Favoritism or bias? Cooperation and competition under different intergroup relationships: evidence from EEG hyperscanning.

Cooperation and competition are the most common forms of social interaction in various social relationships. Intergroup relationships have been posited to influence individuals' interpersonal interactions significantly. Using electroencephalography hyperscanning, this study aimed to establish whether intergroup relationships influence interpersonal cooperation and competition and the underlying neural mechanisms. According to the results, the in-group Coop-index is better than the out-group, whereas the out-group Comp-index is stronger than the in-group. The in-group functional connectivity between the frontal-central region and the right temporoparietal junction in the ß band was stronger in competition than cooperation. The out-group functional connectivity between the frontal-central region and the left temporoparietal junction in the α band was stronger in cooperation than competition. In both cooperation and competition, the in-group exhibited higher interbrain synchronization between the prefrontal cortex and parietal region in the θ band, as well as between the frontal-central region and frontal-central region in the α band, compared to the out-group. The intrabrain phase-locking value in both the α and ß bands can effectively predict performance in competition tasks. Interbrain phase-locking value in both the α and θ bands can be effectively predicted in a performance cooperation task. This study offers neuroscientific evidence for in-group favoritism and out-group bias at an interpersonal level.

A novel surface marker CD49d promotes TNF expression in oyster agranulocytes by mediating the MAPK pathway.

CD49d, encoded by the gene Integrin α4, is a significant member of cell adhesion receptors, which is widely expressed in various immune cells to trigger immune responses against invading pathogens. In the present study, the expression of CgCD49d and its regulatory role in TNF expression were investigated in the Pacific oyster Crassostrea gigas. There were five Int-alpha domains, an Integrin_alpha2 region and a unique FG-GAP repeat region inserted identified in CgCD49d. CgCD49d transcript was specifically expressed in haemocytes, and its mRNA expression level in haemocytes increased after LPS and Vibrio splendidus stimulation. After CgCD49d was blocked by using its antibody, the phosphorylation level of CgJNK in the MAPK signaling pathway and CgTNF transcripts decreased significantly post V. splendidus stimulation. After phosphorylation level of CgJNK was inhibited by using its inhibitor, the nuclear translocation of CgRel was restrained and CgTNF transcripts also decreased significantly post V. splendidus stimulation. Furthermore, CgCD49d was found to be mainly expressed in the agranulocyte subpopulation, and Alexa Fluor 488-conjugated CgCD49d antibody labeled agranulocytes with a circle of green fluorescence signals on CgCD49d+ agranulocyte surface under Confocal microscopy, which accounted for 24.9 ± 4.53% of total haemocytes. Collectively, these results suggested that CgCD49d promoted TNF expression in oyster haemocytes against bacterial invasion by mediating MAPK pathway, and it could be used as a surface marker to type and sort a subset of agranulocyte subpopulation among haemocytes.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning.

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R2P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

Individual Micron-Sized Aerosol Qualitative Analysis-Combined Raman Spectroscopy and Laser-Induced Breakdown Spectroscopy by Optical Trapping in Air.

The attribution of single particle sources of atmospheric aerosols is an essential problem in the study of air pollution. However, it is still difficult to qualitatively analyze the source of a single aerosol particle using noncontact in situ techniques. Hence, we proposed using optical trapping to combine gated Raman spectroscopy with laser-induced breakdown spectroscopy (LIBS) in a single levitated micron aerosol. The findings of the spectroscopic imaging indicated that the particle plasma formed by a single particle ablation with a pulsed laser within 7 ns deviates from the trapped particle location. The LIBS acquisition field of view was expanded using the 19-bundle fiber, which also reduces the fluctuation of a single particle signal. In addition, gated Raman was utilized to suppress the fluorescence and increase the Raman signal-to-noise ratio. Based on this, Raman can measure hard-to-ionize substances with LIBS, such as sulfates. The LIBS radical can overcome the restriction that Raman cannot detect ionic chemicals like fluoride and chloride in halogens. To test the capability of directly identifying distinctive feature compounds utilizing spectra, we detected anions using Raman spectroscopy and cations using LIBS. Four typical mineral aerosols are subjected to precise qualitative evaluations (marble, gypsum, baking soda, and activated carbon adsorbed potassium bicarbonate). To further validate the application potential for substances with indistinctive feature discrimination, we employed machine learning algorithms to conduct a qualitative analysis of the coal aerosol from ten different origin regions. Three data fusion methodologies (early fusion, intermediate fusion, and late fusion) for Raman and LIBS are implemented, respectively. The accuracy of the late fusion model prediction using StackingClassifier is higher than that of the LIBS data (66.7%) and Raman data (86.1%) models, with an average accuracy of 90.6%. This research has the potential to provide online single aerosol analysis as well as technical assistance for aerosol monitoring and early warning.

Metabolite profiling and transcriptomic analyses demonstrate the effects of biocontrol agents on alkaloid accumulation in Fritillaria thunbergii.

BACKGROUND: During Fritillaria thunbergii planting, pests and diseases usually invade the plant, resulting in reduced yield and quality. Previous studies have demonstrated that using biocontrol agents can effectively control grubs and affect the steroid alkaloids content in F. thunbergii. However, the molecular regulatory mechanisms underlying the differences in the accumulation of steroid alkaloids in response to biocontrol agents remain unclear. RESULTS: Combined transcriptomic and metabolic analyses were performed by treating the bulbs of F. thunbergii treated with biocontrol agents during planting. Otherwise, 48 alkaloids including 32 steroid alkaloids, 6 indole alkaloids, 2 scopolamine-type alkaloids, 1 isoquinoline alkaloid, 1 furoquinoline alkaloid, and 6 other alkaloids were identified. The content of steroidal alkaloids particularly peimine, peiminine, and veratramine, increased significantly in the group treated with the biocontrol agents. Transcriptome sequencing identified 929 differential genes using biocontrol agents, including 589 upregulated and 340 downregulated genes. Putative biosynthesis networks of steroid alkaloids have been established and combined with differentially expressed structural unigenes, such as acetyl-CoA C-acetyl-transferase, acelyl-CoAC-acetyltransferase3-hydroxy-3-methylglutaryl-coenzyme A synthase, 1-deoxy-D-xylulose-5-phosphate reductor-isomerase, 2-C-methyl-D-erythritol-4-phosphate cytidylyltransferase and 4-hydroxy-3-methylbut-2-enyl diphosphate reductase. In addition, biological processes such as amino acid accumulation and oxidative phosphorylation were predicted to be related to the synthesis of steroid alkaloids. Cytochrome P450 enzymes also play crucial roles in the steroid alkaloid synthesis. The transcription factor families MYB and bHLH were significantly upregulated after using biocontrol agents. CONCLUSIONS: Biocontrol agents increased the steroid alkaloids accumulation of steroid alkaloids by affecting key enzymes in the steroid alkaloid synthesis pathway, biological processes of oxidative phosphorylation and amino acid synthesis, cytochrome P450 enzymes, and transcription factors. This study revealed the mechanism underlying the difference in steroidal alkaloids in F. thunbergii after using biocontrol agents, laying the groundwork for future industrial production of steroid alkaloids and ecological planting of medicinal materials in the future.

Mechanism of optical limiting in metalloporphyrins under visible continuous radiation.

Here, we present an experimental investigation on the nonlinear optical (NLO) and optical limiting properties of metalloporphyrin compounds (Cu-1-OH, Zn-1-OH, Cu-1-E and Zn-1-E) using spatial self-phase modulation (SSPM) method in the visible range. It is found that all of the samples show a large self-defocusing effect at 532 nm, which is attributed to the thermal nonlinear optical effects with negative nonlinear refractive index coefficient n2 due to the relatively high absorption at 532 nm. In contrast, at 780 nm where absorption is weak for both Zn- and Cu-porphyrins, Zn-porphyrins still exhibit visible self-defocusing while Cu-porphyrins do not show any nonlinear diffraction pattern. Such a phenomenon can be explained by the Kerr effect of Cu-porphyrins at 780 nm. As the thermal nonlinear optical effects (of negative n2) at 780 nm are reduced due to the low absorption, the Kerr effect with positive n2 becomes comparable and the overall nonlinearity is reduced. The Kerr effect of Cu-porphyrins is stronger than that of Zn-porphyrins because of the enhanced π-electron delocalization effect as Cu(II) has a variable number of valence electrons and incompletely filled d atomic orbitals. Finally, the optical limiting performance of Zn-porphyrins is demonstrated as a representative and its dependence on sample position is examined. This work not only enriches the understanding of the physical mechanism of optical limiting in porphyrin materials, but also provides a significant reference to improve the third-order NLO coefficient by adjusting the structure of compounds.

Chemical profiling and quantification analysis of flavonoids in different varieties of Euryales Semen by ultra-high-performance liquid chromatography with tandem mass spectrometry.

Euryales Semen was a traditional Chinese medicine, which has been commonly used to treat spermatorrhea, enuresis, and frequent urination. Flavonoids were a critical ingredient in determining the function and quality of Euryales Semen. At present, no effective method has been established for the qualitative of Euryales Semen flavonoids. In this study, an ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry method was established for flavonoids. By comparison with standard or literature data, 32 flavonoid compounds have been identified in Euryales Semen. Based on the qualitative results, an ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectroscopy method was developed for the main components, and the linearity, the limit of detection, limit of quantification, repeatability, precision, stability, and recovery of the method were verified. The principal component analysis and the hierarchical clustering heatmaps analysis showed that the 30 batches of samples were distinctly separated into the North Gordon Euryale and South Gordon Euryale, and the measured contents of the six flavonoids in North Gordon Euryale were more abundant than in South Gordon Euryale, especially isoquercitrin, hesperetin, and quercetin. It provided a scientific basis for the quality control of Euryales Semen and a theoretical basis for the rational utilization of Euryales Semen resources.

Simultaneous determination of cordycepin and its metabolite 3'-deoxyinosine in rat whole blood by ultra-high-performance liquid chromatography coupled with Q Exactive hybrid quadrupole orbitrap high-resolution accurate mass spectrometry and its application to accurate pharmacokinetic studies.

Cordycepin from Cordyceps possesses excellent pharmacological properties, including anti-inflammation and anti-tumor effects, therefore representing a potential alternative medicine. However, doubts about the pharmacokinetic results of cordycepin had been raised in the previous study due to its rapid deamination. The organic solvent methanol was immediately added to terminate the degradation of cordycepin in anticoagulated blood samples and enable the accurate evaluation of pharmacokinetics in vivo. A sensitive and selective ultra-high-performance liquid chromatography coupled with Q Exactive hybrid quadrupole orbitrap high-resolution accurate mass spectrometry method was developed and validated to simultaneously determine cordycepin and its deamination metabolite 3'-deoxyinosine using 2-chloroadenosine as an internal standard in rat whole blood. The calibration curves of cordycepin and 3'-deoxyinosine showed excellent linearity within the concentration range of 1.05-10 000.00 ng/ml with acceptable accuracy, precision, selectivity, recovery, matrix effect, and stability. This method was successfully applied to the pharmacokinetic study of cordycepin and its metabolite in rat blood. The effect of the adenosine deaminase inhibitor erythro-9-(2-hydroxy-3-nonyl) adenine hydrochloride on the pharmacokinetics of cordycepin was investigated. In summary, the reliable pharmacokinetic parameters of cordycepin and its deamination metabolite 3'-deoxyinosine in rat blood were successfully elucidated. Erythro-9-(2-hydroxy-3-nonyl) adenine hydrochloride considerably prolonged the half-life of cordycepin in vivo.

Neural Basis of the Implicit Learning of Complex Artificial Grammar with Nonadjacent Dependencies.

The capacity for the implicit learning/processing of complex grammar with nonadjacent dependencies is an important feature of human language learning. In this fMRI study, using an implicit AGL paradigm, we explored the neural basis of the implicit learning of the nonadjacent dependency rule, disentangling from sequence-based chunk knowledge (i.e., local sequential regularities or substring) by focusing on the low chunk strength items (which were naturally less similar to training strings), based on tracking neural responses during training and test phases. After listening to and memorizing a series of strings of 10 syllables generated from nonadjacent artificial grammar in the training phase, participants implicitly acquired the knowledge of grammar and chunks. Regarding grammaticality, Broca's area was specifically related to low chunk strength grammatical strings relative to nongrammatical strings in the test phase. This region showed decreased activity with time in the training phase, and a lesser decrease in activity was associated with higher performance in grammar learning. Furthermore, Broca's area showed significantly higher strength of functional connectivity with the left superior temporal gyrus in the low chunk strength grammatical string compared with nongrammatical strings, and this functional connectivity increased with the training time. For the chunks, the performance of accurate discrimination of high chunk strength from low chunk strength nongrammatical strings was predicted by hippocampal activity in the training phase. Converging evidence from the training and test phases showed that Broca's area and its functional connectivity with the left superior temporal gyrus were engaged in the implicit learning/processing of the nonadjacent dependency rule, separating the effects of chunks.

Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies.

The chemical compositions of atmospheric particles have been studied for several decades, and the traditional techniques for particle analysis usually require time-consuming sample preparation. Within this study, simultaneous quantitative detection of multiple metallic species (Zn, Cu, and Ni) in single micro-sized suspended particles was investigated by combining random forest (RF) and variable selection strategies. Laser-induced breakdown spectra of 15 polluted black carbon samples were applied for establishing the RF model, and the movmean smoothing spectral pretreatment method and variable selection methods [variable importance measurement (VIM), genetic algorithm (GA), and variable importance projection (VIP)] were proposed. Finally, the optimized RF calibration model with the evaluation indicators of mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R2) was constructed based on the optimal input variables and model parameters. Compared with the univariate regression method, the VIP-RF (Zn) and VIM-RF (Cu and Ni) models showed a better correlation relationship (Rp2 = 0.9662 for Zn, Rp2 = 0.9596 for Cu, and Rp2 = 0.9548 for Ni). For Zn, Cu, and Ni, the values of RMSEP (RMSE of prediction) decreased by 116.44, 68.94, and 102.10 ppm, while the values of MREP (MRE of prediction) decreased by 67, 55, and 48%, respectively. The values of ratio of prediction to deviation (RPD) of VIP-RF (Zn), VIM-RF (Cu), and VIM-RF (Ni) models were 5.4, 5.0, and 4.7, respectively. The performance of this combined approach displays a notable accuracy improvement in the quantitative analysis of single particles, suggesting that it is a promising tool for real-time air particulate matter pollution monitoring and control in the future.