Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.     

ConSpec – A Formal Language for Policy Specification

The paper presents ConSpec, an automata based policy specification language. The language trades off clean semantics to language expressiveness; a formal semantics for the language is provided as security automata. ConSpec specifications can be used at different stages of the application lifecycle, rendering possible the formalization of various policy enforcement techniques.     

Neural Network Model to Support International Market Entry Decisions

Bidding for international construction projects is a critical decision for companies that aim to position themselves in the global construction market. Determination of attractive projects and markets where the competitive advantage of a company is high requires extensive environmental scanning, forecasting, and learning from the experience of competitors in international markets. In this paper, a neuronet model has been developed as a decision support tool that can classify international projects with respect to attractiveness and competitiveness based on the experiences of Turkish contractors in overseas markets. The model can be used to guide decision makers on which type of data should be collected during international business development and further help them to prepare priority lists during strategic planning. Information derived from the model demonstrates that the most important factors that increase attractiveness of an international project are availability of funds, market volume, economic prosperity, contract type, and country risk rating. Similarly, level of competition, attitude of host government, existence of strict quality requirements, country risk rating, and cultural/religious similarities are the most important factors that affect competitiveness of Turkish contractors in international markets.     

Prediction of disorder with new computational tool: BVDEA

Recognizing that many intrinsically disordered regions in proteins play key roles in vital functions and also in some diseases, identification of the disordered regions has became a demanding process for structure prediction and functional characterization of proteins. Therefore, many studies have been motivated on accurate prediction of disorder. Mostly, machine learning techniques have been used for dealing with the prediction problem of disorder due to the capability of extracting the complex relationships and correlations hidden in large data sets. In this study, a novel method, named Border Vector Detection and Extended Adaptation (BVDEA) was developed for predicting disorder as an alternative accurate classifier. The classifier performs the predictions by using three types of structural features belonging to proteins. For attesting the performance of the method, three computational learning techniques and eleven specific tools were used for comparison. Training was executed based on the data by 5-fold cross validation. When compared with the two learning methods of LVQ and BVDA, the proposed method gives the best success on classification. The BVDEA also provides faster and more robust learning as compared to the others. The new method provides a significant contribution to predicting disorder and order regions of proteins.     

First-principles hydrogen adsorption properties of Li-decorated ThMoB4-type graphene

The hydrogen storage (1–10H₂) properties of single- and double-side lithium decorated ThMoB₄-type graphene (Li/ThMoB₄C) are systematically investigated by density functional first-principles calculations within Dmol³ package. After well-converged geometry optimizations, it is found that the binding energy of Li adatom is higher enough, and there is no adatom clustering. The average adsorption energies of 1–6 H₂ deviate in 0.20–0.27 eV/H₂ range, which is providing a convenient physical adsorption-desorption cycle. A detailed examination of the binding mechanism between the constituents of the H₂ adsorbed Li-decorated systems is presented by density of states, Mulliken charge analysis, electron density and density difference calculations. For further decoration and adsorption with 12Li adatom and 72H₂ molecules, the computation yields a high gravimetric density of 14.5 wt % with the acceptable adsorption energy. In this way, it is concluded that Li/ThMoB₄ system can be considered as a promising hydrogen storage medium.     

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.     

Effect of protein-energy malnutrition on erythropoietin requirement in maintenance hemodialysis patients

Possible interactions between inflammatory and nutritional markers and their impact on recombinant human erythropoietin (rHuEPO) hyporesponsiveness are not well understood. We investigated the role of nutritional status in rHuEPO requirement in maintenance hemodialysis (MHD) patients without evidence of inflammation. This cross-sectional study included 88 MHD patients. The associations between required rHuEPO dose and malnutrition-inflammation score (MIS) and several laboratory values known to be related to nutrition and/or inflammation were analyzed. Anthropometric measures including body mass index, triceps skinfold thickness, and midarm circumferences were also measured. Twenty-three patients with serum C-reactive protein levels >10 mg/L were excluded from the analysis. The remaining 65 patients (male/female, 41/24; age 49.1+/-11.4 years; dialysis duration 99.7+/-63.0 months) were studied. These patients had moderate malnutrition and the average MIS was 7.4 (range 3-17). The average weekly dose of administered rHuEPO was 69.1+/-63.1 U/kg. Malnutrition-inflammation score had a positive correlation with the serum concentration of tumor necrosis factor-alpha, whereas it had a negative correlation with anthropometric measures, total iron-binding capacity, prealbumin, phosphorus, creatinine, and triglyceride. According to Pearson's correlation analysis, significant relationships of increased MIS with increased required rHuEPO dose and rHuEPO responsiveness index (EPO divided by hematocrit) were observed (p=0.008, r=-0.326; p=0.017, r=-0.306, respectively). Recombinant human erythropoietin dose requirement is correlated with MIS and adverse nutritional status in MHD patients without evidence of inflammation. Further research should focus on reversing the undergoing microinflammation for a better outcome in dialysis patients.     

Effect of alkali metal hydroxides on the morphological development and optical properties of ceria nanocubes under hydrothermal conditions

Nanocrystalline cerium(LV) oxide (CeO2, ceria) particles were produced via the hydrothermal treatment of cerium nitrate hexahydrate with various alkali metal hydroxides (MOH: M = Li, Na, K). Experimental conditions such as [MOH], reaction temperature, and reaction time were studied. Particle morphology as well as size of crystallites was precisely controlled by choice of experimental conditions. While rod-shaped particles were obtained at 120 degrees C, well-defined nanocubes were formed at higher temperatures regardless of the choice of MOH. Examination of particle growth kinetics, in the final stages of crystallization, showed that particle growth rate is controlled by two different mechanisms. Grain boundary diffusion controls the particle growth in the presence of NaOH with an activation energy of 113.8 kj/mol and surface diffusion for LiOH ad KOH with the activation energy of 43.0-150.9 kj/mol, respectively. In addition, the particles exhibit strong violet and blue emissions at 400 nm and 370 nm. The former emission originates from excitation of a wide band gap of CeO2. The latter one is attributed to the trivalency of the cerium ion and appears to be sensitive to all the experimental conditions studied. Both extending reaction time and increasing temperature reduce the intensity of the 370 nm emission and increase the intensity of the 400 nm emission.     

Fatty acids of neutral and phospholipids of three endangered trout: Salmo trutta caspius Kessler, Salmo trutta labrax Pallas and Salmo trutta macrostigma Dumeril

Total (TL), neutral (NL) and phospholipid (PL) amounts and fatty acid (FA) composition of female Salmo trutta caspius, Salmo trutta labrax and Salmo trutta macrostigma were investigated during one year. Twenty-three FAs were identified in both NLs and PLs. The principal FAs of both fractions were palmitic acid in saturated fatty acid, oleic acid in monounsaturated fatty acid, docosahexaenoic acid (DHA) in n-3 polyunsaturated fatty acids (n-3 PUFAs) and linoleic acid in n-6 PUFAs. The highest values for TLs, NLs and PLs were found in winter. As a general trend, the highest n-3/n-6 ratios and eicosapentaenoic acid (EPA) + DHA amounts were found in the winter and this coincided with the lowest gonado-somatic index.