LArPix: Demonstration of low-power 3D pixelated charge readout for liquid argon time projection chambers

We report the demonstration of a low-power pixelated readout system designed for three-dimensional ionization charge detection and digital readout of liquid argon time projection chambers (LArTPCs). Unambiguous 3D charge readout was achieved using a custom-designed system-on-a-chip ASIC (LArPix) to uniquely instrument each pad in a pixelated array of charge-collection pads. The LArPix ASIC, manufactured in 180 nm bulk CMOS, provides 32 channels of charge-sensitive amplification with self-triggered digitization and multiplexed readout at temperatures from 80 K to 300 K. Using an 832-channel LArPix-based readout system with 3 mm spacing between pads, we demonstrated low-noise ($<$500 e$^-$ RMS equivalent noise charge) and very low-power ($<$100 $\mu$W/channel) ionization signal detection and readout. The readout was used to successfully measure the three-dimensional ionization distributions of cosmic rays passing through a LArTPC, free from the ambiguities of existing projective techniques. The system design relies on standard printed circuit board manufacturing techniques, enabling scalable and low-cost production of large-area readout systems using common commercial facilities. This demonstration overcomes a critical technical obstacle for operation of LArTPCs in high-occupancy environments, such as the near detector site of the Deep Underground Neutrino Experiment (DUNE).Comment: 19 pages, 10 figures, 1 ancillary animation. V3 includes minor revisions based on referee comment

Don't Worry, I'm in Control! Is Users’ Trust in Automated Driving Different When Using a Continuous Ambient Light HMI Compared to an Auditory HMI?

Ambient LED displays have been used to provide peripheral light-based cues to drivers about a vehicle's current state, along with providing requests for a driver's attention or action. However, few studies have investigated the use of an ambient LED display to improve drivers' trust, perceived safety, and reactions during L3 automated driving. Due to the ambient nature of an LED lightband display, it could be anticipated that it would provide reassurance of the automation status while automation is on, along with providing a gentle cue for non-urgent transitions of control. This video submission presents a methodological overview of a driving simulator study designed to evaluate the effectiveness of an ambient peripheral light display (Lightband HMI) in terms of its potential to improve drivers' trust in L3 automation, along with a comparison of a Lightband and Auditory HMI in terms of their effectiveness in facilitating transitions of control

Transverse spin dependence of pp total cross‐sections

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87331/2/72_1.pd

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Decontamination of pork carcases with hot water or acidified sodium chlorite- a comparison in two Australian abattoirs

A decontamination trial on the effectiveness of hot water and acidified sodium chlorite (SANOVA™) treatment on TVC, E. coli and Salmonella spp. was undertaken on a total of 852 pork carcases prior to primary chilling in two pork abattoirs in Australia using belly-strip excision sampling. Test pigs were selected from herds with a known high level of on-fam1 Salmonella infection. For control carcases at Abattoirs A and B, mean log10 Total Viable Count was 4.06 and 3.00 cfu/gram compared with 1.81 and 2. 09 cfu/gram, for hot water and 2. 76 and 2.53 cfu/gram for SAN OVA ™ treated carcases, respectively

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

A fresh look at the evolution and diversification of photochemical reaction centers

In this review, I reexamine the origin and diversification of photochemical reaction centers based on the known phylogenetic relations of the core subunits, and with the aid of sequence and structural alignments. I show, for example, that the protein folds at the C-terminus of the D1 and D2 subunits of Photosystem II, which are essential for the coordination of the water-oxidizing complex, were already in place in the most ancestral Type II reaction center subunit. I then evaluate the evolution of reaction centers in the context of the rise and expansion of the different groups of bacteria based on recent large-scale phylogenetic analyses. I find that the Heliobacteriaceae family of Firmicutes appears to be the earliest branching of the known groups of phototrophic bacteria; however, the origin of photochemical reaction centers and chlorophyll synthesis cannot be placed in this group. Moreover, it becomes evident that the Acidobacteria and the Proteobacteria shared a more recent common phototrophic ancestor, and this is also likely for the Chloroflexi and the Cyanobacteria. Finally, I argue that the discrepancies among the phylogenies of the reaction center proteins, chlorophyll synthesis enzymes, and the species tree of bacteria are best explained if both types of photochemical reaction centers evolved before the diversification of the known phyla of phototrophic bacteria. The primordial phototrophic ancestor must have had both Type I and Type II reaction centers

Mining multi-item drug adverse effect associations in spontaneous reporting systems

<p>Abstract</p> <p>Background</p> <p>Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.</p> <p>Results</p> <p>Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.</p> <p>Conclusions</p> <p>Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.</p