Pinpointing differentially expressed domains in complex protein mixtures with the cloud service of PatternLab for Proteomics.

Mass-spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. Here we describe a new module integrated into PatternLab for Proteomics that allows the pinpointing of differentially expressed domains. This is accomplished by inferring functional domains through our cloud service, using HMMER3 and Pfam remotely, and then mapping the quantitation values into domains for downstream analysis. In all, spotting which functional domains are changing when comparing biological states serves as a complementary approach to facilitate the understanding of a system's biology. We exemplify the new module's use by reanalyzing a previously published MudPIT dataset of Cryptococcus gattii cultivated under iron-depleted and replete conditions. We show how the differential analysis of functional domains can facilitate the interpretation of proteomic data by providing further valuable insight.

Experimental analysis of lateral impact on planar brittle material: spatial properties of cracks.

The breakup of alkaline glass and alumina plates due to planar impacts on one of their lateral sides is studied. Particular attention is given to investigating the spatial location of the cracks within the plates. Analysis based on a phenomenological model suggests that bifurcations along the cracks' paths are more likely to take place closer to the impact region than far away from it, i.e., the bifurcation probability seems to lower as the perpendicular distance from the impacted lateral increases. It is also found that many observables are not sensitive to the plate material used in this work, as long as the fragment multiplicities corresponding to the fragmentation of the plates are similar. This gives support to the universal properties of the fragmentation process reported in previous experiments. However, even under the just mentioned circumstances, some spatial observables are capable of distinguishing the material of which the plates are made, which therefore suggests that this universality should be carefully investigated.

Experimental analysis of lateral impact on planar brittle material.

The fragmentation of alumina and glass plates due to lateral impact is studied. A few hundred plates have been fragmented at different impact velocities and the produced fragments are analyzed. The method employed in this work allows one to investigate some geometrical properties of the fragments, besides the traditional size distribution usually studied in former experiments. We found that, although both materials exhibit qualitative similar fragment size distribution function, their geometrical properties appear to be quite different. A schematic model for two-dimensional fragmentation is also presented and its predictions are compared to our experimental results. The comparison suggests that the analysis of the fragments' geometrical properties constitutes a more stringent test of the theoretical models' assumptions than the size distribution.

Identifying differences in protein expression levels by spectral counting and feature selection.

Spectral counting is a strategy to quantify relative protein concentrations in pre-digested protein mixtures analyzed by liquid chromatography online with tandem mass spectrometry. In the present study, we used combinations of normalization and statistical (feature selection) methods on spectral counting data to verify whether we could pinpoint which and how many proteins were differentially expressed when comparing complex protein mixtures. These combinations were evaluated on real, but controlled, experiments (yeast lysates were spiked with protein markers at different concentrations to simulate differences), which were therefore verifiable. The following normalization methods were applied: total signal, Z-normalization, hybrid normalization, and log preprocessing. The feature selection methods were: the Golub index, the Student t-test, a strategy based on the weighting used in a forward-support vector machine (SVM-F) model, and SVM recursive feature elimination. The results showed that Z-normalization combined with SVM-F correctly identified which and how many protein markers were added to the yeast lysates for all different concentrations. The software we used is available at http://pcarvalho.com/patternlab.

Avaliação das variáveis clínicas e neurocomportamentais de recém-nascidos pré-termo / Assessment of the clinical and neurobehavioral variables of preterm newborns

OBJETIVOS: Avaliar as variáveis clínicas e neurocomportamentais do desenvolvimento de recém-nascidos pré-termo. MÉTODO: Estudo transversal com amostra de 21 recém-nascidos, com idade gestacional média de 32 semanas (± 1,7) e idade cronológica média de 27 dias (±15,2), de ambos os sexos, avaliados na internação hospitalar. Foi utilizado roteiro de anamnese para a coleta dos dados sobre a gestação, parto, complicações desenvolvidas e Neonatal Medical Index (NMI). Os recém-nascidos foram avaliados pela Neurobehavioral Assessment of the Preterm Infant (NAPI) em sete categorias: sinal de cachecol, desenvolvimento motor e vigor, ângulo poplíteo, alerta e orientação, irritabilidade, choro e percentual de sono. Os dados foram analisados no programa SPSS® com base na estatística descritiva (freqüências, médias e desvios-padrões), teste t de Student para comparação de grupos (amostra do estudo com amostra normativa NAPI) e teste de correlação de Spearman (variáveis clínicas e categorias da NAPI). RESULTADOS: O desempenho dos recém-nascidos pré-termo apresentou diferença estatisticamente significativa em relação ao do grupo normativo NAPI nas varáveis sinal de cachecol, desenvolvimento motor/vigor e choro. O NMI correlacionou-se negativamente com o sinal cachecol (r= -0,60), o estado comportamental alerta inativo correlacionou-se positivamente com o desenvolvimento motor e vigor (r= 0,59) e com a qualidade do choro (r= 0,71). As complicações maternas mais freqüentes foram infecção genitourinária (47 por cento) e hipertensão arterial gestacional (24 por cento), e as neonatais foram síndrome da membrana hialina (86 por cento), infecção neonatal (57 por cento) e hiperbilirrubinemia (47 por cento). CONCLUSÃO: A avaliação neurocomportamental e os dados clínicos são variáveis que devem ser estudadas quando se trabalha com recém-nascidos em risco para atraso no desenvolvimento.

OBJECTIVE: To assess the clinical and neurobehavioral variables for the development of preterm newborns. METHOD: This was a cross-sectional study with a sample of twenty-one preterm newborns of both genders and mean gestational and chronological ages of 32 weeks (± 1.7) and 27 days (± 15.2), respectively, who were assessed while still in hospital. The medical files were used to collect data on the gestation, delivery, complications developed and on the Neonatal Medical Index (NMI). The newborns were evaluated using the Neurobehavioral Assessment of the Preterm Infant (NAPI) in seven categories: scarf sign, motor development and vigor, popliteal angle, alertness and orientation, irritability, cry quality and percent asleep. The data were analyzed using SPSS® statistical software based on descriptive statistics (frequencies, means and standard deviations), Student's t test for comparison of groups (study sample versus normative NAPI sample) and Spearman's correlation test (clinical variables and NAPI categories). RESULTS: The preterm newborns' performance was statistically significantly different from the normative NAPI group in relation to the variables of scarf sign, motor development and vigor and cry quality. The NMI correlated negatively with scarf sign (r= -0.60). Alert behavioral state during inactivity correlated positively with motor development and vigor (r= 0.59) and with cry quality (r= 0.71). The most frequent maternal complications were genitourinary tract infection (47 percent) and gestational arterial hypertension (24 percent), and the neonatal complications were hyaline membrane syndrome (86 percent), neonatal infection (57 percent) and hyperbilirubinemia (47 percent). CONCLUSION: Neurobehavioral assessment and clinical data are variables that must be considered when working with newborns at risk of delayed development.

Learning in the combinatorial neural model.

The combinatorial neural model (CNM) is a type of fuzzy neural network for classification problems and, more generally, for the mapping between fuzzy multidimensional spaces. Learning in CNM is a complex task spanning the learning of input-neuron membership functions, of the network topology, and of connection weights. In this paper we are concerned with these various aspects of learning in CNM, most notably with the learning of connection weights, whose complexity comes from the existence of nondifferentiable, nonconvex error functions associated with the learning process. We introduce several algorithms for weight learning, most based on subgradient techniques borrowed from the field of nondifferentiable optimization. All algorithms are based on essentially "local" rules, and are therefore amenable to distributed/parallel implementations. Experimental results are provided on the large-scale problem of monitoring the deforestation of the Amazon region on satellite images. What these results indicate is that a hybrid CNM system outperforms previous results obtained with variations of error backpropagation techniques. In addition, this hybrid system has demonstrated robustness in the context under consideration, therefore constituting an attractive alternative.