Better Than
Recently Published Documents


(FIVE YEARS 30218)



2022 ◽  
Vol 16 (1) ◽  
pp. 0-0

The number of attacks increased with speedy development in web communication in the last couple of years. The Anomaly Detection method for IDS has become substantial in detecting novel attacks in Intrusion Detection System (IDS). Achieving high accuracy are the significant challenges in designing an intrusion detection system. It also emphasizes applying different feature selection techniques to identify the most suitable feature subset. The author uses Extremely randomized trees (Extra-Tree) for feature importance. The author tries multiple thresholds on the feature importance parameters to find the best features. If single classifiers use, then the classifier's output is wrong, so that the final decision may be wrong. So The author uses an Extra-Tree classifier applied to the best-selected features. The proposed method is estimated on standard datasets KDD CUP'99, NSL-KDD, and UNSW-NB15. The experimental results show that the proposed approach performs better than existing methods in detection rate, false alarm rate, and accuracy.

2021 ◽  
pp. 1-13
Toktam Taghavi ◽  
Alireza Rahemi ◽  
Reza Rafie ◽  
Maru K. Kering

Rapid multiplication of turmeric (Curcuma longa) by micropropagation is needed to produce a continuous source of uniformly sized, high-quality, and disease-free plantlets. Three in vitro experiments were conducted to optimize the medium by evaluating nine media and a full factorial combination (matrix) of two plant growth regulators for direct organogenesis of ‘Hawaiian Red’ turmeric. Two experiments evaluated the media, and the third studied the plant growth regulator matrix. As a result, Driver and Kuniyuki walnut (DKW), Murashige and Skoog (MS), and broadleaf tree basal (BLT) media performed better than woody plant media [Lloyd & McCown woody plant basal medium (L&M), and McCown’s woody plant basal salt mixture (McCown)] for shoot and root formation. The multiplication rate was 18 plants per explant in DKW with 1 mg⋅L−1 6-benzylaminopurine (BAP) and 0.1 mg⋅L−1 1-naphthaleneacetic acid (NAA). After transferring the plants to an ex vitro environment, the survival rate was 97%, and 30% higher than previously reported. DKW produced the highest number of plantlets (with shoots and roots), and BLT produced fewer plants with higher biomass. In the MS media, higher BAP to NAA ratio (2.5 to 0.1 mg⋅L−1) produced the most significant number of shoots; however, the lowest concentration of BAP and NAA (0.1 mg⋅L−1 of both) produced the highest number of rooted plantlets. There are two recommendations for tissue culture of ‘Hawaiian Red’ turmeric. To produce the highest number of plantlets, one should use the higher BAP to NAA ratio (2.5 mg⋅L−1 BAP and 0.1 mg⋅L−1 NAA) for shoot proliferation and then transfer the explants to the root initiation media. However, to reduce the number of subcultures, the explants can be grown in the lowest concentration of both BAP and NAA (0.1 mg⋅L−1) to induce both shoot and root. Although, the number of plantlets (with roots and shoots) will decrease in this method, there is no need for subsequent subcultures and changing of the plant growth regulator combinations.

2021 ◽  
Ho Yin Yuen ◽  
Jesper Jansson

Abstract Background: Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, inaccuracies in high-throughput experiments often result in incomplete PPI data. Computational techniques are thus used to infer missing data and to evaluate confidence scores, with link prediction being one such approach that uses the structure of the network of PPIs known so far to find good candidates for missing PPIs. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. However, the previously developed L3 principle-based link predictors are only an approximate implementation of the L3 principle. As such, not only is the full potential of the L3 principle not realized, they may even lead to candidate PPIs that otherwise fit the L3 principle being penalized. Result: In this article, we propose a formulation of link predictors without approximation that we call ExactL3 (L3E) by addressing missing elements within L3 predictors in the perspective of network modeling. Through statistical and biological metrics, we show that in general, L3E predictors perform better than the previously proposed methods on seven datasets across two organisms (human and yeast) using a reasonable amount of computation time. In addition to L3E being able to rank the PPIs more accurately, we also found that L3-based predictors, including L3E, predicted a different pool of real PPIs than the general-purpose link predictors. This suggests that different types of PPIs can be predicted based on different topological assumptions and that even better PPI link predictors may be obtained in the future by improved network modeling.

2021 ◽  
Vol 11 (1) ◽  
Bing Liu ◽  
Yueqiang Jin ◽  
Dezhi Xu ◽  
Yishu Wang ◽  
Chaoyang Li

AbstractStudies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.

Rajalaxmi Behera ◽  
Ajoy Mandal ◽  
Saroj Rai ◽  
M. Karunakaran ◽  
Mohan Mondal ◽  

Background: Genotype environment interaction plays vital role in animal productivity. Heat stress is one of the major environmental stressor affecting milk production and measured in terms of temperature humidity index (THI). Indian milk industry largely depends on crossbred cows bearing different degree of exotic inheritance. Thus, the role of genotype (genetic group) of the crossbred cows and environment (THI) interaction plays vital role in Indian climate which is mostly tropical in nature. Therefore, study was undertaken to examine the existence of genetic group × THI in crossbred dairy cows reared at institute herd of ICAR-National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal. Methods: A total of 12364 records each of monthly milk yield (MMY) and average daily milk yield in a month (AMY) of crossbred cows spanned over twenty two years (1994-2015) and weather parameters(temperature and relative humidity) for the corresponding years were collected from institute records. The data were classified into 8 genetic groups according to the genetic composition and 3 THI groups (THI less than 72, THI 72-78 and THI above 78). The interaction model was used to study the G×E interaction study using least squares analysis. Result: Effect of non-genetic factors (parity, period of calving and stage of lactation) was found to be highly significant (P less than 0.01) and genetic group × THI was significant (P less than 0.05) of on both MMY and AMY. Genetic group bearing 50% Jersey and 50% Red Sindhi or Tharparkar were the most heat tolerant breeds. Jersey crossbred cows were more heat tolerant than Holstein crossbred cows. Crossbred cows with 50% Jersey inheritance performed better than higher Jersey inheritance during periods of THI above 72.

Mayuree Kanlayavattanakul ◽  
Nattaya Lourith ◽  
Puxvadee Chaikul

Abstract Background Coffee beans contain oil with health benefits from fatty acids. The unprocessed and processed coffee beans are mostly identical in coffee oil quality and are substantively supplied for certain industries. However, the cost-effective valorization of specialty ingredients from spent coffee grounds for cosmetics is sparely presented. Linoleic acid-rich spent coffee oil, as a specialty material for skin lightening and antiaging cosmetics, is objectively to be presented. Results Spent coffee oils were prepared by different methods. The most cost-effective material with a high extraction yield, linoleic acid content and unsaturated/saturated fatty acid (UFA/SFA) ratio (13.21  ±  0.25, 32.09% and 0.97) was modified. The modified oil was boosted in linoleic acid (77.20% or 140.57% improvement) and the UFA/SFA ratio (33.12). The physicochemical properties of the oil were applicable for cosmetics as per its safety profiles in B16F10 melanoma and normal human skin fibroblast cells. The oil significantly better inhibited cellular melanogenesis than kojic and linoleic acids (p  <  0.01), with prominent tyrosinase and TRP-2 inhibitions. The cellular antioxidant activity of the oil was comparable to those of ascorbic and linoleic acids. The collagen stimulating efficacy of the oil was significantly better than that of ascorbic but comparable to that of linoleic acid as indicated by the MMP-2 inhibitory activities (p  <  0.01 and p  <  0.001, respectively). Conclusions The oil is a specialty material for skin brightening and skin wrinkle reduction/skin elasticity improvement products. A successive circular bioeconomy of spent coffee ground waste in a more profitable cosmetic industry is indicated. Graphic abstract

2021 ◽  
Ling-Yu Wang ◽  
Xue Li ◽  
Kun Luo ◽  
Yu-Hao Song ◽  
Ren-Guo Liu

Abstract In this study, cationic polyacrylamide (CPAM) modified diatomite and cetyl trimethyl ammonium bromide (CTAB) modified diatomite were synthesized and used as conditioner in sewage sludge dewatering. The effects of these two types of modified diatomite on the dewaterability and settling performance of activated sludge were studied. The mechanism of the two types of modified diatomite in the activated sludge system was elucidated. The efficiency of CPAM-modified diatomite was better than that of CTAB-modified diatomite in improving the settleability and dewaterability performance of sludge. The results indicated that specific resistance to filtration (SRF) was decreased from 8.52×1012m/Kg to 0.92×1012 m/Kg, and the water content in the remaining sludge cake after pumping filtration was decreased from 92.2% to 68.1%. by adding 0.4% of CPAM-modified diatomite and pH=3.5, which result in optimal sludge settling of activated sludge. Further studies showed that the polymer/surfactant adsorbed in diatomite increased sludge dewaterability and improved the sedimentation rate as a result of stripping extracellular polymer substances (EPS) and damaging the internal structure of sludge conduce bound water releasing. According to scanning electron microscope(SEM) images, two types of modified diatomite powder not only kept the porous, but also shown more complete and uniform structure in comparison to nature diatomite.

2021 ◽  
pp. 875647932110519
Omar Mohammed ◽  
Ahmed Magdy ◽  
Ahmed Askalany ◽  
Sondos Salem ◽  
Mazen Abdel-Rasheed ◽  

Objective: Preeclampsia accounts for 15% of maternal deaths and may cause fetal morbidity and mortality. The aim of this research was to evaluate the efficacy of maternal uterine artery Doppler versus serum beta-human chorionic gonadotropin (β-hCG), during the first trimester, in predicting preeclampsia and intrauterine growth restriction (IUGR). Materials and Methods: In a convenient sample of 388 pregnant women, uterine artery resistive index (RI) and pulsatility index (PI) were assessed, and serum β-hCG level was measured at 11 to 13 weeks of gestation. The patients’ maternal blood pressure and fetal growth were monitored. Results: The patients with preeclampsia (n = 58) showed a significant uterine RI and PI increase with a significant β-hCG decrease compared with the normotensive patients (n = 330). The specificity of uterine PI and RI to predict preeclampsia and IUGR is higher than that of β-hCG. However, the sensitivity of combined diagnostic tools is higher than the singular use of these diagnostic tests. Conclusion: Uterine artery Doppler may be better than serum β-hCG in predicting preeclampsia and IUGR. However, combined diagnostic techniques may be better to screen at-risk patients.

2021 ◽  
François-Marie Bréon ◽  
Leslie David ◽  
Pierre Chatelanaz ◽  
Frédéric Chevallier

Abstract. In David et al (2021), we introduced a neural network (NN) approach for estimating the column-averaged dry air mole fraction of CO2 (XCO2) and the surface pressure from the reflected solar spectra acquired by the OCO-2 instrument. The results indicated great potential for the technique as the comparison against both model estimates and independent TCCON measurements showed an accuracy and precision similar or better than that of the operational ACOS (NASA’s Atmospheric CO2 Observations from Space retrievals – ACOS) algorithm. Yet, subsequent analysis showed that the neural network estimate often mimics the training dataset and is unable to retrieve small scale features such as CO2 plumes from industrial sites. Importantly, we found that, with the same inputs as those used to estimate XCO2 and surface pressure, the NN technique is able to estimate latitude and date with unexpected skill, i.e. with an error whose standard deviation is only 7° and 61 days, respectively. The information about the date mainly comes from the weak CO2 band, that is influenced by the well-mixed and increasing concentrations of CO2 in the stratosphere. The availability of such information in the measured spectrum may therefore allow the NN to exploit it rather than the direct CO2 imprint in the spectrum, to estimate XCO2. Thus, our first version of the NN performed well mostly because the XCO2 fields used for the training were remarkably accurate, but it did not bring any added value. Further to this analysis, we designed a second version of the NN, excluding the weak CO2 band from the input. This new version has a different behaviour as it does retrieve XCO2 enhancements downwind of emission hotspots, i.e. a feature that is not in the training dataset. The comparison against the reference Total Carbon Column Observing Network (TCCON) and the surface-air-sample-driven inversion of the Copernicus Atmosphere Monitoring Service (CAMS) remains very good, as in the first version of the NN. In addition, the difference with the CAMS model (also called innovation in a data assimilation context) for NASA Atmospheric CO2 Observations from Space (ACOS) and the NN estimates are significantly correlated. These results confirm the potential of the NN approach for an operational processing of satellite observations aiming at the monitoring of CO2 concentrations and fluxes.

2021 ◽  
Shengyu Wang ◽  
Junhua Qiao ◽  
Yaping Chen ◽  
Langfei Tian ◽  
Xin Sun

Abstract Hand, foot and mouth disease (HFMD) is a serious threat to the health of infants, which can be caused by enterovirus 71 (EV71). The clinical symptoms are mostly self-limited, but some of them develop into aseptic meningitis with poor prognosis and even death. In this study, we screened Urolithin A (UroA), an intestinal metabolite of ellagic acid, significantly inhibited the replication of EV71 in cells. Further evaluation showed that UroA was better than ribavirin in CC50, IC50 and selection index (SI). Moreover, we found that UroA inhibits the proliferation of EV71 by promoting autophagy and apoptosis of infected cells. Therefore, UroA is a candidate drug for the treatment of EV71 infection.

Sign in / Sign up

Export Citation Format

Share Document