Highlights d Functionally distinct neuronal ensembles exist within a single memory engram d Fosand Npas4-dependent ensembles undergo distinct synaptic modifications after CFC d Fosand Npas4-dependent ensembles drive memoryguided behaviors in opposite directions d Memory generalization and discrimination, respectively, require MEC and CCK + interneurons

While many approaches to predict aqueous pK a values exist, the fast and accurate prediction of non-aqueous pK a values is still challenging. Based on the iBonD experimental pK a database (39 solvents), ah olistic pK a prediction model was established using machine learning.S tructural and physical-organic-parameter-based descriptors (SPOC) were introduced to represent the electronic and structural features of the molecules.The models trained with aneural network or the XGBoost algorithm showed the best prediction performance with alow MAE value of 0.87 pK a units.The approachallows ac omprehensive mapping of all possible pK a correlations between different solvents and it was validated by predicting the aqueous pK a and micro-pK a of pharmaceutical molecules and pK a values of organocatalysts in DMSO and MeCN with high accuracy.Anonline prediction platform was constructed based on the current model, which can providepK a prediction for different types of XÀHacidity in the most commonly used solvents.

Purpose The purpose of this study is to reconcile the positive, non-significant and even negative effects of guanxi on firm performance from two aspects. First, it explores the linear and curvilinear relationships between guanxi and distinct performance dimensions. Second, it examines the moderating effects of both exchange-related behavioral risk (reflected by contract enforcement in this study) and market-related environmental risk (reflected by market turbulence in this study) on the above relationship. Design/methodology/approach Based on data for 206 samples collected from distributors of house furnishings, computers and their components, a moderated regression is used to test the hypotheses. Findings The empirical test generally supports the conceptual model and demonstrates three findings. First, guanxi has a linear, positive effect on financial performance and an inverted U-shaped effect on strategic performance. Second, contract enforcement decreases the effect of guanxi on financial performance and enhances its effect on strategic performance. Third, market turbulence enhances the effect of guanxi on financial performance and weakens its effect on strategic performance. Research limitations/implications First, this study collects data only from China. Future studies should collect data from other emerging markets to allow for either model validation or cross-country comparisons. Second, the data come only from buyers, and suppliers’ viewpoints are not included. Third, in addition to contract enforcement and market turbulence, other important contingencies should be considered in the guanxi–performance link. Practical implications The results provide important implications for managers to manage guanxi in an emerging economy. Managers should be very clear about their primary goal (i.e. pursuing short-term financial revenue or long-term strategic targets); next, they should understand how to match guanxi with various levels of contract enforcement and market turbulence to achieve that goal. Originality/value First, prior research has documented guanxi’s role in channel relationships, but it has not achieved consistent conclusions. Second, although existing studies have analyzed the contingencies of guanxi at the firm level, market level and institutional level, another important contingency “the dyadic relationship condition” is rarely considered. Third, although the extant research has realized the value of guanxi contingent on various market conditions, conflicting views exist. This study contributes by addressing these issues.

Although the developmental maturation of cortical inhibitory synapses is known to be a critical factor in gating the onset of critical period (CP) for experience-dependent cortical plasticity, how synaptic transmission dynamics of other cortical synapses are regulated during the transition to CP remains unknown. Here, by systematically examining various intracortical synapses within layer 4 of the mouse visual cortex, we demonstrate that synaptic temporal dynamics of intracortical excitatory synapses on principal cells (PCs) and inhibitory parvalbumin- or somatostatin-expressing cells are selectively regulated before the CP onset, whereas those of intracortical inhibitory synapses and long-range thalamocortical excitatory synapses remain unchanged. This selective maturation of synaptic dynamics results from a ubiquitous reduction of presynaptic release and is dependent on visual experience. These findings provide an additional essential circuit mechanism for regulating CP timing in the developing visual cortex.

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PurposeThe purpose of this paper is to examine the effect of mentoring on newcomer well-being, as mediated by newcomer socialization and moderated by proactive personality.Design/methodology/approachData were collected at four time points in a sample of 227 newcomers. Regression analysis and bootstrapping method were used to test the hypotheses.FindingsMentoring had a positive and indirect effect on newcomer well-being through socialization. The moderated mediation analysis also revealed that proactive personality augmented the direct effect of mentoring on socialization and its indirect effect on well-being.Research limitations/implicationsOur data were collected in China, thereby limiting the generalization of the research findings. Future research can test our model in different cultural contexts.Practical implicationsOrganizations should consider establishing a mentoring program to foster newcomer socialization and achieve well-being. Within the mentoring context, cultivating newcomers to become more proactive can predict higher socialization levels, resulting in higher well-being.Originality/valuePrevious research largely focused on the development of the well-being of tenured employees. Drawing on socialization resources theory, this study focuses on the newcomer well-being and proposes the influential mechanism and boundary condition of the relationship between mentoring and newcomer well-being. It sheds light on exploring the well-being development for newcomers.

The case of large azimuth misalignment angles in a strapdown inertial navigation system (SINS) is analyzed, and a method of using the adaptive UPF for the initial alignment is proposed. The filter is based on the idea of a strong tracking filter; through the introduction of the attenuation memory factor to effectively enhance the corrections of the current information residual error on the system, it reduces the influence on the system due to the system simplification, and the uncertainty of noise statistical properties to a certain extent; meanwhile, the UPF particle degradation phenomenon is better overcome. Finally, two kinds of non-linear filters, UPF and adaptive UPF, are adopted in the initial alignment of large azimuth misalignment angles in SINS, and the filtering effects of the two kinds of nonlinear filter on the initial alignment were compared by simulation and turntable experiments. The simulation and turntable experiment results show that the speed and precision of the initial alignment using adaptive UPF for a large azimuth misalignment angle in SINS under the circumstance that the statistical properties of the system noise are certain or not have been improved to some extent.

While many approaches to predict aqueous pK a values exist, the fast and accurate prediction of non-aqueous pK a values is still challenging. Based on the iBonD experimental pK a database (39 solvents), ah olistic pK a prediction model was established using machine learning.S tructural and physical-organic-parameter-based descriptors (SPOC) were introduced to represent the electronic and structural features of the molecules.The models trained with aneural network or the XGBoost algorithm showed the best prediction performance with alow MAE value of 0.87 pK a units.The approachallows ac omprehensive mapping of all possible pK a correlations between different solvents and it was validated by predicting the aqueous pK a and micro-pK a of pharmaceutical molecules and pK a values of organocatalysts in DMSO and MeCN with high accuracy.Anonline prediction platform was constructed based on the current model, which can providepK a prediction for different types of XÀHacidity in the most commonly used solvents.