Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries.

Background The availability of fast alignment-free algorithms has greatly reduced the computational burden of RNAseq processing, especially for relatively poorly assembled genomes. Using these approaches, previous RNA-seq datasets could potentially be processed and integrated with newly sequenced libraries. Confounding factors in such integration include sequencing depth and methods of RNA extraction and selection. Different selection methods (typically, either polyA-selection or rRNA-depletion) omit different RNAs, resulting in different fractions of the transcriptome being sequenced. In particular, rRNA-depleted libraries sample a broader fraction of the transcriptome than polyA-selected libraries. This study aimed to develop a systematic means of accounting for library type that allows data from these two methods to be compared. Results The method was developed by comparing two RNA-seq datasets from ovine macrophages, identical except for RNA selection method. Gene-level expression estimates were obtained using a two-part process centred on the high-speed transcript quantification tool Kallisto. Firstly, a set of reference transcripts was defined that constitute a standardised RNA space, with expression from both datasets quantified against it. Secondly, a simple ratio-based correction was applied to the rRNA-depleted estimates. The outcome is an almost perfect correlation between gene expression estimates, independent of library type and across the full range of levels of expression. Conclusion A combination of reference transcriptome filtering and a ratio-based correction can create equivalent expression profiles from both polyA-selected and rRNA-depleted libraries. This approach will allow meta-analysis and integration of existing RNA-seq data into transcriptional atlas projects

The Ternary Rab27a–Myrip–Myosin VIIa Complex Regulates Melanosome Motility in the Retinal Pigment Epithelium

The retinal pigment epithelium (RPE) contains melanosomes similar to those found in the skin melanocytes, which undergo dramatic light-dependent movements in fish and amphibians. In mammals, those movements are more subtle and appear to be regulated by the Rab27a GTPase and the unconventional myosin, Myosin VIIa (MyoVIIa). Here we address the hypothesis that a recently identified Rab27a- and MyoVIIa-interacting protein, Myrip, promotes the formation of a functional tripartite complex. In heterologous cultured cells, all three proteins co-immunoprecipitated following overexpression. Rab27a and Myrip localize to the peripheral membrane of RPE melanosomes as observed by immunofluorescence and immunoelectron microscopy. Melanosome dynamics were studied using live-cell imaging of mouse RPE primary cultures. Wild-type RPE melanosomes exhibited either stationary or slow movement interrupted by bursts of fast movement, with a peripheral directionality trend. Nocodazole treatment led to melanosome paralysis, suggesting that movement requires microtubule motors. Significant and similar alterations in melanosome dynamics were observed when any one of the three components of the complex was missing, as studied in ashen- (Rab27a defective) and shaker-1 (MyoVIIa mutant)-derived RPE cells, and in wild-type RPE cells transduced with adenovirus carrying specific sequences to knockdown Myrip expression. We observed a significant increase in the number of motile melanosomes, exhibiting more frequent and prolonged bursts of fast movement, and inversion of directionality. Similar alterations were observed upon cytochalasin D treatment, suggesting that the Rab27a–Myrip–MyoVIIa complex regulates tethering of melanosomes onto actin filaments, a process that ensures melanosome movement towards the cell periphery

Language Models (Mostly) Know What They Know

We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format. Thus we can approach self-evaluation on open-ended sampling tasks by asking models to first propose answers, and then to evaluate the probability "P(True)" that their answers are correct. We find encouraging performance, calibration, and scaling for P(True) on a diverse array of tasks. Performance at self-evaluation further improves when we allow models to consider many of their own samples before predicting the validity of one specific possibility. Next, we investigate whether models can be trained to predict "P(IK)", the probability that "I know" the answer to a question, without reference to any particular proposed answer. Models perform well at predicting P(IK) and partially generalize across tasks, though they struggle with calibration of P(IK) on new tasks. The predicted P(IK) probabilities also increase appropriately in the presence of relevant source materials in the context, and in the presence of hints towards the solution of mathematical word problems. We hope these observations lay the groundwork for training more honest models, and for investigating how honesty generalizes to cases where models are trained on objectives other than the imitation of human writing.Comment: 23+17 pages; refs added, typos fixe

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful outputs. We make three main contributions. First, we investigate scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B parameters) and 4 model types: a plain language model (LM); an LM prompted to be helpful, honest, and harmless; an LM with rejection sampling; and a model trained to be helpful and harmless using reinforcement learning from human feedback (RLHF). We find that the RLHF models are increasingly difficult to red team as they scale, and we find a flat trend with scale for the other model types. Second, we release our dataset of 38,961 red team attacks for others to analyze and learn from. We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs. Third, we exhaustively describe our instructions, processes, statistical methodologies, and uncertainty about red teaming. We hope that this transparency accelerates our ability to work together as a community in order to develop shared norms, practices, and technical standards for how to red team language models

A cluster-randomized controlled trial to reduce sedentary behavior and promote physical activity and health of 8-9 year olds: The Transform-Us! Study

Background: Physical activity (PA) is associated with positive cardio-metabolic health and emerging evidence suggests sedentary behavior (SB) may be detrimental to children&rsquo;s health independent of PA. The primary aim of the Transform-Us! study is to determine whether an 18-month, behavioral and environmental intervention in the school and family settings results in higher levels of PA and lower rates of SB among 8-9 year old children compared with usual practice (post-intervention and 12-months follow-up). The secondary aims are to determine the independent and combined effects of PA and SB on children&rsquo;s cardio-metabolic health risk factors; identify the factors that mediate the success of the intervention; and determine whether the intervention is cost-effective.Methods/design: A four-arm cluster-randomized controlled trial (RCT) with a 2 &times; 2 factorial design, with schools as the unit of randomization. Twenty schools will be allocated to one of four intervention groups, sedentary behavior (SB-I), physical activity (PA-I), combined SB and PA (SB+PA-I) or current practice control (C), which will be evaluated among approximately 600 children aged 8-9 years in school year 3 living in Melbourne, Australia. All children in year 3 at intervention schools in 2010 (8-9 years) will receive the intervention over an 18-month period with a maintenance &lsquo;booster&rsquo; delivered in 2012 and children at all schools will be invited to participate in the evaluation assessments. To maximize the sample and to capture new students arriving at intervention and control schools, recruitment will be on-going up to the post-intervention time point. Primary outcomes are time spent sitting and in PA assessed via accelerometers and inclinometers and survey.Discussion: To our knowledge, Transform-Us! is the first RCT to examine the effectiveness of intervention strategies for reducing children&rsquo;s overall sedentary time, promoting PA and optimizing health outcomes. The integration of consistent strategies and messages to children from teachers and parents in both school and family settings is a critical component of this study, and if shown to be effective, may have a significant impact on educational policies as well as on pedagogical and parenting practices.<br /

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

Network Topologies and Dynamics Leading to Endotoxin Tolerance and Priming in Innate Immune Cells

The innate immune system, acting as the first line of host defense, senses and adapts to foreign challenges through complex intracellular and intercellular signaling networks. Endotoxin tolerance and priming elicited by macrophages are classic examples of the complex adaptation of innate immune cells. Upon repetitive exposures to different doses of bacterial endotoxin (lipopolysaccharide) or other stimulants, macrophages show either suppressed or augmented inflammatory responses compared to a single exposure to the stimulant. Endotoxin tolerance and priming are critically involved in both immune homeostasis and the pathogenesis of diverse inflammatory diseases. However, the underlying molecular mechanisms are not well understood. By means of a computational search through the parameter space of a coarse-grained three-node network with a two-stage Metropolis sampling approach, we enumerated all the network topologies that can generate priming or tolerance. We discovered three major mechanisms for priming (pathway synergy, suppressor deactivation, activator induction) and one for tolerance (inhibitor persistence). These results not only explain existing experimental observations, but also reveal intriguing test scenarios for future experimental studies to clarify mechanisms of endotoxin priming and tolerance.Comment: 15 pages, 8 figures, submitte

Impact of physical activity level and dietary fat content on passive overconsumption of energy in non-obese adults

Background: Passive overconsumption is the increase in energy intake driven by the high-fat energy-dense food environment. This can be explained in part because dietary fat has a weaker effect on satiation (i.e. process that terminates feeding). Habitually active individuals show improved satiety (i.e. process involved in post-meal suppression of hunger) but any improvement in satiation is unknown. Here we examined whether habitual physical activity mitigates passive overconsumption through enhanced satiation in response to a high-fat meal. Methods: Twenty-one non-obese individuals with high levels of physical activity (HiPA) and 19 individuals with low levels of physical activity (LoPA) matched for body mass index (mean = 22.8 kg/m2) were recruited. Passive overconsumption was assessed by comparing ad libitum energy intake from covertly manipulated high-fat (HFAT; 50% fat) or high-carbohydrate (HCHO; 70% carbohydrate) meals in a randomized crossover design. Habitual physical activity was assessed using SenseWear accelerometers (SWA). Body composition, resting metabolic rate, eating behaviour traits, fasting appetite-related peptides and hedonic food reward were also measured. Results: In the whole sample, passive overconsumption was observed with greater energy intake at HFAT compared to HCHO (p  0.05). SWA confirmed that HiPA were more active than LoPA (p  0.05 for all). Conclusions: Non-obese individuals with high or low physical activity levels but matched for BMI showed similar susceptibility to passive overconsumption when consuming an ad libitum high-fat compared to a high-carbohydrate meal. This occurred despite increased total daily energy expenditure and improved body composition in HiPA. Greater differences in body composition and/or physical activity levels may be required to impact on satiation