Genome-wide analysis of the interaction between the endosymbiotic bacterium Wolbachia and its Drosophila host

BACKGROUND: Intracellular Wolbachia bacteria are obligate, maternally-inherited, endosymbionts found frequently in insects and other invertebrates. The success of Wolbachia can be attributed in part to an ability to alter host reproduction via mechanisms including cytoplasmic incompatibility (CI), parthenogenesis, feminization and male killing. Despite substantial scientific effort, the molecular mechanisms underlying the Wolbachia/host interaction are unknown. RESULTS: Here, an in vitro Wolbachia infection was generated in the Drosophila S2 cell line, and transcription profiles of infected and uninfected cells were compared by microarray. Differentially-expressed patterns related to reproduction, immune response and heat stress response are observed, including multiple genes that have been previously reported to be involved in the Wolbachia/host interaction. Subsequent in vivo characterization of differentially-expressed products in gonads demonstrates that Angiotensin Converting Enzyme (Ance) varies between Wolbachia infected and uninfected flies and that the variation occurs in a sex-specific manner. Consistent with expectations for the conserved CI mechanism, the observed Ance expression pattern is repeatable in different Drosophila species and with different Wolbachia types. To examine Ance involvement in the CI phenotype, compatible and incompatible crosses of Ance mutant flies were conducted. Significant differences are observed in the egg hatch rate resulting from incompatible crosses, providing support for additional experiments examining for an interaction of Ance with the CI mechanism. CONCLUSION: Wolbachia infection is shown to affect the expression of multiple host genes, including Ance. Evidence for potential Ance involvement in the CI mechanism is described, including the prior report of Ance in spermatid differentiation, Wolbachia-induced sex-specific effects on Ance expression and an Ance mutation effect on CI levels. The results support the use of Wolbachia infected cell cultures as an appropriate model for predicting in vivo host/Wolbachia interactions

Organophosphate Ester Flame Retardants and Plasticizers in ocean sediments from the North Pacific to the Arctic Ocean

The occurence of organophosphate ester (OPE) flame retardants and plasticizers in surface sediment from the North Pacific to Arctic Ocean was observed for the first time during the fourth National Arctic Research Expedition of China in the summer of 2010. The samples were analyzed for three halogenated OPEs [tris(2-chloroethyl) phosphate (TCEP), tris(1-chloro-2-propyl) phosphate (TCPP), and tris(dichloroisopropyl) phosphate], three alkylated OPEs [triisobutyl phosphate (TiBP), tri-n-butyl phosphate, and tripentyl phosphate], and triphenyl phosphate. Σ7OPEs (total concentration of the observed OPEs) was in the range of 159–4658 pg/g of dry weight. Halogenated OPEs were generally more abundant than the nonhalogenated OPEs; TCEP and TiBP dominated the overall concentrations. Except for that of the Bering Sea, Σ7OPEs values increased with increasing latitudes from Bering Strait to the Central Arctic Ocean, while the contributions of halogenated OPEs (typically TCEP and TCPP) to the total OPE profile also increased from the Bering Strait to the Central Arctic Ocean, indicating they are more likely to be transported to the remote Arctic. The median budget of 52 (range of 17–292) tons for Σ7OPEs in sediment from the Central Arctic Ocean represents only a very small amount of their total production volume, yet the amount of OPEs in Arctic Ocean sediment was significantly larger than the sum of polybrominated diphenyl ethers (PBDEs) in the sediment, indicating they are equally prone to long-range transport away from source regions. Given the increasing level of production and usage of OPEs as substitutes of PBDEs, OPEs will continue to accumulate in the remote Arctic

AU-PD: An Arbitrary-size and Uniform Downsampling Framework for Point Clouds

Point cloud downsampling is a crucial pre-processing operation to downsample the points in the point cloud in order to reduce computational cost, and communication load, to name a few. Recent research on point cloud downsampling has achieved great success which concentrates on learning to sample in a task-aware way. However, existing learnable samplers can not perform arbitrary-size sampling directly. Moreover, their sampled results always comprise many overlapping points. In this paper, we introduce the AU-PD, a novel task-aware sampling framework that directly downsamples point cloud to any smaller size based on a sample-to-refine strategy. Given a specified arbitrary size, we first perform task-agnostic pre-sampling to sample the input point cloud. Then, we refine the pre-sampled set to make it task-aware, driven by downstream task losses. The refinement is realized by adding each pre-sampled point with a small offset predicted by point-wise multi-layer perceptrons (MLPs). In this way, the sampled set remains almost unchanged from the original in distribution, and therefore contains fewer overlapping cases. With the attention mechanism and proper training scheme, the framework learns to adaptively refine the pre-sampled set of different sizes. We evaluate sampled results for classification and registration tasks, respectively. The proposed AU-PD gets competitive downstream performance with the state-of-the-art method while being more flexible and containing fewer overlapping points in the sampled set. The source code will be publicly available at https://zhiyongsu.github.io/Project/AUPD.html

Trace determination of the flame retardant tetrabromobisphenol A in the atmosphere by gas chromatography–mass spectrometry

A simple and effective method has been developed for analysis of the flame retardant tetrabromobisphenol A (TBBPA) in environmental samples by using modified soxhlet extraction in combination with silica gel clean-up, derivatization with silylation reagent and gas chromatography–mass spectrometry (GC–MS) in selected ion monitoring mode (SIM). Satisfactory recoveries were achieved for the large volume sampling, soxhlet extraction and silica gel clean-up. The overall recovery is 79 ± 1%. The derivatization procedure is simple and fast, and produces stable TBBPA derivative. GC–MS with electronic impact (EI) ionization mode shows better detection power than using negative chemical ionization (NCI) mode. EI gives a method detection limit of 0.04 pg m−3 and enables to determine trace TBBPA in ambient air in remote area. The method was successfully applied to the determination of TBBPA in atmospheric samples collected over land and coastal regions. The concentrations of TBBPA ranged from below the method detection limit (0.04 pg m−3) to 0.85 pg m−3. A declining trend with increasing latitude was present from the Wadden Sea to the Arctic. The atmospheric occurrence of TBBPA in the Arctic is significant and might imply that TBBPA has long-range transport potential

Online Corrupted User Detection and Regret Minimization

In real-world online web systems, multiple users usually arrive sequentially into the system. For applications like click fraud and fake reviews, some users can maliciously perform corrupted (disrupted) behaviors to trick the system. Therefore, it is crucial to design efficient online learning algorithms to robustly learn from potentially corrupted user behaviors and accurately identify the corrupted users in an online manner. Existing works propose bandit algorithms robust to adversarial corruption. However, these algorithms are designed for a single user, and cannot leverage the implicit social relations among multiple users for more efficient learning. Moreover, none of them consider how to detect corrupted users online in the multiple-user scenario. In this paper, we present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors to speed up learning, and identify the corrupted users in an online setting. To robustly learn and utilize the unknown relations among potentially corrupted users, we propose a novel bandit algorithm RCLUB-WCU. To detect the corrupted users, we devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations. We prove a regret upper bound for RCLUB-WCU, which asymptotically matches the lower bound with respect to $T$ up to logarithmic factors, and matches the state-of-the-art results in degenerate cases. We also give a theoretical guarantee for the detection accuracy of OCCUD. With extensive experiments, our methods achieve superior performance over previous bandit algorithms and high corrupted user detection accuracy

Dynamic surface tension of the pure liquid-vapor interface subjected to the cyclic loads

We demonstrate a methodology for computationally investigating the mechanical response of a pure molten lead surface system to the lateral mechanical cyclic loads and try to answer the question: how dose the dynamically driven liquid surface system follow the classical physics of the elastic-driven oscillation? The steady-state oscillation of the dynamic surface tension under cyclic load, including the excitation of high frequency vibration mode at different driving frequencies and amplitudes, was compared with the classical theory of single-body driven damped oscillator. Under the highest studied frequency (50 GHz) and amplitude (5%) of the load, the increase of the (mean value) dynamic surface tension could reach ~5%. The peak and trough values of the instantaneous dynamic surface tension could reach (up to) 40% increase and (up to) 20% decrease compared to the equilibrium surface tension, respectively. The extracted generalized natural frequencies and the generalized damping constants seem to be intimately related to the intrinsic timescales of the atomic temporal-spatial correlation functions of the liquids both in the bulk region and in the outermost surface layers. These insights uncovered could be helpful for quantitative manipulation of the liquid surface tension using ultrafast shockwaves or laser pulses

Online Clustering of Bandits with Misspecified User Models

The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in the long run. A line of works, called the clustering of bandits (CB), utilize the collaborative effect over user preferences and have shown significant improvements over classic linear bandit algorithms. However, existing CB algorithms require well-specified linear user models and can fail when this critical assumption does not hold. Whether robust CB algorithms can be designed for more practical scenarios with misspecified user models remains an open problem. In this paper, we are the first to present the important problem of clustering of bandits with misspecified user models (CBMUM), where the expected rewards in user models can be perturbed away from perfect linear models. We devise two robust CB algorithms, RCLUMB and RSCLUMB (representing the learned clustering structure with dynamic graph and sets, respectively), that can accommodate the inaccurate user preference estimations and erroneous clustering caused by model misspecifications. We prove regret upper bounds of $O(\epsilon_*T\sqrt{md\log T} + d\sqrt{mT}\log T)$ for our algorithms under milder assumptions than previous CB works (notably, we move past a restrictive technical assumption on the distribution of the arms), which match the lower bound asymptotically in $T$ up to logarithmic factors, and also match the state-of-the-art results in several degenerate cases. The techniques in proving the regret caused by misclustering users are quite general and may be of independent interest. Experiments on both synthetic and real-world data show our outperformance over previous algorithms