ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

The role of quantitative cross-case analysis in understanding tropical smallholder farmers’ adaptive capacity to climate shocks

Climate shocks are predicted to increase in magnitude and frequency as the climate changes, notably impacting poor and vulnerable communities across the Tropics. The urgency to better understand and improve communities' resilience is reflected in international agreements such as the Paris Agreement and the multiplication of adaptation research and action programs. In turn, the need for collecting and communicating evidence on the climate resilience of communities has increasingly drawn questions concerning how to assess resilience. While empirical case studies are often used to delve into the context-specific nature of resilience, synthesizing results is essential to produce generalizable findings at the scale at which policies are designed. Yet datasets, methods and modalities that enable cross-case analyses that draw from individual local studies are still rare in climate resilience literature. We use empirical case studies on the impacts of El Niño on smallholder households from five countries to test the application of quantitative data aggregation for policy recommendation. We standardized data into an aggregated dataset to explore how key demographic factors affected the impact of climate shocks, modeled as crop loss. We find that while cross-study results partially align with the findings from the individual projects and with theory, several challenges associated with quantitative aggregation remain when examining complex, contextual and multi-dimensional concepts such as resilience. We conclude that future exercises synthesizing cross-site empirical evidence in climate resilience could accelerate research to policy impact by using mixed methods, focusing on specific landscapes or regional scales, and facilitating research through the use of shared frameworks and learning exercises

Hidden Cues in Random Line Stereograms

Successful fusion of random-line stereograms with breaks in the vernier acuity range has been interpreted to suggest that the interpolation process underlying hyperacuity is parallel and preliminary to stereomatching. In this paper (a) we demonstrate with computer experiments that vernier cues are not needed to solve the stereomatching problem posed by these stereograms and (b) we provide psychophysical evidence that human stereopsis probably does not use vernier cues alone to achieve fusion of these random-line stereograms.MIT Artificial Intelligence Laborator

Can smallholder farmers buffer rainfall variability through conservation agriculture? On-farm practices and maize yields in Kenya and Malawi

Reduced tillage, permanent ground cover and crop diversification are the three core pillars of Conservation Agriculture (CA). We assess and compare on-farm effects of different practices related to the three pillars of CA on maize yields under ENSO-driven rainfall variability in Kenya and Malawi. Reduced tillage practices increased yields per hectare by 250 kg on average in Malawi under below-average rainfall conditions and by 700 kg in Kenya under above-average rainfall, but did not have any significant effect on yields under below-average rainfall conditions in Kenya. Ground cover had a positive impact on yields in Malawi (dry conditions) but not in Kenya (both dry and wet conditions), where mixed crop and livestock systems limited this practice. Crop diversification had positive impacts in Kenya (both dry and wet conditions), where maize-legume crop rotation is practiced, but not in Malawi where landholdings are too small to allow rotation. Our findings suggest that isolated CA techniques can have positive effects on yields even after only a few years of practice under variable rainfall conditions. This strengthens empirical evidence supporting the value of CA in resilience building of agricultural systems, and suggests that both full and partial adoption of CA practices should be supported in areas where climate change is leading to more variable rainfall conditions

Detection of Spectral Variations of Anomalous Microwave Emission with QUIJOTE and C-BASS

Anomalous Microwave Emission (AME) is a significant component of Galactic diffuse emission in the frequency range $10$-$60\,$GHz and a new window into the properties of sub-nanometre-sized grains in the interstellar medium. We investigate the morphology of AME in the $\approx10^{\circ}$ diameter $\lambda$ Orionis ring by combining intensity data from the QUIJOTE experiment at $11$, $13$, $17$ and $19\,$GHz and the C-Band All Sky Survey (C-BASS) at $4.76\,$GHz, together with 19 ancillary datasets between $1.42$ and $3000\,$GHz. Maps of physical parameters at $1^{\circ}$ resolution are produced through Markov Chain Monte Carlo (MCMC) fits of spectral energy distributions (SEDs), approximating the AME component with a log-normal distribution. AME is detected in excess of $20\,\sigma$ at degree-scales around the entirety of the ring along photodissociation regions (PDRs), with three primary bright regions containing dark clouds. A radial decrease is observed in the AME peak frequency from $\approx35\,$GHz near the free-free region to $\approx21\,$GHz in the outer regions of the ring, which is the first detection of AME spectral variations across a single region. A strong correlation between AME peak frequency, emission measure and dust temperature is an indication for the dependence of the AME peak frequency on the local radiation field. The AME amplitude normalised by the optical depth is also strongly correlated with the radiation field, giving an overall picture consistent with spinning dust where the local radiation field plays a key role.Comment: 19 pages, 7 figures, accepted for publication by MNRA

The State-of-Play of Anomalous Microwave Emission (AME) Research

Anomalous Microwave Emission (AME) is a component of diffuse Galactic radiation observed at frequencies in the range $\approx 10$-60 GHz. AME was first detected in 1996 and recognised as an additional component of emission in 1997. Since then, AME has been observed by a range of experiments and in a variety of environments. AME is spatially correlated with far-IR thermal dust emission but cannot be explained by synchrotron or free-free emission mechanisms, and is far in excess of the emission contributed by thermal dust emission with the power-law opacity consistent with the observed emission at sub-mm wavelengths. Polarization observations have shown that AME is very weakly polarized ($\lesssim 1$%). The most natural explanation for AME is rotational emission from ultra-small dust grains ("spinning dust"), first postulated in 1957. Magnetic dipole radiation from thermal fluctuations in the magnetization of magnetic grain materials may also be contributing to the AME, particularly at higher frequencies ($\gtrsim 50$ GHz). AME is also an important foreground for Cosmic Microwave Background analyses. This paper presents a review and the current state-of-play in AME research, which was discussed in an AME workshop held at ESTEC, The Netherlands, June 2016.Comment: Accepted for publication in New Astronomy Reviews. Summary of AME workshop held at ESTEC, The Netherlands, June 2016, 40 pages, 18 figures. Updated to approximately match published versio

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance

Sulforaphane Causes Epigenetic Repression of hTERT Expression in Human Breast Cancer Cell Lines

Sulforaphane (SFN), an isothiocyanate found in cruciferous vegetables, is a common dietary component that has histone deacetylase inhibition activity and exciting potential in cancer prevention. The mechanisms by which SFN imparts its chemopreventive properties are of considerable interest and little is known of its preventive potential for breast cancer. expression facilitated the induction of cellular apoptosis in human breast cancer cells.Collectively, our results provide novel insights into SFN-mediated epigenetic down-regulation of telomerase in breast cancer prevention and may open new avenues for approaches to SFN-mediated cancer prevention

The European Society of Human Reproduction and Embryology guideline for the diagnosis and treatment of endometriosis: an electronic guideline implementability appraisal

<p>Abstract</p> <p>Background</p> <p>Clinical guidelines are intended to improve healthcare. However, even if guidelines are excellent, their implementation is not assured. In subfertility care, the European Society of Human Reproduction and Embryology (ESHRE) guidelines have been inventoried, and their methodological quality has been assessed. To improve the impact of the ESHRE guidelines and to improve European subfertility care, it is important to optimise the implementability of guidelines. We therefore investigated the implementation barriers of the ESHRE guideline with the best methodological quality and evaluated the used instrument for usability and feasibility.</p> <p>Methods</p> <p>We reviewed the ESHRE guideline for the diagnosis and treatment of endometriosis to assess its implementability. We used an electronic version of the guideline implementability appraisal (eGLIA) instrument. This eGLIA tool consists of 31 questions grouped into 10 dimensions. Seven items address the guideline as a whole, and 24 items assess the individual recommendations in the guideline. The eGLIA instrument identifies factors that influence the implementability of the guideline recommendations. These factors can be divided into facilitators that promote implementation and barriers that oppose implementation. A panel of 10 experts from three European countries appraised all 36 recommendations of the guideline. They discussed discrepancies in a teleconference and completed a questionnaire to evaluate the ease of use and overall utility of the eGLIA instrument.</p> <p>Results</p> <p>Two of the 36 guideline recommendations were straightforward to implement. Five recommendations were considered simply statements because they contained no actions. The remaining 29 recommendations were implementable with some adjustments. We found facilitators of the guideline implementability in the quality of decidability, presentation and formatting, apparent validity, and novelty or innovation of the recommendations. Vaguely defined actions, lack of facilities, immeasurable outcomes, and inflexibility within the recommendations formed barriers to implementation. The eGLIA instrument was generally useful and easy to use. However, assessment with the eGLIA instrument is very time-consuming.</p> <p>Conclusions</p> <p>The ESHRE guideline for the diagnosis and treatment of endometriosis could be improved to facilitate its implementation in daily practice. The eGLIA instrument is a helpful tool for identifying obstacles to implementation of a guideline. However, we recommend a concise version of this instrument.</p