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Renewable fuel generation is essential for a low carbon footprint economy. Thus, over the last five decades, a significant effort has been dedicated towards increasing the performance of solar fuels generating devices. Specifically, the solar to hydrogen efficiency of photoelectrochemical cells has progressed steadily towards its fundamental limit, and the faradaic efficiency towards valuable products in CO2 reduction systems has increased dramatically. However, there are still numerous scientific and engineering challenges that must be overcame in order to turn solar fuels into a viable technology. At the electrode and device level, the conversion efficiency, stability and products selectivity must be increased significantly. Meanwhile, these performance metrics must be maintained when scaling up devices and systems while maintaining an acceptable cost and carbon footprint. This roadmap surveys different aspects of this endeavor: system benchmarking, device scaling, various approaches for photoelectrodes design, materials discovery, and catalysis. Each of the sections in the roadmap focuses on a single topic, discussing the state of the art, the key challenges and advancements required to meet them. The roadmap can be used as a guide for researchers and funding agencies highlighting the most pressing needs of the field.

Renewable fuel generation is essential for a low carbon footprint economy. Thus, over the last five decades, a significant effort has been dedicated towards increasing the performance of solar fuels generating devices. Specifically, the solar to hydrogen efficiency of photoelectrochemical cells has progressed steadily towards its fundamental limit, and the faradaic efficiency towards valuable products in CO2 reduction systems has increased dramatically. However, there are still numerous scientific and engineering challenges that must be overcame in order to turn solar fuels into a viable technology. At the electrode and device level, the conversion efficiency, stability and products selectivity must be increased significantly. Meanwhile, these performance metrics must be maintained when scaling up devices and systems while maintaining an acceptable cost and carbon footprint. This roadmap surveys different aspects of this endeavor: system benchmarking, device scaling, various approaches for photoelectrodes design, materials discovery, and catalysis. Each of the sections in the roadmap focuses on a single topic, discussing the state of the art, the key challenges and advancements required to meet them. The roadmap can be used as a guide for researchers and funding agencies highlighting the most pressing needs of the field.

Damage to specific brain circuits can cause specific neuropsychiatric symptoms. Therapeutic stimulation to these same circuits may modulate these symptoms. To determine whether these circuits converge, we studied depression severity after brain lesions (n = 461, five datasets), transcranial magnetic stimulation (n = 151, four datasets) and deep brain stimulation (n = 101, five datasets). Lesions and stimulation sites most associated with depression severity were connected to a similar brain circuit across all 14 datasets (P < 0.001). Circuits derived from lesions, deep brain stimulation and transcranial magnetic stimulation were similar (P < 0.0005), as were circuits derived from patients with major depression versus other diagnoses (P < 0.001). Connectivity to this circuit predicted out-of-sample antidepressant efficacy of transcranial magnetic stimulation and deep brain stimulation sites (P < 0.0001). In an independent analysis, 29 lesions and 95 stimulation sites converged on a distinct circuit for motor symptoms of Parkinson's disease (P < 0.05). We conclude that lesions, transcranial magnetic stimulation and DBS converge on common brain circuitry that may represent improved neurostimulation targets for depression and other disorders.

The performance of halide perovskite-based electronic and optoelectronic devices is often related to interfacial charge transport. To shed light on the underlying physical and chemical properties of CH3NH3PbI3 (MAPbI3) in direct contact with common electrodes Al, Ti, Cr, Ag, and Au, the evolution of interfacial properties and Fermi level pinning is systematically studied. Given a unique experimental facility, pristine interfaces without any exposure to ambient air were prepared. We observe aggregation of substantial amounts of metallic lead (Pb0) at the metal/MAPbI3 interface, resulting from the interfacial reaction between the deposited metal and iodine ions from MAPbI3. It is found that the Schottky barrier height at the metal/MAPbI3 interface is independent of the metal work function due to strong Fermi level pinning, possibly due to the metallic Pb0 aggregates, which act as interfacial trap sites. The charge neutrality level of MAPbI3 is consistent with the energy level of Pb0-related defects, indicating that Pb0 interfacial trap states can be nonradiative recombination sites. This work underlines that control of chemical bonding at interfaces is a key factor for designing future halide perovskite-based devices.

Deep brain stimulation (DBS) is one of the most important clinical therapies for neurological disorders. DBS also has great potential to become a great tool for clinical neuroscience research. Recently, the National Engineering Laboratory for Neuromodulation at Tsinghua University held an international Deep Brain Stimulation Initiative workshop to discuss the cutting-edge technological achievements and clinical applications of DBS. We specifically addressed new clinical approaches and challenges in DBS for movement disorders (Parkinson's disease and dystonia), clinical application toward neurorehabilitation for stroke, and the progress and challenges toward DBS for neuropsychiatric disorders. This review highlighted key developments in (1) neuroimaging, with advancements in 3-Tesla magnetic resonance imaging DBS compatibility for exploration of brain network mechanisms; (2) novel DBS recording capabilities for uncovering disease pathophysiology; and (3) overcoming global healthcare burdens with online-based DBS programming technology for connecting patient communities. The successful event marks a milestone for global collaborative opportunities in clinical development of neuromodulation to treat major neurological disorders.

© 2019 Elsevier B.V. Two dilemmas frequently occur in many real-world clinical prognoses. First, the on-hand data cannot be put entirely into the existing prediction model, since the features from new data do not perfectly match those of the model. As a result, some unique features collected from the patients in the current domain of interest might be wasted. Second, the on-hand data is not sufficient enough to learn a new prediction model. To overcome these challenges, we propose an output-based transfer learning approach with least squares support vector machine (LS-SVM) to make the maximum use of the small dataset and guarantee an enhanced generalization capability. The proposed approach can learn a current domain of interest with limited samples effectively by leveraging the knowledge from the predicted outputs of the existing model in the source domain. Also, the extent of output knowledge transfer from the source domain to the current one can be automatically and rapidly determined using a proposed fast leave-one-out cross validation strategy. The proposed approach is applied to a real-world clinical dataset to predict 5-year overall and cancer-specific mortality of bladder cancer patients after radical cystectomy. The experimental results indicate that the proposed approach achieves better classification performances than the other comparative methods and has the potential to be implemented into the real-world context to deal with small data problems in cancer prediction and prognosis.

© 2013 IEEE. The additive kernel least squares support vector machine (AK-LS-SVM) has been well used in classification tasks due to its inherent advantages. For example, additive kernels work extremely well for some specific tasks, such as computer vision classification, medical research, and some specialized scenarios. Moreover, the analytical solution using AK-LS-SVM can formulate leave-one-out cross-validation error estimates in a closed form for parameter tuning, which drastically reduces the computational cost and guarantee the generalization performance especially on small and medium datasets. However, AK-LS-SVM still faces two main challenges: 1) improving the classification performance of AK-LS-SVM and 2) saving time when performing a grid search for model selection. Inspired by the stacked generalization principle and the transfer learning mechanism, a layer-by-layer combination of AK-LS-SVM classifiers embedded with transfer learning is proposed in this paper. This new classifier is called deep transfer additive kernel least square support vector machine (DTA-LS-SVM) which overcomes these two challenges. Also, considering that imbalanced datasets are involved in many real-world scenarios, especially for medical data analysis, the deep-transfer element is extended to compensate for this imbalance, thus leading to the development of another new classifier iDTA-LS-SVM. In the hierarchical structure of both DTA-LS-SVM and iDTA-LS-SVM, each layer has an AK-LS-SVM and the predictions from the previous layer act as an additional input feature for the current layer. Importantly, transfer learning is also embedded to guarantee generalization consistency between the adjacent layers. Moreover, both iDTA-LS-SVM and DTA-LS-SVM can ensure the minimal leave-one-out error by using the proposed fast leave-one-out cross validation strategy on the training set in each layer. We compared the proposed classifiers DTA-LS-SVM and iDTA-LS-SVM with the traditional LS-SVM and SVM u

© 2013 IEEE. The additive kernel least squares support vector machine (AK-LS-SVM) has been well used in classification tasks due to its inherent advantages. For example, additive kernels work extremely well for some specific tasks, such as computer vision classification, medical research, and some specialized scenarios. Moreover, the analytical solution using AK-LS-SVM can formulate leave-one-out cross-validation error estimates in a closed form for parameter tuning, which drastically reduces the computational cost and guarantee the generalization performance especially on small and medium datasets. However, AK-LS-SVM still faces two main challenges: 1) improving the classification performance of AK-LS-SVM and 2) saving time when performing a grid search for model selection. Inspired by the stacked generalization principle and the transfer learning mechanism, a layer-by-layer combination of AK-LS-SVM classifiers embedded with transfer learning is proposed in this paper. This new classifier is called deep transfer additive kernel least square support vector machine (DTA-LS-SVM) which overcomes these two challenges. Also, considering that imbalanced datasets are involved in many real-world scenarios, especially for medical data analysis, the deep-transfer element is extended to compensate for this imbalance, thus leading to the development of another new classifier iDTA-LS-SVM. In the hierarchical structure of both DTA-LS-SVM and iDTA-LS-SVM, each layer has an AK-LS-SVM and the predictions from the previous layer act as an additional input feature for the current layer. Importantly, transfer learning is also embedded to guarantee generalization consistency between the adjacent layers. Moreover, both iDTA-LS-SVM and DTA-LS-SVM can ensure the minimal leave-one-out error by using the proposed fast leave-one-out cross validation strategy on the training set in each layer. We compared the proposed classifiers DTA-LS-SVM and iDTA-LS-SVM with the traditional LS-SVM and SVM u

© 2013 IEEE. Missing data is a common issue in community health and epidemiological studies. Direct removal of samples with missing data can lead to reduced sample size and information bias, which deteriorates the significance of the results. While data imputation methods are available to deal with missing data, they are limited in performance and could introduce noises into the dataset. Instead of data imputation, a novel method based on additive least square support vector machine (LS-SVM) is proposed in this paper for predictive modeling when the input features of the model contain missing data. The method also determines simultaneously the influence of the features with missing values on the classification accuracy using the fast leave-one-out cross-validation strategy. The performance of the method is evaluated by applying it to predict the quality of life (QOL) of elderly people using health data collected in the community. The dataset involves demographics, socioeconomic status, health history, and the outcomes of health assessments of 444 community-dwelling elderly people, with 5% to 60% of data missing in some of the input features. The QOL is measured using a standard questionnaire of the World Health Organization. Results show that the proposed method outperforms four conventional methods for handling missing data - case deletion, feature deletion, mean imputation, and K-nearest neighbor imputation, with the average QOL prediction accuracy reaching 0.7418. It is potentially a promising technique for tackling missing data in community health research and other applications.

IEEE The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.

© 2013 IEEE. Missing data is a common issue in community health and epidemiological studies. Direct removal of samples with missing data can lead to reduced sample size and information bias, which deteriorates the significance of the results. While data imputation methods are available to deal with missing data, they are limited in performance and could introduce noises into the dataset. Instead of data imputation, a novel method based on additive least square support vector machine (LS-SVM) is proposed in this paper for predictive modeling when the input features of the model contain missing data. The method also determines simultaneously the influence of the features with missing values on the classification accuracy using the fast leave-one-out cross-validation strategy. The performance of the method is evaluated by applying it to predict the quality of life (QOL) of elderly people using health data collected in the community. The dataset involves demographics, socioeconomic status, health history, and the outcomes of health assessments of 444 community-dwelling elderly people, with 5% to 60% of data missing in some of the input features. The QOL is measured using a standard questionnaire of the World Health Organization. Results show that the proposed method outperforms four conventional methods for handling missing data - case deletion, feature deletion, mean imputation, and K-nearest neighbor imputation, with the average QOL prediction accuracy reaching 0.7418. It is potentially a promising technique for tackling missing data in community health research and other applications.

© 2017 IEEE. Many medical applications face a situation that the on-hand data cannot fully fit an existing predictive model or on-line tool, since these models or tools only use the most common predictors and the other valuable features collected in the current scenario are not considered altogether. On the other hand, the training data in the current scenario is not sufficient to learn a predictive model effectively yet. In order to overcome these problems and construct an efficient classifier, for these real situations in medical fields, in this work we present an approach based on the least squares support vector machine (LS-SVM), which utilizes a transfer learning framework to make maximum use of the data and guarantee its enhanced generalization capability. The proposed approach is capable of effectively learning a target domain with limited samples by relying on the probabilistic outputs from the other previously learned model using a heterogeneous method in the source domain. Moreover, it autonomously and quickly decides how much output knowledge to transfer from source domain to the target one using a fast leave-one-out cross validation strategy. This approach is applied on a real-world clinical dataset to predict 5-year mortality of bladder cancer patients after radical cystectomy, and the experimental results indicate that the proposed method can achieve better performances compared to traditional machine learning methods, consistently showing the potential of the proposed method under the circumstances with insufficient data.

© 2017 IEEE. Many medical applications face a situation that the on-hand data cannot fully fit an existing predictive model or on-line tool, since these models or tools only use the most common predictors and the other valuable features collected in the current scenario are not considered altogether. On the other hand, the training data in the current scenario is not sufficient to learn a predictive model effectively yet. In order to overcome these problems and construct an efficient classifier, for these real situations in medical fields, in this work we present an approach based on the least squares support vector machine (LS-SVM), which utilizes a transfer learning framework to make maximum use of the data and guarantee its enhanced generalization capability. The proposed approach is capable of effectively learning a target domain with limited samples by relying on the probabilistic outputs from the other previously learned model using a heterogeneous method in the source domain. Moreover, it autonomously and quickly decides how much output knowledge to transfer from source domain to the target one using a fast leave-one-out cross validation strategy. This approach is applied on a real-world clinical dataset to predict 5-year mortality of bladder cancer patients after radical cystectomy, and the experimental results indicate that the proposed method can achieve better performances compared to traditional machine learning methods, consistently showing the potential of the proposed method under the circumstances with insufficient data.

A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities. © 2013 IEEE.

A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities. © 2013 IEEE.

In general, the calibration process involves minimisation of the deviation between recorded information and the simulation predictions through repeated adjustment of control parameters. Implementation of this process requires temporal and spatial information at an adequate resolution to achieve robust predictions from a catchment modelling system. Unfortunately, the available information usually is not adequate for this purpose and it becomes necessary to either use catchment average values or to use other techniques to infer the necessary information. Developments in information technology and the availability of digital information have facilitated the later approach (see, for example, Choi and Ball, 1999). Using the Centennial Park Catchment in Sydney, Australia as a test catchment, inference models for estimation of the control parameters necessary to implement the Stormwater Management Model were developed. A number of alternative inference models were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each subcatchment having its own unique values. Furthermore, the influence of different measures of deviation between the monitored information and simulation predictions were considered. Presented herein will be the results of these investigations into the complexity and structure of models used in the calibration process.

In general, the calibration process involves minimisation of the deviation between recorded information and the simulation predictions through repeated adjustment of control parameters. Implementation of this process requires temporal and spatial information at an adequate resolution to achieve robust predictions from a catchment modelling system. Unfortunately, the available information usually is not adequate for this purpose and it becomes necessary to either use catchment average values or to use other techniques to infer the necessary information. Developments in information technology and the availability of digital information have facilitated the later approach (see, for example, Choi and Ball, 1999). Using the Centennial Park Catchment in Sydney, Australia as a test catchment, inference models for estimation of the control parameters necessary to implement the Stormwater Management Model were developed. A number of alternative inference models were developed to assess the influence of inference model complexity and structure on the calibration of the catchment modelling system. These inference models varied from the assumption of a spatially invariant value (catchment average) to spatially variable with each subcatchment having its own unique values. Furthermore, the influence of different measures of deviation between the monitored information and simulation predictions were considered. Presented herein will be the results of these investigations into the complexity and structure of models used in the calibration process.

The application of catchment modelling systems is now a common approach for management of catchments. Fundamental to the application of a catchment modelling system is the calibration and validation of the many control parameters used to ensure that the simulated catchment response adequately reproduces the actual catchment response. Recorded information about the actual catchment response is needed if this necessary calibration and validation process is to be undertaken. However, the recorded information typically used for operation of a catchment modelling system rarely, if ever, is error free. These errors have the potential to influence the calibration of the control parameters for a catchment modelling system. Traditional calibration approaches focus only on achieving the parameter values that result in the best curve-fitting between simulated and recorded data. Introduced herein is an Early Stopping Technique which is aimed at avoiding curve-fitting through monitoring improvements in the objective function used for determining the set of optimal control parameters. Application of this approach to the calibration of SWMM (Storm Water Management Model) on the Centennial Park catchment in Sydney is outlined.

The application of catchment modelling systems is now a common approach for management of catchments. Fundamental to the application of a catchment modelling system is the calibration and validation of the many control parameters used to ensure that the simulated catchment response adequately reproduces the actual catchment response. Recorded information about the actual catchment response is needed if this necessary calibration and validation process is to be undertaken. However, the recorded information typically used for operation of a catchment modelling system rarely, if ever, is error free. These errors have the potential to influence the calibration of the control parameters for a catchment modelling system. Traditional calibration approaches focus only on achieving the parameter values that result in the best curve-fitting between simulated and recorded data. Introduced herein is an Early Stopping Technique which is aimed at avoiding curve-fitting through monitoring improvements in the objective function used for determining the set of optimal control parameters. Application of this approach to the calibration of SWMM (Storm Water Management Model) on the Centennial Park catchment in Sydney is outlined.

Accurate estimation of the control parameters for spatially distributed physically based catchment modelling systems requires considerable work to establish credibility. Presented in this paper is a methodology for estimation of control parameter values based on the application of a decision support system within a hydroinformatic system. The proposed methodology uses information contained within a GIS database together with optimisation techniques to infer spatially variable control parameters for utilisation with a catchment modelling system such as the Stormwater Management Model (SWMM). A case study application of the proposed methodology was undertaken using the Musgrave Avenue Stormwater System in Centennial Park, Sydney. Results from this application suggest that the proposed approach is capable of providing accurate spatially distributed control parameters for implementation with physically based catchment modelling systems. © 2002 Published by Elsevier Science Ltd.

Accurate estimation of the control parameters for spatially distributed physically based catchment modelling systems requires considerable work to establish credibility. Presented in this paper is a methodology for estimation of control parameter values based on the application of a decision support system within a hydroinformatic system. The proposed methodology uses information contained within a GIS database together with optimisation techniques to infer spatially variable control parameters for utilisation with a catchment modelling system such as the Stormwater Management Model (SWMM). A case study application of the proposed methodology was undertaken using the Musgrave Avenue Stormwater System in Centennial Park, Sydney. Results from this application suggest that the proposed approach is capable of providing accurate spatially distributed control parameters for implementation with physically based catchment modelling systems. © 2002 Published by Elsevier Science Ltd.

The tumor suppressor retinoblastoma protein (RE) plays a central role in cellular growth regulation, differentiation, and apoptosis. Phosphorylation of RR results in a consequent loss of its ability to inhibit cell cycle progression However, how RE: phosphorylation might be regulated in apoptotic or postmitotic cells, such as neurons, remains unclear. Here we report that neuronal Cdc2-like kinase (Nclk), composed of Cdk5 and a neuronal Cdk5 activator (p25(nck5a)), can bind and phosphorylate RE. Since RE has been shown recently to associate with D-type G(1) cyclins and viral oncoproteins through a common peptide sequence motif of LXCXE, Nclk binding may be mediated by a related sequence motif (LXCXXE) found in p25(nck5a). We demonstrate (i) in vitro binding of bacterially expressed p25(nck5a) to a GST-RB fusion protein, (ii) coprecipitation of GST-RB and reconstituted Cdk5 . p25(nck5a), and (iii) phosphorylation of GST-RB by bacterially expressed Cdk5 . p25(nck5a) kinase and by Cdk5 . p25(nck5a) kinase purified from bovine brain. Finally, we show that immunoprecipitation of RE from embryonic mouse brain homogenate results in the coprecipitation of Cdk5 and that Cdk5 kinase activity is maximal during late embryonic development, a period when programmed cell death of developing neurons is greatest, Taken together, these results suggest that NcIk can bind to and phosphorylate RE in vitro and in vivo. We infer that Nclk may play an important role in regulating the activity of RE in the brain, including perhaps in apoptosing neurons.

The tumor suppressor retinoblastoma protein (RE) plays a central role in cellular growth regulation, differentiation, and apoptosis. Phosphorylation of RR results in a consequent loss of its ability to inhibit cell cycle progression However, how RE: phosphorylation might be regulated in apoptotic or postmitotic cells, such as neurons, remains unclear. Here we report that neuronal Cdc2-like kinase (Nclk), composed of Cdk5 and a neuronal Cdk5 activator (p25(nck5a)), can bind and phosphorylate RE. Since RE has been shown recently to associate with D-type G(1) cyclins and viral oncoproteins through a common peptide sequence motif of LXCXE, Nclk binding may be mediated by a related sequence motif (LXCXXE) found in p25(nck5a). We demonstrate (i) in vitro binding of bacterially expressed p25(nck5a) to a GST-RB fusion protein, (ii) coprecipitation of GST-RB and reconstituted Cdk5 . p25(nck5a), and (iii) phosphorylation of GST-RB by bacterially expressed Cdk5 . p25(nck5a) kinase and by Cdk5 . p25(nck5a) kinase purified from bovine brain. Finally, we show that immunoprecipitation of RE from embryonic mouse brain homogenate results in the coprecipitation of Cdk5 and that Cdk5 kinase activity is maximal during late embryonic development, a period when programmed cell death of developing neurons is greatest, Taken together, these results suggest that NcIk can bind to and phosphorylate RE in vitro and in vivo. We infer that Nclk may play an important role in regulating the activity of RE in the brain, including perhaps in apoptosing neurons.

The tumor suppressor retinoblastoma protein (RE) plays a central role in cellular growth regulation, differentiation, and apoptosis. Phosphorylation of RR results in a consequent loss of its ability to inhibit cell cycle progression However, how RE: phosphorylation might be regulated in apoptotic or postmitotic cells, such as neurons, remains unclear. Here we report that neuronal Cdc2-like kinase (Nclk), composed of Cdk5 and a neuronal Cdk5 activator (p25(nck5a)), can bind and phosphorylate RE. Since RE has been shown recently to associate with D-type G(1) cyclins and viral oncoproteins through a common peptide sequence motif of LXCXE, Nclk binding may be mediated by a related sequence motif (LXCXXE) found in p25(nck5a). We demonstrate (i) in vitro binding of bacterially expressed p25(nck5a) to a GST-RB fusion protein, (ii) coprecipitation of GST-RB and reconstituted Cdk5 . p25(nck5a), and (iii) phosphorylation of GST-RB by bacterially expressed Cdk5 . p25(nck5a) kinase and by Cdk5 . p25(nck5a) kinase purified from bovine brain. Finally, we show that immunoprecipitation of RE from embryonic mouse brain homogenate results in the coprecipitation of Cdk5 and that Cdk5 kinase activity is maximal during late embryonic development, a period when programmed cell death of developing neurons is greatest, Taken together, these results suggest that NcIk can bind to and phosphorylate RE in vitro and in vivo. We infer that Nclk may play an important role in regulating the activity of RE in the brain, including perhaps in apoptosing neurons.