Characteristics of population-based matched controls by frequency of fried food intake at home and outside of the home¹.

<p>Characteristics of population-based matched controls by frequency of fried food intake at home and outside of the home<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192960#t002fn001" target="_blank">¹</a>.</p

Joint effect of eating fried food ≥4 times/week at home and outside of the home.

<p>Odds Ratio (OR) of MI (95%CI) according to the joint category of fried food intake at home and outside of the home; All models used a fixed sample size of 2,154 case-control pairs, ORs conditioned on matching variables (age, sex and area of residence).</p> <p>A:, Adjusted for history of diabetes (yes/no), hypertension (yes/no), smoking (never, past, <10 cigarettes/d, 10–19 cigarettes/d, and ≥20 cigarettes/d), waist-hip-ratio (quintiles), physical activity (quintiles), income (quintiles), educational years, intake of alcohol (never, past, and tertile of current drinkers) and occupation (retired, agriculture, plumbers, semi-skilled or driver, managers and administrators, professionals and others);</p> <p>B: Further adjusted for saturated fat, fiber and total energy intake (all in quintile).</p

Frequency of fried food intake and risk of nonfatal acute myocardial infarction in the Costa Rica Heart Study.

<p>Frequency of fried food intake and risk of nonfatal acute myocardial infarction in the Costa Rica Heart Study.</p

Acetylated debranched rice starch: Structure, characterization, and functional properties

<p>Combining biological and chemical methods for the modification of starch is a very interesting prospect. The goal of this work was to investigate the influence of debranching and acetylation on the structure of rice starch (RS), ultimately to improve the function of RS. Our experimental results showed that RS particles can be completely destroyed by debranching. The crystal structure of RS is A-type, but the structure of debranched rice starch (DRS) and acetylated debranched rice starch (ADRS) is B-type. The crystallinity degree of DRS and ADRS was less than that of RS. The surface of DRS and ADRS particles became very rough, marking a complete departure from the surface of smooth RS granules. Acetylation occurred specifically at the sides of DRS granules. The surface hydroxyl numbers of RS increased after debranching and acetylation, and the thermal characteristics changed substantially. Debranching led to an increase in onset temperature from 97.79 to 107.64°C; acetylation improved the freeze-thaw stability and swelling power of both the RS and DRS. The blue value of RS varied from 0.424 to 0.640 due to debranching.</p

Additional file 1 of Pretreatment with a long-acting GnRH agonist for frozen-thawed embryo transfer cycles: how to improve live birth?

Additional file 1: Supplementary Table 1. Characteristics of patients in GnRHa+ovulation vs. GnRHa+HRT. Supplementary Table 2. Outcomes of FET for GnRHa+ovulation vs. GnRHa+HRT

GSB model experimental results on the NSL-KDD.

With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories’ malware. In the dataset sample, the normal data samples account for the majority, while the attacks’ malware accounts for the minority. However, the minority categories’ attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks’ malware to improve the detection rate of attacks’ malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories’ malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection.</div

The configuration of parameter.

With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories’ malware. In the dataset sample, the normal data samples account for the majority, while the attacks’ malware accounts for the minority. However, the minority categories’ attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks’ malware to improve the detection rate of attacks’ malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories’ malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection.</div

Different category in the dataset.

With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories’ malware. In the dataset sample, the normal data samples account for the majority, while the attacks’ malware accounts for the minority. However, the minority categories’ attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks’ malware to improve the detection rate of attacks’ malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories’ malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection.</div

Innovations and contributions of the paper.

With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories’ malware. In the dataset sample, the normal data samples account for the majority, while the attacks’ malware accounts for the minority. However, the minority categories’ attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks’ malware to improve the detection rate of attacks’ malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories’ malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection.</div

Metabolic Effects of the <i>pksCT</i> Gene on Monascus aurantiacus Li As3.4384 Using Gas Chromatography–Time-of-Flight Mass Spectrometry-Based Metabolomics

Monascus spp. have been used for the production of natural pigments and bioactive compounds in China for several centuries. Monascus can also produce the mycotoxin citrinin, restricting its use. Disruption of the <i>pksCT</i> gene in Monascus aurantiacus Li AS3.4384 reduces citrinin production capacity of this strain (Monascus PHDS26) by over 98%. However, it is unclear how other metabolites of M. aurantiacus Li AS3.4384 (the wild-type strain) are affected by the <i>pksCT</i> gene. Here, we used metabolomic analyses to compare red yeast rice (RYR) metabolite profiles of the wild-type strain and Monascus PHDS26 at different stages of solid-state fermentation. A total of 18 metabolites forming components within the glycolysis, acetyl-CoA, amino acid, and tricarboxylic acid (TCA) cycle metabolic processes were found to be altered between the wild-type strain and Monascus PHDS26 at different stages of solid-state fermentation. Thus, these findings provide important insights into the metabolic pathways affected by the <i>pksCT</i> gene in M. aurantiacus