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As habitats change, species with higher intraspecific variation have more resources to adapt. Medicinal plants in the Himalayas are increasingly threatened by climate change and other anthropogenic influences. The intraspecific variation within and among 17 populations of the high-elevation herb Trillium govanianum Wall. ex D.Don was studied as an indicator of adaptability. The variation in 19 traits of population structure, morphology, and phytochemistry was assessed across habitats that varied in elevation (2452–3432 m a.s.l.), aspect, latitude (30.1–31.7°N), and arboreal community. The morphology and population structure were conserved among populations but varied among regions. The populations in the lower elevation mixed forests of Tirthan Valley produced smaller rhizomes but larger plant densities, such that plant biomass per square metre was conserved. The phytochemistry varied among regions and populations within regions, indicating significant variation among habitats. The aboveground morphology of the species masks considerable variations in belowground morphology and phytochemistry. The observed variations can help the species to adapt to the changing environmental conditions by provoking a functional response.

The commercial demand for Nardostachys jatamansi in the global market has raised concern about its long-term sustainability. The genus Nardostachys is represented by the single species (Nardostachys jatamansi) endemic to the Himalayas. This study reviews biology, uses, threats, knowledge gaps, and conservation concerns, and offers suggestions for its sustainable utilization. Findings indicate that only limited data are available on the biology of the species, principally on population distribution and size, reproductive biology, and ecological niche models. The uses of the species are well documented in traditional as well as modern medicine systems. Overharvesting from the wild to meet global demand is a major threat to existing populations. Several other characteristics, such as the species’ slow growing nature, preference for specific habitat, low population density, and poorly developed ex situ propagation protocols, pose threats to its survival in the foreseeable future. Sustainable utilization and effective conservation measures are urgently needed for both the species’ survival and to ensure it continues to provide community income in the Himalayas.

Water Quality (WQ) modeling and forecasting are very challenging for water management bodies due to the complex and nonlinear relationship between the parameters responsible for determining water quality. The main focus of this paper is the water quality prediction of the Ganges River by analyzing the impact of one of the critical configuration parameters of a neural network known as the learning rate. The proposed prediction model based on an artificial neural network (ANN) consists of different sets of experiments performed by comparing twelve different training functions against the variation in learning rates. A total of 360 experiments have been conducted on the dataset collected over the period 2001 to 2015 with five stations along the Ganges River in the state of Uttarakhand, India. All experiments have been conducted in MATLAB software. The ANN-based program is written in Matlab’s NN-Toolbox. As input parameters, we have used temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and total coliform. The water quality standard set by the Central Pollution Control Board of India has been used. The performance of the developed model has been calculated based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE). Trail training function-based artificial neural network models indicate higher predictive accuracy when compared to other models developed using the remaining eleven training functions when the learning rate is set to 0.04. In conclusion, ANN has the ability to efficiently predict the water quality of rivers and the learning rate has a greater impact on the development of such predictive models. So, it is required to be tuned very carefully.

Aesculus indica Colebr. is a monecious, deciduous woody tree species of the temperate regions of the Himalaya. It is cultivated for its commercial use in agro-forestry practices. The present research on pollination biology and conservation of Aesculus indica growing in Kumaon Himalaya was carried out in the temperate forest ecosystem of Lohaghat, Uttarakhand, India. The field experiments were conducted on five randomly selected populations of the study plant during April 2017 to September 2017 to quantify the diversity and foraging behavior of insect visitors and their influence on fruiting success of the plant. Of the total 18 species of insects recorded as floral visitors, 77.78% of the total species belonged to the order Lepidoptera while the rest belonged to the order Hymenoptera. Butterflies foraged the flowers maximum during 10.00–13.00 h, whereas bees were more active during morning times (07.00–10.00 h). The flower handling time and foraging rate as the parameters of foraging behavior varied among insect floral visitors. Butterflies were recorded as the most frequent floral visitors accounting 62.22% of the total floral visits. Experimentally, it was found that Aesculus indica is an ambophilous plant i.e., relying on both wind and insects for its reproductive success, and exhibits psychophilous and melitophilous syndromes for pollination. The overall results on pollination mechanism of the plant would be useful in developing conservation measures for environmental stability in the face of global climate change.

Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K -means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.

Trillium has fascinated the botanical world with its peculiar biology and high medicinal value. The genus appears in North America and Asia, and is widely used for traditional medicine on both continents. Medicinal value has been validated recently through the isolation and ex situ testing of constituent compounds. This study reviews biology, medicinal value, ecological threats and knowledge gaps, and offers suggestions for conservation and sustainable utilization. Findings indicate that the biology of the genus is well explored and the reproductive constraints are well recognized. Steroidal compounds and pharmaceutical potential of the genus are reported, but controlled clinical trials on humans are yet to be conducted. High medicinal value and the peculiar biology have made the genus susceptible to several threats.

Dactylorhiza hatagirea (D. Don) Soo (Orchidaceae) is a critically endangered high-value medicinal plant collected from wild and extensively used against diabetes, cuts/wound healing, gastric disorders, chronic fever, seminal weakness/dysfunctions, and bone fractures. The tuber of the species yields high quality “Salep” having astringent and aphrodisiac properties, with high economic value. This results in high demand and illegal collection of the species from wild, thus making this species one of the most exploited species in the Indian Himalaya. The populations of D. hatagirea are restricted to small isolated pockets and are subject to destructive harvesting, therefore, need urgent in situ as well as ex situ conservation interventions. In view of this, the present study is an attempt to understand population status, its distribution, threats, and vulnerability of the species. A total of 14 sites in different habitat types along the altitudinal gradient (2500–4500 m) were selected for intensive field study in the Indian Western Himalaya. The results of the study reveal that D. hatagirea prefers moist laden undulating habitats in alpine meadows. The population density of the species ranges between 0.70 and 2.43 individuals/m2 in alpine meadows of the studied sites. Population density and species abundance increased significantly along the altitude within its range of occurrence. The relationship between altitude and total density was curvilinear and significant (r2=0.666, p 0.247) between slope and total density. The results of the study also indicate that the vegetation in alpine ecosystem were more diverse in species composition at mid-altitudes (3400 to 3700 m) than high altitudes. Destructive harvesting of underground tuber and irregular grazing were observed as critical threats to the species. D. hatagirea share habitats with few expanding species (i.e., Polygonum polystachyum) and highly traded species (i.e., Ophiocordyceps sinensis) in the alpine meadows, which affects its population density. The study concluded that short life cycle, poor seed germination rate, specific microhabitat requirement, low population density, and high commercial value limit the widespread distribution of the species in the region. The findings of the study suggest an urgent need for developing conservation and management plan for the species in the region for sustaining traditional health care system and pharmaceutical demand.

Trillium govanianum is an endemic medicinal herb of the Himalayan region. Recently explored active constituents and their pharmacological activities from the species are of prime importance. Its range of distribution is highly specific in Indian Himalayas. Collection of its rhizome has become one of the significant commercial activity in the Indian Himalayas. However, the collection of the species is unsustainable. Understanding the socio-ecological dynamics of a species and carving out the prospects for its sustainable use is a difficult task in the Himalayas. Effective conservation strategies are needed to conserve the species while maintaining community incomes. The present chapter provides an overview of the biology, uses and conservation approaches that can be followed for the sustainable utilization of T. govanianum in Indian Himalayas.

Dactylorhiza hatagirea, an important source of ‘Salep,’ is a highly valued but underutilized medicinal plant of the Himalayan region. Health benefits of the species are associated with the presence of natural antioxidant that alleviates glycemic activity and oxidative stress. The present study aimed to optimize and propose an effective ultrasound-assisted extraction (UAE) method to extract glycoside (Dactylorhin E) and other polyphenol compounds using response surface methodology (RSM). Under optimal extraction conditions, total phenolics (11.42 ± 0.48 mg GAE/g DW), flavonoids content (11.46 ± 0.28 mg QE/g DW), and antioxidant activity, i.e., DPPH (1.37 ± 0.008 mM AAE/g DW) and ABTS (1.06 ± 0.008 mM AAE/g DW) and anti-glycemic, i.e., alpha-amylase (42.81 ± 0.22 % inhibition) and alpha-glucosidase (27.97 ± 0.40 % inhibition) inhibition activity were found near the model predicted values. HPLC-PDA recorded six bioactive compounds in UAE, of which Dactylorhin E (14.14 mg/g DW) and o-coumaric (3.22 mg/g DW) were most prominent. This study prepared optimized glycoside, polyphenol-rich extracts aimed to be used for the formulation of foodstuffs and phytomedicines. Furthermore, the method is reliable, simple, and an alternative to various other extraction techniques.

Predicting the water quality of rivers has attracted a lot of researchers all around the globe. A precise prediction of river water quality may benefit the water management bodies. However, due to the complex relationship existing among various factors, the prediction is a challenging job. Here, the authors attempted to develop a model for forecasting or predicting the water quality of the river Ganga using application of predictive intelligence based on machine learning approach called support vector machine (SVM). The monthly data sets of five water quality parameters from 2001 to 2015 were taken from five sampling stations from Devprayag to Roorkee in the Uttarakhand state of India. The experiments are conducted in Python 2.7.13 language (Anaconda2 4.3.1) using the radial basis function (RBF) as a kernel for developing the non-linear SVM-based classifier as a model for water quality prediction. The results indicated a prediction performance of 96.66% for best parameter combination which proved the significance of predictive intelligence in water quality forecasting.

Trillium govanianum Wall ex D. Don (Melanthiaceae) is a perennial rhizomatous herb endemic to the Himalayas. The species has high commercial demand and is threatened in the Indian Himalayas. The potential of T. govanianum for ex situ regeneration (vegetative and in vitro propagation) was investigated. For vegetative propagation, whole rhizomes with incisions at apical portion, and apical, middle and distal sections of rhizomes were used. In vitro regeneration potential of the species was tested by using roots (R), rhizomes (RH), leaves (L), stems (S), shoot tips (ST) and complete shoot buds (CSB) as explants in MS medium/modified MS medium. The apical portions of the rhizomes were found capable of developing roots and buds. Middle and distal portion of the rhizomes did not respond; they were either rotted or did not show any growth. Multiple buds (2–4) and roots (2–5 each bud) were induced in the rhizomes given incisions at the apical portion. Of the different explants type tested for in vitro propagation; only ST and CSB were found capable of developing shoots and callus-like structures. The CSB (obtained from vegetative propagation) cultured on different media types developed complete plantlets. The maximum leaf area was recorded from the CSB cultured on ½ strength MS medium supplemented with 60 g/l sucrose, 1 % activated charcoal and BA (2.5 ppm). The present study offers first reliable propagation protocol of the species whereby two means of propagation i.e. vegetative propagation and tissue culture complement each other for the generation of a simple protocol.

Recently, Water Quality (WQ) comes out to be the central point of concern all around the globe. The purpose of this work is to develop an automated procedure that can be used to classify the water quality of the River Ganga proficiently in the stretch from Devprayag to Roorkee Uttarakhand, India. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and total coliform (TC) for the time period from 2001 to 2015 is used for this research work. The proposed method involves developing various water quality classification models using one of the concept of data mining called decision tree (DT) for evaluating the WQ classes. The experiments are conducted using Weka data mining tool. Models first developed using (60–40)% data division approach and then using (80–20)% approach of data division. Five different decision tree models are developed named J48 (C4.5), Random Forest, Random Tree, LMT (logistic model tree) and Hoeffding Tree. These classifiers were analyzed to determine the most accurate classifier model for the present dataset by evaluating their performance via measures like Accuracy, Kappa Statistics, Recall, Precision, F-Measure, Mean absolute error and Root mean squared error. The results concluded that the random forest model outperforms all other classifiers with a great accuracy rate of 100% in both approaches and least error rate when developed using the second approach. Such a highly acceptable and attractive results may be helpful for the decision makers in water management and planning.

This is the first report of somatic embryogenesis and synthetic seed production in Angelica glauca, a valuable medicinal plant of Himalaya. Somatic embryogenesis protocol was developed using leaf explants from newly sprouted rhizomes. Mature leaf explants were inoculated in Murishige and Skoog (MS) medium supplemented with 3 µM 2,4-dichlorophenoxyacetic acid (2,4-D) induced 86% callus. Calli (0.5 g) subcultured into different concentration of 1-napthaleneacetic (NAA) and 6- benzylaminopurine (BA) either alone or in combination produced globular structure and their differentiation. NAA (2 µM) in combination of BA (2 µM) germinated 2.2 shoots per culture in 8 weeks time. Plantlets when transferred into sand, soil and peat moss (1:1:1) ratio were found suitable for acclimatization and 75% plantlets survived. Somatic embryos were put into a combination of 3% (w/v) sodium alginate and 100 µM calcium nitrate for 30 min for protecting the somatic embryos and the production of synthetic seeds. Experiments on the evaluation of synthetic seeds are under progress. Key words: Himalaya, Angelica glauca, somatic embryogenesis, synthetic seed.

The Ganga River is the national river of India and recently got the right of living entity. It is necessary to monitor its water quality frequently. But monitoring and assessment of a river WQ is a provocative task in ecological management. In this study, soft computing based one of the computation technique called Artificial Neural Network (ANN) is used to developpredictionor forecasting modelfor estimating the WQ of the river Ganga in the stretch from Devprayag to Roorkee, Uttarakhand, India. Five sampling stations along this river stretch areselected. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen, biochemical oxygen demand and total coliform is usedfrom year 2001 to 2015. In developing the ANN model for the WQ prediction, the feed forward error back propagation method is used to develop predictionmodel for conducting various experimental investigationsfollowing many training parameters by adopting the neural network configurationof 5-10-1. The prediction performance of the developed model is evaluated using one of the statistical means named mean square error. The predicted WQ model resulted in a higher accuracy by producing best MSEvalue of 0.041 when using trainlm(Levenberg-Marquardt backpropagation)as a training function. The present study concluded that the ANNs are capable of predicting WQ of the river Ganga with acceptable accuracy of 95.9%.

Ethnobotanical relevance Trillium govanianum Wall. ex D. Don (Melanthiaceae) is valued as a traditional medicinal herb in the Himalayan region. Applications include treatment of cancer, hypertension, neurasthenia, giddiness, arthritis, dysentery, inflammation, sepsis and reproductive disorders. Its range is highly specific, and limited to cold, shaded and moist habitats at 2400-3500 m a.s.l. Rhizomes are gathered from wild populations for trade, and this has recently emerged as a significant source of income among indigenous people of the Indian Himalayan region. Aim To assess the impact of changing rhizome prices on T. govanianum in the Indian Himalayan region by observing the status of existing populations and assessing the typical methods used for gathering and trade of rhizomes. Material and methods Ecological attributes were assessed in 17 sites from Tirthan Valley (Himachal Pradesh), Munsiyari and Tunghnath (Uttarakhand). Socio-economic attributes assessed through communal focus groups and interviews with 579 medicinal plants gatherers and 19 traders, using semi-structured open ended questionnaires. Results Population decline was highest in Munsiyari, followed by Tirthan Valley and Tunghnath, indicating that the species may become locally extinct in some areas. Methods used for gathering did not consider regeneration. Conclusion Gathering of T. govanianum from wild populations is unselective and unmanaged, raising the potential threat of local extinctions. Sustainable utilization and effective conservation is needed to protect the species and maintain community incomes. A status of ‘threatened’ is justified for the species in the Indian Himalayan region, due to its slow life cycle, specific habitat requirement, low population density, and commercial value.

Axenic cultures of two moss taxa viz. Bryum dichotomum Hedw., an acrocarpous moss and Entodon macropodus (Hedw.) Mull. Hal., a pleurocarpous moss were established by inoculating their spores into agar media. Germination, growth, development and comparative morphogenetic studies of these two growth forms were done. In B. dichotomum, protonemal buds developed on caulonema, while in E. macropodus, buds were produced on chloronema. Sub culturing of B. dichotomum gave rise to new individuals through chloronemal and caulonemal stage, while E. macropodus it directly regenerated into new gametophytes. In old culture under low moisture conditions, asexual diaspores namely, protonemal bulbils B. dichotomum and protonemal brood cells E. macropodus were formed.

Angelica glauca is one of the important medicinal plants and it is widely used by indigenous communities for different purposes. The present study analyzes variability in reproductive characters of A. glauca. The reproductive parts were found having significant positive correlation with altitude (e.g., number of umbellets /umber r= 0.857, p

SummaryFor the first time, an in vitro regeneration system for ‘Green Sweet’ apple has been developed from nucellus-raised callus. Callus induction (100%) occurred in nucellar explants excised from immature fruit when cultivated on Murashige and Skoog (MS) basal medium, free of plant growth regulators (PGR) and supplemented with 3.0% (w/v) sucrose and 0.8% (w/v) agar. When cultured on MS basal medium supplemented with 10.0 µM benzyladenine (BA) and 1.0 µM gibberellic acid (GA3), calli showed 88.9 ± 5.6% shoot regeneration with 4.1 ± 0.1 shoots per explant, and a mean shoot length of 3.5 ± 0.2 cm. Elongated shoots produced roots (44.4 ± 5.6%) when placed in half-strength MS basal medium supplemented with 1.0 µM indole-3-butyric acid (IBA) and 1.0 µM -naphthaleneacetic acid (NAA). Maximum rooting percentage (55.6 ± 5.6%) was obtained on 0.4% (w/v) agar, while the highest number of roots per shoot (2.8 ± 0.4 roots) and root length (3.4 ± 0.2 cm) were recorded on 0.6% (w/v) agar. Acclimatisation of in vitrora...

Himalayan medicinal plants are at risk of over-exploitation. Many medicinal plants of the region are being exploited unsustainably to meet local as well as global demands. Hence, conservation initiatives must be formulated after gathering information on availability and the type and extent of pressure on a particular species and these must be supported by simple propagation methods. The present paper analyses the status of Angelica glauca Edgew. in two centres of Western Himalaya. The availability of the species in the wild is low and it is subject to severe threat largely from overgrazing and anthropogenic pressure. Because of the influence of traders from outside, local inhabitants are losing interest in traditional conservation mechanisms. The existing scenario shows that natural populations of A. glauca need to be preserved. One option for promoting this is seed propagation to offset existing trends and encourage cultivation. Addition of indole-acetic acid (IAA) was found to be effective in improving ...

By means of recent advancements in the wireless networking area a totally new paradigm came into existence in the domain of wireless communication well known as Vehicular Ad-Hoc Network (VANET) employed specially for providing road safety, comfort and convenience to the driver as well as passengers during their journey. A VANET is a type of highly dynamic wireless network that can be formed without the need for any pre-existing infrastructure which aims to improve the transportation system by integrating sensors, wireless networks, GPS, 2G and 3G technologies with the ad-hoc networks. Due to higher mobility of nodes (vehicles), routing becomes the most challenging task in VANETs. A variety of research has been done on routing and several protocols have been proposed with their implementation. In this paper, we evaluate the efficiency of two routing protocols one is proactive (Table-driven) OLSR protocol and another reactive (On-demand) AOMDV by comparing their performances in the city scenario for the map of city of Arlington, Texas using NS-2. The realistic vehicular mobility traces are generated using Intelligent Driver Model (IDM) based tool VanetMobiSim.