Acoustic detection of stem and root borer Neoplocaederus ferrugineus (Coleoptera: Cerambycidae) in cashew

Stem and root borer Neoplocaederus ferrugineus (Linnaeus) (Coleoptera: Cerambycidae) is a severe pest of cashew (Anacardium occidentale L), which affects the cashew tree heavily. The larvae are tissue borers that remain concealed inside the tree and are very difficult to detect at the initial stage of infestation. Adult attractants are not identified, and manual checking of each tree in a plantation is not feasible and laborious. Acoustic technologies have been developed for the non-invasive and early detection of wood borers. The present study was conducted to characterize the acoustic sounds of various larval instars, tissues and field evaluation of the commercially available acoustic device. Burst analysis of different larval instars in cashew logs shows that there are no significant differences between the number of bursts and various larval instars. A relatively less number of bursts are found when the larvae are in the sapwood region than the cambium region. The empty cashew log and healthy trees do not produce any significant noise. The number of burst counts and impulses are used to predict the likelihood of infestation. The detection performance of the acoustic device under field conditions shows that infested trees are correctly detected with 91% accuracy. The possibility of not detecting healthy trees is 85%. This is the first report on the acoustic behaviour of cashew stem and root borer. The results of the study will help in better understanding the acoustic behaviour of the hidden pest and also be useful in the timely identification of stem and root borer-affected trees in cashew plantations.     

NDVI is not reliable as a surrogate of forage abundance for a large herbivore in tropical forest habitat

Remotely sensed vegetation indices are increasingly being used in wildlife studies but field‐based support for their utility as a measure of forage availability comes largely from open‐canopy habitats. We assessed whether normalized difference vegetation index (NDVI) represents forage availability for Asian elephants in a southern Indian tropical forest. We found that the number of food species was a small percentage of all plant species. NDVI was not a good measure of food abundance in any vegetation category partly because of (a) small to moderate proportional abundances of food species relative to the total abundance of all species in that category (herbs and shrubs), (b) abundant overstory vegetation resulting in low correlations between NDVI and food abundance, despite a high proportional abundance of food species and a concordance between total abundance and food species abundance (graminoids), and (c) the relevant variables measured and important as food at the ground level (count and GBH) not being related to primary productivity (trees and recruits). NDVI had a negative relationship with the total abundance of graminoids, which represent a bulk of elephant and other herbivore diet, because of negative interaction with other vegetation and canopy cover that positively explained NDVI. Spatially interpolated total graminoid abundance modeled from field data outperformed NDVI in predicting total graminoid abundance, although interpolation models of food graminoid abundance were not satisfactory. Our results reject the utility of NDVI in mapping elephant forage abundance in tropical forests, a finding that has implications for studies of other herbivores also. Abstract in Kannada is available with online material.     

Diversity of settlement-stage reef fishes captured by light-trap in a tropical south-west Atlantic Ocean coastal reef system

This study reports the results of 5 years of monitoring reef fish post-larvae using light traps in the Bay of Tamandaré, north-east Brazil. An annotated checklist of pre-settlement fish species, their frequency of occurrence and taxonomic characteristics are provided. In total, 4,422 post-larval fishes belonging to 36 families, 56 genera and 76 species were captured. The most species-rich families were Carangidae (7), Lutjanidae (6) and Pomacentridae (4), while the families Gerreidae (30.47%), Holocentridae (16.54%), Blenniidae (12.01%), Labrisomidae (8.36%), Lutjanidae (8.29%) and Acanthuridae (5.95%) were the most abundant. This is the first study of the taxonomic diversity and assemblage structure of settlement-stage reef fishes in the tropical south-west Atlantic Ocean. Although a few common species were not captured due to selectivity of light traps, the composition and taxonomic diversity of this first collection suggests that light traps are useful for studies of the early life history of a wide range of pre-settlement reef fishes.     

Exploring the protein–protein interaction landscape in plants

Protein–protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.     

The use of natural language to communicate the perception of vibrotactile stimuli

This study explores the subjective use of adjectives to verbally communicate vibrotactile stimulation across multiple frequencies. In total, nine different vibrotactile stimulus frequencies (10–300?Hz) were utilized, and subjective evaluation methods, which involved adjectives, were used to assess the sensory representations of the participants (18 healthy male participants; mean age, 22.9 years; standard deviation, 3.5). Sensory terms such as ‘slow,’ ‘protruding,’ and ‘thick’ were used as representative expressions to describe low-frequency (10–100?Hz) vibrotactile stimulations, while ‘fast,’ ‘shallow,’ and ‘tickly’ were used to describe high-frequency (225–300?Hz) vibrotactile stimulations. At the frequencies of 150 and 200?Hz, no characteristic word was found because there was no difference in subjective evaluation scores from other low or high frequencies. The results suggest that vibrotactile stimulation at different frequencies induce diverse sensory representations, owing to not only the motion and shape of the stimuli but also the subjective responses of the perceivers. The results of this study could be utilized in developing affective haptic devices in the future.     

Investigation of deleterious effects of nsSNPs in the POT1 gene: a structural genomics-based approach to understand the mechanism of cancer development

Protection of telomere 1 (POT1) is one of the key components of shelterin complex, implicated in maintaining the telomere homeostasis, and thus stability of the eukaryotic genome. A large number of non-synonymous single nucleotide polymorphisms (nsSNPs) in the POT1 gene have been reported to cause varieties of human diseases, including cancer. In recent years, a number of mutations in POT1 has been markedly increased, and interpreting the effect of these large numbers of mutations to understand the mechanism of associated diseases seems impossible using experimental approaches. Herein, we employ varieties of computational methods such as PROVEAN, PolyPhen-2, SIFT, PoPMuSiC, SDM2, STRUM, and MAESTRO to identify the effects of 387 nsSNPs on the structure and function of POT1 protein. We have identified about 183 nsSNPs as deleterious and termed them as “high-confidence nsSNPs.” Distribution of these high-confidence nsSNPs demonstrates that the mutation in oligonucleotide binding domain 1 is highly deleterious (one in every three nsSNPs), and high-confidence nsSNPs show a strong correlation with residue conservation. The structure analysis provides a detailed insights into the structural changes occurred in consequence of conserved mutations which lead to the cancer progression. This study, for the first time, offers a newer prospective on the role of POT1 mutations on the structure, function, and their relation to associated diseases.     

Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.