Termite mound soil based potting media: a better approach towards sustainable agriculture.

Termite mound soils are known to possess unique physico-chemical and biochemical properties, making them highly fertile. Considering their rich nutrient content, the objective of the current experiment is to assess the physico-chemical properties and enzyme activities of termite mound based potting media and evaluate theirperformance for further exploration in floriculture. Potting media consisting of termite mound soil (TS) of a subterranean termite, Odontotermes obesus were prepared in 7 different combinations with garden soil (GS), sand (S) and farmyard manure (FYM) and a control (without termite mound soil), i.e., T1 (TS, GS, S, FYM (v:v:v:v /1:2:1:1)), T2 (TS, GS, S, FYM (v:v:v:v / 2:1:1:1)), T3 (TS, S, FYM (v:v:v / 2:1:1)), T4 (TS, GS, FYM (v:v:v / 2:1:1)), T5 (TS, GS, S (v:v:v / 2:1:1)), T6 (TS, S, FYM (v:v:v / 3:1:1)), T7 (TS, S, FYM (v:v:v / 1:1:2)) and control (GS, S, FYM (v:v:v / 2:1:1)). The samples were then analysed in laboratory. Experimental analysis on physico-chemical and biological parameters revealed superiority of T7 (TS, S, FYM (v:v:v / 1:1:2)) in terms of pH (7.15), organic carbon (2.13%), available nitrogen (526.02 kg ha-1), available phosphorus (56.60 kg ha-1), available potassium (708.19 kg ha-1), dehydrogenase activity (18.21 µg TTF g-1 soil day-1), Phosphomonoesterase (PME) activity (46.68 54 µg p-nitrophenol/gsoil/h) and urease activity (3.39 µg NH4-N g-1 soil h-1). Whereas T4 (TS, GS, FYM (v:v:v /2:1:1)) registered superiority in terms of PME activity (50.54 µg p-nitrophenol/gsoil/h), Fluorescein diacetate (FDA) activity (11.01 µgfluorescein/gsoil/h) and Soil Microbial Biomass Carbon (SMBC) (262.25 µg/g). Subsequent to the laboratory analysis, two best potting mixtures (T7 & T4) were selected and their performance was assessed by growing a test crop, Tagetes erecta cv. Inca Orange. Considering the growth parameters, the potting media: T7 was found to be significantly superior in terms of plant spread (39.64 cm), leaf area index (4.07), fresh weight (37.72 g), yield (317.81 g/plant), and diameter (9.38 cm) of flower over T4 & control. The Benefit:Cost (B:C) ratio meaning the ratio of net returns to total cost of cultivation was determined. The B:C ratio of raising marigold flower as potted plant in T7 was 1.10 whereas the B:C ratio of the potting mixture of T7 was 2.52. This shows that T7 potting media is also economically viable choice for commercial purposes.

Harnessing weedy rice as functional food and source of novel traits for crop improvement.

A relative of cultivated rice (Oryza sativa L.), weedy or red rice (Oryza spp.) is currently recognized as the dominant weed, leading to a drastic loss of yield of cultivated rice due to its highly competitive abilities like producing more tillers, panicles, and biomass with better nutrient uptake. Due to its high nutritional value, antioxidant properties (anthocyanin and proanthocyanin), and nutrient absorption ability, weedy rice is gaining immense research attentions to understand its genetic constitution to augment future breeding strategies and to develop nutrition-rich functional foods. Consequently, this review focuses on the unique gene source of weedy rice to enhance the cultivated rice for its crucial features like water use efficiency, abiotic and biotic stress tolerance, early flowering, and the red pericarp of the seed. It explores the debating issues on the origin and evolution of weedy rice, including its high diversity, signalling aspects, quantitative trait loci (QTL) mapping under stress conditions, the intricacy of the mechanism in the expression of the gene flow, and ecological challenges of nutrient removal by weedy rice. This review may create a foundation for future researchers to understand the gene flow between cultivated crops and weedy traits and support an improved approach for the applicability of several models in predicting multiomics variables.

Identification and Analysis of Imaging-Genomic Signatures to Study Recurrence in Breast Cancers.

One of the main causes of breast cancer related death is its recurrence. In this study, we investigate the association of gene expression and pathological image features to understand breast cancer recurrence. A total of 172 breast cancer patient data was downloaded from the TCGA-BRCA database. The dataset contained diagnostic whole slide images and RNA-seq data of 80 recurrent and 92 disease-free breast cancer patients. We performed genomic analysis on RNA-seq data to obtain the hub genes related to recurrent breast cancer. We extracted relevant pathomic features from histopathology images. The discriminative ability of the hub genes and pathomic features were evaluated using machine learning classifiers. We used Spearman rank correlation analysis to find statistically significant association between gene expression and pathomic features. We identified that, genes which are related to breast cancer progression is significantly associated (adjusted p-value<0.05) with several pathomic features.Clinical Relevance- Histopathology is the gold standard for cancer detection. It provides us with cellular level information. A strong association between a pathomic feature and a gene expression will help clinicians understand the cellular and molecular mechanism of cancer for better prognosis.

Identification of hub genes to determine drug-disease correlation in breast carcinomas.

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.

Plant growth-promoting traits of culturable seed microbiome of citrus species from Purvanchal Himalaya.

Despite Northeastern India being "Treasure House of Citrus Genetic Wealth," genetic erosion of citrus diversity poses severe concern with a corresponding loss in seed microbial diversity. The seed microbiome of citrus species unique to the Purvanchal Himalaya is seldom explored for their use in sustainable orchard management. Isolation and characterization of culturable seed microbiomes of eight citrus species, namely, Citrus reticulata Blanco, C. grandis (L.) Osbeck, C. latipes Tanaka, C. megaloxycarpa Lushaigton, C. jambhiri Lush, C. sinensis (L.) Osbeck, C. macroptera Montr, and C. indica Tanaka collected from NE India were carried out. The isolates were then screened for an array of plant growth-promoting (PGP) traits [indole acetic acid (IAA) production, N2 fixation, phosphate and zinc complex dissolution, siderophores, and Hydrogen Cyanide (HCN) production]. The pure culture isolates of seed microbiomes were capable of dissolving insoluble Ca3(PO4)2 (1.31-4.84 µg Pi ml-1 h-1), Zn3(PO4)2 (2.44-3.16 µg Pi ml-1 h-1), AlPO4 (1.74-3.61 µg Pi ml-1 h-1), and FePO4 (1.54-4.61µg Pi ml-1 h-1), mineralized phytate (12.17-18.00 µg Pi ml-1 h-1) and produced IAA-like substances (4.8-187.29 µg ml-1 h-1). A few isolates of the seed microbiome were also able to fix nitrogen, secrete siderophore-like compounds and HCN, and dissolve ZnSO4 and ZnO. The 16S ribosomal Ribonucleic Acid (rRNA)-based taxonomic findings revealed that Bacillus was the most dominant genus among the isolates across citrus species. Isolates CG2-1, CME6-1, CME6-4, CME6-5, CME6-9, CJ7-1, CMA10-1, CI11-3, and CI11-4 were identified as promising bioinoculants for development of microbial consortium having multifaceted PGP traits for nutritional benefits of nitrogen, phosphorus and zinc, and IAA hormonal benefits to citrus crops for better fitness in acid soils.

Antimicrobial Traits of Beauveria bassiana Against Rhizoctonia solani, the Causal Agent of Sheath Blight of Rice Under Field Conditions.

Beauveria bassiana, an entomopathogenic fungus, has recently drawn attention worldwide not only as a potential biocontrol agent against insect pests but also for its other beneficial roles as plant disease antagonist, endophyte, plant growth promoter, and beneficial rhizosphere colonizer. In the present study, 53 native isolates of B. bassiana were screened for antifungal ability against Rhizoctonia solani, the causal agent of sheath blight of rice. Also, the mechanisms underlying such interaction and the responsible antimicrobial traits involved were studied. Following this, potential B. bassiana isolates were assayed against the reduction of sheath blight of rice under field conditions. The results showed that B. bassiana exhibited antagonistic behavior against R. solani with a percent mycelial inhibition recorded maximum of up to 71.15%. Mechanisms behind antagonism were the production of cell-wall-degrading enzymes, mycoparasitism, and the release of secondary metabolites. The study also deciphered several antimicrobial traits and the presence of virulent genes in B. bassiana as a determinant of potential plant disease antagonists. Under field conditions, combined application of the B. bassiana microbial consortium as a seed treatment, seedling root dip, and foliar sprays showed reduced sheath blight disease incidence and severity up to 69.26 and 60.50%, respectively, along with enhanced plant-growth-promoting attributes. This is one of the few studies investigating the antagonistic abilities of the entomopathogenic fungus B. bassiana against phytopathogen R. solani and the underlying mechanisms involved.

Molecular interaction between plants and Trichoderma species against soil-borne plant pathogens.

Trichoderma spp. (Hypocreales) are used worldwide as a lucrative biocontrol agent. The interactions of Trichoderma spp. with host plants and pathogens at a molecular level are important in understanding the various mechanisms adopted by the fungus to attain a close relationship with their plant host through superior antifungal/antimicrobial activity. When working in synchrony, mycoparasitism, antibiosis, competition, and the induction of a systemic acquired resistance (SAR)-like response are considered key factors in deciding the biocontrol potential of Trichoderma. Sucrose-rich root exudates of the host plant attract Trichoderma. The soluble secretome of Trichoderma plays a significant role in attachment to and penetration and colonization of plant roots, as well as modulating the mycoparasitic and antibiosis activity of Trichoderma. This review aims to gather information on how Trichoderma interacts with host plants and its role as a biocontrol agent of soil-borne phytopathogens, and to give a comprehensive account of the diverse molecular aspects of this interaction.

A computer-aided tool for automatic volume estimation of hematoma using non-contrast brain CT scans.

The computation of hematoma volume is the key parameter for treatment planning of Intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) imaging is routinely used for the diagnosis of ICH. Hence, the development of computer-aided tools for three-dimensional (3D) computed tomography (CT) image analysis is essential to estimate the gross volume of hematoma. We propose a methodology for automatic estimation of the hematoma volume from 3D CT volumes. Our approach integrates two different methods, multiple abstract splitting (MAS) and seeded region growing (SRG) to develop a unified hematoma detection pipeline from pre-processed CT volumes. The proposed methodology was tested on 80 cases. The volume was estimated from the delineated hematoma region, validated against the ground-truth volumes, and compared with those obtained from the conventional ABC/2 approach. We also compared our results with the U-Net model (supervised technique) to show the applicability of the proposed method. The volume calculated from manually segmented hematoma was considered the ground truth. TheR2correlation coefficient between the volume obtained from the proposed algorithm and the ground truth is 0.86, which is equivalent to theR2value resulting from the comparison between the volume calculated by ABC/2 and the ground truth. The experimental results of the proposed unsupervised approach are comparable to the deep neural architecture (U-Net models). The average computation time was 132.76 ± 14 seconds. The proposed methodology provides a fast and automatic estimation of hematoma volume, which is similar to the baseline user-guided ABC/2 approach. Implementation of our method does not demand a high-end computational setup. Thus, recommended in clinical practice for computer-assistive volume estimation of hematoma from 3D CT volumes and can be implemented in a simple computer system.

Nanotechnological approaches for management of soil-borne plant pathogens.

Soil borne pathogens are significant contributor of plant yield loss globally. The constraints in early diagnosis, wide host range, longer persistence in soil makes their management cumbersome and difficult. Therefore, it is crucial to devise innovative and effective management strategy to combat the losses caused by soil borne diseases. The use of chemical pesticides is the mainstay of current plant disease management practices that potentially cause ecological imbalance. Nanotechnology presents a suitable alternative to overcome the challenges associated with diagnosis and management of soil-borne plant pathogens. This review explores the use of nanotechnology for the management of soil-borne diseases using a variety of strategies, such as nanoparticles acting as a protectant, as carriers of actives like pesticides, fertilizers, antimicrobials, and microbes or by promoting plant growth and development. Nanotechnology can also be used for precise and accurate detection of soil-borne pathogens for devising efficient management strategy. The unique physico-chemical properties of nanoparticles allow greater penetration and interaction with biological membrane thereby increasing its efficacy and releasability. However, the nanoscience specifically agricultural nanotechnology is still in its toddler stage and to realize its full potential, extensive field trials, utilization of pest crop host system and toxicological studies are essential to tackle the fundamental queries associated with development of commercial nano-formulations.

Destructive Phytophthora on orchids: current knowledge and future perspectives.

Anton de Bary first coined the genus, Phytophthora, which means "plant destroyer", viewing its devastating nature on potatoes. Globally plants have faced enormous threat from Phytophthora since its occurrence. In fact, a century ago, Phytophthorapalmivora was first reported on Dendrobium maccarthiae in Sri Lanka. Since then, members of beautiful flowering crops of the family Orchidaceae facing the destructive threat of Phytophthora. Several Phytophthora species have been recorded to infect orchids with economic loss worldwide. To date, orchids are attacked by 12 species of Phytophthora. Five Phytophthora species (P. palmivora, P. nicotianae, P. cactorum, P. multivesiculata, P. meadii) are the major pathogenic Oomycetous Chromista" rather than true fungi frequently occurred on Orchidaceae. Phytophthora palmivora (having ~32 orchid host genera in 15 countries), Phytophthora nicotianae (having ~15 orchid host genera in 16 countries), Phytophthora cactorum (having ~43 orchid host genera in 6 countries), Phytophthora multivesiculata (having 2 orchid host genera in 5 countries) and Phytophthora capsici (having 2 orchid host genera in all Vanilla growing countries) are potential destroyers of Orchidaceae. Most of them are water loving Oomycetes cause disease in moist environments (> 80% RH) at 16-28°C. In artificially constructed orchidaria, anthropogenic factors are mostly contributed to the dissemination Phytophthora diseases in addition to many other factors. Water management, clean cultivation, and agro-chemicals are the major options for effective management of orchid Phytophthora, as the eco-friendly management options like development of resistant hybrids/cultivars, biological disease management, transgenic approaches, RNAi technology remained in the infant stage. In this review, we intended to highlight the insight of Phytophthora diseases associated with the orchid disease with reference to the historical aspect of the diseases, symptoms and signs, the pathogens, taxonomy, geographic distribution, host range within the Orchidaceae, pathogen identification, molecular diagnostics, mating types and races, management options and strategies and future perspectives.

Characterization and identification of fungicide insensitive Pestalotiopsis-like species pathogenic to tea crop in India.

Gray blight, a fungal disease caused by Pestalotiopsis-like species, is a widespread disease affecting tea crop (Camellia sinensis (L.) Kuntze) in many tea-growing countries, including India, resulting in huge losses in tea production. In India, several studies have been conducted to understand the fungal diseases of tea crop, but gray blight has not been well described in major tea growing areas such as in North Bengal, based on its geographic distribution, molecular analysis, or pathogenicity, and even fungicide resistance. The objective of this study was to identify and characterize the causative agents of gray blight disease in symptomatic leaf sample of tea crop collected from 27 tea gardens located in North Bengal, India and to evaluate some common fungicides against them in order to understand the resistance mechanism. In this study, we characterized Pestalotiopsis-like species based on the phylogenies of DNA sequences (internal transcribed spacers) and assessment of conidial characteristics. The study revealed that out of 27 isolates of gray blight pathogens, 17 belonged to the genus Pseudopestalotiopsis (Ps.), six isolates were Neopestalotiopsis, and four were Pestalotiopsis. Two novel species, Ps. thailandica and N. natalensis were introduced through this study. The most frequently isolated genus from C. chinensis was Pseudopestalotiopsis. Pathogenicity tests showed that the isolates displayed significantly different virulence when inoculated onto wounded tea leaves and the mycelial growth rate was positively correlated with pathogenicity (P < 0.01). Based on the 13 ISSR (Inter Simple Sequence Repeat) markers used and principal coordinate analysis, it was found that isolates were very diverse. Out of 27 isolates, IND0P2, DLG0P10, and BHAT0P11 isolates were insensitive against both MBC + M3 (Carbendazim + Mancozeb) and DMI (Hexaconazole) fungicides, while isolates SANY0P18, PAHG0P19, RANG0P24, and SING0P25 were insensitive only against MBC + M3 fungicide. Further, these insensitive isolates were grouped into separate clusters by ISSR, indicating their distinctiveness. However, all the evaluated isolates were susceptible to M1 (copper oxychloride) and another DMI (propiconazole) fungicides. Therefore, to manage gray blight, fungicide resistance management strategies as recommended by Fungicide Resistance Action Committee should be implemented.

Interplay between quantum diffusion and localization in the atom-optics kicked rotor.

Atom-optics kicked rotor represents an experimentally reliable version of the paradigmatic quantum kicked rotor system. In this system, a periodic sequence of kicks are imparted to the cold atomic cloud. After a short initial diffusive phase the cloud settles down to a stationary state due to the onset of dynamical localization. In this paper, to explore the interplay between localized and diffusive phases, we experimentally implement a modification to this system in which the sign of the kick sequence is flipped after every M kicks. This is achieved in our experiment by allowing free evolution for half the Talbot time after every M kicks. Depending on the value of M, this modified system displays a combination of enhanced diffusion followed by asymptotic localization. This is explained as resulting from two competing processes-localization induced by standard kicked rotor type kicks, and diffusion induced by the half Talbot time evolution. The experimental and numerical simulations agree with one another. The evolving states display localized but nonexponential wave function profiles. This provides another route to quantum control in the kicked rotor class of systems.

Endophytic Beauveria bassiana can protect the rice plant from sheath blight of rice caused by Rhizoctonia solani and enhance plant growth parameters.

Beauveria bassiana, a potential entomopathogenic biocontrol agent, has recently drawn attention worldwide for its other additional beneficial roles such as plant disease antagonist, beneficial rhizosphere colonizer, plant growth promoter and an endophyte. In the present study, endophytic colonizing behaviour of five (5) B. bassiana isolates viz., Bb4, Bb16, Bb25, Bb44 and Bb53 were studied in rice following three (3) artificial inoculation techniques viz., seed treatment, root inoculation and foliar spray and the endophytic colonizing ability were determined by culture-based assay. After B. bassiana inoculation, rice plants were challenged with Rhizoctonia solani and disease incidence and plant growth promotion were assessed. Per cent colonization of rice stems, leaves and roots were influenced by inoculation technique, post-inoculation time (7th, 14th, 21st and 28th dpi) and plant growth medium (sterile soil, non-sterile soil), recorded maximum on 14th-day post-inoculation (dpi) i.e., 96% in stems, 92% in leaves and 28% in roots, whereas, lower colonization was recorded on 7th, 21st and 28th dpi. Whereas, the foliar spray was found best as compared to seed and root inoculation techniques, and maximum fungal recovery was observed in stems and leaves and least in roots. Upon colonization, the physical presence of B. bassiana in rice was localized by light microscopy-based studies. Potential B. bassiana strains with endophytic ability were re-isolated and their identity was determined based on morphometric and PCR-based techniques. Further, the present study also identified several virulent genes viz., BbChit1, Cdep1, Bbhog1 and Bbjen1 and extracellular hydrolytic enzymes viz., α-amylase, cellulase, lipase, pectinase and xylanase secreted by endophytic B. bassiana strains as determinants responsible for establishing the endophytic association in rice. On the other hand, a significant reduction in disease incidence was observed in the endophytic B. bassiana Bb4-, Bb16- and Bb44-inoculated plants as compared to the non-endophytic B. bassiana Bb25- and Bb53-inoculated plants along with enhanced plant growth promotion. This is one of the few studies investigating the colonization of B. bassiana in rice and its promising role as a plant disease antagonist and plant growth promoter in rice.

Advances in Nanotechnology as a Potential Alternative for Plant Viral Disease Management.

Plant viruses cause enormous losses in agricultural production accounting for about 47% of the total overall crop losses caused by plant pathogens. More than 50% of the emerging plant diseases are reported to be caused by viruses, which are inevitable or unmanageable. Therefore, it is essential to devise novel and effective management strategies to combat the losses caused by the plant virus in economically important crops. Nanotechnology presents a new tendency against the increasing challenges in the diagnosis and management of plant viruses as well as plant health. The application of nanotechnology in plant virology, known as nanophytovirology, includes disease diagnostics, drug delivery, genetic transformation, therapeutants, plant defense induction, and bio-stimulation; however, it is still in the nascent stage. The unique physicochemical properties of particles in the nanoscale allow greater interaction and it may knock out the virus particles. Thus, it opens up a novel arena for the management of plant viral diseases. The main objective of this review is to focus on the mounting collection of tools and techniques involved in the viral disease diagnosis and management and to elucidate their mode of action along with toxicological concerns.

Isolation and characterization of endophytic bacteria from tomato foliage and their in vitro efficacy against root-knot nematodes.

Fifteen endophytic bacteria were isolated from leaves and stems of Solanum lycopersicum and Solanum pimpinellifolium collected from different locations of the Jorhat district of Assam and characterized by morphological, cultural, biochemical and molecular approaches. An in vitro study was carried out to evaluate their potentiality as biological control agents against second stage juvenile of the root-knot nematode, Meloidogyne incognita race2. Thirty second stage juveniles (J2) of M. incognita race 2 were exposed to cell free culture filtrates of all the 15 bacterial endophytes in a sterile cavity block at a concentration of S(100%), S/2(50%), S/4(25%), S/6(17%) and S/10(10%) for a duration of 6, 12, 24, and 48 hr. The results revealed that all the isolates had the potentiality to significantly increase the mortality of the second stage juveniles (J2). The percent mortality was directly proportional to the duration of exposure time and the concentration of the culture filtrate. The isolate BETL2 showed the best result with 81.47% mortality of juveniles followed by isolates BETL4 (81.43%), BETLI (79.07%), BETS2 (78.87%), and BETL6 (78.17%). The 16S rRNA sequence amplification results indicated that these isolates were Bacillus marisflavi (BETL2), Bacillus altitudinis (BETL4), Microbacterium arborescens (BETL1), Exiguobacterium indicum (BETS2), and Bacillus marisflavi (BETL6). The four most efficient isolates were structurally analyzed using a scanning electron microscope and this revealed that the length and breadth of isolates-BETLI, BETL2, BETL4, and BETS2 were 701.70 nm × 348.30 nm, 954.10 nm × 303.10 nm, 984.10 nm × 332.90 nm and 1422.00 nm × 742.00 nm, respectively. The result of the present study indicated that the above four novel strains of endophytic bacterial isolates enhance the mortality of J2 of M. incognita race2 and has the potentiality as biological control agents against M. incognita.

Antifungal Activity of Biogenically Synthesized Silver and Gold Nanoparticles Against Sheath Blight of Rice.

Biogenically synthesized silver and gold nanoparticles were evaluated for antifungal activity against Rhizoctonia solani causing sheath blight of rice. Both the nanoparticles were tested at 1, 5, 10, 50, 100 and 200 ppm along with chemical check against the pathogen. Silver nanoparticle (Ag NP) at 200 ppm showed the highest inhibition (73.39%) in the radial growth of R. solani, while gold nanoparticle (Au NP) at the same concentration inhibited the growth of the pathogen up to 60.83%. Study on mode of action of nanoparticle by electron microscopy showed that Ag NP accumulate inside the fungal cells thereby cause distortion of fungal cells leading to death of the pathogen. Ag NP at 200 and 100 ppm caused complete inhibition of sclerotial germination of R. solani. Pot experiment conducted to study the efficacy of Ag NP at 200 ppm against sheath blight of rice showed that application of Ag NP increased the plant growth parameters as compared to control, with reduced per cent disease incidence (20.00%) as compared to inoculated control R. solani (88.00%). Application of Ag NP also increased the concentration of vital secondary metabolites like phenols, flavonoids, terpenoids and total soluble sugars.

Ventricle shape analysis using modified WKS for atrophy detection.

Brain ventricle is one of the biomarkers for detecting neurological disorders. Studying the shape of the ventricles will aid in the diagnosis process of atrophy and other CSF-related neurological disorders, as ventricles are filled with CSF. This paper introduces a spectral analysis algorithm based on wave kernel signature. This shape signature was used for studying the shape of segmented ventricles from the brain images. Based on the shape signature, the study groups were classified as normal subjects and atrophy subjects. The proposed algorithm is simple, effective, automated, and less time consuming. The proposed method performed better than the other methods heat kernel signature, scale invariant heat kernel signature, wave kernel signature, and spectral graph wavelet signature, which were used for validation purpose, by producing 94-95% classification accuracy by classifying normal and atrophy subjects correctly for CT, MR, and OASIS datasets.

Radius-optimized efficient template matching for lesion detection from brain images.

Computer-aided detection of brain lesions from volumetric magnetic resonance imaging (MRI) is in demand for fast and automatic diagnosis of neural diseases. The template-matching technique can provide satisfactory outcome for automatic localization of brain lesions; however, finding the optimal template size that maximizes similarity of the template and the lesion remains challenging. This increases the complexity of the algorithm and the requirement for computational resources, while processing large MRI volumes with three-dimensional (3D) templates. Hence, reducing the computational complexity of template matching is needed. In this paper, we first propose a mathematical framework for computing the normalized cross-correlation coefficient (NCCC) as the similarity measure between the MRI volume and approximated 3D Gaussian template with linear time complexity, [Formula: see text], as opposed to the conventional fast Fourier transform (FFT) based approach with the complexity [Formula: see text], where [Formula: see text] is the number of voxels in the image and [Formula: see text] is the number of tried template radii. We then propose a mathematical formulation to analytically estimate the optimal template radius for each voxel in the image and compute the NCCC with the location-dependent optimal radius, reducing the complexity to [Formula: see text]. We test our methods on one synthetic and two real multiple-sclerosis databases, and compare their performances in lesion detection with FFT and a state-of-the-art lesion prediction algorithm. We demonstrate through our experiments the efficiency of the proposed methods for brain lesion detection and their comparable performance with existing techniques.

Modified distance regularized level set evolution for brain ventricles segmentation.

Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%-90%, specificity in the range of 98%-99%, and accuracy in the range of 95%-98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

Wavelet Transform and Texton based Analysis for Detection of Benign and Malignant Masses.

Cancer has affected the human community to a large extent due to its low survival rate towards the end stage of the disease. It is asymptomatic in many cases during the initial stage. Thus the dependency on early diagnosis and regular check up increases manifold. Computer Aided Diagnostic Model is the need of the hour which will increase the diagnostic efficiency. A total of 400 images acquired from the Digital Database for Screening Mammography have been used here for analysis. This paper proposes a novel technique to differentiate benign and malignant breast lesions in mammograms using multiresolution analysis and Schmid Filter Bank, which were not reported earlier. A three level Haar wavelet decomposed image(L1, L2, L3) is obtained for each Region of Interest. In each level Texton based analysis is further investigated through Schmid filter bank. Statistical features and Haralick's Features are obtained from filter response and Gray Level Cooccurence Matrix respectively. Partition Membership Filter is further applied to the feature matrix for feature partitioning. The method shows maximum accuracy of 98.63% and Area under Curve of 0.981 using Random Forest Classifier and ten fold cross validation.