Avian Bornaviruses in Wild Aquatic Birds of the Anseriformes Order in Poland

Bornaviruses are a diverse family of viruses infecting various hosts, including birds. Aquatic bird bornavirus 1 (ABBV-1) and aquatic bird bornavirus 2 (ABBV-2) have been found in wild waterfowl but data on their prevalence are scarce. To gain knowledge on the occurrence of ABBVs in Poland, samples originating from dead birds of the Anseriformes order collected in 2016–2021 were tested with a real time RT-PCR method targeting the ABBVs genome. A total of 514 birds were examined, including 401 swans, 96 ducks and 17 geese. The presence of ABBV-1 RNA was detected in 52 swans (10.1% of all tested birds) from 40 different locations. No positive results were obtained for ducks and geese. Sequences of about 2300 bases were generated for 18 viruses and phylogenetic analysis was performed. A relatively low genetic diversity of the examined ABBV-1 strains was observed as all were gathered in a single cluster in the phylogenetic tree and the minimum nucleotide identity was 99.14%. The Polish strains were closely related to ABBV-1 identified previously in Denmark and Germany, but a limited number of sequences from Europe hinders the drawing of conclusions about interconnections between Polish and other European ABBVs. The results of the present study provide new insights into the distribution and genetic characteristics of ABBVs in wild birds in Europe.

Molecular Dissection for Boll Shedding in Upland Cotton Using Microsatellites

DNA markers application in marker-assisted breeding of cotton is handicapped due to low genetic diversity in cotton germplasm. The present study was designed to identify DNA markers, predominately simple sequence repeats (SSRs), associated with tolerance/resistance to heat stress as a consequence of boll shedding. To find out the genetic diversity a total of 24 cotton genotypes and 50 SSR primers were used. Total 288 alleles were produced with an average of 5.7 alleles per primer. Bootstrap cluster analysis used to generate a dendrogram that cluster the 24 accessions into two main clusters. Eleven out of 24 genotypes fall in a single cluster. Phenotypically H-4074 gives more diversity, while genotypically H-4074 sheared the same genetic background as H-4070, H-4091 and H-4090. Low genetic diversity was observed among both genotypic and phenotypic as maximum varieties fall in single group. This study helps for selecting diverse accessions with multiple phenotypic traits, which were drought to boll shedding. It suggests further elaborating the molecular genetic diversity by using new SSR marker to improve the yield of cotton cultivars. These preliminary results set the stage for initiating in depth marker-trait association studies, which will be instrumental for initiating marker-assisted breeding in cotton.

Subnanometric Cu Clusters on Atomically Fe-doped MoO2 for Furfural Upgrading to Aviation Biofuels

Single cluster catalysts (SCCs) are considered as versatile boosters in heterogeneous catalysis due to their modifiable single cluster sites and supports. In this work, we report subnanometric Cu clusters dispersed on Fe-doped MoO2 support for biomass-derived furfural upgrading. Systematical characterizations suggest uniform Cu clusters (composing four Cu atoms in average) are homogeneously immobilized on the atomically Fe-doped ultrafine MoO2 nanocrystals (Cu4/Fe0.3Mo0.7O2@C). The atomic doping of Fe into MoO2 leads to significantly modified electronic structure and consequently charge redistribution inside the supported Cu clusters. The as-prepared Cu4/Fe0.3Mo0.7O2@C shows superior catalytic performance in the oxidative coupling of furfural with C3~C10 primary/secondary alcohols to produce C8~C15 aldehydes/ketones (aviation biofuel intermediates), outperforming the conventionally prepared counterparts. DFT calculations and control experiments are further carried out to interpret the structural and compositional merits of Cu4/Fe0.3Mo0.7O2@C in the oxidative coupling reaction, and elucidate the reaction pathway and related intermediates.

Universality of noise-induced resilience restoration in spatially-extended ecological systems

Many systems may switch to an undesired state due to internal failures or external perturbations, of which critical transitions toward degraded ecosystem states are prominent examples. Resilience restoration focuses on the ability of spatially-extended systems and the required time to recover to their desired states under stochastic environmental conditions. The difficulty is rooted in the lack of mathematical tools to analyze systems with high dimensionality, nonlinearity, and stochastic effects. Here we show that nucleation theory can be employed to advance resilience restoration in spatially-embedded ecological systems. We find that systems may exhibit single-cluster or multi-cluster phases depending on their sizes and noise strengths. We also discover a scaling law governing the restoration time for arbitrary system sizes and noise strengths in two-dimensional systems. This approach is not limited to ecosystems and has applications in various dynamical systems, from biology to infrastructural systems.

Stellar Spins in the Pleiades, Praesepe, and M35 Open Clusters

We analyze spectroscopic and photometric data to determine the projected inclinations of stars in three open clusters: the Pleiades, Praesepe, and M35. We determine the sin i values of 42, 35, and 67 stars in each cluster, respectively, and from their distributions we find that isotropic spins and moderate alignment are both consistent with the Pleiades and Praesepe data. While it is difficult to distinguish between these scenarios for a single cluster, an ensemble of such distributions may facilitate a distinction. The M35 inclination distribution is most consistent with a superposition of isotropic and anisotropic spins, the source of which could be systematic error or a physical grouping of aligned stars. We also study internal cluster kinematics using radial velocities and proper motions. Our kinematics analysis reveals significant plane-of-sky rotation in Praesepe, with a mean velocity of 0.132 ± 0.022 km s−1 in a clockwise direction.

Comparative genomics of Nocardia seriolae reveals recent importation and subsequent widespread dissemination in mariculture farms in South Central Coast, Vietnam

Between 2010 and 2015, nocardiosis outbreaks caused by Nocardia seriolae affected many permit farms throughout Vietnam, causing mass fish mortalities. To understand the biology, origin, and epidemiology of these outbreaks, 20 N. seriolae strains collected from farms in four provinces in the South-Central Coast of Vietnam, along with two Taiwanese strains, were analysed using genetics and genomics. Pulsed-field gel electrophoresis identified a single cluster amongst all Vietnamese strains that was distinct from the Taiwanese strains. Like the PFGE findings, phylogenomic and single-nucleotide polymorphism (SNP) genotyping analyses revealed that all Vietnamese N. seriolae strains belonged to a single, unique clade. Strains fell into two subclades that differed by 103 SNPs, with almost no diversity within clades (0-2 SNPs). There was no association between geographic origin and subclade placement, suggesting frequent N. seriolae transmission between Vietnamese mariculture facilities during the outbreaks. Vietnamese strains shared a common ancestor with strains from Japan and China, with the closest strain, UTF1 from Japan, differing by just 217 SNPs from the Vietnamese ancestral node. Draft Vietnamese genomes range from 7.55-7.96 Mbp in size, have an average G+C content of 68.2%, and encode 7,602-7,958 predicted genes. Several putative virulence factors were identified, including genes associated with host cell adhesion, invasion, intracellular survival, antibiotic and toxic compound resistance, and haemolysin biosynthesis. Our findings provide important new insights into N. seriolae epidemiology and pathogenicity and will aid future vaccine development and disease management strategies, with the ultimate goal of nocardiosis-free aquaculture.

An Intellectual Energy Device for Household Appliances Using Artificial Neural Network

This article highlights the importance of implementing intelligent monitoring devices with the internet of things (IoT) for observing the amount of charges on different appliances in each household. In India, it has been observed that 20% of power is wasted due to commercial appliances where the amount of charge flow is much excess to corresponding appliances. Therefore, to perceive information about the flow of charges, it is necessary to implement an intelligent device, and it is possible to obtain exact information on the flow of charges with the help of wireless sensor networks (WSN). Even most of the researchers have developed an intelligent device for monitoring the amount of charges but delay, energy consumption, and cost of implementation are much higher. It is always necessary to extract precise information at corresponding time periods for reducing the delay in packet transmission of a specific network. To excerpt such real-time data in the network layer, an active procedure should be followed by integrating dissimilar network areas inside a single cluster, and binary coded artificial neural network (BCANN) is introduced to acquire information about hidden layers. To prove the effect of such integration process, several tests have been prepared using online and offline analyses where simulation results prove to be much effective in case of all different scenarios to an extent of 52.4% when compared to existing methods.

Measuring muon tracks in Baikal-GVD using a fast reconstruction algorithm

The Baikal Gigaton Volume Detector (Baikal-GVD) is a km$$^3$$ 3 -scale neutrino detector currently under construction in Lake Baikal, Russia. The detector consists of several thousand optical sensors arranged on vertical strings, with 36 sensors per string. The strings are grouped into clusters of 8 strings each. Each cluster can operate as a stand-alone neutrino detector. The detector layout is optimized for the measurement of astrophysical neutrinos with energies of $$\sim $$ ∼ 100 TeV and above. Events resulting from charged current interactions of muon (anti-)neutrinos will have a track-like topology in Baikal-GVD. A fast $$\chi ^2$$ χ 2 -based reconstruction algorithm has been developed to reconstruct such track-like events. The algorithm has been applied to data collected in 2019 from the first five operational clusters of Baikal-GVD, resulting in observations of both downgoing atmospheric muons and upgoing atmospheric neutrinos. This serves as an important milestone towards experimental validation of the Baikal-GVD design. The analysis is limited to single-cluster data, favoring nearly-vertical tracks.