Mapping of soil suitability for medicinal plants using machine learning methods.

Inadequate conservation of medicinal plants can affect their productivity. Traditional assessments and strategies are often time-consuming and linked with errors. Utilizing herbs has been an integral part of the traditional system of medicine for centuries. However, its sustainability and conservation are critical due to climate change, over-harvesting and habitat loss. The study reveals how machine learning algorithms, geographic information systems (GIS) being a powerful tool for mapping and spatial analysis, and soil information can contribute to a swift decision-making approach for actual forethought and intensify the productivity of vulnerable curative plants of specific regions to promote drug discovery. The data analysis based on machine learning and data mining techniques over the soil, medicinal plants and GIS information can predict quick and effective results on a map to nurture the growth of the herbs. The work incorporates the construction of a novel dataset by using the quantum geographic information system tool and recommends the vulnerable herbs by implementing different supervised algorithms such as extra tree classifier (EXTC), random forest, bagging classifier, extreme gradient boosting and k nearest neighbor. Two unique approaches suggested for the user by using EXTC, firstly, for a given subregion type, its suitable soil classes and secondly, for soil type from the user, its respective subregion labels are revealed, finally, potential medicinal herbs and their conservation status are visualised using the choropleth map for classified soil/subregion. The research concludes on EXTC as it showcases outstanding performance for both soil and subregion classifications compared to other models, with an accuracy rate of 99.01% and 98.76%, respectively. The approach focuses on serving as a comprehensive and swift reference for the general public, bioscience researchers, and conservationists interested in conserving medicinal herbs based on soil availability or specific regions through maps.

Two-stage lung nodule detection framework using enhanced UNet and convolutional LSTM networks in CT images.

Lung cancer is one of the leading causes of cancer deaths globally, and lung nodules are the primary indicators that aid in early detection. The computer-aided detection (CADe) system acts as a second reader, reducing the variability in lung cancer risk assessment across physicians. This work aims to improve the performance of CADe systems by developing high sensitive and resilient detection networks using deep learning. This paper proposes a novel CADe framework to detect nodules from CT scans using an enhanced UNet in conjunction with a pyramid dilated convolutional long short term memory (PD-CLSTM) network. The proposed CADe system works in two stages: nodule detection and false nodule elimination. In the first stage, a modified UNet-based model, Atrous UNet+, is proposed to detect nodule candidates from axial slices using dilation and ensemble mechanisms. Dilated convolution is a powerful technique for dense prediction by incorporating larger context information without increasing the computation load. Ensemble skip connections fuse multilevel semantic features that help detect nodules of diverse sizes. In the second stage, The pyramid dilated convolutional LSTM network is proposed to identify true nodules using inter-slice and intra-slice spatial features of 3D nodule patches. In this work, a novel idea of applying convolution long short-term memory (ConvLSTM) is attempted to categorize true nodules from false nodules and help to eliminate false nodules. Experimental results on the LUNA16 dataset show that our proposed CADe system achieves the best average sensitivity of 0.930 at seven predefined FPRs: 1/8, 1/4, 1/2, 1, 2, 4, and 8 FPs per scan. Also, the proposed CADe system detects small nodules in the range of 5-9 mm with a sensitivity of 0.92 and other nodules (>10 mm) with a sensitivity of 0.93, resulting in a high detection rate in recognizing nodules of diverse sizes.

A Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique.

Purpose: In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification. Materials and Methods: A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule. In the meantime, they also reduce the need for a large-scale annotated dataset for CNNs. For classifying malignant nodules and benign nodules, radial basis function support vector machine is used. The proposed hybridized model is evaluated on the LIDC-IDRI dataset. Results: It has achieved an accuracy of 97.53%, sensitivity of 98.62%, specificity of 96.88%, precision of 95.04%, F1 score of 0.9679, false-positive rate of 3.117%, and false-negative rate of 1.38% and has been compared with other state of the art techniques. Conclusions: The performance of the proposed hybridized feature-based classification technique is better than the deep features-based classification technique in lung nodule classification.

A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications.

Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night's sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

Written Advice Given by African American Smokers to Their Peers: Qualitative Study of Motivational Messages.

BACKGROUND: Although African Americans have the lowest rates of smoking onset and progression to daily smoking, they are less likely to achieve long-term cessation. Interventions tailored to promote use of cessation resources in African American individuals who smoke are needed. In our past work, we demonstrated the effectiveness of a technology-assisted peer-written message intervention for increasing smoking cessation in non-Hispanic White smokers. In this formative study, we have adapted this intervention to be specific for African American smokers. OBJECTIVE: We aimed to report on the qualitative analysis of messages written by African American current and former smokers for their peers in response to hypothetical scenarios of smokers facing cessation challenges. METHODS: We recruited African American adult current and former smokers (n=41) via ResearchMatch between April 2017 and November 2017. We asked participants to write motivational messages for their peers in response to smoking-related hypothetical scenarios. We also collected data on sociodemographic factors and smoking characteristics. Thematic analysis was conducted to identify cessation strategies suggested by the study participants. RESULTS: Among the study participants, 60% (25/41) were female. Additionally, more than half (23/41, 56%) were thinking about quitting, 29% (12/41) had set a quit date, and 27% (11/41) had used electronic cigarettes in the past 30 days. Themes derived from the qualitative analysis of peer-written messages were (1) behavioral strategies, (2) seeking help, (3) improvements in quality of life, (4) attitudes and expectations, and (5) mindfulness/religious or spiritual practices. Under the behavioral strategies theme, distraction strategies were the most frequently suggested strategies (referenced 84 times in the 318 messages), followed by use of evidence-based treatments/cessation strategies. Within the seeking help theme, subthemes included seeking help or support from family/friends or close social networks (referenced 56 times) and health care professionals (referenced 22 times). The most frequent subthemes that emerged from improvements in the quality of life theme included improving one's health (referenced 22 times) and quality of life (referenced 21 times). Subthemes that emerged from the attitude and expectations theme included practicing positive self-talk (referenced 27 times), autonomy/independence from the smoking habit (referenced six times), and financial cost of smoking (referenced five times). The two subthemes that emerged from the mindfulness/religious or spiritual practices theme were use of self-awareness techniques (referenced 36 times) and religious or spiritual practices to cope (referenced 13 times). CONCLUSIONS: Our approach to adapt a prior peer-message intervention to African American smokers yielded a set of evidence-based messages that may be suitable for smokers at all phases of motivation to quit (ready to quit or not ready to quit). In future research, we plan to assess the impact of texting these messages to African American smokers in a smoking cessation trial.

Appraisal of Deep-Learning Techniques on Computer-Aided Lung Cancer Diagnosis with Computed Tomography Screening.

AIMS: Deep-learning methods are becoming versatile in the field of medical image analysis. The hand-operated examination of smaller nodules from computed tomography scans becomes a challenging and time-consuming task due to the limitation of human vision. A standardized computer-aided diagnosis (CAD) framework is required for rapid and accurate lung cancer diagnosis. The National Lung Screening Trial recommends routine screening with low-dose computed tomography among high-risk patients to reduce the risk of dying from lung cancer by early cancer detection. The evolvement of clinically acceptable CAD system for lung cancer diagnosis demands perfect prototypes for segmenting lung region, followed by identifying nodules with reduced false positives. Recently, deep-learning methods are increasingly adopted in medical image diagnosis applications. SUBJECTS AND METHODS: In this study, a deep-learning-based CAD framework for lung cancer diagnosis with chest computed tomography (CT) images is built using dilated SegNet and convolutional neural networks (CNNs). A dilated SegNet model is employed to segment lung from chest CT images, and a CNN model with batch normalization is developed to identify the true nodules from all possible nodules. The dilated SegNet and CNN models have been trained on the sample cases taken from the LUNA16 dataset. The performance of the segmentation model is measured in terms of Dice coefficient, and the nodule classifier is evaluated with sensitivity. The discriminant ability of the features learned by a CNN classifier is further confirmed with principal component analysis. RESULTS: Experimental results confirm that the dilated SegNet model segments the lung with an average Dice coefficient of 0.89 ± 0.23 and the customized CNN model yields a sensitivity of 94.8 on categorizing cancerous and noncancerous nodules. CONCLUSIONS: Thus, the proposed CNN models achieve efficient lung segmentation and two-dimensional nodule patch classification in CAD system for lung cancer diagnosis with CT screening.

In vivo interpretation of model predicted inhibition in acrylate pathway engineered Lactococcus lactis.

To maximize the productivity of engineered metabolic pathway, in silico model is an established means to provide features of enzyme reaction dynamics. In our previous study, Escherichia coli engineered with acrylate pathway yielded low propionic acid titer. To understand the bottleneck behind this low productivity, a kinetic model was developed that incorporates the enzymatic reactions of the acrylate pathway. The resulting model was capable of simulating the fluxes reported under in vitro studies with good agreement, suggesting repression of propionyl-CoA transferase (Pct) by carboxylate metabolites as the main limiting factor for propionate production. Furthermore, the predicted flux control coefficients of the pathway enzymes under steady state conditions revealed that the control of flux is shared between Pct and lactoyl-CoA dehydratase. Increase in lactate concentration showed gradual decrease in flux control coefficients of Pct that in turn confirmed the control exerted by the carboxylate substrate. To interpret these in silico predictions under in vivo system, an organized study was conducted with a lactic acid bacteria strain engineered with acrylate pathway. Analysis reported a decreased product formation rate on attainment of inhibitory titer by suspected metabolites and supported the model.

Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images.

A satellite image transmitted from satellite to the ground station is corrupted by different kinds of noises such as impulse noise, speckle noise and Gaussian noise. The traditional methods of denoising can remove the noise components but cannot preserve the quality of the image and lead to over-blurring of the edges in the image. To overcome these drawbacks, this paper develops an optimized bilateral filter for image denoising and preserving the edges using different nature inspired optimization algorithms which can effectively denoise the image without blurring the edges in the image. Denoising the image using a bilateral filter requires the decision of the control parameters so that the noise is removed and the edge details are preserved. With the help of optimization algorithms such as Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Adaptive Cuckoo Search (ACS), the control parameters in the bilateral filter are decided for optimal performance. It is observed that the proposed Adaptive Cuckoo Search based bilateral filter denoising gives better results in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Feature Similarity Index (FSIM), Entropy and CPU time in comparison to traditional methods such as Median filter and RGB spatial filter.

Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN).

Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC.

Assessment of the Diversity of Fungal Community Composition Associated With Vachellia pachyceras and Its Rhizosphere Soil From Kuwait Desert.

This research examined the general soil fungi and AM fungal communities associated with a Lonely Tree species (Vachellia pachyceras) existing in the Sabah Al-Ahmad Natural Reserve located at the Kuwait desert. The goals of the study were to describe the general fungal and AM fungal communities present in the rhizospheric, non-rhizospheric soils and roots of V. pachyceras, respectively, as well as local and non-local V. pachyceras seedlings when grown under standard nursery growing environments. Soil and root samples were analyzed for an array of characteristics including soil physicochemical composition, and culture-independent method termed PCR-cloning, intermediate variable region of rDNA, the large subunit (LSU) and internal transcribed spacer (ITS) region sequence identifications. The results reveal that the fungal phylotypes were classified in four major fungal phyla namely Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota. The largest assemblage of fungal analyses showed communities dominated by members of the phylum Ascomycota. The assays also revealed a wealth of incertae sedis fungi, mostly affiliated to uncultured fungi from diverse environmental conditions. Striking difference between rhizosphere and bulk soils communities, with more fungal diversities and Operational Taxonomic Units (OTUs) richness associated with both the field and nursery rhizosphere soils. In contrast, a less diverse fungal community was found in the bulk soil samples. The characterization of AM fungi from the root system demonstrated that the most abundant and diversified group belongs to the family Glomeraceae, with the common genus Rhizophagus (5 phylotypes) and another unclassified taxonomic group (5 phylotypes). Despite the harsh climate that prevails in the Kuwait desert, studied roots displayed the existence of considerable number of AM fungal biota. The present work thus provides a baseline of the fungal and mycorrhizal community associated with rhizosphere and non-rhizosphere soils and roots of only surviving V. pachyceras tree from the Kuwaiti desert and seedlings under nursery growing environments.

Evaluating the diversity and composition of bacterial communities associated with Vachellia pachyceras - the only existing native tree species in the Kuwait desert.

We investigated the diversity and composition of bacterial communities in rhizospheric and non-rhizospheric bulk soils as well as root nodule bacterial communities of Vachellia pachyceras - the only native tree species existing in the Kuwait desert. Community fingerprinting comparisons and 16S rDNA sequence identifications were used for characterization of the bacterial population using specific primers. The bacterial characterization of soil samples revealed four major phyla: Acidobacteria, Bacteroidetes, Firmicutes, and Proteobacteria. In situ (desert) samples of both rhizospheric and non-rhizospheric bulk soil were dominated by the bacterial phyla Firmicutes and Bacteroidetes, whereas the phylum Betaproteobacteria was present only in non-rhizospheric bulk soil. Ex situ (nursery growing condition) V. pachyceras resulted in restricted bacterial communities dominated by members of a single phylum, Bacteroidetes. Results indicated that the soil organic matter and rhizospheric environments might drive the bacterial community. Despite harsh climatic conditions, data demonstrated that V. pachyceras roots harbor endophytic bacterial populations. Our findings on bacterial community composition and structure have major significance for evaluating how Kuwait's extreme climatic conditions affect bacterial communities. The baseline data obtained in this study will be useful and assist in formulating strategies in ecological restoration programs, including the application of inoculation technologies.

Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network.

Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process. This paper presents a new Modified Hopfield Neural Network (MHNN) for abnormality classification from human retinal images. It relies on the idea that both weight values and output values can be adjusted simultaneously. The novelty of the proposed method lies in the training algorithm. In conventional method, the weights remain fixed but the weights are changing in the proposed method. Experimental performed on the Lotus Eye Care Hospital containing 540 images collected showed that the proposed MHNN yields an average sensitivity and specificity of 0.99 and accuracy of 99.25%. The proposed MHNN is better than HNN and other neural network approaches for Diabetic Retinopathy Diagnosis from Retinal Images.

Durham Smith Vest-Over-Pant Technique: Simple Procedure for a Complex Problem (Post-Hypospadias Repair Fistula).

OBJECTIVE: Urethrocutaneous fistula, which occurs after hypospadias surgery, is often a baffling problem and its treatment is challenging. The study aimed to evaluate the results of the simple procedure (Durham Smith vest-over-pant technique) for this complex problem (post-hypospadias repair fistula). METHODS: During the period from 2011 to 2015, 20 patients with post-hypospadias repair fistulas underwent Durham Smith repair. Common age group was between 5 and 12 years. Site wise distribution of fistula was coronal 2 (10%), distal penile 7 (35%), mid-penile 7 (35%), and proximal-penile 4 (20%). Out of 20 patients, 15 had fistula of size <5 mm (75%) and 5 patients had fistula of size >5 mm (25%). All cases were repaired with Durham Smith vest-over-pant technique by a single surgeon. In case of multiple fistulas adjacent to each other, all fistulas were joined to form single fistula and repaired. RESULTS: We have successfully repaired all post-hypospadias surgery urethrocutaneous fistulas using the technique described by Durham Smith with 100% success rate. CONCLUSION: Durham Smith vest-over-pant technique is a simple solution for a complex problem (post hypospadias surgery penile fistulas) in properly selected patients.

A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms.

BACKGROUND AND OBJECTIVE: Early detection and diagnosis of breast cancer through mammography screening reduces breast cancer mortality by around 20%. However it is often a complex process to differentiate abnormalities due to the ill-defined margins and subtle appearances. METHOD: This paper investigates a new computer aided approach to detect the abnormalities in the digital mammograms using a Dual Stage Adaptive Thresholding (DuSAT). The suspicious mass region is identified using global histogram and local window thresholding method. The global thresholding is done based on the Histogram Peak Analysis (HPA) of the entire image and the threshold is obtained by maximizing the proposed threshold selection criteria. The local thresholding is carried out for each pixel in a defined neighborhood window that provides precise segmentation results. RESULTS: The algorithm is verified with 300 images in the DDSM database and 170 images in the mini-MIAS database. Experimental results show that the proposed algorithm achieves an average sensitivity of 92.5% with 1.06 FP/image for DDSM database and an average sensitivity of 93.5% with 0.62 FP/image for mini-MIAS database. CONCLUSION: The achieved results depict that the proposed approach provides better results compared to other state-of-art methods for mass detection that helps the radiologists in diagnosis of breast cancer at early stage.

Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm.

Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature.

Biochemical and molecular analysis of Camellia sinensis (L.) O. Kuntze tea from the selected P/11/15 clone.

Green tea is one of the most important beverages consumed across the world and it possesses various phytotherapeutics. Polyphenol oxidase (PPO) activity, total polyphenols, catechins, amino acid content and enzymatic antioxidants are considered to be potential parameters in tea characterization. P/11/15 clone (Camellia sinensis (L) O. Kuntze) was chosen to analyze the biochemical characterization and to analyze the gene expression pattern. The selected P/11/15 clone (C. sinensis (L) O. Kuntze) possess potent Polyphenol oxidase (49.62 U/mg of protein), sufficient catechin (20.75%), Polyphenol (20.01%), Peroxidase (450.08 µM of O2 formed min-1 g-1 dry weight), Catalase (1.20 µM H2O2 reduced min-1 mg-1 protein) and Super Oxide Dismutase (45.11 U/mg proteins). Flavonoid gene expression reveals ANR (1.66%) and F3H (1.02%) were up regulated in the selected P/11/15 clone. The results obtained suggest that P/11/15 clone showed adequate enzyme levels, thus an increased antioxidant activity.

Cone Beam Computed Tomography-Dawn of A New Imaging Modality in Orthodontics.

Today, we are in a world of innovations, and there are various diagnostics aids that help to take a decision regarding treatment in a well-planned way. Cone beam computed tomography (CBCT) has been a vital tool for imaging diagnostic tool in orthodontics. This article reviews case reports during orthodontic treatment and importance of CBCT during the treatment evaluation.

Mammogram segmentation using maximal cell strength updation in cellular automata.

Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.