Deriving physical connectivity from neuronal morphology

 A model is presented that allows prediction of the probability for the formation of appositions between the axons and dendrites of any two neurons based only on their morphological statistics and relative separation. Statistics of axonal and dendritic morphologies of single neurons are obtained from 3D reconstructions of biocytin-filled cells, and a statistical representation of the same cell type is obtained by averaging across neurons according to the model. A simple mathematical formulation is applied to the axonal and dendritic statistical representations to yield the probability for close appositions. The model is validated by a mathematical proof and by comparison of predicted appositions made by layer 5 pyramidal neurons in the rat somatosensory cortex with real anatomical data. The model could be useful for studying microcircuit connectivity and for designing artificial neural networks. Received: 11 February 2002 / Accepted: 5 November 2002 / Published online: 20 February 2003 Correspondence to: H. Markram (e-mail: Henry.Markram@epfl.ch Tel.: +41-21-6939537, Fax: +41-21-6935350) Acknowledgements. This study was supported by the National Alliance for Autism Research, the Minerva Foundation, the US Navy, the Ebner Center for Biomedical Research, and the Edith Blum Foundation.     

Deriving site-specific species pools from large databases

Defining the species pool of a community is crucial for many types of ecological analyses, providing a foundation to metacommunity, null modelling or dark diversity frameworks. It is a challenge to derive the species pool empirically from large and heterogeneous databases. Here, we propose a method to define a site-specific species pool (SSSP), i.e. the probabilistic set of species that may co-occur with the species of a target community. Using large databases with geo-referenced records that comprise full plant community surveys, our approach characterizes each site by its own species pool without requiring a pre-defined habitat classification. We calculate the probabilities of each species in the database to occur in the target community using Beals’ index of sociological favourability, and then build sample-based rarefaction curves from neighbouring records with similar species composition to estimate the asymptotic species pool size. A corresponding number of species is then selected among the species having the highest occurrence probability, thus defining both size and composition of the species pool. We tested the robustness of this approach by comparing SSSPs obtained with different spatial extents and dissimilarity thresholds, fitting different models to the rarefaction curves, and comparing the results obtained when using Beals co-occurrence probabilities or presence/absence data. As an example application, we calculated the SSSPs for all calcareous grassland records in the German Vegetation Reference Database, and show how our method could be used to 1) produce grain-dependent estimations of species richness across plots, 2) derive scalable maps of species richness and 3) define the full list of species composing the SSSP of each target site. By deriving the species pool exclusively from community characteristics, the SSSP framework presented here provides a robust approach to bridge biodiversity estimations across spatial scales.     

Deriving dispersal distances from genetic data.

Dispersal is one of the most important factors determining the genetic structure of a population, but good data on dispersal distances are rare because it is difficult to observe a large sample of dispersal events. However, genetic data contain unbiased information about the average dispersal distances in species with a strong sex bias in their dispersal rates. By plotting the genetic similarity between members of the philopatric sex against some measure of the distance between them, the resulting regression line can be used for estimating how far dispersing individuals of the opposite sex have moved before settling. Dispersers showing low genetic similarity to members of the opposite sex will on average have originated from further away. Applying this method to a microsatellite dataset from lions (Panthera leo) shows that their average dispersal distance is 1.3 home ranges with a 95% confidence interval of 0.4-3.0 home ranges. These results are consistent with direct observations of dispersal from our study population and others. In this case, direct observations of dispersal distance were not detectably biased by a failure to detect long-range dispersal, which is thought to be a common problem in the estimation of dispersal distance.     

Deriving quantitative conclusions from microarray expression data

MOTIVATION: The last few years have seen the development of DNA microarray technology that allows simultaneous measurement of the expression levels of thousands of genes. While many methods have been developed to analyze such data, most have been visualization-based. Methods that yield quantitative conclusions have been diverse and complex. RESULTS: We present two straightforward methods for identifying specific genes whose expression is linked with a phenotype or outcome variable as well as for systematically predicting sample class membership: (1) a conservative, permutation-based approach to identifying differentially expressed genes; (2) an augmentation of K-nearest-neighbor pattern classification. Our analyses replicate the quantitative conclusions of Golub et al. (1999; Science, 286, 531-537) on leukemia data, with better classification results, using far simpler methods. With the breast tumor data of Perou et al. (2000; Nature, 406, 747-752), the methods lend rigorous quantitative support to the conclusions of the original paper. In the case of the lymphoma data in Alizadeh et al. (2000; Nature, 403, 503-511), our analyses only partially support the conclusions of the original authors. AVAILABILITY: The software and supplementary information are available freely to researchers at academic and non-profit institutions at http://cc.ucsf.edu/jain/public     

Deriving biomedical diagnostics from NMR spectroscopic data

Biomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS). We demonstrate the application of the SCS by three examples of its use in analyzing ¹H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract.     

Deriving respiratory cell types from stem cells

The reported pluripotential capabilities of many human stem cell types has made them an attractive area of research, given the belief they may hold considerable therapeutic potential for treating a wide range of human diseases and injuries. Although the bulk of stem cell based research has focused on developing procedures for the treatment of pancreatic, neural, cardiovascular and haematopoietic diseases, the potential for deriving respiratory cell types from stem cells for treatment of respiratory specific diseases has also been explored. It is suggested that stem cell derivatives may be used for lung replacement/regeneration therapeutics and high though-put pharmacological screening strategies for a variety of respiratory injuries and diseases including: cystic fibrosis, chronic obstructive pulmonary disease, respiratory distress syndrome, pulmonary fibrosis and pulmonary edema. This review will explore recent progress in characterizing adult respiratory and bone marrow derived stem cells with respiratory potential as well as the endogenous mechanisms directing the homing of these cells to the diseased and injured lung. In addition, the potential for embryonic stem cell based therapies in this domain as well as the histological, anatomical and molecular aspects of respiratory development will be summarized.     

Phonological and morphological conditions on affix order in Huave

Huave, a language isolate of Mexico, has “mobile” affixes, which surface as prefixes or suffixes depending on morphophonological properties of the base to which they attach. This paper shows that despite affix mobility, the hierarchical structure of affixes is morphologically fixed. Meanwhile, the linearization algorithm for individual mobile affixes is analyzed with an Optimality-Theoretic P >> M ranking schema, where phonological well-formedness constraints outrank morphological alignment constraints. The proposed model is a revision of previous P >> M models in that morphological constraints are argued to be of the idiosyncratic, language-specific type previously used in subcategorization approaches to the morphology–phonology interface.     

Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat

The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.     

Deriving protein dynamical properties from weighted protein contact number

It has recently been shown that in proteins the atomic mean-square displacement (or B-factor) can be related to the number of the neighboring atoms (or protein contact number), and that this relationship allows one to compute the B-factor profiles directly from protein contact number. This method, referred to as the protein contact model, is appealing, since it requires neither trajectory integration nor matrix diagonalization. As a result, the protein contact model can be applied to very large proteins and can be implemented as a high-throughput computational tool to compute atomic fluctuations in proteins. Here, we show that this relationship can be further refined to that between the atomic mean-square displacement and the weighted protein contact-number, the weight being the square of the reciprocal distance between the contacting pair. In addition, we show that this relationship can be utilized to compute the cross-correlation of atomic motion (the B-factor is essentially the auto-correlation of atomic motion). For a nonhomologous dataset comprising 972 high-resolution X-ray protein structures (resolution <2.0 A and sequence identity <25%), the mean correlation coefficient between the X-ray and computed B-factors based on the weighted protein contact-number model is 0.61, which is better than those of the original contact-number model (0.51) and other methods. We also show that the computed correlation maps based on the weighted contact-number model are globally similar to those computed through normal model analysis for some selected cases. Our results underscore the relationship between protein dynamics and protein packing. We believe that our method will be useful in the study of the protein structure-dynamics relationship.     

Deriving heart period variability from blood pressure waveforms.

International standards for calculating heart period variability (HPV) from a series of R-wave intervals (R-R) in an electrocardiographic (ECG) recording have been widely accepted. It is possible, and potentially useful in various settings, to use systolic blood pressure waveform intervals to estimate HPV, but the validity of HPV derived from blood pressure (BP) waveforms has not been established. To test the reliability between BP- and ECG-derived HPV indexes, we evaluated data from 234 healthy adults in four studies of HPV reactivity to stress. Study conditions included resting baseline, arithmetic, Stroop test, speech presentation, and orthostatic tilt. Continuous ECG and BP recordings were sampled at a rate of 500 Hz, scored by the same methods, and used to calculate heart rate and time- and frequency-domain measures of HPV. Overall, reliability between the two methods was very high for computing heart rate and HPV indexes. High-frequency HPV indexes were somewhat less reliably computed. In conclusion, in healthy adults, with the use of appropriate methods, BP waveforms can produce reliable indexes of HPV.     

Deriving excitatory neurons of the neocortex from pluripotent stem cells

The human cerebral cortex is an immensely complex structure that subserves critical functions that can be disrupted in developmental and degenerative disorders. Recent innovations in cellular reprogramming and differentiation techniques have provided new ways to study the cellular components of the cerebral cortex. Here, we discuss approaches to generate specific subtypes of excitatory cortical neurons from pluripotent stem cells. We review spatial and temporal aspects of cortical neuron specification that can guide efforts to produce excitatory neuron subtypes with increased resolution. Finally, we discuss distinguishing features of human cortical development and their translational ramifications for cortical stem cell technologies.     

Deriving Restoration Goals for Acidified Lakes from Ataxonomic Phytoplankton Studies

The ataxonomic phytoplankton composition and abundance (biomass classes) in lakes of differing acidity were examined by flow cytometry. The ataxonomic parameters applied here were photosynthesis pigments and cellular protein content. Up to 1000 cells per second can be assessed by this method. Consequently, enough cells to create biomass spectra can be counted within only a few minutes. Photosynthesis pigment autofluorescence was used to separate algal cells from detritus and to classify the phytoplankton organisms into different pigment groups. Chlorophyll fluorescence ratio (CFR) at different excitation wave lengths proved to be a sensitive tool. As expected, the diversity of CFR-determined pigment groups decreased with increasing acidification. Some groups were acid-insensitive and occurred even at pH below 3.0. However, picoplanktic cyanobacteria ( Synechoccocus/Synechocystis -like particles [SLPs]) were absent at pHs below 4.5–4.0, with their accompanying high metal concentrations. Thus, the reappearance of cyanobacterial picoplankton may serve as a first major restoration goal in strongly acidified lakes. Protein staining using fluorescein isothiocyanate enables fast estimates of phytoplankton biomass and establishment of biomass spectra as an estimate of the integrity of plankton communities. Although the phytoplankton investigations presented are only snapshots of the situation on the sampling days, the feasibility of flow cytometrical methods for preparing phytoplankton biomass spectra has been demonstrated. The completeness of such biomass spectra, such as the presence or absence of SLP-picoplankters, as well as the variances around regression lines (linear or parabolic), may serve as goals in restoring lakes acidified to different degrees.     

Deriving phylogenetic trees from the similarity analysis of metabolic pathways

MOTIVATION: Comparative analysis of metabolic pathways in different genomes can give insights into the understanding of evolutionary and organizational relationships among species. This type of analysis allows one to measure the evolution of complete processes (with different functional roles) rather than the individual elements of a conventional analysis. We present a new technique for the phylogenetic analysis of metabolic pathways based on the topology of the underlying graphs. A distance measure between graphs is defined using the similarity between nodes of the graphs and the structural relationship between them. This distance measure is applied to the enzyme-enzyme relational graphs derived from metabolic pathways. Using this approach, pathways and group of pathways of different organisms are compared to each other and the resulting distance matrix is used to obtain a phylogenetic tree. RESULTS: We apply the method to the Citric Acid Cycle and the Glycolysis pathways of different groups of organisms, as well as to the Carbohydrate metabolic networks. Phylogenetic trees obtained from the experiments were close to existing phylogenies and revealed interesting relationships among organisms.     

Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller

This article presents modular recurrent neural network controllers for single legs of a biomimetic six-legged robot equipped with standard DC motors. Following arguments of Ekeberg et?al. (Arthropod Struct Dev 33:287?C300, 2004), completely decentralized and sensori-driven neuro-controllers were derived from neuro-biological data of stick-insects. Parameters of the controllers were either hand-tuned or optimized by an evolutionary algorithm. Employing identical controller structures, qualitatively similar behaviors were achieved for robot and for stick insect simulations. For a wide range of perturbing conditions, as for instance changing ground height or up- and downhill walking, swing as well as stance control were shown to be robust. Behavioral adaptations, like varying locomotion speeds, could be achieved by changes in neural parameters as well as by a mechanical coupling to the environment. To a large extent the simulated walking behavior matched biological data. For example, this was the case for body support force profiles and swing trajectories under varying ground heights. The results suggest that the single-leg controllers are suitable as modules for hexapod controllers, and they might therefore bridge morphological- and behavioral-based approaches to stick insect locomotion control.     

Post-natal imprinting: evidence from marsupials

Genomic imprinting has been identified in therian (eutherian and marsupial) mammals but not in prototherian (monotreme) mammals. Imprinting has an important role in optimising pre-natal nutrition and growth, and most imprinted genes are expressed and imprinted in the placenta and developing fetus. In marsupials, however, the placental attachment is short-lived, and most growth and development occurs post-natally, supported by a changing milk composition tailor-made for each stage of development. Therefore there is a much greater demand on marsupial females during post-natal lactation than during pre-natal placentation, so there may be greater selection for genomic imprinting in the mammary gland than in the short-lived placenta. Recent studies in the tammar wallaby confirm the presence of genomic imprinting in nutrient-regulatory genes in the adult mammary gland. This suggests that imprinting may influence infant post-natal growth via the mammary gland as it does pre-natally via the placenta. Similarly, an increasing number of imprinted genes have been implicated in regulating feeding and nurturing behaviour in both the adult and the developing neonate/offspring in mice. Together these studies provide evidence that genomic imprinting is critical for regulating growth and subsequently the survival of offspring not only pre-natally but also post-natally.