Effect of Sample Transportation on the Proteome of Human Circulating Blood Extracellular Vesicles

Circulating extracellular vesicles (cEV) are released by many kinds of cells and play an important role in cellular communication, signaling, inflammation modulation, coagulation, and tumor growth. cEV are of growing interest, not only as biomarkers, but also as potential treatment targets. However, very little is known about the effect of transporting biological samples from the clinical ward to the diagnostic laboratory, notably on the protein composition. Pneumatic tube systems (PTS) and human carriers (C) are both routinely used for transport, subjecting the samples to different ranges of mechanical forces. We therefore investigated qualitatively and quantitatively the effect of transport by C and PTS on the human cEV proteome and particle size distribution. We found that samples transported by PTS were subjected to intense, irregular, and multidirectional shocks, while those that were transported by C mostly underwent oscillations at a ground frequency of approximately 4 Hz. PTS resulted in the broadening of nanoparticle size distribution in platelet-free (PFP) but not in platelet-poor plasma (PPP). Cell-type specific cEV-associated protein abundances remained largely unaffected by the transport type. Since residual material of lymphocytes, monocytes, and platelets seemed to dominate cEV proteomes in PPP, it was concluded that PFP should be preferred for any further analyses. Differential expression showed that the impact of the transport method on cEV-associated protein composition was heterogeneous and likely donor-specific. Correlation analysis was nonetheless able to detect that vibration dose, shocks, and imparted energy were associated with different terms depending on the transport, namely in C with cytoskeleton-regulated cell organization activity, and in PTS with a release of extracellular vesicles, mainly from organelle origin, and specifically from mitochondrial structures. Feature selection algorithm identified proteins which, when considered together with the correlated protein-protein interaction network, could be viewed as surrogates of network clusters.     

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.     

Sea-level rise and its possible impacts given a 'beyond 4°C world' in the twenty-first century

The range of future climate-induced sea-level rise remains highly uncertain with continued concern that large increases in the twenty-first century cannot be ruled out. The biggest source of uncertainty is the response of the large ice sheets of Greenland and west Antarctica. Based on our analysis, a pragmatic estimate of sea-level rise by 2100, for a temperature rise of 4°C or more over the same time frame, is between 0.5 m and 2 m--the probability of rises at the high end is judged to be very low, but of unquantifiable probability. However, if realized, an indicative analysis shows that the impact potential is severe, with the real risk of the forced displacement of up to 187 million people over the century (up to 2.4% of global population). This is potentially avoidable by widespread upgrade of protection, albeit rather costly with up to 0.02 per cent of global domestic product needed, and much higher in certain nations. The likelihood of protection being successfully implemented varies between regions, and is lowest in small islands, Africa and parts of Asia, and hence these regions are the most likely to see coastal abandonment. To respond to these challenges, a multi-track approach is required, which would also be appropriate if a temperature rise of less than 4°C was expected. Firstly, we should monitor sea level to detect any significant accelerations in the rate of rise in a timely manner. Secondly, we need to improve our understanding of the climate-induced processes that could contribute to rapid sea-level rise, especially the role of the two major ice sheets, to produce better models that quantify the likely future rise more precisely. Finally, responses need to be carefully considered via a combination of climate mitigation to reduce the rise and adaptation for the residual rise in sea level. In particular, long-term strategic adaptation plans for the full range of possible sea-level rise (and other change) need to be widely developed.     

Excitonic Mechanism of Local Phase Transformations by Optical Pumping

Transformations of cooperative electronic states by impacts of optical pumping and/or electrostatic doping is a new mainstream in physics of correlated systems. Here we present a semi-phenomenological modeling of spatio-temporal effects in a system where the light absorption goes through a channel creating the excitons—intra-molecular ones or bound electron–hole pairs—and finally the condensate of optical excitons feeds and stimulates phase transformations. Interacting with a near-critical order parameter and deformations, the excitons are subject to self-trapping. That locally enhances their density which can surpass a critical value to trigger the phase transformation, even if the mean density is below the required threshold. The model can be used e.g. as a simplified version of optically induced neutral-ionic transitions in organic chain compounds.     

CO2-efflux measurements for evaluating source zone natural attenuation rates in a petroleum hydrocarbon contaminated aquifer

In order to gain regulatory approval for source zone natural attenuation (SZNA) at hydrocarbon-contaminated sites, knowledge regarding the extent of the contamination, its tendency to spread, and its longevity is required. However, reliable quantification of biodegradation rates, an important component of SZNA, remains a challenge. If the rate of CO(2) gas generation associated with contaminant degradation can be determined, it may be used as a proxy for the overall rate of subsurface biodegradation. Here, the CO(2)-efflux at the ground surface is measured using a dynamic closed chamber (DCC) method to evaluate whether this technique can be used to assess the areal extent of the contaminant source zone and the depth-integrated rate of contaminant mineralization. To this end, a field test was conducted at the Bemidji, MN, crude oil spill site. Results indicate that at the Bemidji site the CO(2)-efflux method is able to both delineate the source zone and distinguish between the rates of natural soil respiration and contaminant mineralization. The average CO(2)-efflux associated with contaminant degradation in the source zone is estimated at 2.6 μmol m(-2) s(-1), corresponding to a total petroleum hydrocarbon mineralization rate (expressed as C(10)H(22)) of 3.3 g m(-2) day(-1).     

On a decidable generalized quantifier logic corresponding to a decidable fragment of first-order logic

Van Lambalgen (1990) proposed a translation from a language containing a generalized quantifierQ into a first-order language enriched with a family of predicatesR _i, for every arityi (or an infinitary predicateR) which takesQxg(x, y₁,..., y_n) to x(R(x, y₁,..., y₁) (x,y₁,...,y_n)) (y ₁,...,y_n are precisely the free variables ofQx). The logic ofQ (without ordinary quantifiers) corresponds therefore to the fragment of first-order logic which contains only specially restricted quantification. We prove that it is decidable using the method of analytic tableaux. Related results were obtained by Andréka and Németi (1994) using the methods of algebraic logic.     

Miniemulsion polymers as solid support for transition metal catalysts

A pentadentate salen-type ligand was immobilized in a poly[(styrene)]-co-(butyl acrylate)] matrix by miniemulsion polymerization. The obtained polymer beads revealed a particle size of 50 nm in the dry state by transmission electron microscopy. Dynamic light scattering experiments in methanol and water showed a solvent-dependent average particle size with a mean particle diameter of up to 233 nm in methanol. These results provide valuable insights for the optimization of macromolecular oxidation catalysts and their future use as enzyme-like entities in aqueous media. The particle stability was demonstrated over a wide pH range (3-11) by gel permeation chromatography, and initial results for the metal ion binding ability were obtained.     

Molecular Signatures in Urologic Tumors

Urologic tumors continue to represent a huge fraction of cancer cases in the United States, with over 376,310 estimated new diagnoses in 2013. As with many types of tumors, urologic tumors vary greatly in their phenotype, ranging from minimally invasive to malignancies possessing great metastatic potential. The increasing need for more efficient and less invasive methods of cancer detection, as well as the ability to predict severity of the disease phenotype is readily evident—yet reliable methods remain elusive in a clinical setting today. Comprehensive panels of gene clusters are being developed toward the generation of molecular signatures in order to better diagnose urologic malignancies, and identify effective treatment strategies in the emerging era of personalized medicine. In this review, we discuss the current literature on the credibility and biomarker value of such molecular signatures in the context of clinical significance relating to the pathological aggressiveness of urologic tumors (prostate, bladder and renal cancer)—also exploiting their predictive potential in the response to treatment.     

An Overview of Scaling Laws in Ad Hoc and Cognitive Radio Networks

Currently, wireless communications are changing along the lines of three main thrusts. The first is the introduction of secondary spectrum licensing (SSL). Regulations on the usage of licensed spectra are being loosened, encouraging unused primary spectrum to be licensed, often in an opportunistic manner, to secondary devices. The second is the introduction of cognitive radios. These wireless devices are able to sense and adapt in a “smart” manner to their wireless environment, making them prime candidates to becoming secondary users in SSL initiatives. Finally, as we approach the communication limits of point-to-point channels, and as wireless devices become cheap and ubiquitous, the focus is shifting from single to multiple communication links, or networks. In this paper, we provide an overview of the recently established theoretical limits, in the form of sum-rates, or throughput, of two main types of networks: ad hoc networks, in which the devices are homogeneous, and cognitive networks, in which a mixture of primary and secondary (or cognitive) devices are present. We summarize and provide intuition on how the throughput of a network scales with its number of nodes n, as n → ∞, under different network and node capability assumptions.

Bayesian networks for system reliability reassessment

This paper proposes a methodology to apply Bayesian networks to structural system reliability reassessment, with the incorporation of two important features of large structures: (1) multiple failure sequences, and (2) correlations between component-level limit states. The proposed method is validated by analytical comparison with the traditional reliability analysis methods for series and parallel systems. The Bayesian network approach is combined with the branch-and-bound method to improve its efficiency and to facilitate its application to large structures. A framed structure with multiple potential locations of plastic hinges and multiple failure sequences is analyzed to illustrate the proposed method.