Aeschynomene evenia has emerged as a new model legume for the deciphering of the molecular mechanisms of an alternative symbiotic process that is independent of the Nod factors. Whereas most of the research on nitrogen-fixing symbiosis, legume genetics and genomics has so far focused on Galegoid and Phaseolid legumes, A. evenia falls in the more basal and understudied Dalbergioid clade along with peanut ( Arachis hypogaea ). To provide insights into the symbiotic genes content and the structure of the A. evenia genome, we established a gene-based genetic map for this species. Firstly, an RNAseq analysis was performed on the two parental lines selected to generate a F ₂ mapping population. The transcriptomic data were used to develop molecular markers and they allowed the identification of most symbiotic genes. The resulting map comprised 364 markers arranged in 10 linkage groups (2 n = 20). A comparative analysis with the sequenced genomes of Arachis duranensis and A. ipaensis , the diploid ancestors of peanut, indicated blocks of conserved macrosynteny. Altogether, these results provided important clues regarding the evolution of symbiotic genes in a Nod factor-independent context. They provide a basis for a genome sequencing project and pave the way for forward genetic analysis of symbiosis in A. evenia .

Koi herpesvirus disease (KHVD) is an emerging disease that causes mass mortality in koi and common carp,Cyprinus carpioL. Its causative agent is Cyprinid herpesvirus 3 (CyHV-3), also known as koi herpesvirus (KHV). Although data on the pathogenesis of this deadly virus is relatively abundant in the literature, still little is known about its genomic diversity and about the molecular mechanisms that lead to such a high virulence. In this context, we developed a new strategy for sequencing full-length CyHV-3 genomes directly from infected fish tissues. Total genomic DNA extracted from carp gill tissue was specifically enriched with CyHV-3 sequences through hybridization to a set of nearly 2 million overlapping probes designed to cover the entire genome length, using KHV-J sequence (GenBank accession numberAP008984) as reference. Applied to 7 CyHV-3 specimens from Poland and Indonesia, this targeted genomic enrichment enabled recovery of the full genomes with >99.9% reference coverage. The enrichment rate was directly correlated to the estimated number of viral copies contained in the DNA extracts used for library preparation, which varied between ∼5000 and ∼2×10⁷. The average sequencing depth was >200 for all samples, thus allowing the search for variants with high confidence. Sequence analyses highlighted a significant proportion of intra-specimen sequence heterogeneity, suggesting the presence of mixed infections in all investigated fish. They also showed that inter-specimen genetic diversity at the genome scale was very low (>99.95% of sequence identity). By enabling full genome comparisons directly from infected fish tissues, this new method will be valuable to trace outbreaks rapidly and at a reasonable cost, and in turn to understand the transmission routes of CyHV-3.

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the “BeeTogether” dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives’ extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony’s belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.

We study a new technique for reducing the number of iterations of the block Cimmino method by replicating rows in the partitioned system, so that we obtain a nondisjoint partitioning of the rows. Since rows in different partitions that are close to colinear produce a poorly conditioned iteration matrix for the block Cimmino method, row replication can get around this problem. With intelligent replication choices, we can reduce the number of iterations for convergence of the replicated block Cimmino method. The downside is a slight increase of the computational workload associated with each partition. In order to find a trade-off between a lower number of iterations and a higher cost per iteration, selecting the proper set of rows for replication is crucial. In this paper, we use graph-based techniques to find good candidates for replication. Since the block Cimmino method can be interpreted as a nonoverlapping additive Schwartz method applied to the normal equations, the replication techniques correspond to introducing an overlap between the subdomains defined by the partitions. We show analytically in the case of a two-block partitioning how the replication improves the condition number of the block Cimmino iteration matrix. We then use challenging two-dimensional PDE problems to show that our algebraic approach targets physically meaningful phenomena on the interface between partitions. We demonstrate the efficiency of the proposed method in improving the performance of the block Cimmino solver, even with a small amount of replication, on problems from the SuiteSparse Matrix Collection. Finally, we compare our approach to a BiCGStab preconditioned with an additive Schwartz method and show that our replication technique can be used to define the subdomains and overlaps in the context of domain decomposition methods.