A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model

We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules. Experimentally, such regions can be inferred from fluorescence measurements obtained by binding a complementary probe with fluorescence markers to the targeted regions. We use a biophysical model which shows that the fluorescence ratio under the log scale has a sparse linear relationship with the coefficients describing the accessibility of each nucleotide, since not all sites are accessible (due to the folding of the molecule). The SpKG algorithm uniquely combines the Bayesian ranking and selection problem with the frequentist $\ell_1$ regularized regression approach Lasso. We use this algorithm to identify the sparsity pattern of the linear model as well as sequentially decide the best regions to test before experimental budget is exhausted. Besides, we also develop two other new algorithms: batch SpKG algorithm, which generates more suggestions sequentially to run parallel experiments; and batch SpKG with a procedure which we call length mutagenesis. It dynamically adds in new alternatives, in the form of types of probes, are created by inserting, deleting or mutating nucleotides within existing probes. In simulation, we demonstrate these algorithms on the Group I intron (a mid-size RNA molecule), showing that they efficiently learn the correct sparsity pattern, identify the most accessible region, and outperform several other policies

Improving machine translation systems via isotopic replacement

Machine translation plays an essential role in people’s daily international communication. However, machine translation systems are far from perfect. To tackle this problem, researchers have proposed several approaches to testing machine translation. A promising trend among these approaches is to use word replacement, where only one word in the original sentence is replaced with another word to form a sentence pair. However, precise control of the impact of word replacement remains an outstanding issue in these approaches. To address this issue, we propose CAT, a novel word-replacement-based approach, whose basic idea is to identify word replacement with controlled impact (referred to as isotopic replacement). To achieve this purpose, we use a neural-based language model to encode the sentence context, and design a neural-network-based algorithm to evaluate context-aware semantic similarity between two words. Furthermore, similar to TransRepair, a state-of-the-art word-replacement-based approach, CAT also provides automatic fixing of revealed bugs without model retraining. Our evaluation on Google Translate and Transformer indicates that CAT achieves significant improvements over TransRepair. In particular, 1) CAT detects seven more types of bugs than TransRepair; 2) CAT detects 129% more translation bugs than TransRepair; 3) CAT repairs twice more bugs than TransRepair, many of which may bring serious consequences if left unfixed; and 4) CAT has better efficiency than TransRepair in input generation (0.01s v.s. 0.41s) and comparable efficiency with TransRepair in bug repair (1.92s v.s. 1.34s)

Outer mitochondrial membrane localization of apoptosis-inducing factor: mechanistic implications for release

Poly(ADP-ribose) polymerase-1-dependent cell death (known as parthanatos) plays a pivotal role in many clinically important events including ischaemia/reperfusion injury and glutamate excitotoxicity. A recent study by us has shown that uncleaved AIF (apoptosis-inducing factor), but not calpain-hydrolysed truncated-AIF, was rapidly released from the mitochondria during parthanatos, implicating a second pool of AIF that might be present in brain mitochondria contributing to the rapid release. In the present study, a novel AIF pool is revealed in brain mitochondria by multiple biochemical analyses. Approx. 30% of AIF loosely associates with the outer mitochondrial membrane on the cytosolic side, in addition to its main localization in the mitochondrial intermembrane space attached to the inner membrane. Immunogold electron microscopic analysis of mouse brain further supports AIF association with the outer, as well as the inner, mitochondrial membrane in vivo. In line with these observations, approx. 20% of uncleaved AIF rapidly translocates to the nucleus and functionally causes neuronal death upon NMDA (N-methyl-d-aspartate) treatment. In the present study we show for the first time a second pool of AIF in brain mitochondria and demonstrate that this pool does not require cleavage and that it contributes to the rapid release of AIF. Moreover, these results suggest that this outer mitochondrial pool of AIF is sufficient to cause cell death during parthanatos. Interfering with the release of this outer mitochondrial pool of AIF during cell injury paradigms that use parthanatos hold particular promise for novel therapies to treat neurological disorders

Thalassospira xiamenensis sp. nov. and Thalassospira profundimaris sp. nov

Two bacterial strains, M-5T and WP0211T, were isolated from the surface water of a waste-oil pool in a coastal dock and from a deep-sea sediment sample from the West Pacific Ocean, respectively. Analysis of 16S rRNA gene sequences indicated that both strains belonged to the class Alphaproteobacteria and were closely related to Thalassospira lucentensis (96.1 and 96.2%, gene sequence similarity, respectively). Based on the results of physiological and biochemical tests, as well as DNA-DNA hybridization experiments, it is suggested that these isolates represent two novel species of the genus Thalassospira. Various traits allow both novel strains to be differentiated from Thalassospira lucentensis, including oxygen requirement, nitrate reduction and denitrification abilities and major fatty acid profiles, as well as their ability to utilize six different carbon sources. Furthermore, the novel strains may be readily distinguished from each other by differences in their motility, flagellation, growth at 4 °C and 40 °C, their ability to hydrolyse Tween 40 and Tween 80, their utilization of 19 different carbon sources and by quantitative differences in their fatty acid contents. It is proposed that the isolates represent two novel species for which the names Thalassospira xiamenensis sp. nov. (type strain, M-5T = DSM 17429T = CGMCC 1.3998T) and Thalassospira profundimaris sp. nov. (type strain, WP0211T = DSM 17430T = CGMCC 1.3997T) are proposed. © 2007 IUMS.link_to_OA_fulltex

Thalassospira xiamenensis sp. nov. and Thalassospira profundimaris sp. nov

Two bacterial strains, M-5T and WP0211T, were isolated from the surface water of a waste-oil pool in a coastal dock and from a deep-sea sediment sample from the West Pacific Ocean, respectively. Analysis of 16S rRNA gene sequences indicated that both strains belonged to the class Alphaproteobacteria and were closely related to Thalassospira lucentensis (96.1 and 96.2%, gene sequence similarity, respectively). Based on the results of physiological and biochemical tests, as well as DNA-DNA hybridization experiments, it is suggested that these isolates represent two novel species of the genus Thalassospira. Various traits allow both novel strains to be differentiated from Thalassospira lucentensis, including oxygen requirement, nitrate reduction and denitrification abilities and major fatty acid profiles, as well as their ability to utilize six different carbon sources. Furthermore, the novel strains may be readily distinguished from each other by differences in their motility, flagellation, growth at 4 °C and 40 °C, their ability to hydrolyse Tween 40 and Tween 80, their utilization of 19 different carbon sources and by quantitative differences in their fatty acid contents. It is proposed that the isolates represent two novel species for which the names Thalassospira xiamenensis sp. nov. (type strain, M-5T = DSM 17429T = CGMCC 1.3998T) and Thalassospira profundimaris sp. nov. (type strain, WP0211T = DSM 17430T = CGMCC 1.3997T) are proposed. © 2007 IUMS.link_to_OA_fulltex