Helping others, warming yourself: Altruistic behaviors increase warmth feelings of the ambient environment

Altruistic behaviors typically improve the welfare of the recipient at the cost of the performer’s resources and energy. Do altruistic performers obtain any positive internal reward from altruistic behaviors? We conducted six experiments to explore whether altruistic behaviors could increase performer’s warmth perception of the ambient environment. The first three studies focused on crisis situations. A retrospective field study (Study 1, with Hurricane Sandy) and two laboratory studies (Study 2a and 2b, with an earthquake scenario) found that people who helped others felt warmer of the ambient environment than people who did not. We extended to daily life situations and found that participants who performed helping behaviors in laboratory (either voluntarily in Study 3a or randomly assigned to in Study 3b) and passers-by who donated to a charity (Study 4) reported warmer perception of the ambient environment than those who did not. These findings suggested an immediate internal reward of altruism

Modeling analysis of the relationship between atherosclerosis and related inflammatory factors

Objective: To establish early diagnosis model of inflammatory factors for atherosclerosis (AS), providing theoretical evidence for early detection of AS and development of plaques. Methods: Serum samples were collected to detect the inflammatory factors including CysC, Hcy, hs-CRP, UA, FIB, D-D, LP (a), IL-6, SAA, sCD40L and MDA. Using Logistic regression analysis, the inflammatory factors used for modeling were screened out, and then the AS early diagnosis models were established based on receiver operating characteristic (ROC) curve, support vector machine and BP neural network respectively. Results: No significant difference exists between the general materials of two groups. All 11 inflammatory factors had higher level in AS group than in control group. As shown in ROC curve, all inflammatory factors were helpful in AS diagnosis. In terms of sensitivity, UA ranked first (98) and FIB ranked last (55.5); in terms of specificity, UA ranked first (99) and FIB ranked last (78); in terms of area under the curve, UA and SAA ranked first (both were 0.995) and FIB ranked last (0.721). Based on Logistic regression equation, six factors were screened out, including Hcy, Hs-CRP, IL-6, D-D, CysC and MDA. According to classification, the final sixth steps had a prediction accuracy of 99%. When six inflammatory factors included in Logistic regression equation were detected jointly, the sensitivity, specificity and area under the curve were 57%, 97% and 0.821 respectively, while those of the model excluding D-D were 64%, 90% and 0.828, generally superior to results of joint detection including six factors. The ROC curve based on Hcy, Hs-CRP and MDA had a sensitivity of 87%, a specificity of 94% and an area under the curve of 0.869, being inferior to those of the ROC curve based on IL-6, D-D and Cys C, which were 87%, 92% and 0.936 respectively. The accuracy of SVM-AS diagnosis model and BP neural network model were 82.5% and 77.5% respectively. Conclusion: All 11 inflammatory factors are valuable in AS diagnosis. AS early diagnosis models based on Logistic regression analysis, ROC curve, support vector machine and BP neural network possess diagnostic value and can provide reference for clinical diagnosis

Pyramid-VAE-GAN: Transferring hierarchical latent variables for image inpainting

Abstract Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN

Algorithm Selection for Protein-Ligand Docking: A Case Study on ACE with AutoDock

The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein-ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein-ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein-ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein-ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA’s parameters. As it pertains to protein-ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance

Research on Flutter Characterization of Flexible Blade Response under Typhoon Operating Conditions

Wind turbine blades, being flexible, are susceptible to damage during typhoons. Studying the aeroelastic response of these blades in typhoon conditions is crucial for providing a theoretical foundation for their optimization and design. This research focuses on the NREL 5 MW flexible blade, employing the B-L stall model for dynamic inflow and geometrically exact beam theory to develop an aeroelastic model capable of predicting the blade’s flutter limit. Through quantitative analysis, we assess the stability of the wind turbine’s flexible blade under typhoon conditions and examine the blade tip’s transient response. The findings indicate that the model’s flutter speed is 21.5 rpm, marked by a significant increase in tip deflection’s mean square error of over 80% and a coupling of flapwise and torsional modes at 4.81 Hz. The blade tip’s transient response under typhoon conditions does not satisfy the flutter criterion, thus preventing instability. Under typhoon conditions, the deflection in the flapwise, edgewise, and twist directions of the blade shows an increase of 12.1%, 10.5%, and 119.2%, respectively, compared to standard operating conditions

RNA-Interference-Mediated Aphid Control in Crop Plants: A Review

peer reviewedCrop plants suffer severe yield losses due to the significant damages caused by aphids. RNA interference (RNAi) technology is a versatile and environmentally friendly method for pest management in crop protection. Transgenic plants expressing siRNA/dsRNA and non-transformative methods such as spraying, microinjection, feeding, and a nanocarrier-delivery-mediated RNAi approach have been successfully applied for agricultural insect pest management. In this review, we summarize the application of host-induced gene silencing (HIGS)-mediated RNAi, spray-induced gene silencing (SIGS)-mediated RNAi, and other delivery-method-mediated RNAi methods for aphid control. We further discuss the challenges in RNAi application and propose potential solutions to enhance RNAi efficiency

RNA-Interference-Mediated Aphid Control in Crop Plants: A Review

Crop plants suffer severe yield losses due to the significant damages caused by aphids. RNA interference (RNAi) technology is a versatile and environmentally friendly method for pest management in crop protection. Transgenic plants expressing siRNA/dsRNA and non-transformative methods such as spraying, microinjection, feeding, and a nanocarrier-delivery-mediated RNAi approach have been successfully applied for agricultural insect pest management. In this review, we summarize the application of host-induced gene silencing (HIGS)-mediated RNAi, spray-induced gene silencing (SIGS)-mediated RNAi, and other delivery-method-mediated RNAi methods for aphid control. We further discuss the challenges in RNAi application and propose potential solutions to enhance RNAi efficiency

Effects of Mg/Zn ratio and pre-aging on microstructure and mechanical properties of Al–Mg–Zn–Cu alloys

The alloy composition and aging process are significant factors determining the type of precipitates in Al alloys. In this work, the effects of Mg/Zn ratio and pre-aging process on the microstructure and mechanical properties of Al–Mg–Zn–Cu alloys were systematically investigated using transmission electron microscopy (TEM), hardness measurements and tensile tests. The results show that reducing the Mg/Zn ratio significantly increases the density of T′-Mg32(AlZnCu)49 precipitates, thereby enhancing and accelerating the aging response. A detailed comparison has been made between Al–Mg–Zn–Cu alloys aged by single-step and two-step artificial aging processes. With the same composition, the alloy exhibits a higher yield strength under the two-stage aging process, resulting from the higher precipitation strengthening effect (Orowan dislocation bypassing mechanism) due to the smaller size and higher density of precipitates. Furthermore, it is particularly interesting that there may be two evolution paths for GPII zones with (111)Al habit plane: one is that the GPII zones are dissolved into the matrix, and the other is that the GPII zones gradually evolve into the T′ precipitates. This work sheds some light on the precipitation sequence in Al–Mg–Zn–Cu alloys and is greatly helpful for designing age-hardening Al–Mg based alloys