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

Optimization of Extraction Process of Total Triterpenoids from Crataegus songarica by Response Surface Methodology and Its Purification Process

In order to improve the high-value utilization of Crataegus songarica, the single factor experiment, orthogonal experiment and response surface design were used to optimize the extraction process of total triterpenes from Crataegus songarica by compound enzyme assisted extraction, and the purification process was optimized. The results showed that the optimal extraction process of total triterpenes from Crataegus songarica was as follows: The addition amount of cellulase, pectinase and papain was 4%, 4% and 2%, respectively, the ratio of material to liquid was 1:24 g/mL, the temperature was 51 ℃, the pH was 4.5, and the enzymolysis time was 76 min. Under these conditions, the extraction amount of total triterpenes from Crataegus songarica was (36.570±0.332) mg/g, and the extraction amount of total triterpenes was (53.782±0.673) mg/g by ultrasonic extraction again. The purification process was as follows: The concentration of the sample solution was 6 mg/mL, the flow rate was 1 mL/min, the sample volume was 15 mL, the elution condition was 50 mL, and 70% ethanol. The purity of the total triterpenoids was 29.93%, and the recovery rate was 81.25%. The above results indicate that the compound enzymatic method is feasible and stable for the extraction and purification of total triterpenoids from Crataegus songarica by macroporous resin

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

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

ObjectiveTo investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.MethodsA total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the “rms” package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.ResultsThe mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.ConclusionPD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC

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