorithm was confirmed by doing exhaustive comparisons of seven segmentation algorithms. The comparisons showed that the best algorithm is CfitK-means as it has a maximum IoU score value of 95.75.

An enhanced version of Harris Hawks Optimization by dimension learning-based hunting for Breast Cancer Detection.

Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique.

Segmentation of retinal blood vessels based on feature-oriented dictionary learning and sparse coding using ensemble classification approach.

Accurate segmentation of the blood vessels from a retinal image plays a significant role in the prudent examination of the vessels. A supervised blood vessel segmentation technique to extract blood vessels from a retinal image is proposed. The uniqueness of the work lies in the implementation of feature-oriented dictionary learning and sparse coding for the accurate classification of the pixels in an image. First, the image is split into patches and for each patch, Gabor features are extracted at multiple scales and orientations to create a set of feature vectors (this is done for the whole training set). Then, an overcomplete feature-oriented dictionary is trained from the extracted Gabor features (selected on the basis of standard deviation) using the generalized K-means for singular value decomposition dictionary learning technique. Sparse representations are subsequently calculated for the corresponding features from the dictionary. The combination of feature vectors and sparse representations constitutes the final feature vector. This feature vector is then fed into the ensemble classifier for the classification of pixels into either blood vessel pixels or nonblood vessel pixels. The method is evaluated on publicly available DRIVE and STARE datasets, as they contain ground truth images precisely marked by experts. The results obtained on both of the datasets show that the proposed technique outperforms most of the state-of-the-art techniques reported in the literature.

Bacillus shivajii sp. nov., isolated from a water sample of Sambhar salt lake, India.

A novel Gram-stain-positive, rod-shaped, motile, spore-forming, strictly aerobic, alkali- and halo- tolerant bacterium, designated strain AK72T, was isolated from a water sample collected from Sambhar salt lake, Rajasthan, India. The colony appears circular, shiny, smooth, translucent or slightly pale in colour and convex with an entire margin after 48 h incubation at 37 °C with pH 9. Growth of the bacterium occurred at 10-42 °C (optimum, 25-37 °C), at salinities of 0.5-10 % (w/v) NaCl (optimum 3-5 % NaCl) and pH of 6-10 (optimum pH 9). Strain AK72T was positive for oxidase, catalase, nitrate reductase, phenylalanine deaminase, ornithine decarboxylase, aesculinase, lipase and urease activities. The predominant fatty acids were iso-C15 : 0, anteiso-C15 : 0 and iso-C16 : 0 and the cell-wall peptidoglycan contained meso-diaminopimelic acid as the diagnostic diamino acid. The major polar lipids of the strain were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, an unidentified aminophospholipid, three unidentified phospholipids and three unidentified lipids. The genomic DNA G+C content of the strain AK72T was 36.8 mol%. Phylogenetic analysis of the 16S rRNA gene sequence indicated that strain AK72T was closely related to Bacillus cellulosilyticus (96.5 %) and Bacillus vedderi (96.3 %), but the novel strain AK72T formed a separate clade with Bacillus aurantiacus whereas B. cellulosilyticus and B. vedderi were clustered in a separate clade. The above data in combination with the phenotypic characteristics and phylogenetic data inferred that strain AK72T represents a novel species of the genus Bacillus, for which the name Bacillusshivajii sp. nov. is proposed. The type strain is AK72T (=MTCC 12636T=KCTC 33981T=JCM 32183T).

Bacillus alkalilacus sp. nov., isolated from a sediment sample from a lake in India.

An aerobic, endospore-forming, haloalkali-tolerant, Gram-stain-positive, motile, rod-shaped bacterium, designated strain AK73T, was isolated from a sediment sample collected from Sambhar lake, Jaipur, Rajasthan, India. Colonies were circular, 1-2 mm in diameter, glossy, smooth, yellowish and convex with an entire margin after 48 h growth on marine agar at pH 9 and 37 °C. Growth occurred at 15-42 °C, 0-10 % (w/v) NaCl and at a pH range of 7-12. Strain AK73T was positive for catalase and arginine dihydrolase 2 activities, hydrolysis of Tweens 20, 40 and 80, and negative for esculinase, caseinase, gelatinase, ß-galactosidase, lipase (Tween 60) and urease activities. The fatty acids were dominated by branched iso-, anteiso-, saturated fatty acids with a high abundance of iso-C15 : 0, anteiso-C15 : 0, C16 : 0 and anteiso-C17 : 0; MK-7 was the major menaquinone. The cell-wall peptidoglycan contained meso-diaminopimelic acid as the diagnostic diamino acid. The polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, one unidentified aminophospholipid, four unidentified phospholipids and three unidentified lipids. The DNA G+C content of strain AK73T was 54 mol%. Analysis based on comparative 16S rRNA gene sequence analysis indicated that Bacillus alcalophilus was the nearest phylogenetic neighbour, with a pair-wise sequence similarity of 96.0 %. Phylogenetic analysis showed that strain AK73T formed a separate lineage but was loosely associated with a peripheral cluster of organisms that contained Bacillus gibsonii, Bacillus murimartini and Bacillus plakortidis with similarity values of 93.6, 93.5 and 93.4 %, respectively. Based on its phenotypic characteristics and on phylogenetic inference, strain AK73T represents a novel species of the genus Bacillus, for which the name Bacillus alkalilacus sp. nov. is proposed. The type strain is AK73T (=JCM 32184T=MTCC 12637T=KCTC 33880T).

Bacillus lacus sp. nov., isolated from a water sample of a salt lake in India.

A strictly aerobic, alkaliphilic, Gram-stain-positive, motile, rod-shaped bacterium, designated strain AK74T, was isolated from a water sample collected from Sambhar salt lake, Rajasthan, India. Colonies were circular, 1.2 mm in diameter, shiny, smooth, whitish and convex with an entire margin after 48 h growth at 37 °C with pH 9.0. Growth occurred at 25-42 °C, 0-4 % (w/v) NaCl and at a pH of 7-12. Strain AK74T was positive for aesculinase, caseinase, lipase activities and negative for oxidase, catalase, amylase, cellulase, DNase, gelatinase and urease activities. The fatty acids were dominated by branched iso-, anteiso- and saturated fatty acids with a high abundance of iso-C15 : 0, anteiso-C15 : 0, C16 : 0 and C16 : 1 and the cell-wall peptidoglycan contained meso-diaminopimelic acid as the diagnostic diamino acid. The DNA G+C content of strain AK74T was 51.6 mol%. A blast sequence similarity search based on 16S rRNA gene sequences indicated that Bacillus niabensis, Bacillus idriensisand Bacillus halosaccharovorans were the nearest phylogenetic neighbours, with a pair-wise sequence similarities of 96.6, 96.6 and 96.5%, respectively. Phylogenetic analysis showed that strain AK74T clustered with Bacillus mangrove and together clustered with Bacillus idriensisand Bacillus indicus. Based on its phenotypic characteristics and on phylogenetic inference, strain AK74T represents a novel species of the genus Bacillus, for which the name Bacilluslacus sp. nov. is proposed. The type strain is AK74T (=MTCC 12638T=KCTC 33946T=JCM 32185T).

Automatic segmentation of tumors in B-Mode breast ultrasound images using information gain based neutrosophic clustering.

BACKGROUND: Since breast ultrasound images are of low contrast, contain inherent noise and shadowing effect due to its imaging process, segmentation of breast tumors depicting ultrasound image is a challenging task. Thus, a robust breast ultrasound image segmentation technique is inevitable. OBJECTIVE: To develop an automatic lesion segmentation technique for breast ultrasound images. METHODS: First, the technique automatically detects the suspicious tumor region of interest and discards the unwanted complex background regions. Next, based on the concept of information gain, the technique applies an existing neutrosophic clustering method to the detected region to segment the desired tumor area. The proposed technique computes information gain values from the local neighbourhood of each pixel, which is further used to update the membership values and the cluster centers for the neutrosophic clustering process. Integrating the concept of entropy and neutrosophic logic features into the technique enabled to generate better segmentation results. RESULTS: Results of proposed method were compared both qualitatively and quantitatively with fuzzy c-means, neutrosophic c-means and neutrosophic ℓ-means clustering methods. It was observed that the proposed method outperformed the other three methods and yielded the best Mean (TP: 94.72, FP: 5.85, SI: 93.75, HD: 8.2, AMED: 2.4) and Standard deviation (TP: 3.2, FP: 3.7, SI: 3.8, HD: 2.6, AMED: 1.3) values for different quality metrics on the current set of breast ultrasound images. CONCLUSION: Study demonstrated that the proposed technique is robust to the shadowing effect and produces more accurate segmentation of the tumor region, which is very similar to that visually segmented by Radiologist.

Lactic acid bacteria isolated from yak milk show probiotic potential.

Probiotic industries strive for new, efficient and promising probiotic strains that impart a positive impact on consumer health. Challenges are persisting in isolation, screening, and selection of the new indigenous probiotic strains. In the present research, we explored the probiotic potential of 17 lactic acid bacteria isolated from Yak milk in a series of in vitro tests. We also demonstrated their health benefits, i.e., cholesterol degradation, lactose digestion, antimicrobial activity, antioxidant, and anticancer activities. Principal component analysis revealed that more than 50% of the strains fulfilled the examined criteria, e.g., survival in acidic pH, bile concentrations, and adherent property. Approximately all the strains produced antimicrobial substances against the maximum number of tested strains including clinical strains. Most strains degraded cholesterol in comparison to the reference probiotic strain whereas strain Yc showed 1.5 times higher the degradation efficiency of the control strain. Lan4 strain exhibited remarkable anticancer activity and induced the maximum apoptosis (87%) in the Hela cells and was non-toxic to the non-cancerous HEK293 cells. Around ten strains showed positive lactose digestion. Overall, this can be concluded that selected lactic acid bacteria revealed excellent probiotic properties along with desirable health benefits. These strains need to be further investigated in details for their application in the development of novel probiotic preparations for the improvement of public health.

Determination of a selection of synthetic cannabinoids and metabolites in urine by UHPSFC-MS/MS and by UHPLC-MS/MS.

Two different analytical techniques, ultra-high performance supercritical fluid chromatography-tandem mass spectrometry (UHPSFC-MS/MS) and reversed phase ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), were used for the determination of two synthetic cannabinoids and eleven metabolites in urine; AM-2201 N-4-OH-pentyl, AM-2233, JWH-018 N-5-OH-pentyl, JWH-018 N-pentanoic acid, JWH-073 N-4-OH-butyl, JWH-073 N-butanoic acid, JWH-122 N-5-OH-pentyl, MAM-2201, MAM-2201 N-4-OH-pentyl, RCS-4 N-5-OH-pentyl, UR-144 degradant N-pentanoic acid, UR-144 N-4-OH-pentyl, and UR-144 N-pentanoic acid. Sample preparation included a liquid-liquid extraction after deconjugation with ß-glucuronidase. The UHPSFC-MS/MS method used an Acquity UPC(2 TM) BEH column with a mobile phase consisting of CO2 and 0.3% ammonia in methanol, while the UHPLC-MS/MS method used an Acquity UPLC® BEH C18 column with a mobile phase consisting of 5 mM ammonium formate (pH 10.2) and methanol. MS/MS detection was performed with positive electrospray ionization and two multiple reaction monitoring transitions. Deuterated internal standards were used for six of the compounds. Limits of quantification (LOQs) were between 0.04 and 0.4 µg/L. Between-day relative standard deviations at concentrations ≥ LOQ were ≤20%, with biases within ±19%. Recoveries ranged from 40 to 90%. Corrected matrix effects were within 100 ± 10%, except for MAM-2201 with UHPSFC-MS/MS, and for UR-144 N-pentanoic acid and MAM-2201 N-4-OH-pentyl with UHPLC-MS/MS. Elution order obtained by UHPSFC-MS/MS was almost opposite to that obtained by UHPLC-MS/MS, making this instrument setup an interesting combination for screening and confirmation analyses in forensic cases. The UHPLC-MS/MS method has, since August 2014, been successfully used for confirmation of synthetic cannabinoids in urine samples revealing a positive immunoassay screening result. Copyright © 2015 John Wiley & Sons, Ltd.