Genetic transfer of resistance to powdery mildew and of an associated molecular marker from Aegilops ventricosa to hexaploid wheat.

Resistance to powdery mildew, caused by the fungus Erysiphe graminis f.sp. tritici, has been transferred from Aegilops ventricosa (genomes DvMv) to hexaploid wheat (Triticum aestivum, ABD). In two transfer lines, H-93-8 and H-93-35, the resistance gene was linked to a gene encoding protein U-1, whereas one line, H-93-33, was resistant but lacked the molecular marker, and another line, H-93-1, was susceptible but carried the gene for U-1, indicating that the original Mv chromosome from Ae. ventricosa, carrying the two genes, had undergone recombination with a wheat chromosome in the last two lines

Resistance to the cereal cyst nematode (Heterodera avenae) transferred from the wild grass Aegilops ventricosa to hexaploid wheat by a "stepping-stone" procedure

Transfer of resistance toHeterodera avenae, the cereal cyst nematode (CCN), by a stepping-stoneprocedure from the wild grassAegilops ventricosa to hexaploid wheat has been demonstrated. The number of nematodes per plant was lower, and reached a plateau much earlier, in the resistant introgression line H93-8 (1–2 nematodes per plant) than in the recipient H10-15 wheat (14–16 nematodes per plant). Necrosis (hypersensitive reaction) near the nematode, little cell fusion, and few, often degraded syncytia were observed in infested H93-8 roots, while abundant, well-formed syncytia were present in the susceptible H10-15 wheat. Line H93-8 was highly resistant to the two Spanish populations tested, as well as the four French races (Fr1-Fr4), and the British pathotype Hall, but was susceptible to the Swedish pathotypes HgI and HgIII. Resistance was inherited as though determined by a single quasi-dominant factor in the F2 generations resulting from crosses of H93-8 with H10-15 and with Loros, a resistant wheat carrying the geneCre1 (syn.Ccn1). The resistance gene in H93-8 (Cre2 orCcn2) is not allelic with respect to that in Loros. RFLPs and other markers, together with the cytogenetical evidence, indicate that theCre2 gene has been integrated into a wheat chromosome without affecting its meiotic pairing ability. Introduction ofCre2 by backcrossing into a commercial wheat backgroud increases grain yield when under challenge by the nematode and is not detrimental in the absence of infestation

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis

Model-based Cross-correlation Search for Gravitational Waves from the Low-mass X-Ray Binary Scorpius X-1 in LIGO O3 Data

We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run of Advanced LIGO and Advanced Virgo. This is a semicoherent search that uses details of the signal model to coherently combine data separated by less than a specified coherence time, which can be adjusted to balance sensitivity with computing cost. The search covered a range of gravitational-wave frequencies from 25 to 1600 Hz, as well as ranges in orbital speed, frequency, and phase determined from observational constraints. No significant detection candidates were found, and upper limits were set as a function of frequency. The most stringent limits, between 100 and 200 Hz, correspond to an amplitude h0 of about 10−25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4 × 10−26 assuming the optimal orientation. The sensitivity of this search is now probing amplitudes predicted by models of torque balance equilibrium. For the usual conservative model assuming accretion at the surface of the neutron star, our isotropically marginalized upper limits are close to the predicted amplitude from about 70 to 100 Hz; the limits assuming that the neutron star spin is aligned with the most likely orbital angular momentum are below the conservative torque balance predictions from 40 to 200 Hz. Assuming a broader range of accretion models, our direct limits on gravitational-wave amplitude delve into the relevant parameter space over a wide range of frequencies, to 500 Hz or more

Model-based Cross-correlation Search for Gravitational Waves from the Low-mass X-Ray Binary Scorpius X-1 in LIGO O3 Data

We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run of Advanced LIGO and Advanced Virgo. This is a semicoherent search that uses details of the signal model to coherently combine data separated by less than a specified coherence time, which can be adjusted to balance sensitivity with computing cost. The search covered a range of gravitational-wave frequencies from 25 to 1600 Hz, as well as ranges in orbital speed, frequency, and phase determined from observational constraints. No significant detection candidates were found, and upper limits were set as a function of frequency. The most stringent limits, between 100 and 200 Hz, correspond to an amplitude h0 of about 10−25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4 × 10−26 assuming the optimal orientation. The sensitivity of this search is now probing amplitudes predicted by models of torque balance equilibrium. For the usual conservative model assuming accretion at the surface of the neutron star, our isotropically marginalized upper limits are close to the predicted amplitude from about 70 to 100 Hz; the limits assuming that the neutron star spin is aligned with the most likely orbital angular momentum are below the conservative torque balance predictions from 40 to 200 Hz. Assuming a broader range of accretion models, our direct limits on gravitational-wave amplitude delve into the relevant parameter space over a wide range of frequencies, to 500 Hz or more