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Due to complex polyploid, sugarcane whole genome sequencing and characterization lag far behind other crops. PCR-based DNA markers are a viable low-cost option to evaluate genetic diversity and verify genotypes. In this study, the 5S ribosomal RNA-intergenic spacer (ITS) of 171 accessions of Saccharum spp. and Tripidium spp. was dissected, including 30 accessions of S. officinarum, 71 of S. spontaneum, 17 of S. robustum, 25 of S. barberi, 13 of S. sinense, 2 of S. edule, 5 sugarcane cultivars (Saccharum spp. hybrids), 6 of Tripidium spp. (formally Erianthus spp.), and 2 of unknown species. The ITS spacers were amplified from 10 ng of the leaf DNA of each accession with the universal PCR primers PI and PII. The PCR-amplified spacers (amplicons) were analyzed by both agarose gel and capillary electrophoresis (CE). While agarose gel electrophoresis revealed five banding patterns, a total of 42 polymorphic amplicons, ranging from 60 to 506 bp, were detected by CE. Three amplicons, 234-, 235-, and 236-bp in size, were amplified from all accessions of six Saccharum species, except for three S. robustum accessions (Molokai 5573, NG 57-054, and NG 77-235) that lacked the 236-bp amplicon. The 234-, 235-, 236-bp banding pattern found in S. spontaneum was less consistent than other Saccharum species, sometimes missing a few but not all the bands in this region. An amplicon of 61-bp was amplified only from the sugarcane hybrid varieties. The PI/PII patterns indicated diversity and subpopulations within Saccharum, which could potentially be used in Breeding. Moreover, all Saccharum-specific amplicons were mostly absent in Tripidium spp. accessions, which produced 405-bp and 406-bp amplicons, and any pattern of the exceptions indicated misidentification. The T. bengalense accession Kalimpong had a unique CE-banding pattern that was different from all other accessions. Although the clustering pattern of the 42 amplicons only discriminated at the genus level, these amplicons helped identify nine misclassified accessions. This study further demonstrates that these PI/PII amplicons could be particularly useful markers for breeders at sugarcane field stations to quickly confirm and discriminate among the accessions of germplasm collections. Full article

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area. Full article

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017–2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region. Full article

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system. Full article

Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Système Hydrologique Européen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region. Full article

Landslides are among the most harmful natural hazards for human beings. This study aims to delineate landslide hazard zones in the Darjeeling and Kalimpong districts of West Bengal, India using a novel ensemble approach combining the weight-of-evidence (WofE) and support vector machine (SVM) techniques with remote sensing datasets and geographic information systems (GIS). The study area currently faces severe landslide problems, causing fatalities and losses of property. In the present study, the landslide inventory database was prepared using Google Earth imagery, and a field investigation carried out with a global positioning system (GPS). Of the 326 landslides in the inventory, 98 landslides (30%) were used for validation, and 228 landslides (70%) were used for modeling purposes. The landslide conditioning factors of elevation, rainfall, slope, aspect, geomorphology, geology, soil texture, land use/land cover (LULC), normalized differential vegetation index (NDVI), topographic wetness index (TWI), sediment transportation index (STI), stream power index (SPI), and seismic zone maps were used as independent variables in the modeling process. The weight-of-evidence and SVM techniques were ensembled and used to prepare landslide susceptibility maps (LSMs) with the help of remote sensing (RS) data and geographical information systems (GIS). The landslide susceptibility maps (LSMs) were then classified into four classes; namely, low, medium, high, and very high susceptibility to landslide occurrence, using the natural breaks classification methods in the GIS environment. The very high susceptibility zones produced by these ensemble models cover an area of 630 km² (WofE& RBF-SVM), 474 km² (WofE& Linear-SVM), 501km² (WofE& Polynomial-SVM), and 498 km² (WofE& Sigmoid-SVM), respectively, of a total area of 3914 km². The results of our study were validated using the receiver operating characteristic (ROC) curve and quality sum (Qs) methods. The area under the curve (AUC) values of the ensemble WofE& RBF-SVM, WofE & Linear-SVM, WofE & Polynomial-SVM, and WofE & Sigmoid-SVM models are 87%, 90%, 88%, and 85%, respectively, which indicates they are very good models for identifying landslide hazard zones. As per the results of both validation methods, the WofE & Linear-SVM model is more accurate than the other ensemble models. The results obtained from this study using our new ensemble methods can provide proper and significant information to decision-makers and policy planners in the landslide-prone areas of these districts. Full article

Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 hours with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system. Full article

Despite several pharmacological applications of Baccaurea ramiflora Lour., studies on the influence of its polyphenol content on pharmacological activity such as anti-inflammatory properties have been scarce. Here we evaluated in vitro antioxidant activity, poyphenolics by HPLC and the anti-inflammatory potential of the methanolic leaf extract of Baccaurea ramiflora (BME) and its protective effects in carrageenan-induced paw edema model of inflammation in rats. The BME extract contained 79.06 ± 0.03 mg gallic acid equivalent (GAE)/g total polyphenols, 28.80 ± 0.01 mg quercetin equivalent (QE)/g flavonoid and 29.42 ± 0.01 ?g cathechin equivalent/g proanthocyanidin respectively and rosmarinic acid (8 mg/kg) as a main component was identified by HPLC. Results demonstrate that administration of BME at the dose of 200 mg/kg can reduce paw edema by over 63%, and it exhibits a dose-response effect. Depending on concentration, the extract exerted scavenging activity on DPPH radical (IC₅₀ 36.4 ?g/mL), significantly inhibited IL-1? (4.4 pg/mg protein) and TNF-? (0.21 ng/?g protein). Therefore, we conclude BME causes a substantial reduction of inflammation in in vivo models. We propose that rosmarinic acid and similar phenolic compounds may be useful in the therapy of inflammation-related injuries. Full article