Hierarchically clustered adaptive quantization CMAC and its learning convergence

The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC), learning convergence, nonuniform quantization

GPCR Genes Are Preferentially Retained after Whole Genome Duplication

One of the most interesting questions in biology is whether certain pathways have been favored during evolution, and if so, what properties could cause such a preference. Due to the lack of experimental evidence, whether select gene families have been preferentially retained over time after duplication in metazoan organisms remains unclear. Here, by syntenic mapping of nonchemosensory G protein-coupled receptor genes (nGPCRs which represent half the receptome for transmembrane signaling) in the vertebrate genomes, we found that, as opposed to the 8–15% retention rate for whole genome duplication (WGD)-derived gene duplicates in the entire genome of pufferfish, greater than 27.8% of WGD-derived nGPCRs which interact with a nonpeptide ligand were retained after WGD in pufferfish Tetraodon nigroviridis. In addition, we show that concurrent duplication of cognate ligand genes by WGD could impose selection of nGPCRs that interact with a polypeptide ligand. Against less than 2.25% probability for parallel retention of a pair of WGD-derived ligands and a pair of cognate receptor duplicates, we found a more than 8.9% retention of WGD-derived ligand-nGPCR pairs–threefold greater than one would surmise. These results demonstrate that gene retention is not uniform after WGD in vertebrates, and suggest a Darwinian selection of GPCR-mediated intercellular communication in metazoan organisms

Widespread divergence of the CEACAM/PSG genes in vertebrates and humans suggests sensitivity to selection

In mammals, carcinoembryonic antigen cell adhesion molecules (CEACAMs) and pregnancy-specific glycoproteins (PSGs) play important roles in the regulation of pathogen transmission, tumorigenesis, insulin signaling turnover, and fetal–maternal interactions. However, how these genes evolved and to what extent they diverged in humans remain to be investigated specifically. Based on syntenic mapping of chordate genomes, we reveal that diverging homologs with a prototypic CEACAM architecture–including an extracellular domain with immunoglobulin variable and constant domain-like regions, and an intracellular domain containing ITAM motif–are present from cartilaginous fish to humans, but are absent in sea lamprey, cephalochordate or urochordate. Interestingly, the CEACAM/PSG gene inventory underwent radical divergence in various vertebrate lineages: from zero in avian species to dozens in therian mammals. In addition, analyses of genetic variations in human populations showed the presence of various types of copy number variations (CNVs) at the CEACAM/PSG locus. These copy number polymorphisms have 3–80% frequency in select populations, and encompass single to more than six PSG genes. Furthermore, we found that CEACAM/PSG genes contain a significantly higher density of nonsynonymous single nucleotide polymorphism (SNP) compared to the chromosome average, and many CEACAM/PSG SNPs exhibit high population differentiation. Taken together, our study suggested that CEACAM/PSG genes have had a more dynamic evolutionary history in vertebrates than previously thought. Given that CEACAM/PSGs play important roles in maternal–fetal interaction and pathogen recognition, these data have laid the groundwork for future analysis of adaptive CEACAM/PSG genotype-phenotypic relationships in normal and complicated pregnancies as well as other etiologies.Chia Lin Chang, Jenia Semyonov, Po Jen Cheng, Shang Yu Huang, Jae Il Park, Huai-Jen Tsai, Cheng-Yung Lin, Frank Grützner, Yung Kuei Soong, James J. Cai, Sheau Yu Teddy Hs

K-Means Clustering with Neural Networks for ATM Cash Repository Prediction

Optimal forecasting of ATM cash repository in an optimal way is a complex task. This paper deals with cash demand forecasting of NN5 time series data using neural networks. NN5 reduced Dataset is a subsample of 11 time series of complete dataset of 111 daily time series drawn from homogeneous population of empirical cash demand time series. Main objective of this paper is to forecast cash demand forecasting of NN5 data with neural networks. Further, the same process is applied on clusters of ATMs. Discrete time wrapping is used as distance measure. Root mean square error has been calculated for such clustered group of ATMs and average is calculated. Root Mean Square error indicates applications of clustering before applying Neural Network increases precision in forecasting of ATM Cash Repository

Supportive supervision and constructive relationships with healthcare workers support CHW performance: Use of a qualitative framework to evaluate CHW programming in Uganda

Abstract Background While evidence supports community health worker (CHW) capacity to improve maternal and newborn health in less-resourced countries, key implementation gaps remain. Tools for assessing CHW performance and evidence on what programmatic components affect performance are lacking. This study developed and tested a qualitative evaluative framework and tool to assess CHW team performance in a district program in rural Uganda. Methods A new assessment framework was developed to collect and analyze qualitative evidence based on CHW perspectives on seven program components associated with effectiveness (selection; training; community embeddedness; peer support; supportive supervision; relationship with other healthcare workers; retention and incentive structures). Focus groups were conducted with four high/medium-performing CHW teams and four low-performing CHW teams selected through random, stratified sampling. Content analysis involved organizing focus group transcripts according to the seven program effectiveness components, and assigning scores to each component per focus group. Results Four components, ‘supportive supervision’, ‘good relationships with other healthcare workers’, ‘peer support’, and ‘retention and incentive structures’ received the lowest overall scores. Variances in scores between ‘high’/‘medium’- and ‘low’-performing CHW teams were largest for ‘supportive supervision’ and ‘good relationships with other healthcare workers.’ Our analysis suggests that in the Bushenyi intervention context, CHW team performance is highly correlated with the quality of supervision and relationships with other healthcare workers. CHWs identified key performance-related issues of absentee supervisors, referral system challenges, and lack of engagement/respect by health workers. Other less-correlated program components warrant further study and may have been impacted by relatively consistent program implementation within our limited study area. Conclusions Applying process-oriented measurement tools are needed to better understand CHW performance-related factors and build a supportive environment for CHW program effectiveness and sustainability. Findings from a qualitative, multi-component tool developed and applied in this study suggest that factors related to (1) supportive supervision and (2) relationships with other healthcare workers may be strongly associated with variances in performance outcomes within a program. Careful consideration of supervisory structure and health worker orientation during program implementation are among strategies proposed to increase CHW performance