Yushania anceps is a temperate woody bamboo taxon of high socio-economic importance occurring in the sub-alpine zone of the western Himalayas. This study was carried out to delineate the potential distribution of Y. anceps in the western Himalayas through species distribution modelling (SDM), and genetic characterization using sequence-tagged microsatellite (STMS) markers. The present study revealed an endemic distribution of this species in the Uttarakhand Himalayas, with an estimated area of 211.59 km2. The maximum probability of occurrence was recorded in the moderately dense forests between the altitudinal ranges of 2500 and 2700 m. The model output was well supported with high values of different statistical measures, such as the AUC (0.911) and Kappa coefficient (K = 0.513). Environmental variables related to precipitation, temperature and topography were identified as the most contributory in current SDM. In addition, diversity measures, namely allelic richness (Ar), expected heterozygosity (He), and fixation index (FST), were calculated in five sampled populations with eight STMS markers, which indicated high genetic diversity (Ar = 4.24; He = 0.689) and little differentiation (FST = 0.062). The diversity maps displayed that the populations located in the Kumaon region captured relatively more genetic diversity than the Garhwal region. Further, genetic clustering and STRUCTURE analysis revealed a substantial level of genetic admixing across the analysed populations, and as a result, no sub-structuring was detected. Due to the rare and endemic distribution of Y. anceps, it requires immediate conservation measures, and the knowledge base generated here will be of paramount importance to forest managers, researchers and policymakers.

Nanowire-based nanocomposite materials are being developed as transparent and flexible electrodes or as stretchable conductors and dielectrics for biosensing. Here, we theoretically investigate the use of scanning dielectric microscopy (SDM) to characterize these materials in a nondestructive way, with a special focus on the achievable spatial resolution and the possibility of detection of the capacitive coupling between nearby nanowires. Numerical calculations with models involving single and multiple buried nanowires have been performed. We demonstrate that the capacitance gradient spread function of a single buried nanowire consists of a modified Lorenzianan with a cubic decay. We show that the achievable spatial resolution can be determined with good accuracy with the help of this spread function. It is shown that, in general, the spatial resolution worsens when any system parameter decreases the maximum of the nanowire spread function or increases its width, or both. Finally, we show that SDM measurements are also sensitive to the capacitive coupling between nearby nanowires. This latter result is of utmost relevance since the macroscopic electric properties of nanowire nanocomposites largely depend on the electric interaction between nearby nanowires. The present results show that SDM can be a valuable nondestructive subsurface characterization technique for nanowire nanocomposite materials.

Fuscopannaria leucosticta is a rare and understudied cyanolichen with an interesting and unusual distribution in tertiary relict hotspots worldwide. There is a relatively large population in eastern North America, where it occurs mostly throughout the Appalachian Mountains and reaches its northernmost extent in New Brunswick and Nova Scotia, Canada. The ability to detect this species, and thus determine its habitat requirements, is critical for understanding how it might be affected by human-induced environmental degradation. Maximum entropy modelling with MaxEnt was used to predict the distribution of suitable habitat for this species in Nova Scotia using 62 presence locations, 1405 pseudo-absence locations and four environmental covariates: depth to water table (a proxy for relative soil moisture), distance to the coast and mean annual temperature and precipitation. Our predictive maps identify important habitat features and areas of high suitability in Nova Scotia with an area under the curve value of 0·85. The predicted distribution of this lichen was most affected by temperature. This study elucidates locations as well as species-habitat relationships for F. leucosticta, providing land managers with baseline data that can aid in the discovery of additional populations and provide a better understanding of its ecological requirements which will support the development of sound conservation strategies for this rare lichen.

Cassava (Manihot esculenta Crantz) is one of the most important staple crops for small farmers in the tropics, feeding about 800 million people worldwide. It is currently cultivated in South and Central America, Africa and Asia. The genus Vatiga is widespread throughout the Neotropical region. Its species are sympatric and feed exclusively on cassava. The main objectives of this paper are: (1) to assess the potential distribution of Vatiga, one of the most relevant pests of cassava; (2) to project the resulting models onto the world; (3) to recognize areas with suitable and optimal climates (and thus, high probability) for future colonization, and (4) to compare this model with the harvested area of cassava analyzing the climatic variables required by both the host and the pest species. Species distribution models were built using Maxent (v3.3.3k) with bioclimatic variables from the WorldClim database in 2.5 arc min resolution across the globe. Our model shows that Vatiga has the potential to expand its current distribution into other suitable areas, and could invade other regions where cassava is already cultivated, e.g., Central Africa and Asia. Considering the results and the high host specificity of Vatiga, its recent appearance in Réunion Island (Africa) poses a serious threat, as nearby areas are potentially suitable for invasion and could serve as dispersal routes enabling Vatiga to reach the continent. The present work may help prevention or early detection of Vatiga spp. in areas where cassava is grown.

Robot navigation is a large area of research, where many different approaches have already been tried, including navigation based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high-dimensional binary spaces. It exhibits characteristics, such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach followed here was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store, adjusting memory parameters to the level of noise and inferring new paths from the learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. This paper reports novel results of the limits of the model under different typical navigation problems. The SDM showed to be very robust to illumination and scenario changes, occlusion and saturation. An algorithm to build a topological map of the environment based on the visual memories is also described.