Notes

The covalent organic crate ingredient acting as any supramolecular darkness mask to the regioselective functionalization of C60.
Multi-class classification for highly imbalanced data is a challenging task in which multiple issues must be resolved simultaneously, including (i) accuracy on classifying highly imbalanced multi-class data; (ii) training efficiency for large data; and (iii) sensitivity to high imbalance ratio (IR). In this paper, a novel sequential ensemble learning (SEL) framework is designed to simultaneously resolve these issues. SEL framework provides a significant property over traditional AdaBoost, in which the majority samples can be divided into multiple small and disjoint subsets for training multiple weak learners without compromising accuracy (while AdaBoost cannot). To ensure the class balance and majority-disjoint property of subsets, a learning strategy called balanced and majority-disjoint subsets division (BMSD) is developed. Unfortunately it is difficult to derive a general learner combination method (LCM) for any kind of weak learner. In this work, LCM is specifically designed for extreme learning machine, called LCM-ELM. The proposed SEL framework with BMSD and LCM-ELM has been compared with state-of-the-art methods over 16 benchmark datasets. In the experiments, under highly imbalanced multi-class data (IR up to 14K; data size up to 493K), (i) the proposed works improve the performance in different measures including G-mean, macro-F, micro-F, MAUC; (ii) training time is significantly reduced.In this work we develop analytical techniques to investigate a broad class of associative neural networks set in the high-storage regime. These techniques translate the original statistical-mechanical problem into an analytical-mechanical one which implies solving a set of partial differential equations, rather than tackling the canonical probabilistic route. We test the method on the classical Hopfield model - where the cost function includes only two-body interactions (i.e., quadratic terms) - and on the "relativistic" Hopfield model - where the (expansion of the) cost function includes p-body (i.e., of degree p) contributions. Under the replica symmetric assumption, we paint the phase diagrams of these models by obtaining the explicit expression of their free energy as a function of the model parameters (i.e., noise level and memory storage). Further, since for non-pairwise models ergodicity breaking is non necessarily a critical phenomenon, we develop a fluctuation analysis and find that criticality is preserved in the relativistic model.Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitly account for the correlation in the dataset in terms of graph Laplacian. We will give two variants; in the first one the graph is computed from the data and fixed during the operation. In the second, the graph is learnt iteratively from the data during operation. The first technique will be applied for clustering, and the second one for solving inverse problems.It has been hypothesized that noise-induced cochlear synaptopathy in humans may result in functional deficits such as a weakened middle ear muscle reflex (MEMR) and degraded speech perception in complex environments. Although relationships between noise-induced synaptic loss and the MEMR have been demonstrated in animals, effects of noise exposure on the MEMR have not been observed in humans. The hypothesized relationship between noise exposure and speech perception has also been difficult to demonstrate conclusively. Given that the MEMR is engaged at high sound levels, relationships between speech recognition in complex listening environments and noise exposure might be more evident at high speech presentation levels. In this exploratory study with 41 audiometrically normal listeners, a combination of behavioral and physiologic measures thought to be sensitive to synaptopathy were used to determine potential links with speech recognition at high presentation levels. We found decreasing speech recognition as a function of presentation level (from 74 to 104 dBA), which was associated with reduced MEMR magnitude. We also found that reduced MEMR magnitude was associated with higher estimated lifetime noise exposure. Together, these results suggest that the MEMR may be sensitive to noise-induced synaptopathy in humans, and this may underlie functional speech recognition deficits at high sound levels.Virus detection in food requires appropriate elution and concentration techniques which need to be adapted for different food matrices. ISO/TS-15216-12017 and ISO/TS-15216-22019 describe standard methods for hepatitis A virus (HAV) research in some food only. Milk-based products containing one or more types of fruit are not covered by ISO procedures, even though they can be contaminated by fruit added to these products or by the food handlers. The aim of this work was to identify an efficient method for the detection of HAV in milk-based products. Four methods were tested to recover HAV from artificially contaminated milk, yoghurt and ice cream containing soft fruits. Results showed that the efficiency of the tested methods depends on the analyzed matrix. In milk we obtained a mean recovery from 13.4% to 1.9%; method based on high speed centrifuge gave the best values. The average recovery in yoghurt was between 3.3% and 114.4%, the latter value achieved by method with beef extract at 3% as eluent. compound library inhibitor Finally, two methods gave the best results in ice cream with similar recoveries 29.1% and 27.7% respectively. The first method used glycine as eluent while the other one was based on high speed centrifugation. The ISO method has never proved to be the most efficient in the matrices studied. Therefore, based on the results obtained, a complete rethinking of the ISO method may be necessary to improve its recovery for some products such as milk, while only small changes would be sufficient for other products, such as yoghurt and ice cream.ε-Polylysine (ε-PL) is a natural and highly effective cationic antimicrobial, of which antibacterial activity is limited in food matrix because of ε-PL's charged amino groups that form complexes with food polyanions. Whey protein-ε-PL complexes delivery system was found to be able to solve the problem and keep the antibacterial activity. This study investigated the antibacterial activity of the complexes and its mechanism against Escherichia coli. The minimal inhibitory concentration of ε-PL was in the range 11.72-25.00 g/mL for the complexes containing different amount of ε-PL and was similar to that of free ε-PL. The results of scanning electron microscopy showed that the complexes could destroy the structure of E. coli cell membrane surface, leaving holes on the surface of the bacteria, leading to the death of the bacteria. The molecular dynamics simulation results showed that the mechanism of the antibacterial activity of the complexes was as follows under electrostatic interaction, the complexes captured the phospholipid molecules of the bacterial membrane through the hydrogen bonds between the positively charged amino groups of ε-PL and the oxygen atom of the phosphate head groups of the membrane, which could create holes on the surface of the bacteria and lead to the death of the bacteria.
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