Maternal smoking, consumption of alcohol, and caffeinated beverages during pregnancy and the risk of childhood brain tumors: a meta-analysis of observational studies.

BACKGROUND: We conducted this meta-analysis to investigate the potential association between maternal smoking, alcohol and caffeinated beverages consumption during pregnancy and the risk of childhood brain tumors (CBTs). METHODS: A thorough search was carried out on PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Internet to identify pertinent articles. Fixed or random effects model was applied to meta-analyze the data. RESULTS: The results suggested a borderline statistically significant increased risk of CBTs associated with maternal smoking during pregnancy (OR 1.04, 95% CI 0.99-1.09). We found that passive smoking (OR 1.12, 95% CI 1.03-1.20), rather than active smoking (OR 1.00, 95% CI 0.93-1.07), led to an increased risk of CBTs. The results suggested a higher risk in 0-1 year old children (OR 1.21, 95% CI 0.94-1.56), followed by 0-4 years old children (OR 1.12, 95% CI 0.97-1.28) and 5-9 years old children (OR 1.11, 95% CI 0.95-1.29). This meta-analysis found no significant association between maternal alcohol consumption during pregnancy and CBTs risk (OR 1.00, 95% CI 0.80-1.24). An increased risk of CBTs was found to be associated with maternal consumption of caffeinated beverages (OR 1.16, 95% CI 1.07-1.26) during pregnancy, especially coffee (OR 1.18, 95% CI 1.00-1.38). CONCLUSIONS: Maternal passive smoking, consumption of caffeinated beverages during pregnancy should be considered as risk factors for CBTs, especially glioma. More prospective cohort studies are warranted to provide a higher level of evidence.

Low-count PET image reconstruction based on truncated inverse radon layer and U-shaped network.

Objective.Positron emission tomography (PET) is a functional imaging widely used in various applications such as tumour detection. PET image reconstruction is an ill-posed inverse problem, and the model-based iterative reconstruction methods commonly used in clinical practice have disadvantages such as long time consumption and low signal-to-noise ratio, especially at low doses.Approach.In this study, we propose a deep learning-based reconstruction method that is capable of reconstructing images directly from low-count sinograms. Our network consists of two parts, a truncated inverse radon layer for implementing domain transform and a U-shaped network for image enhancement.Main result.We validated our method on both simulation data and real data. Compared to ordered subset expectation maximization with a post-Guassian filter, the structural similarity can be improved from 0.9357 to 0.9613 and the peak signal-to-noise ratio can be improved by 5 dB.Significance.The proposed method can directly convert low-count sinograms into PET images, while obtaining improved image quality and having less time consumption than iterative reconstruction algorithms and the state-of-the-art convolutional neural network.

A simulation study of 1D U-Net-based inter-crystal scatter event recovery of PET detectors.

To achieve high spatial resolution of reconstructed images in positron emission tomography (PET), the size of the scintillation crystal element is set small in current PET systems, which greatly increases the inter-crystal scattering (ICS) frequency. The ICS is a type of Compton scattering of the gamma photons from one crystal element to its neighborhood element, which obscures the determination of the first interaction position. In this study, we propose a 1D U-Net convolutional neural network to predict the first interaction position, which provides a universal way to efficiently solve the ICS recovery problem. The network is trained using the dataset collected from the GATE Monte Carlo simulation. The 1D U-Net structure is applied due to its capability of synthesizing both low-level and high-level information, which shows superiority in solving the ICS recovery problem. After being well trained, the 1D U-Net can generate a prediction accuracy of 78.1%. Compared to the coincidence events only composed from two photoelectric gamma photons, the sensitivity is improved by 149%. The contrast-to-noise ratio of the reconstructed contrast phantom increases from 6.973 to 10.795 for the 16 mm hot sphere. Compared to the take-energy-centroid method, the spatial resolution of the reconstructed resolution phantom can obtain the best improvement of 33.46%. Compared with the previous deep learning method based on the fully connected network, the proposed 1D U-Net can work more stably with considerably fewer network parameters. The 1D U-Net network model shows good universality when predicting different phantoms, and the computation speed is fast.

Multisource single-cell data integration by MAW barycenter for Gaussian mixture models.

One key challenge encountered in single-cell data clustering is to combine clustering results of data sets acquired from multiple sources. We propose to represent the clustering result of each data set by a Gaussian mixture model (GMM) and produce an integrated result based on the notion of Wasserstein barycenter. However, the precise barycenter of GMMs, a distribution on the same sample space, is computationally infeasible to solve. Importantly, the barycenter of GMMs may not be a GMM containing a reasonable number of components. We thus propose to use the minimized aggregated Wasserstein (MAW) distance to approximate the Wasserstein metric and develop a new algorithm for computing the barycenter of GMMs under MAW. Recent theoretical advances further justify using the MAW distance as an approximation for the Wasserstein metric between GMMs. We also prove that the MAW barycenter of GMMs has the same expectation as the Wasserstein barycenter. Our proposed algorithm for clustering integration scales well with the data dimension and the number of mixture components, with complexity independent of data size. We demonstrate that the new method achieves better clustering results on several single-cell RNA-seq data sets than some other popular methods.

ARBEE: Towards Automated Recognition of Bodily Expression of Emotion in the Wild.

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9876 video clips of body movements and 13,239 human characters, named Body Language Dataset (BoLD), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named Automated Recognition of Bodily Expression of Emotion (ARBEE), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans.

Leveraging Long and Short-Term Information in Content-Aware Movie Recommendation via Adversarial Training.

Movie recommendation systems provide users with ranked lists of movies based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: 1) long-term models and 2) session-based models. The long-term-based models represent the interactions between users and movies that are supposed to change slowly across time, while the session-based models encode the information of users' interests and changing dynamics of movies' attributes in short terms. In this paper, we propose the LSIC model, leveraging long and short-term information for content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and achieves the state-of-the-art results.

Aggregated Wasserstein Distance and State Registration for Hidden Markov Models.

We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian. For such HMMs, the marginal distribution at any time position follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs. We refer to such HMMs as HMM. The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions. Specifically, the components of the marginal GMMs are matched by solving an optimal transport problem where the cost between components is the Wasserstein metric for Gaussian distributions. The solution of the optimization problem is a fast approximation to the Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is a semi-metric and can be computed without generating Monte Carlo samples. It is invariant to relabeling or permutation of states. The distance is defined meaningfully even for two HMMs that are estimated from data of different dimensionality, a situation that can arise due to missing variables. This distance quantifies the dissimilarity of HMMs by measuring both the difference between the two marginal GMMs and that between the two transition matrices. Our new distance is tested on tasks of retrieval, classification, and t-SNE visualization of time series. Experiments on both synthetic and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the Kullback-Leibler divergence.

Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data.

We proposed a probabilistic approach to joint modeling of participants' reliability and humans' regularity in crowdsourced affective studies. Reliability measures how likely a subject will respond to a question seriously; and regularity measures how often a human will agree with other seriously-entered responses coming from a targeted population. Crowdsourcing-based studies or experiments, which rely on human self-reported affect, pose additional challenges as compared with typical crowdsourcing studies that attempt to acquire concrete non-affective labels of objects. The reliability of participants has been massively pursued for typical non-affective crowdsourcing studies, whereas the regularity of humans in an affective experiment in its own right has not been thoroughly considered. It has been often observed that different individuals exhibit different feelings on the same test question, which does not have a sole correct response in the first place. High reliability of responses from one individual thus cannot conclusively result in high consensus across individuals. Instead, globally testing consensus of a population is of interest to investigators. Built upon the agreement multigraph among tasks and workers, our probabilistic model differentiates subject regularity from population reliability. We demonstrate the method's effectiveness for in-depth robust analysis of large-scale crowdsourced affective data, including emotion and aesthetic assessments collected by presenting visual stimuli to human subjects.

Expression of Toll-like Receptor 2 and Toll-like Receptor 4 in Tuberculous Pleural Effusion.

BACKGROUND: Toll-like receptor-2 (TLR2) and Toll-like receptor-4 (TLR4) have been reported to play a crucial role in tuberculosis, however, little is known about their expression in tuberculous pleuritis. OBJECTIVE: The goal of this work is to explore the expressions of TLR2 and TLR4 in tuberculous pleuritis and their predominant expressions on cells. METHODS: Levels of soluble TLR2 and TLR4 by enzyme linked immunosorbent assay (ELISA) in 58 patients with tuberculous pleural effusion (PE) and 43 patients with malignant PE were determined. The related genes were analyzed by RT-PCR and the membrane expressions of TLR2 and TLR4 on CD3+, CD14+, and CD19+ monocytes were assessed by using flow cytometry in 20 of 58 patients with tuberculous pleuritis. RESULTS: Our results showed that the levels of ADA, IL-27 and IFN-γ in tuberculous PE were obviously higher than in malignant PE. Moreover, the concentrations of soluble TLR2 and soluble TLR4 in PE were significantly higher than those in peripheral blood of the same patients, as well as the levels of soluble TLR2 in tuberculous PE were significantly higher than those in malignant effusions. Furthermore, the levels of TLR2, TLR4 and IFN-γ mRNA expression were marked increased in the tuberculous PE when compared with the correspondent serum. Importantly, we found that the predominant expressions of TLR2 in monocyte were on CD19 B cells, and the predominant expressions of TLR4 were on CD14 monocytes/macrophages. CONCLUSION: Our findings provided the evidence of a role for TLRs expression in tuberculous PE.

Phenotypic and molecular characteristics of carbapenem-non-susceptible Enterobacteriaceae from a teaching hospital in Wenzhou, southern China.

Carbapenem resistance in Enterobacteriaceae is increasing and has become a matter of great concern. The aim of this study was to characterize carbapenem-non-susceptible Enterobacteriaceae from a teaching hospital. A total of 49 carbapenem-non-susceptible Enterobacteriaceae clinical isolates recovered in 2007-2010 from the First Affiliated Hospital of Wenzhou Medical College were analyzed by antimicrobial susceptibility testing. The carbapenemase phenotype, outer membrane protein profiles, and clonal relatedness were investigated using the modified Hodge test, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and pulsed-field gel electrophoresis (PFGE). Multilocus sequence typing (MLST) of Klebsiella pneumoniae was also performed. ß-Lactamase genes were examined by PCR and sequencing, and the transferability of carbapenemase genes was determined by a conjugation experiment. The rates of imipenem, meropenem, and ertapenem resistance were 59.2%, 40.8%, and 96.0%, respectively. Thirty isolates exhibited carbapenemase activity, and 32 isolates carried carbapenemase genes. Furthermore, 10 and 9 clinical isolates posessed AmpC ß-lactamase and extended-spectrum ß-lactamase (ESBL) genes, respectively. Eight of 32 carbapenemase-producing isolates were proved to be carried by conjugative plasmids, and there was porin loss in 34.7% (17/49) of the isolates. PFGE analysis demonstrated that 9 KPC-2-producing Serratia marcescens belonged to a clonal strain, suggesting the clonal dissemination of these KPC-2-bearing isolates among different wards. The MLST of K. pneumoniae revealed that two KPC-2 producers were ST11. This study suggests that KPC-2-type carbapenemase is the main contributor to carbapenems resistance in carbapenemase-producing Enterobacteriaceae, and that ESBL, AmpC ß-lactamase overproduction, and porin loss contribute to the resistance level among these isolates; in carbapenemase-non-producing Enterobacteriaceae, ESBL, AmpC enzyme, and porin loss contribute to the carbapenems resistance of Enterobacteriaceae, especially the ertapenem resistance of Enterobacter cloacae.

Observation of delay enhancement in an ion-doped crystal.

We report the investigation of the reduction of the group velocity propagation resulting from the steep change of the refractive index by the coherent population oscillation in an erbium ion-doped optical fiber. We study fully the influences of the ion density and the temperature on the fractional and time delay. We find that the fractional delay can be decreased at high or low temperature. Moreover, we conclude that the temperature can be used as a control parameter to reduce distortion.