Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation

Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches

Relocation of active site carboxylates in major facilitator superfamily multidrug transporter LmrP reveals plasticity in proton interactions

The expression of polyspecific membrane transporters is one important mechanism by which cells can obtain resistance to structurally different antibiotics and cytotoxic agents. These transporters reduce intracellular drug concentrations to subtoxic levels by mediating drug efflux across the cell envelope. The major facilitator superfamily multidrug transporter LmrP from $\textit{Lactococcus}$ lactis catalyses drug efflux in a membrane potential and chemical proton gradient-dependent fashion. To enable the interaction with protons and cationic substrates, LmrP contains catalytic carboxyl residues on the surface of a large interior chamber that is formed by transmembrane helices. These residues co-localise together with polar and aromatic residues, and are predicted to be present in two clusters. To investigate the functional role of the catalytic carboxylates, we generated mutant proteins catalysing membrane potential-independent dye efflux by removing one of the carboxyl residues in Cluster 1. We then relocated this carboxyl residue to six positions on the surface of the interior chamber, and tested for restoration of wildtype energetics. The reinsertion at positions towards Cluster 2 reinstated the membrane potential dependence of dye efflux. Our data uncover a remarkable plasticity in proton interactions in LmrP, which is a consequence of the flexibility in the location of key residues that are responsible for proton/multidrug antiport.A.V.N. is a research associate funded by the Biotechnology and Biological Sciences Research Council (BBSRC). H.S., S.R. and Z.T. received scholarships from the Cambridge Commonwealth, European and International Trust. A.N. is the recipient of a Herchel-Smith Scholarship. K.A. is funded through a programme grant from the Human Frontier Science Program

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

Disparities and risks of sexually transmissible infections among men who have sex with men in China: a meta-analysis and data synthesis.

BACKGROUND: Sexually transmitted infections (STIs), including Hepatitis B and C virus, are emerging public health risks in China, especially among men who have sex with men (MSM). This study aims to assess the magnitude and risks of STIs among Chinese MSM. METHODS: Chinese and English peer-reviewed articles were searched in five electronic databases from January 2000 to February 2013. Pooled prevalence estimates for each STI infection were calculated using meta-analysis. Infection risks of STIs in MSM, HIV-positive MSM and male sex workers (MSW) were obtained. This review followed the PRISMA guidelines and was registered in PROSPERO. RESULTS: Eighty-eight articles (11 in English and 77 in Chinese) investigating 35,203 MSM in 28 provinces were included in this review. The prevalence levels of STIs among MSM were 6.3% (95% CI: 3.5-11.0%) for chlamydia, 1.5% (0.7-2.9%) for genital wart, 1.9% (1.3-2.7%) for gonorrhoea, 8.9% (7.8-10.2%) for hepatitis B (HBV), 1.2% (1.0-1.6%) for hepatitis C (HCV), 66.3% (57.4-74.1%) for human papillomavirus (HPV), 10.6% (6.2-17.6%) for herpes simplex virus (HSV-2) and 4.3% (3.2-5.8%) for Ureaplasma urealyticum. HIV-positive MSM have consistently higher odds of all these infections than the broader MSM population. As a subgroup of MSM, MSW were 2.5 (1.4-4.7), 5.7 (2.7-12.3), and 2.2 (1.4-3.7) times more likely to be infected with chlamydia, gonorrhoea and HCV than the broader MSM population, respectively. CONCLUSION: Prevalence levels of STIs among MSW were significantly higher than the broader MSM population. Co-infection of HIV and STIs were prevalent among Chinese MSM. Integration of HIV and STIs healthcare and surveillance systems is essential in providing effective HIV/STIs preventive measures and treatments. TRIAL REGISTRATION: PROSPERO NO: CRD42013003721

A Double-Voltage-Controlled Effective Thermal Conductivity Model of Graphene for Thermoelectric Cooling

© 1963-2012 IEEE. Graphene provides a new opportunity for thermoelectric study based on its unique heat transfer behavior controllable by a gate voltage. In this paper, an effective thermal conductivity model of graphene for thermoelectric cooling is proposed. The model is based on a double-voltage-control mechanism. According to the law of Fourier heat conduction, an effective thermal conductivity model of the proposed thermoelectric cooling device is derived taking a tunable external voltage into account. Then, a gate voltage is used which can change graphene's thermoelectric characteristics. To verify the correctness and effectiveness of the proposed model, a circuit simulation model using HSPICE is built based on the thermoelectric duality. The simulation results from HSPICE and the calculated results from the mathematic model show good agreements with each other. This paper provides a novel precisely controlling method for thermoelectric cooling

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Using jasmonates and salicylates to reduce losses within the fruit supply chain

The fresh produce industry is constantly growing, due to increasing consumer demand. The shelf-life of some fruit, however, is relatively short, limited by microbial contamination or visual, textural and nutritional quality loss. Thus, techniques for reducing undesired microbial contamination, spoilage and decay, as well as maintaining product’s visual, textural and nutritional quality are in high demand at all steps within the supply chain. The postharvest use of signalling molecules, i.e. jasmonates and salicylates seems to have unexplored potential. The focus of this review is on the effects of treatment with jasmonates and salicylates on the fresh produce quality, defined by decay incidence and severity, chilling injury, maintenance of texture, visual quality, taste and aroma, and nutritional content. Postharvest treatments with jasmonates and salicylates have the ability to reduce decay by increasing fruit resistance to diseases and reducing chilling injury in numerous products. These treatments also possess the ability to improve other quality characteristics, i.e. appearance, texture maintenance and nutritional content. Furthermore, they can easily be combined with other treatments, e.g. heat treatment, ultrasound treatment. A good understanding of all the benefits and limitations related to the postharvest use of jasmonates and salicylates is needed, and relevant information has been reviewed in this paper

Original article

Background: Proteinuria is one of the risk factors for the progression of renal diseases including Alport syndrome (AS), a hereditary glomerular renal disease. This study aimed to evaluate the effi cacy of angiotensin converting enzyme inhibitors (ACEIs) and/or tripterygium, a Chinese herbal medicine widely used in Chinese patients with hematuria and proteinuria, on proteinuria in patients with AS. Methods: Twenty-nine children were enrolled into this retrospective study. Patients were divided into 3 therapy groups: ACEI group, tripterygium group, and ACEI plus tripterygium group. Results: In the 29 children, 23 were male and 6 female. In the ACEI group and the tripterygium group, the effective rate was 87.5% and 25.0%, respectively and in the ACEI plus tripterygium group was 42.9%. Conclusions: ACEI is effective in controlling proteinuria of AS patients. Tripterygium should be carefully administered in controlling proteinuria of AS patients. World J Pediatr 2009;5(4):308-31