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PubMed2005

Structure of liquid formic acid investigated by first principle and classical molecular dynamics simulations.

The structure of liquid formic acid has been investigated by Car-Parrinello and classical molecular dynamics simulations, focusing on the characterization of the H-bond network and on the mutual arrangement of pairs of bonded molecules. In agreement with previous computational studies, two levels of H-bonded structures have been found. Small clusters, characterized by O-H...O bonds, are held together by weak C-H...O bonds to form large branched structures. From the ab initio simulation we infer the importance of cyclic H-bond dimer configurations, typical of the gas phase. Most of these dimer structures are however found to be embedded into H-bonded chains. When only O-H...O bonds are taken into account, linear H-bond chains are detected as basic structures of the liquid. More branched structures occur when C-H...O bonds are also considered. Regarding the arrangement of molecular pairs, we observed that O-H...O bonds favor the occurrence of configurations with parallel molecular planes, whereas no preferential orientation is observed for molecules forming C-H...O bonds.

Journal Article↗

PubMed2004

Specific reading disability (dyslexia): what have we learned in the past four decades?

We summarize some of the most important findings from research evaluating the hypothesized causes of specific reading disability ('dyslexia') over the past four decades. After outlining components of reading ability, we discuss manifest causes of reading difficulties, in terms of deficiencies in component reading skills that might lead to such difficulties. The evidence suggests that inadequate facility in word identification due, in most cases, to more basic deficits in alphabetic coding is the basic cause of difficulties in learning to read. We next discuss hypothesized deficiencies in reading-related cognitive abilities as underlying causes of deficiencies in component reading skills. The evidence in these areas suggests that, in most cases, phonological skills deficiencies associated with phonological coding deficits are the probable causes of the disorder rather than visual, semantic, or syntactic deficits, although reading difficulties in some children may be associated with general language deficits. Hypothesized deficits in general learning abilities (e.g., attention, association learning, cross-modal transfer etc.) and low-level sensory deficits have weak validity as causal factors in specific reading disability. These inferences are, by and large, supported by research evaluating the biological foundations of dyslexia. Finally, evidence is presented in support of the idea that many poor readers are impaired because of inadequate instruction or other experiential factors. This does not mean that biological factors are not relevant, because the brain and environment interact to produce the neural networks that support reading acquisition. We conclude with a discussion of the clinical implications of the research findings, focusing on the need for enhanced instruction.

Brain↗

PubMed2006

Molecular recognition in the assembly of collagens: terminal noncollagenous domains are key recognition modules in the formation of triple helical protomers.

The alpha-chains of the collagen superfamily are encoded with information that specifies self-assembly into fibrils, microfibrils, and networks that have diverse functions in the extracellular matrix. A key self-organizing step, common to all collagen types, is trimerization that selects, binds, and registers cognate alpha-chains for assembly of triple helical protomers that subsequently oligomerize into specific suprastructures. In this article, we review recent findings on the mechanism of chain selection and infer that terminal noncollagenous domains function as recognition modules in trimerization and are therefore key determinants of specificity in the assembly of suprastructures. This mechanism is also illustrated with computer-generated animations.

Biopolymers↗

PubMed2002

BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development.

Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.

Algorithms↗

PubMed2024

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes.

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

Arabidopsis↗

PubMed2026

Bit-reproducible parallel phylogenetic tree inference.

MOTIVATION: Phylogenetic trees describe the evolutionary history among biological species based on their genomic data. Maximum likelihood (ML) based phylogenetic inference tools search for the tree and evolutionary model that best explain the observed genomic data. Given the independence of likelihood score calculations between different genomic sites, parallel computation is commonly deployed. This is followed by a parallel summation over the per-site scores to obtain the overall likelihood score of the tree. However, basic arithmetic operations on IEEE 754 floating-point numbers, such as addition and multiplication, inherently introduce rounding errors. Consequently, the order by which floating-point operations are executed affects the exact resulting likelihood value since these operations are not associative. Moreover, parallel reduction algorithms in numerical codes re-associate operations as a function of the core count and cluster network topology, inducing different round-off errors. These low-level deviations can cause heuristic searches to diverge and induce high-level result discrepancies (e.g. yield topologically distinct phylogenies). This effect has also been observed in multiple scientific fields beyond phylogenetics. RESULTS: We observe that varying the degree of parallelism results in diverging phylogenetic tree searches (high-level results) for over 31% out of 10 179 empirical datasets. More importantly, 8% of these diverging datasets yield trees that are statistically significantly worse than the best-known ML tree for the dataset (AU-test, P < .05). To alleviate this, we develop a variant of the widely used phylogenetic inference tool RAxML-NG, which does yield bit-reproducible results under varying core-counts, with a slowdown of only 0%-12.7% (median 0.8%) on up to 768 cores. For this, we introduce the ReproRed reduction algorithm, which yields bit-identical results under varying core-counts, by maintaining a fixed operation order that is independent of the communication pattern. ReproRed is thus applicable to all associative reduction operations-in contrast to competitors, which are confined to summation. Our ReproRed reduction algorithm only exchanges the theoretical minimum number of messages, overlaps communication with computation, and utilizes fast base-cases for local reductions. ReproRed is able to all-reduce (via a subsequent broadcast) 4.1×106 operands across 48-768 cores in 19.7-48.61 μs, thereby exhibiting a slowdown of 13%-93% over a non-reproducible all-reduce algorithm. ReproRed outperforms the state-of-the-art reproducible all-reduction algorithm ReproBLAS (offers summation only) beyond 10 000 elements per core. In summary, we re-assess non-reproducibility in parallel phylogenetic inference, present the first bit-reproducible parallel phylogenetic inference tool, as well as introduce a general algorithm and open-source code for conducting reproducible associative parallel reduction operations. AVAILABILITY AND IMPLEMENTATION: ReproRed: https://doi.org/10.5281/zenodo.15004918 (LGPL)-Reproducible RAxML-NG version https://doi.org/10.5281/zenodo.15017407 (GPL).

Phylogeny↗

PubMed2001

How people make support judgments: individual differences in the traits used to infer supportiveness in others.

Social support and other social judgments are composed of several distinct components, of which relationship effects are an important part. With regard to support judgments, relationship effects refer to the fact that when judging the same targets, people differ systematically in whom they see as supportive. One explanation for this effect is that people differ in how they combine information about targets to judge supportiveness. Participants rated the supportiveness of hypothetical targets and targets from their own social networks. Multilevel modeling identified the traits participants used to make support judgments. There were significant differences in the extent to which participants used different target personality traits to judge supportiveness. In addition, participant neuroticism predicted the extent to which participants used target neuroticism and agreeableness to judge supportiveness.

Adolescent↗

PubMed1989

Isoproterenol-induced ultrastructural alterations in hearts of alloxan-diabetic rabbits.

1. The effects of isoproterenol (ISO) on the ultrastructure of hearts from 10-week alloxan diabetic rabbits were examined. 2. Following alloxan injection, all rabbits developed severe hyperglycemia, hyperlipidemia and hypoinsulinemia. 3. Injection of ISO induced marked alterations in both control and diabetic rabbit hearts including accumulation of lipid and swelling of sarcoplasmic reticulum. 4. Myofibrils in both groups of animals were dispersed and appeared as a homogeneous mass with poorly defined Z-bands. 5. The most marked effect of ISO treatment in both groups of animals was damage to mitochondria. Mitochondria were extensively damaged and showed partial or complete disruption of their cristae network. 6. Glycogen granules were few in number or not detectable in both groups of animals. 7. The diabetic animals treated with ISO showed greater clumping and margination of nuclear chromatin, fewer intact mitochondria and a greater number of amorphous dense bodies in and around the mitochondria. 8. The presence of greater sarcolemmal damage in diabetic animals was inferred from the significantly greater accumulation of calcium and decreased magnesium in the myocardium.

Animals↗

PubMed2000

Conceptual structure and social functions of behavior explanations: beyond person--situation attributions.

The traditional approach to studying behavior explanations involves treating them as either person causes or situation causes and assessing them by using rating scales. An analysis of people's free-response behavior explanations reveals, however, that the conceptual distinctions people use in their explanations are more complex and sophisticated than the person-situation dichotomy suggests. The authors, therefore, introduce a model of the conceptual structure of folk behavior explanations (the network of concepts and assumptions on which explanations are based) and test it in 4 studies. The modes and features of behavior explanations within this conceptual structure also have specific social functions. In 2 additional studies, the authors demonstrate that people alter distinct features of their explanations when pursuing particular impression-management goals and that listeners make inferences about explainers' goals on the basis of these features.

Adult↗

PubMed2001

A highly variable segment of human subterminal 16p reveals a history of population growth for modern humans outstide Africa.

We have sequenced a highly polymorphic subterminal noncoding region from human chromosome 16p13.3 flanking the 5' end of the hypervariable minisatellite MS205, in 100 chromosomes sampled from different African and Euroasiatic populations. Coalescence analysis indicates that the time to the most recent common ancestor (approximately 1 million years) predates the appearance of anatomically modern human forms. The root of the network describing this variability lies in Africa. African populations show a greater level of diversity and deeper branches. Most Euroasiatic variability seems to have been generated after a recent out-of-Africa range expansion. A history of population growth is the most likely scenario for the Euroasiatic populations. This pattern of nuclear variability can be reconciled with inferences based on mitochondrial DNA.

Africa↗

PubMed2026

Protamine gene expression is associated with sperm motility in rams: An integrative experimental and gene network analysis.

Protamine 1 (PRM1) and protamine 2 (PRM2) are essential regulators of sperm chromatin condensation and genome integrity, and their dysregulation has been associated with impaired male fertility. However, their role in rams remains insufficiently characterized. This study investigated the relationship between protamine gene expression and semen quality in rams and explored their potential upstream regulatory mechanisms using gene regulatory network (GRN) analysis. Fifteen ejaculates from five rams were analyzed. Based on total sperm motility using computer-assisted sperm analysis (CASA), ejaculates were classified into a high-motility group (n = 8) and a low-motility group (n = 7). PRM1 and PRM2 expression levels were quantified by RT-qPCR. Following normality confirmation (p > 0.05), parametric tests were applied using the ejaculate as the biological experimental unit. Samples with reduced motility showed significantly lower expression of both protamines (p < 0.01). Moreover, progressive sperm motility was strongly correlated with both PRM1 (r = 0.71, p = 0.019) and PRM2 (r = 0.69, p = 0.03) transcript levels. Cross-species GRN inference using scGeneRAI and a reference human spermatogenesis dataset identified several hypothesis-generating candidate transcription factors, including HMGB4, HMGB1, H2AFZ, NKX6-1, and SMC3, consistently supported across multiple bootstrap resampling runs. These findings demonstrate a strong association between protamine expression and sperm motility in rams. While the identified candidate regulators provide a valuable framework for future species-specific validation, they also highlight promising candidate molecular biomarkers of male fertility in livestock.

Gene regulatory networks↗

PubMed2005

Papain entrapment in alginate beads for stability improvement and site-specific delivery: physicochemical characterization and factorial optimization using neural network modeling.

This work examines the influence of various process parameters (like sodium alginate concentration, calcium chloride concentration, and hardening time) on papain entrapped in ionotropically cross-linked alginate beads for stability improvement and site-specific delivery to the small intestine using neural network modeling. A 3(3) full-factorial design and feed-forward neural network with multilayer perceptron was used to investigate the effect of process variables on percentage of entrapment, time required for 50% and 90% of the enzyme release, particle size, and angle of repose. Topographical characterization was conducted by scanning electron microscopy, and entrapment was confirmed by Fourier transform infrared spectroscopy and differential scanning calorimetry. Times required for 50% (T(50)) and 90% (T(90)) of enzyme release were increased in all 3 of the process variables. Percentage entrapment and particle size were found to be directly proportional to sodium alginate concentration and inversely proportional to calcium chloride concentration and hardening time, whereas angle of repose and degree of cross-linking showed exactly opposite proportionality. Beads with >90% entrapment and T(50) of <10 minutes could be obtained at the low levels of all 3 of the process variables. The inability of beads to dissolve in acidic environment, with complete dissolution in buffer of pH >or=6.8, showed the suitability of beads to release papain into the small intestine. The shelf-life of the capsules prepared using the papain-loaded alginate beads was found to be 3.60 years compared with 1.01 years of the marketed formulation. It can be inferred from the above results that the proposed methodology can be used to prepare papain-loaded alginate beads for stability improvement and site-specific delivery.

Alginates↗

PubMed2002

Building a bioinformatics ontology using OIL.

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualization is encoded using the ontology inference layer (OIL), a knowledge representation language that combines the modeling style of frame-based systems with the expressiveness and reasoning power of description logics (DLs). This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying application are described. This ontology (TaO) holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalizes upon features of OIL-The conceptualization afforded by the frame-based view of OIL's syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both handcrafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic framework of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logic's reasoning power used to reclassify and infer further relationships. This cycle of elaboration and refinement is iterated on each portion of the ontology until a satisfactory ontology has been created.

Algorithms↗

PubMed2005

A Bayesian approach to modeling dynamic effective connectivity with fMRI data.

A state-space modeling approach for examining dynamic relationship between multiple brain regions was proposed in Ho, Ombao and Shumway (Ho, M.R., Ombao, H., Shumway, R., 2005. A State-Space Approach to Modelling Brain Dynamics to Appear in Statistica Sinica). Their approach assumed that the quantity representing the influence of one neuronal system over another, or effective connectivity, is time-invariant. However, more and more empirical evidence suggests that the connectivity between brain areas may be dynamic which calls for temporal modeling of effective connectivity. A Bayesian approach is proposed to solve this problem in this paper. Our approach first decomposes the observed time series into measurement error and the BOLD (blood oxygenation level-dependent) signals. To capture the complexities of the dynamic processes in the brain, region-specific activations are subsequently modeled, as a linear function of the BOLD signals history at other brain regions. The coefficients in these linear functions represent effective connectivity between the regions under consideration. They are further assumed to follow a random walk process so to characterize the dynamic nature of brain connectivity. We also consider the temporal dependence that may be present in the measurement errors. ML-II method (Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis (2nd ed.). Springer, New York) was employed to estimate the hyperparameters in the model and Bayes factor was used to compare among competing models. Statistical inference of the effective connectivity coefficients was based on their posterior distributions and the corresponding Bayesian credible regions (Carlin, B.P., Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman and Hall, Boca Raton). The proposed method was applied to a functional magnetic resonance imaging data set and results support the theory of attentional control network and demonstrate that this network is dynamic in nature.

Attention↗

PubMed1997

Method for segmenting chest CT image data using an anatomical model: preliminary results.

We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.

Computer Simulation↗

PubMed1996

[Neuro-cognitive models of spelling and Alzheimer disease: mutual clarification].

The neuro-cognitive models developed over the last twenty years have considerably added to the understanding of disorders of spelling and writing resulting from brain lesions. These models differentiate between central processes, damage to which explains linguistic disorders, and peripheral processes, damage to which results in praxia and related disorders. This article analyses studies which have applied these distinctions to disorders observed in Alzheimer's disease. This disease causes unusual pathological situations which show certain types of agraphia rarely seen in the relevant literature, particularly certain peripheral forms. Several studies considered demonstrate the frequency of lexical agraphia in Alzheimer's disease. These lexical disorders of written expression seem to be independent of the lexico-semantic disorders found in other areas of the intellectual processes, Central writing disorders-lexical but also phonological-would appear to be the result of relatively focussed dysfunctionings, the left gyrus angularis and left gyrus supramarginalis respectively, as is suggested in brain metabolism studies. Moreover, certain patients develop peripheral writing disorders which are at present less well known, on a cognitive and neurobiological point of view. Inferences from other pathological contexts reveals that these disorders are often associated with diffuse cerebral lesions. Therefore disorders of written expression in Alzheimer's disease may be the result of two different pathological mechanisms: central disorders arising from localized areas of dysfunctioning at the left temporo-parietal intersection; peripheral disorders linked to the dysfunctioning of a much wider network involving the parietal and frontal regions of both cerebral hemispheres. To account for the diversity of clinical pictures, we suggest a physiopathological hypothesis which could also be used as a therapeutic model in Alzheimer's disease.

Agraphia↗

PubMed1992

The genome of the Mediterranean fruitfly Ceratitis capitata: localization of molecular markers by in situ hybridization to salivary gland polytene chromosomes.

We hybridized cloned DNA segments to salivary gland polytene chromosomes of the medfly, Ceratitis capitata, and thus established molecular markers for 24 sites on 6 out of 10 autosomal arms. An additional marker identified a medfly repetitive element that hybridizes to approximately 100 autosomal sites as well as a granular network that is thought to represent the X chromosome. Some of the markers correspond to 9 characterized transcription units, while 17 remain anonymous; at least 3 of the latter are restriction fragment length polymorphism (RFLP) markers. The characterized transcription units document that chromosomal arm 5L of C. capitata is homologous to the Drosophila melanogaster X chromosome, in agreement with previous inferences based on the extensive conservation of linkage groups in Diptera.

Amino Acid Sequence↗

PubMed2005

Modelling and simulation for metabolomics data analysis.

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying 'biochemistry' of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

Algorithms↗