Feeding habits of Pacific anchovy, Engraulis japonicus (Actinopterygii: Clupeiformes: Engraulidae), captured off the southern coasts of Korea

Understanding the feeding ecology of anchovies in the southern waters of Korea is crucial for improving ecosystem management. However, to date, few studies have examined seasonal changes in the diet of Pacific anchovy, Engraulis japonicus Temminck et Schlegel, 1846, in Korean waters, with the majority of these focusing on the larval and adult stages. The presently reported study provides updates on the feeding habits of E. japonicus off the southern coast of Korea. We analyzed 347 individuals. One-way analysis of similarity was performed to evaluate the differences in diet composition among size classes and seasons of E. japonicus, and correspondence analysis was conducted using the matrix of the percentage by number (%N) data for prey with occurrence of less than 10% to determine the distribution of prey across all size classes and each season. The diet of E. japonicus was investigated according to season and four size classes. The fork length of these specimens ranged from 5.4 to 14.1 cm. A total of 55 prey taxa of varying sizes between 0.33 mm (diatom Coscinodiscus spp.) and 5.8 mm (fish larvae) were recorded. Anchovies were exclusively planktivorous, and copepods were the most common prey, comprising 82.1% of the identified food items and 84.3% of anchovy stomach contents analyzed. However, their occurrence and abundance varied according to season and Pacific anchovy size class. According to the percentage of the index of relative importance (%IRI), the most important prey items were the copepods Calanus sinicus (48.0%), Paracalanus orientalis (31.7%), bivalve larvae (5.8%), Ditrichocorycaeus affinis (4.2%), and calanoid copepods (2.4%). Analysis of similarities and similarity percentage analysis indicated that a distinct diet of Pacific anchovy in the southern waters of Korea is potentially driven by differences in hydrological conditions. Correspondence analysis revealed that anchovies had the most significant impact on the differences between size classes. The results deepen our understanding of prey species diversity and intraspecific food competition off the southern coast of Korea

What factors of early-stage innovative projects are likely to drive projects’ success? A longitudinal analysis of Korean entrepreneurial firms

Previous studies have identified the factors affecting successful technology commercialization as outcomes of R&D projects. However, most of them have used cross‐sectional data, whereas there is a dearth of literature using longitudinal data analysis. Longitudinal analysis is essential for investigating the characteristics of early‐stage innovative projects due to the inherent time lag between project evaluation and commercialization. Therefore, this study examines the early‐stage project characteristics that can be used as meaningful evaluation criteria for predicting success, particularly in technology commercialization. We collected data on the ex‐ante evaluation results and ex‐post commercialization results of R&D projects pursued by entrepreneurial firms. We then conducted a logistic regression analysis and identified three market‐related factors as significant in driving technology commercialization success in the early stages of technology development: market potential, commercialization plan, and market condition

Determination and optimization of spatial samples for distributed measurements.

There are no accepted standards for determining how many measurements to take during part inspection or where to take them, or for assessing confidence in the evaluation of acceptance based on these measurements. The goal of this work was to develop a standard method for determining the number of measurements, together with the spatial distribution of measurements and the associated risks for false acceptance and false rejection. Two paths have been taken to create a standard method for selecting sampling points. A wavelet-based model has been developed to select measurement points and to determine confidence in the measurement after the points are taken. An adaptive sampling strategy has been studied to determine implementation feasibility on commercial measurement equipment. Results using both real and simulated data are presented for each of the paths

Donepezil Regulates LPS and Aβ-Stimulated Neuroinflammation through MAPK/NLRP3 Inflammasome/STAT3 Signaling

The acetylcholinesterase inhibitors donepezil and rivastigmine have been used as therapeutic drugs for Alzheimer’s disease (AD), but their effects on LPS-and Aβ-induced neuroinflam-matory responses and the underlying molecular pathways have not been studied in detail in vitro and in vivo. In the present study, we found that 10 or 50 μM donepezil significantly decreased the LPS-induced increases in the mRNA levels of a number of proinflammatory cytokines in BV2 mi-croglial cells, whereas 50 μM rivastigmine significantly diminished only LPS-stimulated IL-6 mRNA levels. In subsequent experiments in primary astrocytes, donepezil suppressed only LPS-stimulated iNOS mRNA levels. To identify the molecular mechanisms by which donepezil regulates LPS-induced neuroinflammation, we examined whether donepezil alters LPS-stimulated proin-flammatory responses by modulating LPS-induced downstream signaling and the NLRP3 inflam-masome. Importantly, we found that donepezil suppressed LPS-induced AKT/MAPK signaling, the NLRP3 inflammasome, and transcription factor NF-kB/STAT3 phosphorylation to reduce neuroin-flammatory responses. In LPS-treated wild-type mice, a model of neuroinflammatory disease, donepezil significantly attenuated LPS-induced microglial activation, microglial density/morphol-ogy, and proinflammatory cytokine COX-2 and IL-6 levels. In a mouse model of AD (5xFAD mice), donepezil significantly reduced Aβ-induced microglial and astrocytic activation, density, and mor-phology. Taken together, our findings indicate that donepezil significantly downregulates LPS-and Aβ-evoked neuroinflammatory responses in vitro and in vivo and may be a therapeutic agent for neuroinflammation-associated diseases such as AD. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

d-Ribose stabilizes precursor and mature ribose-binding proteins of Escherichia coli

AbstractHeat- and guanidine hydrochloride-induced unfolding and refolding of precursor as well as mature ribose-binding proteins of Escherichia coli were studied in the presence of d-ribose using intrinsic tyrosine fluorescence and circular dichroism. The precursor and mature proteins have shown virtually identical unfolding-folding behavior. It was observed that d-ribose refolds partially unfolded precursor and mature ribose binding proteins into native structure and decreases the unfolding rate of the these proteins. The conformational stabilities of these proteins were found to increase with increasing d-ribose concentration

Selection of<i>Lecanicillium</i>Strain with High Virulence against Developmental Stages of<i>Bemisia tabaci</i>

Selection of fungal strains with high virulence against the developmental stages of Bemisia tabaci was performed using internal transcribed spacer regions. The growth rate of hyphae was measured and bioassay of each developmental stage of B. tabaci was conducted for seven days. All of the fungal strains tested were identified as Lecanicillium spp., with strain 4078 showing the fastest mycelium growth rate (colony diameter, 16.3 ± 0.9 mm) among the strains. Compared to strain 4075, which showed the slowest growth rate, the growth rate of strain 4078 was increased almost 2-fold after seven days. Strains 4078 and Btab01 were most virulent against the egg and larva stages, respectively. The virulence of fungal strains against the adult stage was high, except for strains 41185 and 3387. Based on the growth rate of mycelium and level of virulence, strains 4078 and Btab01 were selected as the best fungal strains for application to B. tabaci, regardless of developmental stage

Tweaking Deep Neural Networks

Deep neural networks are trained so as to achieve a kind of the maximum overall accuracy through a learning process using given training data. Therefore, it is difficult to fix them to improve the accuracies of specific problematic classes or classes of interest that may be valuable to some users or applications. To address this issue, we propose the synaptic join method to tweak neural networks by adding certain additional synapses from the intermediate hidden layers to the output layer across layers and additionally training only these synapses, if necessary. To select the most effective synapses, the synaptic join method evaluates the goodness of all the possible candidate synapses between the hidden neurons and output neurons based on the distribution of all the possible proper weights. The experimental results show that the proposed method can effectively improve the accuracies of specific classes in a controllable way. CCBYTRU