While previous research has identified the performance implications of leaders’ positive implicit followership theories (IFTs, i.e., personal expectations regarding followers’ positive characteristics), this study focuses on the effect of leader–follower congruence in positive IFTs on followers’ job performance. To test our predictions, we conducted two complementary studies. The results of Study 1 (an experiment, N = 200) show that leader–follower congruence (versus incongruence) in positive IFTs is positively related to followers’ relational identification with the leader, which, in turn, is positively related to followers’ job performance. Moreover, followers’ uncertainty avoidance strengthens this relationship. These findings were replicated in Study 2 (a three-wave survey, N = 223) through polynomial regression and response surface analysis. This study improves our understanding of IFTs by showing that leader–follower congruence in this domain is related to followers’ outcomes.

The assist-as-needed (AAN) controller is effective in robot-assisted rehabilitation. However, variations of the engagement of subjects with fixed controller often lead to unsatisfying results. Therefore, adaptive AAN that adjusts control parameters based on individualized engagement is essential to enhance the training effect further. Nevertheless, current approaches mainly focus on the within-trial real-time engagement estimation, and the presence of measurement noise may cause improper evaluation of engagement. In addition, most studies on human-in-loop optimization strategies modulate the controller by greedy strategies, which are prone to fall into local optima. These shortcomings in previous studies could significantly limit the efficacy of AAN. This paper proposes an adaptive AAN to promote engagement by providing subjects with a subject-adaptive assistance level based on trial-wise engagement estimation and performance. Firstly, the engagement is estimated from energy information, which assesses the work done by the subject during a full trial to reduce the influence of measurement outliers. Secondly, the AAN controller is adapted by Bayesian optimization (BO) to maximize the subject’s performance according to historical trial-wise performance. The BO algorithm is good at dealing with noisy signals within limited steps. Experiments with ten healthy subjects resulted in a decrease of 34.59$\%$ in their average trajectory error, accompanied by a reduction of 9.71$\%$ in their energy consumption, thus verifying the superiority of the proposed method to prior work. These results suggest that the proposed method could potentially improve the effect of upper limb rehabilitation.

The relationship between erythrocyte membrane n-3 PUFA and breast cancer risk is controversial. We aimed to examine the associations of erythrocyte membrane n-3 PUFA with odds of breast cancer among Chinese women by using a relatively large sample size. A case–control study was conducted including 853 newly diagnosed, histologically confirmed breast cancer cases and 892 frequency-matched controls (5-year interval). Erythrocyte membrane n-3 PUFA were measured by GC. Logistic regression and restricted cubic spline were used to quantify the association between erythrocyte membrane n-3 PUFA and odds of breast cancer. Erythrocyte membrane α-linolenic acid (ALA), docosapentaenoic acid (DPA) and total n-3 PUFA were inversely and non-linearly associated with odds of breast cancer. The OR values (95 % CI), comparing the highest with the lowest quartile (Q), were 0·57 (0·43, 0·76), 0·43 (0·32, 0·58) and 0·36 (0·27, 0·49) for ALA, DPA and total n-3 PUFA, respectively. Erythrocyte membrane EPA and DHA were linearly and inversely associated with odds of breast cancer ((EPA: ORQ4 v. Q1 (95 % CI) = 0·59 (0·45, 0·79); DHA: ORQ4 v. Q1 (95 % CI) = 0·50 (0·37, 0·67)). The inverse associations were observed between ALA and odds of breast cancer in postmenopausal women, and between DHA and oestrogen receptor+ breast cancer. This study showed that erythrocyte membrane total and individual n-3 PUFA were inversely associated with odds of breast cancer. Other factors, such as menopause and hormone receptor status, may warrant further investigation when examining the association between n-3 PUFA and odds of breast cancer.

One of the most significant issues in hydrodynamic interfacial instabilities is the growth rate of the interfacial perturbations, which plays an important role in both scientific research (e.g. supernova explosion) and engineering applications (e.g. inertial confinement fusion). Yet the underlying mechanisms of such flow phenomena remain unclear or controversial. In this paper the decoupled mechanisms of two effects are found to dominate the interface growth of the single-mode Rayleigh–Taylor instability (RTI) and Richtmyer–Meshkov instability (RMI) via Layzer's potential-flow model. One is the inertial effect induced by the interfacial density gradient and the acceleration, and the other is the frontal distortion effect stemming from interface shape evolution. The former determines the dominant features of interface evolution, while the latter influences the local concavity and convexity of growth rate such as the overshoot phenomenon. These two effects can be approximated as a linearly decoupled analytical solution if their nonlinear interaction term is neglected. With the decoupled solution, the theoretical growth rates agree well with high-fidelity numerical simulation results. The present result indicates that the long-time evolution of fluid interface in both RTI and RMI at all density ratios can be accurately predicted if both inertia and frontal distortion effects are taken into account. Furthermore, the strong dependence of instability evolution on initial amplitude is quantified based on the effects of decoupling, which sheds light on the physical origin of the overshoot phenomenon.

To contribute to the treatment decision and optimize coagulase-negative staphylococcus (CNS) control programs, we conducted a meta-analysis to investigate the epidemiology and antimicrobial resistance rates of coagulase-negative staphylococcus associated with bovine mastitis in China. Three databases (PubMed, Google scholar and China National Knowledge Infrastructure database) were utilized to obtain relevant publications. A total of 18 publications were included in our research, and 3 of them included antimicrobial resistant (AMR) test. The pooled prevalence of coagulase-negative staphylococcus was 17.28%. Subgroup analysis revealed that the prevalence was higher in South China than in North China, was higher in 2011–2020 than in 2000–2010 and was higher in clinical bovine mastitis cases than in subclinical cases. The pooled AMR were most resistant to β-lactams, followed by tetracyclines, quinolones, nitrofurans, lincosamides, sulfonamides, amphenicol and aminoglycosides. The pooled AMR rate of coagulase-negative staphylococcus was lower in 2011–2020 than in 2000–2010. Although the prevalence of CNS showed an increasing trend over 20 years, the AMR rate showed a decreasing trend, and the clinical type of mastitis was the most frequent and the prevalence was highest in South China. Finally, CNS was most resistant to β-lactams amongst the eight groups of antimicrobial agents.

Objectives: Pollution exposure is associated with several dermatological conditions including acne, atopic dermatitis, and psoriasis. Antimicrobial peptides (AMPs) are key effectors of innate defense, and some AMPs are involved in inflammatory skin conditions. In this study, we aimed to characterize expression changes of human AMPs under different in-vitro pollution exposures. Methods: RNA-seq profiling was conducted on normal human primary epidermal keratinocytes (NHEK) treated with either a vehicle control, or benzo[a]pyrene (BaP) and on pigmented living skin equivalent models (pLSE) treated with either a vehicle control, ozone, or vehicle exhaust. Differential expressed genes (DEGs) were identified with R scripts. DEGs of PM2.5 were obtained from the literature and the GEO database. Also, 180 human AMP genes were obtained from a UDAMP database. UpSetPlot was used to plot DEGs overlaps. MetaVolcano was used to identify frequently changed AMPs. Results: We used in-house and published transcriptome profiles to identify AMP genes that displayed altered expression under in-vitro pollution exposure. Of the 180 AMP genes under investigation, 37 showed significant changes in expression in at least 1 of the 5 experiments. Using MetaVolcano, 13 AMP genes were identified to be frequently and consistently changed. Several AMPs associated with inflammation and skin diseases were frequently upregulated, including S100A8, S100A9, LCN2, HBD3, RNASE7, and CXCL1. Only 3 frequently downregulated AMP genes were identified, including CXCL14, which is reported to be a noninflammatory AMP that is highly expressed in healthy skin and is downregulated in skin diseases. Conclusions: The data sets suggest that expression of both proinflammatory and homeostatic AMPs can be perturbed by pollution. These findings provide new clues to explain how pollution affects skin innate defense, host–microbe interactions and contributes to abnormal skin conditions. Normalizing aberrant AMP expression may be a potential approach to treat pollution associated skin disorders in the future.

Objectives: Cohort and epidemiology studies have previously revealed potential associations between air pollution exposure and acne vulgaris. However, the molecular mechanisms that drive these associations are not currently well understood. In this study, we compared the molecular signatures of acne and PM2.5-exposed skin to infer whether common underlying biological mechanisms exist. Methods: Acne microarray data sets were downloaded from GEO. RMAExpress was used for microarray normalization, and TMeV was used to identify differential expressed genes (DEGs). A random-effects model in MetaVolcanoR was used to determine fold changes and P values. DEGs of PM2.5-exposed skin-cell models were obtained from the literature. DEGs were compared using GeneOverlap and a custom R script. Analyses of pathways, upstream regulators, and causal networks were conducted using ingenuity pathway analysis (IPA). Results: The molecular signatures of acne skin and the effect of PM2.5 on skin in vitro were compared at 3 levels: (1) gene expression, (2) pathway activity, and (3) upstream regulators. Significant concordant overlaps of both upregulated (P < 3e-23) and downregulated DEGs (P< .005) were observed in acne skin and PM2.5-exposed keratinocytes. However, for the PM2.5-exposed 3D skin model, significant overlap with acne skin was only observed for upregulated DEGs (P < 8e-14). Fold changes of DEGs in both acne and PM2.5-exposed data sets showed significant correlation (Pearson correlation coefficient > 0.6; P < .001). An IPA analysis identified 13 pathways commonly enriched in acne and PM2.5 data sets, including IL17, IL6, Toll receptor PPAR, LXR–RXR, and acute-phase response pathways. Common upstream regulators were further identified including TNFα, NFκB, CAMP, AhR, and IL17A. Finally, causal network analysis revealed several potential hub regulators shared in acne pathogenesis and PM2.5-exposed skin, including HIF1α, TNF, IL1α, and CCL5. Conclusions: Our analysis revealed significant concordant molecular signatures between acne and PM2.5-exposed skin. Biological insights from this study offer clues that build the causal links between air pollution and acne pathogenesis.

The use of molecular markers is one of the most sensitive, powerful technologies for genetic purity assessment of seed lots. In this study, we aimed to develop a set of insertion and deletion (InDel) markers, through bioinformatics approaches, that may effectively distinguish three representative japonica rice (Oryza sativa L.) varieties, Nipponbare, Taichung 65 and Zhonghua11. The published whole-genome sequences of these varieties were aligned using BWA-MEM, followed by manual inspection for InDels of more than ten base pairs in size with the tview function of SAMtools. A set of ten InDel markers were thus identified and then validated by PCR in the three japonica rice varieties and their intercross F1 hybrids. Results showed that the InDel markers developed in this study could reliably distinguish these three japonica rice varieties. These molecular markers together with the detection method developed here can be applied to DNA-based genetic purity evaluation in rice breeding.

An abnormal alanine aminotransferase (ALT) level is predictive of disease and all-cause mortality and may indicate liver injury. Using twin modeling, the genetic and environmental factors that affect human serum ALT levels have been well studied for the populations in the different countries, and the results showed moderate-to-high heritability. However, the heritability of ALT level has not been explored in Chinese population. Thus, we recruited 369 pairs of twins (233 monozygotic and 136 dizygotic) from the Qingdao Twin Registry in China with a median age of 50 years (40−80 years). Correlation analysis and a structural equation model (SEM) were conducted to evaluate the heritability of ALT level. The data for age, gender, body mass index and alcohol consumption were set as covariates. Intrapair correlation in monozygotic twins was 0.64 (95%CI [.56, .71]) and 0.42 (95% CI [.28, .55]) in dizygotic twins. The SEM analysis indicated that 65% (95% CI [57%, 71%]) of the variation in ALT levels can be explained by additive genetics and 35% (95% CI [29%, 44%]) of the variation is attributed to unique environmental factors or residuals. Shared environmental influences were not significant. In conclusion, serum ALT variations exhibited strong genetic effects. The variation could also be explained by unique environmental factors. However, shared environmental factors have a minor impact on the serum ALT level.

This study compared late bilinguals’ pitch feedback control in L1 and L2 production using a frequency-altered feedback paradigm in which participants read target words while presented with unexpected pitch-shift in their voice feedback. Variables of language (L1 or L2) and perturbation magnitudes (0, 100, 200, or 400 cents) were manipulated. Behaviorally, participants produced larger magnitudes but longer latencies of vocal compensation in L2 than in L1 production, suggesting that L2 pitch feedback control has greater importance but lower efficiency. Event-related potential findings demonstrated that 400-cent shifts elicited greater N1 amplitudes than those in the 0-cent baseline condition in L1 production. This difference was non-significant in L2 production, implying different neural processing of unaltered feedback and externally-generated feedback in L1 and L2 production. Participants’ vocal compensation and P2 amplitudes were similarly modulated by pitch-shift in L1 and L2 production, implying a similar gating mechanism to correct internal and external errors.

As optical parametric chirped pulse amplification has been widely adopted for the generation of extreme intensity laser sources, nonlinear crystals of large aperture are demanded for high-energy amplifiers. Yttrium calcium oxyborate (YCa4O(BO3)3, YCOB) is capable of being grown with apertures exceeding 100 mm, which makes it possible for application in systems of petawatt scale. In this paper, we experimentally demonstrated for the first time to our knowledge, an ultra-broadband non-collinear optical parametric amplifier with YCOB for petawatt-scale compressed pulse generation at 800 nm. Based on the SG-II 5 PW facility, amplified signal energy of approximately 40 J was achieved and pump-to-signal conversion efficiency was up to 42.3%. A gain bandwidth of 87 nm was realized and supported a compressed pulse duration of 22.3 fs. The near-field and wavefront aberration represented excellent characteristics, which were comparable with those achieved in lithium triborate-based amplifiers. These results verified the great potential for YCOB utilization in the future.

Plasma vertical displacement control is essential for the stable operation of tokamak devices. The traditional plasma vertical displacement calculation method is not suitable for balancing speed and accuracy simultaneously, which is necessary for real-time feedback control. In this study, neural networks are used to rapidly detect vertical displacement recognition. Based on a fully connected neural network, the vertical displacement calculation model is trained and tested using magnetic data of approximately 2000 shots. To compare the effects of different inputs on vertical displacement calculation, different magnetic measurement diagnostic signals are used to train and test the model. Compared with a full magnetic measurement dataset, 39 magnetic measurement signals (38 magnetic probes and plasma current) show better accuracy with mean square error <0.0005. The model is tested using historical experimental data, and it demonstrates accurate vertical displacement calculation even in the case of a vertical displacement event. In general, neural network algorithm has great application potential in vertical displacement calculation.

Electron resonant interactions with electromagnetic whistler-mode waves play an important role in electron flux dynamics in various space plasma systems: planetary radiation belts, bow shocks, solar wind and magnetic reconnection regions. Two key wave characteristics determining the regime of wave–particle interactions are the wave intensity and the wave coherency. The classical quasi-linear diffusion approach describes well electron diffusion by incoherent and low-amplitude waves, whereas the nonlinear resonant models describe electron phase bunching and trapping by highly coherent intense waves. This study is devoted to the investigation of the regime of electron resonant interactions with incoherent but intense waves. Although this regime is characterized by electron diffusion, we show that diffusion rates scale linearly with the wave amplitude, $D\propto B_w$, in contrast to the quasi-linear diffusion scaling $D_{QL}\propto B_w^2$. Using observed wave amplitude distributions, we demonstrate that the quasi-linear diffusion model significantly overestimates electron scattering by incoherent, but intense whistler-mode waves. We discuss the results obtained in the context of simulations of long-term electron flux dynamics in space plasma systems.

One challenge facing humans (and nonhuman animal) is that some options that appear attractive locally may not turn out best in the long run. To analyse this human learning problem, we explore human performance in a dynamic decision-making task that places local and global rewards in conflict. We found that experiences that included previous choices and rewards are not easily incorporated into people’s strategy to enhance their performance. Our results suggest that humans are easily driven by concerns about recent feedback, and that choice of a suboptimal behaviour option may be overcome by providing informative cues that indicate a clear immediate outcome for a better option.

An initially perturbed interface between two fluids of different densities is usually unstable when driven by an acceleration or a shock wave; it is known as a Rayleigh–Taylor instability or a Richtmyer–Meshkov instability. One of the most significant issues in these instabilities is the spatiotemporal development of fingers generated at the interface, which plays an important role in both scientific research (e.g. supernova explosion) and engineering applications (e.g. inertial confinement fusion). Accurate theoretical solution of these interfacial fingers remains as an unsolved and challenging problem since Taylor's seminal work more than seven decades ago. This paper reports a unified theory established for such phenomena by combining the classical potential-flow theory and a dual-source model to address the long-standing difficulty highlighted by the initial-value sensitivity and strong nonlinearity. It is the first time for a theory to accurately predict the long-time developments in both growth rate and shape curvature of interfacial fingers at all density ratios in two and three dimensions. Moreover, the new theory clearly reveals the nonlinear coupling mechanism for interfacial evolution, and especially explains the origin of overshot in the growth rate curve.

This work elucidated the performance and mechanisms of Pb2+ adsorption by kaolinite, montmorillonite, goethite and ferrihydrite using batch experiments. The contributions of various adsorption mechanisms were quantified using a stepwise extraction method. Several characterizations (scanning electron microscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, point of zero charge analysis and X-ray fluorescence) were utilized to analyse the physicochemical properties and the potential adsorption mechanisms. The results indicated that the adsorption processes of montmorillonite and goethite approached equilibrium within 20 min, while 60 min were required for the adsorption processes of kaolinite and ferrihydrite. The adsorption processes of Pb2+ by the four minerals best fit the pseudo-second order model. The adsorption capacities of the four minerals for Pb2+ followed the order: montmorillonite > goethite > ferrihydrite > kaolinite, and the maximum adsorption capacities were 69.20, 46.95, 34.32 and 18.62 mg g–1, respectively. The stepwise extraction test showed that the adsorption mechanism of Pb2+ was dominated by ion exchange for montmorillonite, precipitation and complexation for goethite and complexation for kaolinite and ferrihydrite.

High-power continuous-wave ultraviolet lasers are useful for many applications. As ultraviolet laser sources, the wavelength switching capability and compact structure are very important to extend the applicability and improve the flexibility in practical applications. In this work, we present two simple and relatively compact schemes by laser diode pumping to obtain a watt-level single-wavelength 348.7-nm laser and discrete wavelength tunable ultraviolet lasers around 349 nm (from 334.7 to 364.5 nm) by intracavity frequency doubling based on Pr3+:YLF and $\unicode{x3b2}$ -BBO crystals. The maximum output power of the single-wavelength 348.7-nm laser is 1.033 W. The output powers of the discrete wavelength tunable lasers are at the level of tens of milliwatts, except for two peaks at 348.7 and 360.3 nm with output powers of approximately 500 mW. In addition, simulations are carried out to explain the experimental results and clarify the tuning mechanisms.