Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

Prohibited item detection in X-ray images is an effective measure to maintain public safety. Recent prohibited item detection methods based on deep learning has achieved impressive performance. Some methods improve prohibited item detection performance by introducing prior knowledge of prohibited items, such as the edge and size of an object. However, items within baggage are often placed randomly, resulting in cluttered X-ray images, which can seriously affect the correctness and effectiveness of prior knowledge. In particular, we find that different material items in X-ray images have clear distinctions according to their atomic number Z information, which is vital to suppress the interference of irrelevant background information by mining material cues. Inspired by this observation, in this paper, we combined the atomic number Z feature and proposed a novel atomic number Z Prior Guided Network (ZPGNet) to detect prohibited objects from heavily cluttered X-ray images. Specifically, we propose a Material Activation (MA) module that cross-scale flows the atomic number Z information through the network to mine material clues and reduce irrelevant information interference in detecting prohibited items. However, collecting atomic number images requires much labor, increasing costs. Therefore, we propose a method to automatically generate atomic number Z images by exploring the color information of X-ray images, which significantly reduces the manual acquisition cost. Extensive experiments demonstrate that our method can accurately and robustly detect prohibited items from heavily cluttered X-ray images. Furthermore, we extensively evaluate our method on HiXray and OPIXray, and the best result is 2.1% mAP50 higher than the state-of-the-art models on HiXray

Inhibition or Stimulation of Autophagy Affects Early Formation of Lipofuscin-Like Autofluorescence in the Retinal Pigment Epithelium Cell

The accumulation of lipofuscin in the retinal pigment epithelium (RPE) is dependent on the effectiveness of photoreceptor outer segment material degradation. This study explored the role of autophagy in the fate of RPE lipofuscin degradation. After seven days of feeding with either native or modified rod outer segments, ARPE-19 cells were treated with enhancers or inhibitors of autophagy and the autofluorescence was detected by fluorescence-activated cell sorting. Supplementation with different types of rod outer segments increased lipofuscin-like autofluorescence (LLAF) after the inhibition of autophagy, while the induction of autophagy (e.g., application of rapamycin) decreased LLAF. The effects of autophagy induction were further confirmed by Western blotting, which showed the conversion of LC3-I to LC3-II, and by immunofluorescence microscopy, which detected the lysosomal activity of the autophagy inducers. We also monitored LLAF after the application of several autophagy inhibitors by RNA-interference and confocal microscopy. The results showed that, in general, the inhibition of the autophagy-related proteins resulted in an increase in LLAF when cells were fed with rod outer segments, which further confirms the effect of autophagy in the fate of RPE lipofuscin degradation. These results emphasize the complex role of autophagy in modulating RPE autofluorescence and confirm the possibility of the pharmacological clearance of RPE lipofuscin by small molecules

Experimental quantum adversarial learning with programmable superconducting qubits

Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 $\mu$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.Comment: 26 pages, 17 figures, 8 algorithm

Classification of 5-HT1A Receptor Ligands on the Basis of Their Binding Affinities by Using PSO-Adaboost-SVM

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT1A selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies

Relevance of JAK2V617F positivity to hematological diseases - survey of samples from a clinical genetics laboratory

<p>Abstract</p> <p>Background</p> <p>JAK2V617F is found in the majority of patients with Ph- myeloproliferative neoplasms (MPNs) and has become a valuable marker for diagnosis of MPNs. However, it has also been found in many other hematological diseases, and some studies even detected the presence of JAK2V617F in normal blood samples. This casts doubt on the primary role of JAK2V617F in the pathogenesis of MPNs and its diagnostic value.</p> <p>Methods</p> <p>In the present study, we analyzed JAK2V617F positivity with 232 normal blood samples and 2663 patient blood, bone marrow, and amniotic fluid specimens obtained from a clinical genetics laboratory by using a simple DNA extraction method and a sensitive nested allele-specific PCR strategy.</p> <p>Results</p> <p>We found JAK2V617F present in the majority (78%) of MPN patients and in a small fraction (1.8-8.7%) of patients with other specific hematological diseases but not at all in normal healthy donors or patients with non-hematological diseases. We also revealed associations of JAK2V617F with novel as well as known chromosomal abnormalities.</p> <p>Conclusions</p> <p>Our study suggests that JAK2V617F positivity is associated with specific hematological malignancies and is an excellent diagnostic marker for MPNs. The data also indicate that the nested allele-specific PCR method provides clinically relevant information and should be conducted for all cases suspected of having MPNs as well as for other related diseases.</p

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control

The genomic evolution of H1 influenza A viruses from swine detected in the United States between 2009 and 2016

Transmission of influenza A virus (IAV) from humans to swine occurs with relative frequency and is a critical contributor to swine IAV diversity. Subsequent to the introduction of these human seasonal lineages, there is often reassortment with endemic viruses and antigenic drift. To address whether particular genome constellations contributed to viral persistence following the introduction of the 2009 H1N1 human pandemic virus to swine in the USA, we collated and analyzed 616 whole genomes of swine H1 isolates. For each gene, sequences were aligned, the best-known maximum likelihood phylogeny was inferred, and each virus was assigned a clade based upon its evolutionary history. A time-scaled Bayesian approach was implemented for the hemagglutinin (HA) gene to determine the patterns of genetic diversity over time. From these analyses, we observed an increase in genome diversity across all H1 lineages and clades, with the H1-γ and H1-δ1 genetic clades containing the greatest number of unique genome patterns. We documented 74 genome patterns from 2009 to 2016, of which 3 genome patterns were consistently detected at a significantly higher level than others across the entire time period. Eight genome patterns increased significantly while 5 genome patterns were shown to decline in detection over time. Viruses with genome patterns identified as persisting in the U.S. swine population may possess a greater capacity to infect and transmit in swine. This study highlights the emerging genetic diversity of U.S. swine IAV from 2009 to 2016 with implications for swine and public health and vaccine control efforts.</p

Impact of different cumulus convective parameterization schemes on the simulation of precipitation over China

The impact of two cumulus convective schemes on the simulation of the precipitation over China is investigated using the Weather Research and Forecasting (WRF) model. Simulations for the period of 1982–2004 are performed at a horizontal resolution of 30 km and forced by NCEP Reanalysis II data. Results show precipitation simulated with the WRF model is quite sensitive to the choice of Kain–Fritsch and Grell cumulus schemes. Both the schemes have distinct skills in predicting the seasonal mean pattern, annual cycle and interannual variation in precipitation. The results show that the Kain–Fritsch scheme tends to overestimate the magnitude of the summer and annual mean precipitation over the main rain-belts, while the Grell scheme tends to underestimate these effects, particularly the simulation of the summer extreme precipitation. However, the Kain–Fritsch scheme is more skilful in capturing the seasonal mean pattern and annual cycle with higher spatial correlations in the main rain-belts. The Grell scheme shows some advantages for northern China and the Tibetan Plateau, especially in representing the interannual variation. The optimal ensemble approach is used to determine the best combination of the two schemes, with the results giving a better overall performance than the individual schemes alone in predicting summer precipitation. The temporal correlation coefficient of precipitation for the ensemble is significantly higher, while the root mean square error of extreme precipitation is reduced compared with the Kain–Fritsch and Grell results. This shows that the ensemble approach based on the optimal ensemble weight combines the advantages of the two cumulus schemes efficiently

Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels