Neural Mechanisms of Drosophila Circadian Rhythms

Animals show circadian rhythms in a variety of physiological functions and behaviors. In Drosophila melanogaster, behavioral rhythms are driven by circadian clock genes that are oscillating in ~150 circadian pacemaker neurons. To explain how circadian neurons encode time and regulate different behavioral rhythms, I performed 24-hour in vivo whole-brain calcium imaging using light-sheet microscopy. First, I found that different groups of circadian neurons show circadian rhythms in spontaneous neural activity with diverse phases. The neural activity phases of the M and E pacemaker groups, which are associated with the morning and evening locomotor activities respectively, occur ~4 hours before their respective behaviors. I also showed that neural activity rhythms are generated by circadian clock gene oscillations, which regulate the expression of IP3R and T-type calcium channels. Next, I asked how the diverse phases of neural activity are generated from the in-phase clock gene oscillations. Groups of circadian neurons inhibit each other via long-duration neuromodulation, mediated by neuropeptides PDF and sNPF, such that their activity phases are properly staggered across the day and night. Certain activity phases are also regulated by environmental light inputs. I then identified an output pathway by which circadian neurons regulate the locomotor activity rhythm. M and E pacemaker groups independently activate a common pre-motor center (termed ellipsoid body ring neurons) through the agency of specific dopaminergic interneurons. Finally, using methods including whole-brain pan-neuronal imaging, I further identified several output circuits downstream of circadian neurons. Circadian neural activity rhythms propagate through these circuits to regulate different behavioral outputs including sleep, olfaction, mating, and feeding rhythms. Together, my findings show how circadian clocks regulate diverse behavioral outputs by two steps; first, circadian clock genes generate diverse circadian neural activity rhythms within a network of interacting pacemaker neurons; then, sequentially-active pacemaker neurons independently and together regulate diverse behavioral outputs by generating diverse circadian neural activity rhythms in different downstream output circuits

Adaptive MCMC for Bayesian variable selection in generalised linear models and survival models

Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions for the marginal likelihood. The RJMCMC approach can be employed to samples model and coefficients jointly, but effective design of the transdimensional jumps of RJMCMC can be challenge, making it hard to implement. Alternatively, the marginal likelihood can be derived using data-augmentation scheme e.g. Polya-gamma data argumentation for logistic regression) or through other estimation methods. However, suitable data-augmentation schemes are not available for every generalised linear and survival models, and using estimations such as Laplace approximation or correlated pseudo-marginal to derive marginal likelihood within a locally informed proposal can be computationally expensive in the "large n, large p" settings. In this paper, three main contributions are presented. Firstly, we present an extended Point-wise implementation of Adaptive Random Neighbourhood Informed proposal (PARNI) to efficiently sample models directly from the marginal posterior distribution in both generalised linear models and survival models. Secondly, in the light of the approximate Laplace approximation, we also describe an efficient and accurate estimation method for the marginal likelihood which involves adaptive parameters. Additionally, we describe a new method to adapt the algorithmic tuning parameters of the PARNI proposal by replacing the Rao-Blackwellised estimates with the combination of a warm-start estimate and an ergodic average. We present numerous numerical results from simulated data and 8 high-dimensional gene fine mapping data-sets to showcase the efficiency of the novel PARNI proposal compared to the baseline add-delete-swap proposal

Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection

We introduce a framework for efficient Markov chain Monte Carlo algorithms targeting discrete-valued high-dimensional distributions, such as posterior distributions in Bayesian variable selection problems. We show that many recently introduced algorithms, such as the locally informed sampler of Zanella (J Am Stat Assoc 115(530):852–865, 2020), the locally informed with thresholded proposal of Zhou et al. (Dimension-free mixing for high-dimensional Bayesian variable selection, 2021) and the adaptively scaled individual adaptation sampler of Griffin et al. (Biometrika 108(1):53–69, 2021), can be viewed as particular cases within the framework. We then describe a novel algorithm, the adaptive random neighbourhood informed sampler, which combines ideas from these existing approaches. We show using several examples of both real and simulated data-sets that a computationally efficient point-wise implementation (PARNI) provides more reliable inferences on a range of variable selection problems, particularly in the very large p setting

Adaptive MCMC for Bayesian variable selection in generalised linear models and survival models

Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) approach can be employed to jointly sample models and coefficients, but the effective design of the trans-dimensional jumps of RJMCMC can be challenging, making it hard to implement. Alternatively, the marginal likelihood can be derived conditional on latent variables using a data-augmentation scheme (e.g., Pólya-gamma data augmentation for logistic regression) or using other estimation methods. However, suitable data-augmentation schemes are not available for every generalised linear model and survival model, and estimating the marginal likelihood using a Laplace approximation or a correlated pseudo-marginal method can be computationally expensive. In this paper, three main contributions are presented. Firstly, we present an extended Point-wise implementation of Adaptive Random Neighbourhood Informed proposal (PARNI) to efficiently sample models directly from the marginal posterior distributions of generalised linear models and survival models. Secondly, in light of the recently proposed approximate Laplace approximation, we describe an efficient and accurate estimation method for marginal likelihood that involves adaptive parameters. Additionally, we describe a new method to adapt the algorithmic tuning parameters of the PARNI proposal by replacing Rao-Blackwellised estimates with the combination of a warm-start estimate and the ergodic average. We present numerous numerical results from simulated data and eight high-dimensional genetic mapping data-sets to showcase the efficiency of the novel PARNI proposal compared with the baseline add–delete–swap proposal

Task-Technology Fit and Employees’ Exploration of Enterprise Systems: Moderating Role of Local Management Commitment

Based on task-technology fit theory and adaptive structuration theory, we propose that employees’ exploration of enterprise systems is mainly influenced by three fundamental components: task, technology, and organizational environment. Accordingly, a research model is developed to interpret how task variety, system modularity, and local management commitment jointly affect employees’ system exploration. The model is tested with a survey of enterprise system users in six firms, and several meaningful findings are yielded. First, all of the three antecedents can directly affect system exploration. Second, task variety can positively moderate the effects of system modularity on system exploration. Third, local management commitment can strengthen the effects of system modularity and task variety on employees’ system exploration. The limitations and implications for research and practice are discussed

Circadian pacemaker neurons display cophasic rhythms in basal calcium level and in fast calcium fluctuations

SignificanceDaily rhythms in the molecular clock, in calcium, and in electrical activity all interact to support the functions of circadian pacemaker neurons. However, the regulatory mechanisms that unify these properties are not defined. Here, we utilize the cellular resolution of th

The dynamics of pattern matching in camouflaging cuttlefish

Many cephalopods escape detection using camouflage. This behaviour relies on a visual assessment of the surroundings, on an interpretation of visual-texture statistics and on matching these statistics using millions of skin chromatophores that are controlled by motoneurons located in the brain. Analysis of cuttlefish images proposed that camouflage patterns are low dimensional and categorizable into three pattern classes, built from a small repertoire of components. Behavioural experiments also indicated that, although camouflage requires vision, its execution does not require feedback, suggesting that motion within skin-pattern space is stereotyped and lacks the possibility of correction. Here, using quantitative methods, we studied camouflage in the cuttlefish Sepia officinalis as behavioural motion towards background matching in skin-pattern space. An analysis of hundreds of thousands of images over natural and artificial backgrounds revealed that the space of skin patterns is high-dimensional and that pattern matching is not stereotyped-each search meanders through skin-pattern space, decelerating and accelerating repeatedly before stabilizing. Chromatophores could be grouped into pattern components on the basis of their covariation during camouflaging. These components varied in shapes and sizes, and overlay one another. However, their identities varied even across transitions between identical skin-pattern pairs, indicating flexibility of implementation and absence of stereotypy. Components could also be differentiated by their sensitivity to spatial frequency. Finally, we compared camouflage to blanching, a skin-lightening reaction to threatening stimuli. Pattern motion during blanching was direct and fast, consistent with open-loop motion in low-dimensional pattern space, in contrast to that observed during camouflage.journal articl

Task-technology fit and employees\u27 exploration of enterprise systems: Moderating role of local management commitment

Based on task-technology fit theory and adaptive structuration theory, we propose that employees\u27 exploration of enterprise systems is mainly influenced by three fundamental components: task, technology, and organizational environment. Accordingly, a research model is developed to interpret how task variety, system modularity, and local management commitment jointly affect employees\u27 system exploration. The model is tested with a survey of enterprise system users in six firms, and several meaningful findings are yielded. First, all of the three antecedents can directly affect system exploration. Second, task variety can positively moderate the effects of system modularity on system exploration. Third, local management commitment can strengthen the effects of system modularity and task variety on employees\u27 system exploration. The limitations and implications for research and practice are discussed

Employees\u27 exploration of complex systems: An integrative view

Based on the theory of effective use and adaptive structuration theory, we propose that employees\u27 system exploration behavior can be affected by factors related to three major components: task, system, and organizational environment. Specifically, we examine how task characteristics (job autonomy and task variety), system complexity, and innovation climate jointly affect employees\u27 exploration, which, in turn, leads to extended use of enterprise systems. A field survey of enterprise resource planning (ERP) users yields several interesting findings. First, job autonomy and task variety directly enhance system exploration. Second, system complexity plays a moderating role by strengthening the relationship between job autonomy and exploration and weakening the relationship between task variety and exploration. Third, innovation climate, also acting as a moderator, strengthens both the impact of job autonomy on exploration and the impact of system exploration on extended use. This research contributes to information systems (IS) research by theoretically articulating that system exploration is subject to the simultaneous influences of task, system, and organizational environment factors and empirically testing these factors\u27 main effects and interactions to shed new light on system exploration research. It also contributes to IS practice by suggesting that organizations could enhance employees\u27 system exploration and facilitate the transition from exploration to extended use by increasing job autonomy and task variety, designing personalized training programs to reduce system complexity, and developing organizational climates that foster innovations

Tree Species Classification over Cloudy Mountainous Regions by Spatiotemporal Fusion and Ensemble Classifier

Accurate mapping of tree species is critical for the sustainable development of the forestry industry. However, the lack of cloud-free optical images makes it challenging to map tree species accurately in cloudy mountainous regions. In order to improve tree species identification in this context, a classification method using spatiotemporal fusion and ensemble classifier is proposed. The applicability of three spatiotemporal fusion methods, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the flexible spatiotemporal data fusion (FSDAF), and the spatial and temporal nonlocal filter-based fusion model (STNLFFM), in fusing MODIS and Landsat 8 images was investigated. The fusion results in Helong City show that the STNLFFM algorithm generated the best fused images. The correlation coefficients between the fusion images and actual Landsat images on May 28 and October 19 were 0.9746 and 0.9226, respectively, with an average of 0.9486. Dense Landsat-like time series at 8-day time intervals were generated using this method. This time series imagery and topography-derived features were used as predictor variables. Four machine learning methods, i.e., K-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and light gradient boosting machine (LightGBM), were selected for tree species classification in Helong City, Jilin Province. An ensemble classifier combining these classifiers was constructed to further improve the accuracy. The ensemble classifier consistently achieved the highest accuracy in almost all classification scenarios, with a maximum overall accuracy improvement of approximately 3.4% compared to the best base classifier. Compared to only using a single temporal image, utilizing dense time series and the ensemble classifier can improve the classification accuracy by about 20%, and the overall accuracy reaches 84.32%. In conclusion, using spatiotemporal fusion and the ensemble classifier can significantly enhance tree species identification in cloudy mountainous areas with poor data availability