Hypopharyngeal oxygen concentration and pressures delivered by low flow nasal cannula in preterm infants: Relationship with flow, gas mixture, and infant's weight.

BACKGROUND: Low flow nasal cannula (LFNC) are frequently used in preterm infants. However, the delivered inspired oxygen concentration and airway pressures are not well established. OBJECTIVE: To determine the fraction of inspired oxygen (FiO2 ) and hypopharyngeal pressures generated by LFNC at different gas flows, gas mixture concentrations and infant's weight. DESIGN/METHODS: Serial samples of hypopharyngeal gas were obtained in 33 very low birth weight infants who were receiving oxygen with LFNC. Measurements were obtained with different gas flows and oxygen concentrations. FiO2 was measured using an electrochemical cell analyzer and hypopharyngeal pressures with a pressure transducer. RESULTS: 33 infants with a mean BW of 910 ± 284 g and 27 ± 1.7 weeks gestational age were studied at 36 ± 22 days after birth. FiO2 increased proportionally to gas flow, but with large variability: median (range) FiO 2 were 0.33 (0.23-0.54), 0.44 (0.29-0.67), 0.57 (0.33-0.81), and 0.69 (0.51-0.92) at 0.1, 0.3, 0.5, and 1 L/minute, respectively. Significantly higher mean FiO 2 were observed despite low flows in infants ≤ 1000 g compared to those > 1000 g (0.5 ± 0.1 vs 0.4 ± 0.07 at 0.3 L/minute; 0.66 ± 0.09 vs 0.5 ± 0.08 with 0.5 L/minute, respectively, P < .05). Hypopharyngeal pressures increased proportionally to gas flow with high variability: mean ± standard deviation pressures were 1.5 ± 0.8; 2.8 ± 1.2; 4.6 ± 1.3; 6.1 ± 1.6 cm H 2 O at 0.5, 1, 2, and 3 L/minute of gas flow. Peak pressures > 15 cm H 2 O were frequently observed with gas flows ≥ 2 L/min. CONCLUSIONS: Large variability in FiO2 and hypopharyngeal pressures were observed with oxygen administration through LFNC. Very high FiO 2 were observed despite low flows in infants < 1000 g. Excessive peak pressures can be generated with flows ≥ 2 L/minute especially among infants < 1000 g.

Multitask learning improves prediction of cancer drug sensitivity.

Precision oncology seeks to predict the best therapeutic option for individual patients based on the molecular characteristics of their tumors. To assess the preclinical feasibility of drug sensitivity prediction, several studies have measured drug responses for cytotoxic and targeted therapies across large collections of genomically and transcriptomically characterized cancer cell lines and trained predictive models using standard methods like elastic net regression. Here we use existing drug response data sets to demonstrate that multitask learning across drugs strongly improves the accuracy and interpretability of drug prediction models. Our method uses trace norm regularization with a highly efficient ADMM (alternating direction method of multipliers) optimization algorithm that readily scales to large data sets. We anticipate that our approach will enhance efforts to exploit growing drug response compendia in order to advance personalized therapy.

Memory of Inflammation in Regulatory T Cells.

Eukaryotic cells can "remember" transient encounters with a wide range of stimuli, inducing lasting states of altered responsiveness. Regulatory T (Treg) cells are a specialized lineage of suppressive CD4 T cells that act as critical negative regulators of inflammation in various biological contexts. Treg cells exposed to inflammatory conditions acquire strongly enhanced suppressive function. Using inducible genetic tracing, we analyzed the long-term stability of activation-induced transcriptional, epigenomic, and functional changes in Treg cells. We found that the inflammation-experienced Treg cell population reversed many activation-induced changes and lost its enhanced suppressive function over time. The "memory-less" potentiation of Treg suppressor function may help avoid a state of generalized immunosuppression that could otherwise result from repeated activation.

Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation.

We carried out an integrative analysis of enhancer landscape and gene expression dynamics during hematopoietic differentiation using DNase-seq, histone mark ChIP-seq and RNA sequencing to model how the early establishment of enhancers and regulatory locus complexity govern gene expression changes at cell state transitions. We found that high-complexity genes-those with a large total number of DNase-mapped enhancers across the lineage-differ architecturally and functionally from low-complexity genes, achieve larger expression changes and are enriched for both cell type-specific and transition enhancers, which are established in hematopoietic stem and progenitor cells and maintained in one differentiated cell fate but lost in others. We then developed a quantitative model to accurately predict gene expression changes from the DNA sequence content and lineage history of active enhancers. Our method suggests a new mechanistic role for PU.1 at transition peaks during B cell specification and can be used to correct assignments of enhancers to genes.

An allelic series of miR-17 ∼ 92-mutant mice uncovers functional specialization and cooperation among members of a microRNA polycistron.

Polycistronic microRNA (miRNA) clusters are a common feature of vertebrate genomes. The coordinated expression of miRNAs belonging to different seed families from a single transcriptional unit suggests functional cooperation, but this hypothesis has not been experimentally tested. Here we report the characterization of an allelic series of genetically engineered mice harboring selective targeted deletions of individual components of the miR-17 ∼ 92 cluster. Our results demonstrate the coexistence of functional cooperation and specialization among members of this cluster, identify a previously undescribed function for the miR-17 seed family in controlling axial patterning in vertebrates and show that loss of miR-19 selectively impairs Myc-driven tumorigenesis in two models of human cancer. By integrating phenotypic analysis and gene expression profiling, we provide a genome-wide view of how the components of a polycistronic miRNA cluster affect gene expression in vivo. The reagents and data sets reported here will accelerate exploration of the complex biological functions of this important miRNA cluster.

In vivo, Argonaute-bound microRNAs exist predominantly in a reservoir of low molecular weight complexes not associated with mRNA.

MicroRNAs repress mRNA translation by guiding Argonaute proteins to partially complementary binding sites, primarily within the 3' untranslated region (UTR) of target mRNAs. In cell lines, Argonaute-bound microRNAs exist mainly in high molecular weight RNA-induced silencing complexes (HMW-RISC) associated with target mRNA. Here we demonstrate that most adult tissues contain reservoirs of microRNAs in low molecular weight RISC (LMW-RISC) not bound to mRNA, suggesting that these microRNAs are not actively engaged in target repression. Consistent with this observation, the majority of individual microRNAs in primary T cells were enriched in LMW-RISC. During T-cell activation, signal transduction through the phosphoinositide-3 kinase-RAC-alpha serine/threonine-protein kinase-mechanistic target of rapamycin pathway increased the assembly of microRNAs into HMW-RISC, enhanced expression of the glycine-tryptophan protein of 182 kDa, an essential component of HMW-RISC, and improved the ability of microRNAs to repress partially complementary reporters, even when expression of targeting microRNAs did not increase. Overall, data presented here demonstrate that microRNA-mediated target repression in nontransformed cells depends not only on abundance of specific microRNAs, but also on regulation of RISC assembly by intracellular signaling.

Clinical utility of early amplitude integrated EEG in monitoring term newborns at risk of neurological injury / Utilidade clínica de um monitoramento por EEG de amplitude integrada precoce em recém-nascidos a termo com risco de lesão neurológica

OBJECTIVE: to test the clinical utility of an early amplitude-integrated electroencephalography (aEEG) to predict short-term neurological outcome in term newborns at risk of neurology injury. METHODS: this was a prospective, descriptive study. The inclusion criteria were neonatal encephalopathy, neurologic disturbances, and severe respiratory distress syndrome. Sensitivity, specificity, positive and negative predictive values, and likelihood ratio (LR) were calculated. Clinical and demographic data were analyzed. Neurological outcome was defined as the sum of clinical, electroimaging, and neuroimaging findings. RESULTS: ten of the 21 monitored infants (48%) presented altered short-term neurologic outcome. The aEEG had 90% sensitivity, 82% specificity, 82% positive predictive value, and 90% negative predictive value. The positive LR was 4.95, and the negative LR was 0.12. In three of 12 (25%) encephalopathic infants, the aEEG allowed for a better definition of the severity of their condition. Seizures were detected in eight infants (38%), all subclinical at baseline, and none had a normal aEEG background pattern. The status of three infants (43%) evolved and required two or more drugs for treatment. CONCLUSIONS: in infants with encephalopathy or other severe illness, aEEG disturbances occur frequently. aEEG provided a better classification of the severity of encephalopathy, detected early subclinical seizures, and allowed for monitoring of the response to treatment. aEEG was a useful tool at the neonatal intensive care unit for predicting poor short-term neurological outcomes for all sick newborn. .

OBJETIVO: testar a utilidade clínica do aEEG precoce em recém-nascidos a termo com risco delesão neurológica, para prever resultados neurológicos de curto prazo. MÉTODOS: estudo prospectivo e descritivo. Os critérios de inclusão foram encefalopatia neonatal, distúrbios neurológicos e bebês com SARA grave. Sensibilidade, especificidade, valor preditivo positivo e negativo e razão de verossimilhança foram calculados. Dados clínicos edemográficos foram analisados. O resultado neurológico foi definido como a soma de conclusões clínicas, de eletro e de neuroimagem. RESULTADOS: dentre os 21 neonatos monitorados, dez (48%) apresentaram resultado neurológico de curto prazo alterado. O aEEG apresentou sensibilidade de 90%, especificidade de 82%, valor preditivo positivo de 82% e valor preditivo negativo de 90%. A VR positiva foi de 4,95, e a RV negativa de 0,12. Em três dos 12 (25%) neonatos com encefalopatia foi possível definir melhora gravidade de sua condição pelo aEEG. Foram detectadas convulsões em oito neonatos (38%), todas subclínicas no início do estudo, e nenhum apresentou um padrão histórico normal no aEEG. O estado de três neonatos (43%) evoluiu e exigiu dois ou mais medicamentos para tratamento. CONCLUSÕES: em neonatos com encefalopatia ou outra doença grave, os distúrbios no aEEGocorrem com mais frequência. O aEEG forneceu uma classificação melhor da gravidade da encefalopatia, detectou convulsões subclínicas precoces e permitiu que fosse feito o monitoramento da resposta ao tratamento. O aEEG é uma ferramenta útil para prever resultados neurológicos de curto prazo em todos os bebês doentes na UTIN. .

Clinical utility of early amplitude integrated EEG in monitoring term newborns at risk of neurological injury.

OBJECTIVE: to test the clinical utility of an early amplitude-integrated electroencephalography (aEEG) to predict short-term neurological outcome in term newborns at risk of neurology injury. METHODS: this was a prospective, descriptive study. The inclusion criteria were neonatal encephalopathy, neurologic disturbances, and severe respiratory distress syndrome. Sensitivity, specificity, positive and negative predictive values, and likelihood ratio (LR) were calculated. Clinical and demographic data were analyzed. Neurological outcome was defined as the sum of clinical, electroimaging, and neuroimaging findings. RESULTS: ten of the 21 monitored infants (48%) presented altered short-term neurologic outcome. The aEEG had 90% sensitivity, 82% specificity, 82% positive predictive value, and 90% negative predictive value. The positive LR was 4.95, and the negative LR was 0.12. In three of 12 (25%) encephalopathic infants, the aEEG allowed for a better definition of the severity of their condition. Seizures were detected in eight infants (38%), all subclinical at baseline, and none had a normal aEEG background pattern. The status of three infants (43%) evolved and required two or more drugs for treatment. CONCLUSIONS: in infants with encephalopathy or other severe illness, aEEG disturbances occur frequently. aEEG provided a better classification of the severity of encephalopathy, detected early subclinical seizures, and allowed for monitoring of the response to treatment. aEEG was a useful tool at the neonatal intensive care unit for predicting poor short-term neurological outcomes for all sick newborn.

Predicting interacting residues using long-distance information and novel decoding in hidden Markov models.

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with an early traceback mechanism, and apply it to interaction profile hidden Markov models (ipHMMs) to enable optimized incorporation of long-distance correlations between interacting residues, leading to improved prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness and robustness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, as well as reversed sequence orientation. It was demonstrated that the prediction consistently improves as the correlations increase and is not significantly affected by sequence orientation.

Prediction of contact matrix for protein-protein interaction.

MOTIVATION: Prediction of protein-protein interaction has become an important part of systems biology in reverse engineering the biological networks for better understanding the molecular biology of the cell. Although significant progress has been made in terms of prediction accuracy, most computational methods only predict whether two proteins interact but not their interacting residues-the information that can be very valuable for understanding the interaction mechanisms and designing modulation of the interaction. In this work, we developed a computational method to predict the interacting residue pairs-contact matrix for interacting protein domains, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact. RESULTS: Our method is based on supervised learning using support vector machines. For each domain involved in a given domain-domain interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain family, and then each residue position for a member domain sequence is represented as a 20-dimension vector of Fisher scores, characterizing how similar it is as compared with the family profile at that position. Each element of the contact matrix for a sequence pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs, and is tested by leave-one-out cross validation. The performance averaged over a set of 115 DDIs collected from the 3 DID database shows significant improvement (sensitivity up to 85%, and specificity up to 85%), as compared with a multiple sequence alignment-based method (sensitivity 57%, and specificity 78%) previously reported in the literature. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting ligand binding residues and functional sites using multipositional correlations with graph theoretic clustering and kernel CCA.

We present a new computational method for predicting ligand binding residues and functional sites in protein sequences. These residues and sites tend to be not only conserved, but also exhibit strong correlation due to the selection pressure during evolution in order to maintain the required structure and/or function. To explore the effect of correlations among multiple positions in the sequences, the method uses graph theoretic clustering and kernel-based canonical correlation analysis (kCCA) to identify binding and functional sites in protein sequences as the residues that exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure-based functional classification of the proteins in the context of a functional family. The results of testing the method on two well-curated data sets show that the prediction accuracy as measured by Receiver Operating Characteristic (ROC) scores improves significantly when multipositional correlations are accounted for.

MicroRNAs as master regulators of the plant NB-LRR defense gene family via the production of phased, trans-acting siRNAs.

Legumes and many nonleguminous plants enter symbiotic interactions with microbes, and it is poorly understood how host plants respond to promote beneficial, symbiotic microbial interactions while suppressing those that are deleterious or pathogenic. Trans-acting siRNAs (tasiRNAs) negatively regulate target transcripts and are characterized by siRNAs spaced in 21-nucleotide (nt) "phased" intervals, a pattern formed by DICER-LIKE 4 (DCL4) processing. A search for phased siRNAs (phasiRNAs) found at least 114 Medicago loci, the majority of which were defense-related NB-LRR-encoding genes. We identified three highly abundant 22-nt microRNA (miRNA) families that target conserved domains in these NB-LRRs and trigger the production of trans-acting siRNAs. High levels of small RNAs were matched to >60% of all ∼540 encoded Medicago NB-LRRs; in the potato, a model for mycorrhizal interactions, phasiRNAs were also produced from NB-LRRs. DCL2 and SGS3 transcripts were also cleaved by these 22-nt miRNAs, generating phasiRNAs, suggesting synchronization between silencing and pathogen defense pathways. In addition, a new example of apparent "two-hit" phasiRNA processing was identified. Our data reveal complex tasiRNA-based regulation of NB-LRRs that potentially evolved to facilitate symbiotic interactions and demonstrate miRNAs as master regulators of a large gene family via the targeting of highly conserved, protein-coding motifs, a new paradigm for miRNA function.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines.

BACKGROUND: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. RESULTS: In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. CONCLUSIONS: We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows.

Clustering exact matches of pairwise sequence alignments by weighted linear regression.

BACKGROUND: At intermediate stages of genome assembly projects, when a number of contigs have been generated and their validity needs to be verified, it is desirable to align these contigs to a reference genome when it is available. The interest is not to analyze a detailed alignment between a contig and the reference genome at the base level, but rather to have a rough estimate of where the contig aligns to the reference genome, specifically, by identifying the starting and ending positions of such a region. This information is very useful in ordering the contigs, facilitating post-assembly analysis such as gap closure and resolving repeats. There exist programs, such as BLAST and MUMmer, that can quickly align and identify high similarity segments between two sequences, which, when seen in a dot plot, tend to agglomerate along a diagonal but can also be disrupted by gaps or shifted away from the main diagonal due to mismatches between the contig and the reference. It is a tedious and practically impossible task to visually inspect the dot plot to identify the regions covered by a large number of contigs from sequence assembly projects. A forced global alignment between a contig and the reference is not only time consuming but often meaningless. RESULTS: We have developed an algorithm that uses the coordinates of all the exact matches or high similarity local alignments, clusters them with respect to the main diagonal in the dot plot using a weighted linear regression technique, and identifies the starting and ending coordinates of the region of interest. CONCLUSION: This algorithm complements existing pairwise sequence alignment packages by replacing the time-consuming seed extension phase with a weighted linear regression for the alignment seeds. It was experimentally shown that the gain in execution time can be outstanding without compromising the accuracy. This method should be of great utility to sequence assembly and genome comparison projects.

Management of High-Throughput DNA Sequencing Projects: Alpheus.

High-throughput DNA sequencing has enabled systems biology to begin to address areas in health, agricultural and basic biological research. Concomitant with the opportunities is an absolute necessity to manage significant volumes of high-dimensional and inter-related data and analysis. Alpheus is an analysis pipeline, database and visualization software for use with massively parallel DNA sequencing technologies that feature multi-gigabase throughput characterized by relatively short reads, such as Illumina-Solexa (sequencing-by-synthesis), Roche-454 (pyrosequencing) and Applied Biosystem's SOLiD (sequencing-by-ligation). Alpheus enables alignment to reference sequence(s), detection of variants and enumeration of sequence abundance, including expression levels in transcriptome sequence. Alpheus is able to detect several types of variants, including non-synonymous and synonymous single nucleotide polymorphisms (SNPs), insertions/deletions (indels), premature stop codons, and splice isoforms. Variant detection is aided by the ability to filter variant calls based on consistency, expected allele frequency, sequence quality, coverage, and variant type in order to minimize false positives while maximizing the identification of true positives. Alpheus also enables comparisons of genes with variants between cases and controls or bulk segregant pools. Sequence-based differential expression comparisons can be developed, with data export to SAS JMP Genomics for statistical analysis.