Adaptive subspace sampling for class imbalance processing

© 2016 IEEE. This paper presents a novel oversampling technique that addresses highly imbalanced data distribution. At present, the imbalanced data that have anomalous class distribution and underrepresented data are difficult to deal with through a variety of conventional machine learning technologies. In order to balance class distributions, an adaptive subspace self-organizing map (ASSOM) that combines the local mapping scheme and globally competitive rule is proposed to artificially generate synthetic samples focusing on minority class samples. The ASSOM is conformed with feature-invariant characteristics, including translation, scaling and rotation, and it retains the independence of basis vectors in each module. Specifically, basis vectors generated via each ASSOM module can avoid generating repeated representative features that offer nothing but heavy computational load. Several experimental results demonstrate that the proposed ASSOM method with supervised learning manner is superior to other existing oversampling techniques

Fuzzy Integral with Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface

© 2016 IEEE. A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications

The ALMaQUEST Survey: The Molecular Gas Main Sequence and the Origin of the Star-forming Main Sequence

The origin of the star forming main sequence ( i.e., the relation between star formation rate and stellar mass, globally or on kpc-scales; hereafter SFMS) remains a hotly debated topic in galaxy evolution. Using the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we show that for star forming spaxels in the main sequence galaxies, the three local quantities, star-formation rate surface density (\sigsfr), stellar mass surface density (\sigsm), and the \h2~mass surface density (\sigh2), are strongly correlated with one another and form a 3D linear (in log) relation with dispersion. In addition to the two well known scaling relations, the resolved SFMS (\sigsfr~ vs. \sigsm) and the Schmidt-Kennicutt relation (\sigsfr~ vs. \sigh2; SK relation), there is a third scaling relation between \sigh2~ and \sigsm, which we refer to as the `molecular gas main sequence' (MGMS). The latter indicates that either the local gas mass traces the gravitational potential set by the local stellar mass or both quantities follow the underlying total mass distributions. The scatter of the resolved SFMS ($\sigma \sim 0.25$ dex) is the largest compared to those of the SK and MGMS relations ($\sigma \sim$ 0.2 dex). A Pearson correlation test also indicates that the SK and MGMS relations are more strongly correlated than the resolved SFMS. Our result suggests a scenario in which the resolved SFMS is the least physically fundamental and is the consequence of the combination of the SK and the MGMS relations

CLEC5A-mediated enhancement of the inflammatory response in myeloid cells contributes to influenza virus pathogenicity in vivo

Human infections with influenza viruses exhibit mild to severe clinical outcomes as a result of complex virus-host interactions. Induction of inflammatory mediators via pattern recognition receptors may dictate subsequent host responses for pathogen clearance and tissue damage. We identified that human C-type lectin domain family 5 member A (CLEC5A) interacts with the hemagglutinin protein of influenza viruses expressed on lentiviral pseudoparticles through lectin screening. Silencing CLEC5A gene expression, blocking influenza-CLEC5A interactions with anti-CLEC5A antibodies, or dampening CLEC5A-mediated signaling using a spleen tyrosine kinase inhibitor consistently reduced the levels of proinflammatory cytokines produced by human macrophages without affecting the replication of influenza A viruses of different subtypes. Infection of bone marrow-derived macrophages from CLEC5A-deficient mice showed reduced levels of tumor necrosis factor alpha (TNF-α) and IP-10 but elevated alpha interferon (IFN-α) compared to those of wild-type mice. The heightened type I IFN response in the macrophages of CLEC5A-deficient mice was associated with upregulated TLR3 mRNA after treatment with double-stranded RNA. Upon lethal challenges with a recombinant H5N1 virus, CLEC5A-deficient mice showed reduced levels of proinflammatory cytokines, decreased immune cell infiltration in the lungs, and improved survival compared to the wild-type mice, despite comparable viral loads noted throughout the course of infection. The survival difference was more prominent at a lower dose of inoculum. Our results suggest that CLEC5A-mediated enhancement of the inflammatory response in myeloid cells contributes to influenza pathogenicity in vivo and may be considered a therapeutic target in combination with effective antivirals. Well-orchestrated host responses together with effective viral clearance are critical for optimal clinical outcome after influenza infections.published_or_final_versio

ALMaQUEST. IV. The ALMA-MaNGA QUEnching and STar Formation (ALMaQUEST) Survey

The ALMaQUEST (ALMA-MaNGA QUEnching and STar formation) survey is a program with spatially-resolved $^{12}$CO(1-0) measurements obtained with the Atacama Large Millimeter Array (ALMA) for 46 galaxies selected from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) DR15 optical integral-field spectroscopic survey. The aim of the ALMaQUEST survey is to investigate the dependence of star formation activity on the cold molecular gas content at kpc scales in nearby galaxies. The sample consists of galaxies spanning a wide range in specific star formation rate (sSFR), including starburst (SB), main-sequence (MS), and green valley (GV) galaxies. In this paper, we present the sample selection and characteristics of the ALMA observations, and showcase some of the key results enabled by the combination of spatially-matched stellar populations and gas measurements. Considering the global (aperture-matched) stellar mass, molecular gas mass, and star formation rate of the sample, we find that the sSFR depends on both the star formation efficiency (SFE) and the molecular gas fraction ($f_{\rm H_{2}}$), although the correlation with the latter is slightly weaker. Furthermore, the dependence of sSFR on the molecular gas content (SFE or $f_{\rm H_{2}}$) is stronger than that on either the atomic gas fraction or the molecular-to-atomic gas fraction, albeit with the small HI sample size. On kpc scales, the variations in both SFE and $f_{\rm H_{2}}$ within individual galaxies can be as large as 1-2 dex thereby demonstrating that the availability of spatially-resolved observations is essential to understand the details of both star formation and quenching processes.STFC ER

Centre selection for clinical trials and the generalisability of results: a mixed methods study.

BACKGROUND: The rationale for centre selection in randomised controlled trials (RCTs) is often unclear but may have important implications for the generalisability of trial results. The aims of this study were to evaluate the factors which currently influence centre selection in RCTs and consider how generalisability considerations inform current and optimal practice. METHODS AND FINDINGS: Mixed methods approach consisting of a systematic review and meta-summary of centre selection criteria reported in RCT protocols funded by the UK National Institute of Health Research (NIHR) initiated between January 2005-January 2012; and an online survey on the topic of current and optimal centre selection, distributed to professionals in the 48 UK Clinical Trials Units and 10 NIHR Research Design Services. The survey design was informed by the systematic review and by two focus groups conducted with trialists at the Birmingham Centre for Clinical Trials. 129 trial protocols were included in the systematic review, with a total target sample size in excess of 317,000 participants. The meta-summary identified 53 unique centre selection criteria. 78 protocols (60%) provided at least one criterion for centre selection, but only 31 (24%) protocols explicitly acknowledged generalisability. This is consistent with the survey findings (n = 70), where less than a third of participants reported generalisability as a key driver of centre selection in current practice. This contrasts with trialists' views on optimal practice, where generalisability in terms of clinical practice, population characteristics and economic results were prime considerations for 60% (n = 42), 57% (n = 40) and 46% (n = 32) of respondents, respectively. CONCLUSIONS: Centres are rarely enrolled in RCTs with an explicit view to external validity, although trialists acknowledge that incorporating generalisability in centre selection should ideally be more prominent. There is a need to operationalize 'generalisability' and incorporate it at the design stage of RCTs so that results are readily transferable to 'real world' practice

Microdevices for extensional rheometry of low viscosity elastic liquids : a review

Extensional flows and the underlying stability/instability mechanisms are of extreme relevance to the efficient operation of inkjet printing, coating processes and drug delivery systems, as well as for the generation of micro droplets. The development of an extensional rheometer to characterize the extensional properties of low viscosity fluids has therefore stimulated great interest of researchers, particularly in the last decade. Microfluidics has proven to be an extraordinary working platform and different configurations of potential extensional microrheometers have been proposed. In this review, we present an overview of several successful designs, together with a critical assessment of their capabilities and limitations

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

© 2020 IEEE. Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-The-Art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem

Are mice good models for human neuromuscular disease? Comparing muscle excursions in walking between mice and humans

The mouse is one of the most widely used animal models to study neuromuscular diseases and test new therapeutic strategies. However, findings from successful pre-clinical studies using mouse models frequently fail to translate to humans due to various factors. Differences in muscle function between the two species could be crucial but often have been overlooked. The purpose of this study was to evaluate and compare muscle excursions in walking between mice and humans

Novel, synergistic antifungal combinations that target translation fidelity

There is an unmet need for new antifungal or fungicide treatments, as resistance to existing treatments grows. Combination treatments help to combat resistance. Here we develop a novel, effective target for combination antifungal therapy. Different aminoglycoside antibiotics combined with different sulphate-transport inhibitors produced strong, synergistic growth-inhibition of several fungi. Combinations decreased the respective MICs by ≥8 fold. Synergy was suppressed in yeast mutants resistant to effects of sulphate-mimetics (like chromate or molybdate) on sulphate transport. By different mechanisms, aminoglycosides and inhibition of sulphate transport cause errors in mRNA translation. The mistranslation rate was stimulated up to 10-fold when the agents were used in combination, consistent with this being the mode of synergistic action. A range of undesirable fungi were susceptible to synergistic inhibition by the combinations, including the human pathogens Candida albicans, C. glabrata and Cryptococcus neoformans, the food spoilage organism Zygosaccharomyces bailii and the phytopathogens Rhizoctonia solani and Zymoseptoria tritici. There was some specificity as certain fungi were unaffected. There was no synergy against bacterial or mammalian cells. The results indicate that translation fidelity is a promising new target for combinatorial treatment of undesirable fungi, the combinations requiring substantially decreased doses of active components compared to each agent alone