Retinex-guided Channel-grouping based Patch Swap for Arbitrary Style Transfer

The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps. Since the finite features harvested from one single aesthetic style image are inadequate to represent the rich textures of the content natural image, existing techniques treat the full-channel style feature patches as simple signal tensors and create new style feature patches via signal-level fusion, which ignore the implicit diversities existed in style features and thus fail for generating better stylised results. In this paper, we propose a Retinex theory guided, channel-grouping based patch swap technique to solve the above challenges. Channel-grouping strategy groups the style feature maps into surface and texture channels, which prevents the winner-takes-all problem. Retinex theory based decomposition controls a more stable channel code rate generation. In addition, we provide complementary fusion and multi-scale generation strategy to prevent unexpected black area and over-stylised results respectively. Experimental results demonstrate that the proposed method outperforms the existing techniques in providing more style-consistent textures while keeping the content fidelity

Towards Mixture Proportion Estimation without Irreducibility

\textit{Mixture proportion estimation} (MPE) is a fundamental problem of practical significance, where we are given data from only a \textit{mixture} and one of its two \textit{components} to identify the proportion of each component. All existing MPE methods that are distribution-independent explicitly or implicitly rely on the \textit{irreducible} assumption---the unobserved component is not a mixture containing the observable component. If this is not satisfied, those methods will lead to a critical estimation bias. In this paper, we propose \textit{Regrouping-MPE} that works without irreducible assumption: it builds a new irreducible MPE problem and solves the new problem. It is worthwhile to change the problem: we prove that if the assumption holds, our method will not affect anything; if the assumption does not hold, the bias from problem changing is less than the bias from violation of the irreducible assumption in the original problem. Experiments show that our method outperforms all state-of-the-art MPE methods on various real-world datasets

PO-090 Effects of 6-week Hypoxic Exercise on Glucose Metabolism in overweight/obese Males

Objective Early studies have shown that exercise can have positive impacts on the body's glucose metabolism, but there has been no experiment revealing the different effects between normal and hypoxia, two different exercise conditions, on the glucose metabolism of adult males. The aim of this study is to expose the effects of hypoxic exercise intervention on glucose metabolism in 18-45 years old overweight/obese males. In this study, 40 males were given exercise intervention with different exercise condition. The research aims to discriminate the exercise environment that has a better influence on glucose metabolism by detecting and calculating the changes in glucose metabolism-related indicators during the different oxygen content environments exercise. Methods A parallel group design was used to study 40 healthy 18-47 years old overweight/obese males. The overweight standard is BMI≥24 and the obesity standard is BMI≥28. All 40 males were randomly divided into the hypoxia group(HG) and normal group(NG) matched on BMI and age at the pretest. The HG was provided a hypoxic exercise environment by wearing a suction-type atmospheric hypoxic device, and the oxygen content of the inhaled mixed gas is 16%; the NG was provided a normal environment. Nutritional education was given to 40 males prior to the start of exercise intervention, but diet was not restricted during exercise intervention. Both groups involved a 6-week exercise intervention which three times per week and there will be a one-day recovery time after each exercise. The intervention consists of a strength training session and an endurance training session, each intervention was generally composed of a 5minutes warm-up, 30minutes strength training, 30minute endurance training, and 5minute cooldown. The strength training contains deadlift, upright row, squat, shoulder press, calf jump, bow step, biceps curl, triceps extension, all these training loading 12RM, repeating twice and there being 0.5mins rest between sets. The treadmill was used for the endurance training, adjusting running speed according to the target heart rate interval. The calculation method of the target heart rate interval is (220-ages) ×60%~(220-ages) ×70%, and the slope is 0°. Both groups were measured body weight and taken of fasting venous blood samples, measured fasting blood glucose (GLU), glycosylated hemoglobin (GHb) and insulin (INS), calculated insulin resistance index(HOMA-IR) before and after the exercise intervention.  Results After the intervention, the fasting blood GLU, INS and HOMA-IR level in the HG were significantly lower (P≤0.05). The fasting blood GLU, INS and HOMA-IR level in the NG were increased, but there was no statistically significant difference before and after the intervention (P>0.05). There was a significant difference when compared the HG with NG in the fasting blood GLU, INS and HOMA-IR level (P≤0.05). After the intervention, the GHb levels in the HG and NG both increased, but there was no significant difference compared with the pre-intervention group (P>0.05). There was no significant difference in the GHb change rate between the HG and the NG (P >0.05), either. Conclusions Through  6-week intervention, the exercise in the hypoxic environment can more effectively improve the indicators of glucose metabolism in adult obese men compared with the normal environment. The condition of hypoxic mode has more significant benign effects especially for fasting blood GLU, INS, and HOMA-IR. For the GHb results of this experiment, because this index reflects the overall glycemic control in the past 1-2 months, and this study only carried out six weeks of uncontrolled diet exercise intervention, there may be insufficient time for exercise intervention，or the long, excessive glucose intake during the intervention, resulting in no significant differences in the comparison before and after the intervention

Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and define the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we find that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution. Thus, DNNs that fit the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the difference between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the efficacy of the proposed method. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability

Sequencing and Genetic Variation of Multidrug Resistance Plasmids in Klebsiella pneumoniae

BACKGROUND: The development of multidrug resistance is a major problem in the treatment of pathogenic microorganisms by distinct antimicrobial agents. Characterizing the genetic variation among plasmids from different bacterial species or strains is a key step towards understanding the mechanism of virulence and their evolution. RESULTS: We applied a deep sequencing approach to 206 clinical strains of Klebsiella pneumoniae collected from 2002 to 2008 to understand the genetic variation of multidrug resistance plasmids, and to reveal the dynamic change of drug resistance over time. First, we sequenced three plasmids (70 Kb, 94 Kb, and 147 Kb) from a clonal strain of K. pneumoniae using Sanger sequencing. Using the Illumina sequencing technology, we obtained more than 17 million of short reads from two pooled plasmid samples. We mapped these short reads to the three reference plasmid sequences, and identified a large number of single nucleotide polymorphisms (SNPs) in these pooled plasmids. Many of these SNPs are present in drug-resistance genes. We also found that a significant fraction of short reads could not be mapped to the reference sequences, indicating a high degree of genetic variation among the collection of K. pneumoniae isolates. Moreover, we identified that plasmid conjugative transfer genes and antibiotic resistance genes are more likely to suffer from positive selection, as indicated by the elevated rates of nonsynonymous substitution. CONCLUSION: These data represent the first large-scale study of genetic variation in multidrug resistance plasmids and provide insight into the mechanisms of plasmid diversification and the genetic basis of antibiotic resistance

Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer\u27s Disease Progression

Alzheimer\u27s disease (AD) displays a long asymptomatic stage before dementia. We characterize AD stage-associated molecular networks by profiling 14,513 proteins and 34,173 phosphosites in the human brain with mass spectrometry, highlighting 173 protein changes in 17 pathways. The altered proteins are validated in two independent cohorts, showing partial RNA dependency. Comparisons of brain tissue and cerebrospinal fluid proteomes reveal biomarker candidates. Combining with 5xFAD mouse analysis, we determine 15 Aβ-correlated proteins (e.g., MDK, NTN1, SMOC1, SLIT2, and HTRA1). 5xFAD shows a proteomic signature similar to symptomatic AD but exhibits activation of autophagy and interferon response and lacks human-specific deleterious events, such as downregulation of neurotrophic factors and synaptic proteins. Multi-omics integration prioritizes AD-related molecules and pathways, including amyloid cascade, inflammation, complement, WNT signaling, TGF-β and BMP signaling, lipid metabolism, iron homeostasis, and membrane transport. Some Aβ-correlated proteins are colocalized with amyloid plaques. Thus, the multilayer omics approach identifies protein networks during AD progression