Preimplantation chromosomal mosaics, chimaeras and confined placental mosaicism

Some human preimplantation embryos are chromosomally mosaic. For technical reasons, estimates of the overall frequency vary widely from 90% and the true frequency remains unknown. Aneuploid/diploid and aneuploid/aneuploid mosaics typically arise during early cleavage stages before the embryonic genome is fully activated and when cell cycle checkpoints are not operating normally. Other mosaics include chaotic aneuploid mosaics and mixoploids, some of which arise by abnormal chromosome segregation at the first cleavage division. Chimaeras are similar to mosaics, in having two genetically distinct cell populations, but they arise from more than one zygote and occur less often. After implantation, the frequency of mosaic embryos declines to about 2% and most are trisomic/diploid mosaics, with trisomic cells confined to the placenta. Thus, few babies are born with chromosomal mosaicism. This review discusses the origin of different types of chromosomal mosaics and chimaeras; their fate and the relationship between preimplantation chromosomal mosaicism and confined placental mosaicism in human conceptuses and animal models. Abnormal cells in mosaic embryos may be depleted by cell death, other types of cell selection or cell correction but the most severely affected mosaic embryos probably die. Trisomic cells could become restricted to placental lineages if cell selection or correction is less effective in placental lineages and/or they are preferentially allocated to a placental lineage. However, the relationship between preimplantation mosaicism and confined placental mosaicism may be complex because the specific chromosome(s) involved will influence whether chromosomally abnormal cells survive predominately in the placental trophoblast and/or placental mesenchyme. LAY SUMMARY: Human cells normally have 23 pairs of chromosomes, which carry the genes. During the first few days of development, some human embryos are chromosomal mosaics. These mosaic embryos have both normal cells and cells with an abnormal number of chromosomes, which arise from the same fertilised egg. (More rarely, the different cell populations arise from more than one fertilised egg and these embryos are called chimaeras.) If chromosomally abnormal cells survive to term, they could cause birth defects. However, few abnormal cells survive and those that do are usually confined to the placenta, where they are less likely to cause harm. It is not yet understood how this restriction occurs but the type of chromosomal abnormality influences which placental tissues are affected. This review discusses the origin of different types of chromosomally abnormal cells, their fate and how they might become confined to the placenta in humans and animal models

Preconditioning and triggering of offshore slope failures and turbidity currents revealed by most detailed monitoring yet at a fjord-head delta

Rivers and turbidity currents are the two most important sediment transport processes by volume on Earth. Various hypotheses have been proposed for triggering of turbidity currents offshore from river mouths, including direct plunging of river discharge, delta mouth bar flushing or slope failure caused by low tides and gas expansion, earthquakes and rapid sedimentation. During 2011, 106 turbidity currents were monitored at Squamish Delta, British Columbia. This enables statistical analysis of timing, frequency and triggers. The largest peaks in river discharge did not create hyperpycnal flows. Instead, delayed delta-lip failures occurred 8–11 h after flood peaks, due to cumulative delta top sedimentation and tidally-induced pore pressure changes. Elevated river discharge is thus a significant control on the timing and rate of turbidity currents but not directly due to plunging river water. Elevated river discharge and focusing of river discharge at low tides cause increased sediment transport across the delta-lip, which is the most significant of all controls on flow timing in this setting

The effects of minimal tillage, contour cultivation and in-field vegetative barriers on soil erosion and phosphorus loss.

Runoff, sediment, total phosphorus and total dissolved phosphorus losses in overland flow were measured for two years on unbounded plots cropped with wheat and oats. Half of the field was cultivated with minimum tillage (shallow tillage with a tine cultivator) and half was conventionally ploughed. Within each cultivation treatment there were different treatment areas (TA). In the first year of the experiment, one TA was cultivated up and down the slope, one TA was cultivated on the contour, with a beetle bank acting as a vegetative barrier partway up the slope, and one had a mixed direction cultivation treatment, with cultivation and drilling conducted up and down the slope and all subsequent operations conducted on the contour. In the second year, this mixed treatment was replaced with contour cultivation. Results showed no significant reduction in runoff, sediment losses or total phosphorus losses from minimum tillage when compared to the conventional plough treatment, but there were increased losses of total dissolved phosphorus with minimum tillage. The mixed direction cultivation treatment increased surface runoff and losses of sediment and phosphorus. Increasing surface roughness with contour cultivation reduced surface runoff compared to up and down slope cultivation in both the plough and minimum tillage treatment areas, but this trend was not significant. Sediment and phosphorus losses in the contour cultivation treatment followed a very similar pattern to runoff. Combining contour cultivation with a vegetative barrier in the form of a beetle bank to reduce slope length resulted in a non-significant reduction in surface runoff, sediment and total phosphorus when compared to up and down-slope cultivation, but there was a clear trend towards reduced losses. However, the addition of a beetle bank did not provide a significant reduction in runoff, sediment losses or total phosphorus losses when compared to contour cultivation, suggesting only a marginal additional benefit. The economic implications for farmers of the different treatment options are investigated in order to assess their suitability for implementation at a field scale

Assessing Feature Representations for Instance-Based Cross-Domain Anomaly Detection in Cloud Services Univariate Time Series Data

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets

The interaction of normalisation and clustering in sub-domain definition for multi-source transfer learning based time series anomaly detection

This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the target data can result in improved model performance, as compared to identifying sub-domains through defining clusters using the multi-source dataset

Assessing Feature Representations for Instance-Based Cross-Domain Anomaly Detection in Cloud Services Univariate Time Series Data

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best feature set is statistically significant. Furthermore, our analysis of the features used by the most successful model indicates that features related to mean and variance are the most informative for anomaly detection. We also find that features based on model forecast errors are useful for anomaly detection for some but not all datasets

Valuing initial teacher education at Master's level

The future of Master’s-level work in initial teacher education (ITE) in England seems uncertain. Whilst the coalition government has expressed support for Master’s-level work, its recent White Paper focuses on teaching skills as the dominant form of professional development. This training discourse is in tension with the view of professional learning advocated by ITE courses that offer Master’s credits. Following a survey of the changing perceptions of Master’s-level study during a Post Graduate Certificate in Education course by student teachers in four subject groups, this paper highlights how the process of professional learning can have the most impact on how they value studying at a higher level during their early professional development

Decoherence in Josephson Qubits from Dielectric Loss

Dielectric loss from two-level states is shown to be a dominant decoherence source in superconducting quantum bits. Depending on the qubit design, dielectric loss from insulating materials or the tunnel junction can lead to short coherence times. We show that a variety of microwave and qubit measurements are well modeled by loss from resonant absorption of two-level defects. Our results demonstrate that this loss can be significantly reduced by using better dielectrics and fabricating junctions of small area $\lesssim 10 \mu \textrm{m}^2$. With a redesigned phase qubit employing low-loss dielectrics, the energy relaxation rate has been improved by a factor of 20, opening up the possibility of multi-qubit gates and algorithms.Comment: shortened version submitted to PR