Graph neural networks for preference social recommendation.

Social recommendation aims to improve the performance of recommendation systems with additional social network information. In the state of art, there are two major problems in applying graph neural networks (GNNs) to social recommendation: (i) Social network is connected through social relationships, not item preferences, i.e., there may be connected users with completely different preferences, and (ii) the user representation of current graph neural network layer of social network and user-item interaction network is the output of the mixed user representation of the previous layer, which causes information redundancy. To address the above problems, we propose graph neural networks for preference social recommendation. First, a friend influence indicator is proposed to transform social networks into a new view for describing the similarity of friend preferences. We name the new view the Social Preference Network. Next, we use different GNNs to capture the respective information of the social preference network and the user-item interaction network, which effectively avoids information redundancy. Finally, we use two losses to penalize the unobserved user-item interaction and the unit space vector angle, respectively, to preserve the original connection relationship and widen the distance between positive and negative samples. Experiment results show that the proposed PSR is effective and lightweight for recommendation tasks, especially in dealing with cold-start problems.

Functional Characterization of a New Degradation Peptide BmTX4-P1 from Traditional Chinese Scorpion Medicinal Material.

Thermally processed Buthus martensii Karsch scorpion is an important traditional Chinese medical material that has been widely used to treat various diseases in China for over one thousand years. Our recent work showed that thermally processed Buthus martensii Karsch scorpions contain many degraded peptides; however, the pharmacological activities of these peptides remain to be studied. Here, a new degraded peptide, BmTX4-P1, was identified from processed Buthus martensii Karsch scorpions. Compared with the venom-derived wild-type toxin peptide BmTX4, BmTX4-P1 missed some amino acids at the N-terminal and C-terminal regions, while containing six conserved cysteine residues, which could be used to form disulfide bond-stabilized α-helical and ß-sheet motifs. Two methods (chemical synthesis and recombinant expression) were used to obtain the BmTX4-P1 peptide, named sBmTX4-P1 and rBmTX4-P1. Electrophysiological experimental results showed that sBmTX4-P1 and rBmTX4-P1 exhibited similar activities to inhibit the currents of hKv1.2 and hKv1.3 channels. In addition, the experimental electrophysiological results of recombinant mutant peptides of BmTX4-P1 indicated that the two residues of BmTX4-P1 (Lys22 and Tyr31) were the key residues for its potassium channel inhibitory activity. In addition to identifying a new degraded peptide, BmTX4-P1, from traditional Chinese scorpion medicinal material with high inhibitory activities against the hKv1.2 and hKv1.3 channels, this study also provided a useful method to obtain the detailed degraded peptides from processed Buthus martensii Karsch scorpions. Thus, the study laid a solid foundation for further research on the medicinal function of these degraded peptides.

Extraction, structural characterization, and antioxidant activity of polysaccharides derived from Arctium lappa L.

Introduction: Arctium lappa L. root has high nutritional and medicinal values and has been identified as a healthy food raw material by the Ministry of Health of the People's Republic of China. Methods: In the present study, an aqueous two-phase system (ATPS) of polyethylene glycol (PEG)-(NH4)2SO4 was used to extract Arctium lappa L. polysaccharides (ALPs) from the Arctium lappa L. roots, the optimal extraction conditions of crude ALPs were optimized by using the single-factor experiment and response surface methodology. The structure and composition of ALPs were determined by fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and high-performance liquid chromatography (HPLC). At the same time, the antioxidant activity of ALPs was investigated by in vitro antioxidant experiment. Results: The optimized extraction parameters for extraction ALPs were as follows: the PEG relative molecular weight of 6,000, a quality fraction of PEG 25%, a quality fraction of (NH4)2SO4 18%, and an extraction temperature of 80°C. Under these conditions, the extraction rate of ALPs could reach 28.83%. FTIR, SEM and HPLC results showed that ALPs were typical acidic heteropolysaccharides and had uneven particle size distribution, an irregular shape, and a rough surface. The ALPs were chiefly composed of glucose, rhamnose, arabinose, and galactose with a molar ratio of 70.19:10.95:11.16:6.90. In addition, the ALPs had intense antioxidant activity in vitro with IC50 values in the ·OH radical (1.732 mg/ml), DPPH radical (0.29 mg/ml), and superoxide anion (0.15 mg/ml) scavenging abilities. Discussion: The results showed that ATPS was an efficient method to extract polysaccharides and could be used for the extraction of other polysaccharides. These results indicated that ALPs had great prospects as a functional food and could be exploited in multiple fields.

Purification, structural characterization and immunological activity of Sibiraea laexigata (L.) Maxim polysaccharide.

Sibiraea laexigata (L.) Maxim (SLM) has been used as an herbal tea for treating stomach discomfort and indigestion for a long time in china. Polysaccharides have been identified as one of the major bioactive compounds in the SLM. In the present paper, ultrasonic-assisted enzymatic extraction (UAEE) method was employed in polysaccharides extraction derived from SLM using polyethylene glycol (PEG) as extraction solvent, two SLM polysaccharides (SLMPs) fractions (SLMPs-1-1 and SLMPs-2-1) were purified by DEAE Cellulose-52 and Sephadex G-100 chromatography in sequence. Then, the preliminarily structure of the two factions were characterized by chemical composition analysis, molecular weight measurement, UVS, HPLC-PMP, FT-IR, nuclear magnetic resonance (NMR) spectra analysis and SEM. The results showed that SLMPs-1-1 and SLMPs-2-1 with different molecular weights of 1.03 and 1.02 kDa, mainly composed of glucose (46.76 and 46.79%), respectively. The results of structural characterization from FT-IR, 1H NMR, and SEM revealed that SLMPs-1-1 and SLMPs-2-1 contained the typical pyranoid polysaccharide with α-glycosidic bond and ß-glycosidic bond. Furthermore, it was found that SLMPs-1-1 could increase the levels of tumor necrosis factor-α (TNF-α) and interleukin-2 (IL-2), and alleviated the immune organs tissue damage of cyclophosphamide (Cy)-treated mice. RT-qPCR and Western-Blot analysis showed that SLMPs-1-1 could significantly up-regulated the levels of NF-κB, TLR4, which revealed that SLMPs-1-1 could participate in immunosuppressive protection of Cy-treated mice. These findings suggested that the potential of SLMPs-1-1 as an alternative immunostimulator could be used in food and pharmaceutical industries.

Structure characterization and antioxidant activity analysis of polysaccharides from Lanzhou Lily.

Lanzhou Lily (Lilium davidii var. unicolor) is a traditional medicinal plant and popular edible vegetable bulb in China. In this study, the polysaccharides of Lanzhou Lily (LLPs) were extracted by polyethylene glycol-based ultrasonic-assisted enzymatic extraction method (PEG-UAEE). The optimum process conditions were obtained by single-factor experiments and response surface methodology (RSM). Then, the preliminarily structure of LLPs was characterized by HPLC, FT-IR, and SEM, and its antioxidant activities were evaluated. The results showed that LLPs yield reached 14.75% under the optimized conditions: E/S ratio 1,400 U/g; pH 5.0, ultrasonic time 30 min; and ultrasonic temperature 50 °C. The LLPs has pyranoid ring, uronic acid, and the characteristic absorption peaks of -OH, C = O, and C-H. The results of scanning electron microscope indicated that the LLPs had irregular distribution, dispersed structure, and many holes. The HPLC analysis showed that the LLPs were heteropolysaccharide containing galactose (6.36%), glucose (76.03%), rhamnose (2.02%), and arabinose (7.09%). Moreover, the LLPs showed obvious antioxidant effect in vitro.

Medical image augmentation for lesion detection using a texture-constrained multichannel progressive GAN.

Lesion detectors based on deep learning can assist doctors in diagnosing diseases. However, the performance of current detectors is likely to be unsatisfactory due to the scarcity of training samples. Therefore, it is beneficial to use image generation to augment the training set of a detector. However, when the imaging texture of the medical image is relatively delicate, the synthesized image generated by an existing method may be too poor in quality to meet the training requirements of the detectors. In this regard, a medical image augmentation method, namely, a texture-constrained multichannel progressive generative adversarial network (TMP-GAN), is proposed in this work. TMP-GAN uses joint training of multiple channels to effectively avoid the typical shortcomings of the current generation methods. It also uses an adversarial learning-based texture discrimination loss to further improve the fidelity of the synthesized images. In addition, TMP-GAN employs a progressive generation mechanism to steadily improve the accuracy of the medical image synthesizer. Experiments on the publicly available dataset CBIS-DDMS and our pancreatic tumor dataset show that the precision/recall/F1-score of the detector trained on the TMP-GAN augmented dataset improves by 2.59%/2.70%/2.77% and 2.44%/2.06%/2.36%, respectively, compared to the optimal results of other data augmentation methods. The FROC curve of the detector is also better than the curve from the contrast-augmented trained dataset. Therefore, we believe the proposed TMP-GAN is a practical technique to efficiently implement lesion detection case studies.

Generative Adversarial Networks in Medical Image augmentation: A review.

OBJECT: With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed. METHOD: This paper reviews 105 medical image augmentation related papers, which mainly collected by ELSEVIER, IEEE Xplore, and Springer from 2018 to 2021. We counted these papers according to the parts of the organs corresponding to the images, and sorted out the medical image datasets that appeared in them, the loss function in model training, and the quantitative evaluation metrics of image augmentation. At the same time, we briefly introduce the literature collected in three journals and three conferences that have received attention in medical image processing. RESULT: First, we summarize the advantages of various augmentation models, loss functions, and evaluation metrics. Researchers can use this information as a reference when designing augmentation tasks. Second, we explore the relationship between augmented models and the amount of the training set, and tease out the role that augmented models may play when the quality of the training set is limited. Third, the statistical number of papers shows that the development momentum of this research field remains strong. Furthermore, we discuss the existing limitations of this type of model and suggest possible research directions. CONCLUSION: We discuss GAN-based medical image augmentation work in detail. This method effectively alleviates the challenge of limited training samples for medical image diagnosis and treatment models. It is hoped that this review will benefit researchers interested in this field.

Structural and functional biomarkers of the insula subregions predict sex differences in aggression subscales.

Aggression is a common and complex social behavior that is associated with violence and mental diseases. Although sex differences were observed in aggression, the neural mechanism for the effect of sex on aggression behaviors remains unclear, especially in specific subscales of aggression. In this study, we investigated the effects of sex on aggression subscales, gray matter volume (GMV), and functional connectivity (FC) of each insula subregion as well as the correlation of aggression subscales with GMV and FC. This study found that sex significantly influenced (a) physical aggression, anger, and hostility; (b) the GMV of all insula subregions; and (c) the FC of the dorsal agranular insula (dIa), dorsal dysgranular insula (dId), and ventral dysgranular and granular insula (vId_vIg). Additionally, mediation analysis revealed that the GMV of bilateral dIa mediates the association between sex and physical aggression, and left dId-left medial orbital superior frontal gyrus FC mediates the relationship between sex and anger. These findings revealed the neural mechanism underlying the sex differences in aggression subscales and the important role of the insula in aggression differences between males and females. This finding could potentially explain sexual dimorphism in neuropsychiatric disorders and improve dysregulated aggressive behavior.

Sex-related Difference in Mental Rotation Performance is Mediated by the special Functional Connectivity Between the Default Mode and Salience Networks.

The mental rotation task is a particular spatial skill that helps people process visual information and is associated with intelligence and academic performance. Previous studies have found consistent sex difference in mental rotation. However, the neural mechanism of the sex-related difference in mental rotation remains unclear. This study investigates the association between sex, mental rotation and the functional connectivity (FC) of resting-state networks (RSNs) to explore neural correlates of different mental rotation abilities between males and females. Compared with females, males performed better on the mental rotation test. The mental rotation scores were significantly correlated with the special FC between the default mode network (DMN) and salience network (SN). The results of the mediation analysis revealed that the special FC between the DMN and SN mediated the association between sex and mental rotation. Based on these findings, males had higher FC between the DMN and SN, which subsequently promoted their mental rotation performance. These results emphasized the importance of sex in spatial cognition studies of both healthy people and individuals with neuropsychiatric disorders and deepened our understanding of the neural mechanisms underlying sex difference in mental rotation.

BmK86-P1, a New Degradation Peptide with Desirable Thermostability and Kv1.2 Channel-Specific Activity from Traditional Chinese Scorpion Medicinal Material.

Thermally processed Buthus martensii Karsch scorpions are a traditional Chinese medical material for treating various diseases. However, their pharmacological foundation remains unclear. Here, a new degraded peptide of scorpion toxin was identified in Chinese scorpion medicinal material by proteomics. It was named BmK86-P1 and has six conserved cysteine residues. Homology modeling and circular dichroism spectra experiments revealed that BmK86-P1 not only contained representative disulfide bond-stabilized α-helical and ß-sheet motifs but also showed remarkable stability at test temperatures from 20-95 °C. Electrophysiology experiments indicated that BmK86-P1 was a highly potent and selective inhibitor of the hKv1.2 channel with IC50 values of 28.5 ± 6.3 nM. Structural and functional dissection revealed that two residues of BmK86-P1 (i.e., Lys19 and Ile21) were the key residues that interacted with the hKv1.2 channel. In addition, channel chimeras and mutagenesis experiments revealed that three amino acids (i.e., Gln357, Val381 and Thr383) of the hKv1.2 channel were responsible for BmK86-P1 selectivity. This research uncovered a new bioactive peptide from traditional Chinese scorpion medicinal material that has desirable thermostability and Kv1.2 channel-specific activity, which strongly suggests that thermally processed scorpions are novel peptide resources for new drug discovery for the Kv1.2 channel-related ataxia and epilepsy diseases.

Why Am I Willing to Speak Up? The Impact of Spiritual Leadership on Employee Voice Behavior.

The sustainability of organizations highlights the significance of inspiring employees, especially their inner lives or spiritual identities, and leaders play a vital role. Consistent with social cognitive theory, the purpose of this paper is to explore the linking mechanisms and conditional processes underlying the relationship between spiritual leadership and employee voice behavior. Three-wave survey data were collected from 366 full-time employees and their line managers, and a moderated mediation analysis was performed. The results show that career success expectation fully mediates the relationship between spiritual leadership and employee voice behavior. Additionally, felt obligation is found to indirectly strengthen the effect of spiritual leadership on employee voice behavior via career success expectation. In light of the results, practical implications are provided for managers and future researchers to enhance the sustainability of organizations.

Career Adaptability, Work Engagement, and Employee Well-Being Among Chinese Employees: The Role of Guanxi.

The present study examined whether and how career adaptability predicts employee well-being (EWB) based on career construction theory. A three-wave questionnaire design was used to collect the data, and 338 employees participated in the study. The results suggest that career adaptability has a significant effect on work engagement, which, in turn, predicts EWB. In addition to developing a mediation model, we tested the effect of guanxi as a moderator on the former part of the model. Thus, a moderated-mediation model was constructed in this research. In addition to the finding of the mediating role of work engagement, the discussion of guanxi represents a more important novel aspect that draws attention to contextual factors that may shape how employees respond to career adaptability. The results revealed that the indirect effect of career adaptability on EWB through work engagement when guanxi is low is stronger than that when guanxi is high. Furthermore, we discuss the limitations of this study and the implications for future research on career adaptability and EWB.

A Framework for Bus Trajectory Extraction and Missing Data Recovery for Data Sampled from the Internet.

This paper presents a novel framework for trajectories' extraction and missing data recovery for bus traveling data sampled from the Internet. The trajectory extraction procedure is composed of three main parts: trajectory clustering, trajectory cleaning and trajectory connecting. In the clustering procedure, we focus on feature construction and parameter selection for the fuzzy C-means clustering method. Following the clustering procedure, the trajectory cleaning algorithm is implemented based on a new introduced fuzzy connecting matrix, which evaluates the possibility of data belonging to the same trajectory and helps detect the anomalies in a ranked context-related order. Finally, the trajectory connecting algorithm is proposed to solve the issue that occurs in some cases when a route trajectory is incorrectly partitioned into several clusters. In the missing data recovery procedure, we developed the contextual linear interpolation for the cases of missing data occurring inside the trajectory and the median value interpolation for the cases of missing data outside the trajectory. Extensive experiments are conducted to demonstrate that the proposed framework offers a powerful ability to extract and recovery bus trajectories sampled from the Internet.