Perishing rich, expanding poor: Demography and population genetic patterns in two congeneric butterflies.

In human-altered landscapes, specialist butterflies typically form spatially restricted populations, genetically differentiated due to dispersal restrictions. Generalists, in contrast, display minimum differentiation but high genetic diversity. While local-level actions suffice to conserve specialists and landscape-level actions are necessary for generalists, minimum information exists regarding conservation of species with intermediate features. We targeted two congeneric butterflies, the recently re-expanding Argynnis adippe and the strongly declining A. niobe, co-occurring in the pastoral landscape of the Carpathian Mountains, Czech Republic. We integrated species distribution models, mark-recapture and microsatellite analysis to compare their habitat requirements, adult demography, dispersal and genetic patterns, and expanded the genetic analysis across the Carpathian Arc and beyond to delimit spatial conservation units. In two mountain valleys, both species formed interconnected populations numbering thousands of individuals. Mobility patterns suggested the populations' interconnection across the Czech Carpathians. Genetic diversity was extremely poor in the nonthreatened A. adippe and moderate in the declining A. niobe. No population differentiation was detected within the Czech Carpathians (~1500 km2 ). Low genetic diversity and no differentiation was preserved in A. adippe across East Central Europe, whereas in A. niobe, populations from Serbia were differentiated from the Carpathian Arc + Alps. The high adult mobility linked to low differentiation probably reflects the distribution of larval resources, historically widespread but sparse and currently declining for A. niobe (grazing-disturbed grounds), while currently increasing for A. adippe (abandonment scrub, disturbed woodlands). Units as large as entire mountain systems define population boundaries, and hence conservation management units, for both species.

Inhibition of Fungal Strains Isolated from Cereal Grains via Vapor Phase of Essential Oils.

Fungal contamination in stored food grains is a global concern and affects food economics and human and animal health. It is clear that there is a need to develop new technologies with improved performances that are also eco-friendly in nature. Due to the bioactivity of essential oils (EOs) in the vapor phase, their low toxicity for humans, and their biodegradability and antifungal properties, EOs could be a suitable solution. In this study, we explored the potential of thyme, oregano, lemongrass, clove, and cajeput EOs in the vapor phase. For 17 days, inhibitory activity was assessed against five strains of postharvest pathogens-Aspergillus spp., Fusarium s. l. spp., and Penicilliumochrochloron-isolated from cereal grains. A modified disc volatilization method was used, which is more effective in comparison to traditional screening methods. Three concentrations were tested (250, 125, and 62.5 µL/L). The two highest concentrations resulted in complete inhibition of fungal growth; however, even 62.5 µL/L showed a significant antifungal effect. The efficiency of EOs followed this order: thyme > oregano > lemongrass > clove > cajeput. From our findings, it appears that the use of EOs vapors is a better option not only for laboratory experiments, but for subsequent practice.

Capturing the Geometry of Object Categories from Video Supervision.

We propose an unsupervised method to learn the 3D geometry of object categories by looking around them. Differently from traditional approaches, this method does not require CAD models or manual supervision. Instead, using only video sequences showing object instances from a moving viewpoint, the method learns a deep neural network that can predict several aspects of the 3D geometry of such objects from single images. The network has three components. The first is a Siamese viewpoint factorization network that robustly aligns the input videos and learns to predict the absolute viewpoint of the object from a single image. The second is a depth estimation network that performs monocular depth prediction. The third is a shape completion network that predicts the full 3D shape of the object from the output of the monocular depth prediction module. While the three modules solve very different task, we show that they all benefit significantly from allowing networks to perform probabilistic predictions. This results in a self-assessment mechanism which is crucial for obtaining high quality predictions. Our network achieves state-of-the-art results on viewpoint prediction, depth estimation, and 3D point cloud estimation on public benchmarks.

Manipulating particle trajectories with phase-control in surface acoustic wave microfluidics.

We present a 91 MHz surface acoustic wave resonator with integrated microfluidics that includes a flow focus, an expansion region, and a binning region in order to manipulate particle trajectories. We demonstrate the ability to change the position of the acoustic nodes by varying the electronic phase of one of the transducers relative to the other in a pseudo-static manner. The measurements were performed at room temperature with 3 µm diameter latex beads dispersed in a water-based solution. We demonstrate the dependence of nodal position on pseudo-static phase and show simultaneous control of 9 bead streams with spatial control of -0.058 µm/deg ± 0.001 µm/deg. As a consequence of changing the position of bead streams perpendicular to their flow direction, we also show that the integrated acoustic-microfluidic device can be used to change the trajectory of a bead stream towards a selected bin with an angular control of 0.008 deg/deg ± 0.000(2) deg/deg.

Complex Permittivity of Planar Building Materials Measured With an Ultra-Wideband Free-Field Antenna Measurement System.

Building materials are often incorporated into complex, multilayer macrostructures that are simply not amenable to measurements using coax or waveguide sample holders. In response to this, we developed an ultra-wideband (UWB) free-field measurement system. This measurement system uses a ground-plane-based system and two TEM half-horn antennas to transmit and receive the RF signal. The material samples are placed between the antennas, and reflection and transmission measurements made. Digital signal processing techniques are then applied to minimize environmental and systematic effects. The processed data are compared to a plane-wave model to extract the material properties with optimization software based on genetic algorithms.