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Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures. However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and therefore it is not known a priori which objectives are useful. Thus, MOBO methods that return the full Pareto front may be suboptimal in these cases. Here we propose a novel MOBO method that adaptively updates the scalarization function using properties of the posterior of a multi-output Gaussian process surrogate function. This approach selects useful objectives based on a flexible set of desirable criteria, allowing the functional form of each objective to guide optimization. We demonstrate the qualitative behaviour of our method on toy data and perform proof-of-concept analyses of single-cell RNA sequencing and highly multiplexed imaging datasets.

Background The World Health Organisation (WHO) recommends the vaccination against Hepatitis B virus in all infants and children up to the age of 18 years. In addition, adults in high-risk groups should also be vaccinated. This study investigated the prevalence and factors associated with Hepatitis B Virus (HBV) infections among health professional students in the city of Mwanza, Tanzania in order to provide data that can assist in devising prevention and control strategies in this special group. Methods A cross-sectional study involving health professional students of the Catholic University of health and Allied Sciences was conducted between April and July 2016. Hepatitis B surface antigen was detected using rapid antigen test while the anti-hepatitis B surface antibodies(anti-HBs) were quantified using Enzygnost Anti-HBs II assay and anti-HBV core antibodies tested using enzyme immunoassay. Results A total of 1211 health professional students with median age of 22 interquartile range (IQR):21–24 years were enrolled. The slighlty majority (57.5%) of these students were males and 475(39.2%) were in clinical practices. Out of 1211 students, 37 (3.1%) were Hepatitis B surface antigen positive. Of 1174 students tested for anti-HBs, 258 (22%) had titres > 10 IU/L indicating HBV immunity. The median anti-HBs titres was 47.7 IU/L(IQR:16–3-113). A total of 230(89.2%) students among those who were positive for anti-HBs were also positive for HBV core antibodies indicating HBV natural infections. Male sex (adjusted odd ratio(AOR):1.77, p p = 0.002) and being in clinical practices (AOR:1.39, p = 0.028) independenlty predicted anti-HBs positivity. Conclusion A significant proportion of health professional students was naturally immune to Hepatitis B virus. There is a need to measure anti-HBs in order to reduce the cost of unnecessary vaccination especially in the countries with high endemicity of HBV.

RNA-binding proteins play a key role in shaping gene expression profiles during stress, however, little is known about the dynamic nature of these interactions and how this influences the kinetics of gene expression. To address this, we developed kinetic cross-linking and analysis of cDNAs (χCRAC), an ultraviolet cross-linking method that enabled us to quantitatively measure the dynamics of protein–RNA interactions in vivo on a minute time-scale. Here, using χCRAC we measure the global RNA-binding dynamics of the yeast transcription termination factor Nab3 in response to glucose starvation. These measurements reveal rapid changes in protein–RNA interactions within 1 min following stress imposition. Changes in Nab3 binding are largely independent of alterations in transcription rate during the early stages of stress response, indicating orthogonal transcriptional control mechanisms. We also uncover a function for Nab3 in dampening expression of stress-responsive genes. χCRAC has the potential to greatly enhance our understanding of in vivo dynamics of protein–RNA interactions., Protein RNA interactions are dynamic and regulated in response to environmental changes. Here the authors describe ‘kinetic CRAC’, an approach that allows time resolved analyses of protein RNA interactions with minute time point resolution and apply it to gain insight into the function of the RNA-binding protein Nab3.

BUM-HMM is a statistically robust modeling pipeline for interpreting high-throughput RNA structure probing data, including that from transcriptome-wide experiments. Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.

The relationship between early family environment and later creative achievement is rather ambiguous. On the one hand, a context of optimal support and stimulation seems necessary. On the other hand, the lives of some of the greatest creative geniuses contradict this notion, being full of early trauma and tragedy. On the basis of longitudinal studies of young artists and talented adolescents, as well as a retrospective study of mature creative individuals, we explore the outcomes of various family environments. It seems that the two extremes of optimal and pathological experience are both represented disproportionately in the backgrounds of creative individuals. However, creative persons whose childhood was more traumatic appear less satisfied with themselves and their work. So, although a difficult childhood might be more conducive to creative achievement, it does not seem to lead to a serene adulthood. Our study of talented teenagers showed that students who came from a 'complex' family environment that provided them with both support and stimulation were more likely to take on new challenges in their area of talent and to enjoy working on and developing their skills. Such students reported feeling happy more often than those from other family types, and were significantly happier when spending time alone or in productive work.

What constitutes enjoyment of life? Optimal Experience offers a comprehensive survey of theoretical and empirical investigations of the 'flow' experience, a desirable or optimal state of consciousness that enhances a person's psychic state. The authors show the diverse contexts and circumstances in which flow is reported in different cultures, and describe its positive emotional impacts. They reflect on ways in which the ability to experience flow affects work satisfaction, academic success, and the overall quality of life

Mutational signatures are patterns of mutation types, many of which are linked to known mutagenic processes. Signature activity represents the proportion of mutations a signature generates. In cancer, cells may gain advantageous phenotypes through mutation accumulation, causing rapid growth of that subpopulation within the tumour. The presence of many subclones can make cancers harder to treat and have other clinical implications. Reconstructing changes in signature activities can give insight into the evolution of cells within a tumour. Recently, we introduced a new method, TrackSig, to detect changes in signature activities across time from single bulk tumour sample. By design, TrackSig is unable to identify mutation populations with different frequencies but little to no difference in signature activity. Here we present an extension of this method, TrackSigFreq, which enables trajectory reconstruction based on both observed density of mutation frequencies and changes in mutational signature activities. TrackSigFreq preserves the advantages of TrackSig, namely optimal and rapid mutation clustering through segmentation, while extending it so that it can identify distinct mutation populations that share similar signature activities.

A report on the Wellcome Trust Conference on Computational RNABiology held in Hinxton, UK, 17 to 19 October 2016.