Impact of dendritic polymers on nanomaterials

For many years scientists have employed dendritic polymers (dendrimers and hyperbranched polymers) in association with other nanomaterials (such as graphene, carbon nanotubes, proteins and peptides, as well as metallic nanoparticles) to synthesize hybrid nanomaterials with improved biocompatibility, biodegradability, functionality, physicochemical properties and the capability of carrying other molecules. However, more recent studies demonstrate that one of the less noticed effects and newly observed facets of dendritic polymers is their role in changing the structure (shape, size and sheet multiplicity) of the obtained hybrid nanomaterials, upon covalent and noncovalent interactions. In this review, we intend to have a more specialized look at these reports and discuss the ‘whys’ and ‘hows’ of this phenomenon

End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods

Residues and dissipation kinetics of two imidacloprid nanoformulations on bean (Phaseolus vulgaris L.) under field conditions

The current study investigates the dissipation kinetics of two imidacloprid (IMI) nanoformulations (entitled: Nano-IMI and Nano-IMI/TiO2) on common bean (Phaseolus vulgaris) seeds under field conditions and compares them with 35% Suspension Concentrate (SC) commercial formulation. To do so, it sprays P. vulgaris plants at 30 and 60 g/ha within green bean stage, sampling them during the 14-day period after the treatment. Following extraction and quantification of IMI residues, dissipation data have been fitted to simple-first order kinetic model (SFOK) and to first-order double-exponential decay (FODED) models, with 50% and 90% dissipation times (DT50 and DT90, respectively) assessed along the pre-harvest interval (PHI). With the exception of Nano-IMI at 60 g/ha, other decline curves are best fitted to the FODED model. In general, dissipation is faster for Nano-IMI (at 30 g/ha: DT50 = 1.09 days, DT90 = 4.30 days, PHI = 1.23 days; at 60 g/ha: DT50 = 1.29 days, DT90 = 4.29 days, PHI = 2.95 days) and Nano-IMI/TiO2 (at 30 g/ha: DT50 = 1.15 days, DT90 = 4.40 days, PHI = 1.08 days; at 60 g/ha: DT50 = 0.86 days, DT90 = 4.92 days, PHI = 3.02 days), compared to 35% SC (at 30 g/ha: DT50 = 1.58, DT90 = 6.45, PHI = 1.93; at 60 g/ha: DT50 = 1.58 days, DT90 = 14.50 days, PHI = 5.37 days). These results suggest the suitability of Nano-IMI and Nano-IMI/TiO2 application at both rates in terms of their residues on P. vulgaris seeds

Investigating fish purchase patterns and preferences among the consumers of Sari

The present study aimed at looking into the fish consumption preferences and purchase patterns among 266 households of Sari in order to identify the fish market at Mazandaran province. To do so, a questionnaire was provided to be administered among consumers to state their preferences towards the type of fish species, purchased forms of fish as well as fish purchasing pattern in their family. Descriptive statistics as well as referential analysis was conducted through Friedman Test. Results showed that meat consumption priority among the households of Sari constituted the consumption of poultry meat, lamb, fish and beef, respectively. As to the investigation of fish purchase pattern, it was that almost two-thirds of households in Sari bought fish less than once a month and only a few percentage of them (4.1 percent) purchased them on a weekly basis. It was also revealed that consumers in Sari prefer marine fish more than farmed fish, and cold-water fish more than warm-water fish. Fresh, live and canned fish were the preferred forms of purchase for the consumers in Sari, and other forms of fish such as frozen, smoked and salted fish (total 7.9 percent) were rarely preferred by the consumers. Findings of the current research can contribute to powerful decision-making of companies and suppliers in terms of which product should be provided to the market for more sale. Therefore, recognizing the needs and desires of consumers and understanding their purchase behavior are effective steps to meet their expectations and ultimately increasing fish consumption

Effect of Early Post Cesarean Feeding on Gastrointestinal Complications

Background: Gastrointestinal complications are the main complication in patients after cesarean section. Previous studies have reported different results about the effect of early post cesarean feeding on vomiting, nausea, flatulence and illus. Objectives: To identify the effect of early post cesarean feeding on gastrointestinal complications. Materials and Methods: This randomized controlled trial was conducted on 82 women who underwent cesarean section in Mashhad Omolbanin hospital. They were randomly assigned to two equal experimental and control groups. The experimental group started oral fluids four hours after surgery, followed by a regular diet after bowel sounds returned. Mothers in the control group received fluid intravenously during the initial 12 hours, and then if bowel sounds were heard, they were permitted to receive oral fluids and they could start a solid diet if they had defecation. Vomiting and flatulence were assessed with a visual analog scale. Nausea was assessed with an observation questionnaire and illus was assessed via bowel sounds, gas passing and defecation 4, 12, 24, 36 and 48, hours post surgery in the two groups. Also, they were studied for the time of gas passing, bowel sound return, defecation, sitting, walking and breast-feeding. Data were analyzed using the chi-square, Fisher's exact test, t-test and Man-Whitney U test. Results: No mother experienced nausea, vomiting and illus. Flatulence severity 4 and 12 hours after surgery was similar in both groups (P = 0.856, P = 0.392). However, flatulence severity 24, 36 and 48 hours after surgery, was less in the experimental group (P = 0.030, P = 0.016, P = 0.001). Also, bowel sound return, time of gas passing, defecation, sitting and walking were less in the experimental group (P = 0.001). Conclusion: This study showed that early feeding decreased post cesarean gastrointestinal complications

Seizure characterisation using frequency-dependent multivariate dynamics

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy