Improved Neurodevelopmental Outcomes at 5.5 Years of Age in Children Who Received Bovine Milk Fat Globule Membrane and Lactoferrin in Infant Formula Through 12 Months: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the neurodevelopmental outcomes at 5.5 years of age in children who were previously randomized to cow milk-based infant formula (control) or similar formula (milk fat globule membrane + lactoferrin) with added sources of bovine milk fat globule membrane and bovine lactoferrin through 12 months of age. DESIGN: Children who completed study feeding were invited to participate in follow-up assessments: cognitive development across multiple domains (primary outcome; Wechsler Preschool & Primary Scale of Intelligence, 4th Edition), inhibitory control/rule learning (Stroop Task), flexibility/rule learning (Dimensional Change Card Sort), and behavior/emotion (Child Behavior Checklist). RESULTS: Of 292 eligible participants (control: 148, milk fat globule membrane + lactoferrin: 144), 116 enrolled and completed assessments (control: 59, milk fat globule membrane + LF: 57). There were no group demographic differences except family income (milk fat globule membrane + lactoferrin significantly higher). Wechsler Preschool & Primary Scale of Intelligence, 4th Edition composite scores (mean ± standard error) for Visual Spatial (100.6 ± 1.7 vs 95.3 ± 1.7; P = .027), Processing Speed (107.1 ± 1.4 vs 100.0 ± 1.4; P < .001), and Full-Scale IQ (98.7 ± 1.4 vs 93.5 ± 1.5; P = .012) were significantly higher for milk fat globule membrane + lactoferrin versus control, even after controlling for demographic/socioeconomic factors. Stroop Task scores were significantly higher in milk fat globule membrane + lactoferrin versus control (P < .001). Higher Dimensional Change Card Sort scores (P = .013) in the border phase (most complex/challenging) were detected, and more children passed the border phase (32% vs 12%; P = .039) for milk fat globule membrane versus control. No group differences in Child Behavior Checklist score were detected. CONCLUSIONS: Children who received infant formula to 12 months of age with added bovine milk fat globule membrane and bovine lactoferrin versus standard formula demonstrated improved cognitive outcomes in multiple domains at 5.5 years of age, including measures of intelligence and executive function. TRIAL REGISTRATION: Clinicaltrials.gov: https://clinicaltrials.gov/ct2/show/NCT04442477.

An assembly-free method of phylogeny reconstruction using short-read sequences from pooled samples without barcodes.

A current strategy for obtaining haplotype information from several individuals involves short-read sequencing of pooled amplicons, where fragments from each individual is identified by a unique DNA barcode. In this paper, we report a new method to recover the phylogeny of haplotypes from short-read sequences obtained using pooled amplicons from a mixture of individuals, without barcoding. The method, AFPhyloMix, accepts an alignment of the mixture of reads against a reference sequence, obtains the single-nucleotide-polymorphisms (SNP) patterns along the alignment, and constructs the phylogenetic tree according to the SNP patterns. AFPhyloMix adopts a Bayesian inference model to estimate the phylogeny of the haplotypes and their relative abundances, given that the number of haplotypes is known. In our simulations, AFPhyloMix achieved at least 80% accuracy at recovering the phylogenies and relative abundances of the constituent haplotypes, for mixtures with up to 15 haplotypes. AFPhyloMix also worked well on a real data set of kangaroo mitochondrial DNA sequences.

Estimation of evolutionary parameters using short, random and partial sequences from mixed samples of anonymous individuals.

BACKGROUND: Over the last decade, next generation sequencing (NGS) has become widely available, and is now the sequencing technology of choice for most researchers. Nonetheless, NGS presents a challenge for the evolutionary biologists who wish to estimate evolutionary genetic parameters from a mixed sample of unlabelled or untagged individuals, especially when the reconstruction of full length haplotypes can be unreliable. We propose two novel approaches, least squares estimation (LS) and Approximate Bayesian Computation Markov chain Monte Carlo estimation (ABC-MCMC), to infer evolutionary genetic parameters from a collection of short-read sequences obtained from a mixed sample of anonymous DNA using the frequencies of nucleotides at each site only without reconstructing the full-length alignment nor the phylogeny. RESULTS: We used simulations to evaluate the performance of these algorithms, and our results demonstrate that LS performs poorly because bootstrap 95% Confidence Intervals (CIs) tend to under- or over-estimate the true values of the parameters. In contrast, ABC-MCMC 95% Highest Posterior Density (HPD) intervals recovered from ABC-MCMC enclosed the true parameter values with a rate approximately equivalent to that obtained using BEAST, a program that implements a Bayesian MCMC estimation of evolutionary parameters using full-length sequences. Because there is a loss of information with the use of sitewise nucleotide frequencies alone, the ABC-MCMC 95% HPDs are larger than those obtained by BEAST. CONCLUSION: We propose two novel algorithms to estimate evolutionary genetic parameters based on the proportion of each nucleotide. The LS method cannot be recommended as a standalone method for evolutionary parameter estimation. On the other hand, parameters recovered by ABC-MCMC are comparable to those obtained using BEAST, but with larger 95% HPDs. One major advantage of ABC-MCMC is that computational time scales linearly with the number of short-read sequences, and is independent of the number of full-length sequences in the original data. This allows us to perform the analysis on NGS datasets with large numbers of short read fragments. The source code for ABC-MCMC is available at https://github.com/stevenhwu/SF-ABC.

A Bayesian model for classifying all differentially expressed proteins simultaneously in 2D PAGE gels.

BACKGROUND: Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used to identify differentially expressed proteins under two or more experimental or observational conditions. Wu et al (2009) developed a univariate probabilistic model which was used to identify differential expression between Case and Control groups, by applying a Likelihood Ratio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statistical approaches, this model takes into account the two possible causes of missing values in 2D PAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls below the limit of detection. RESULTS: We develop a global Bayesian model which extends the previously described model. Unlike the univariate approach, the model reported here is able treat all differentially expressed proteins simultaneously. Whereas each protein is modelled by the univariate likelihood function previously described, several global distributions are used to model the underlying relationship between the parameters associated with individual proteins. These global distributions are able to combine information from each protein to give more accurate estimates of the true parameters. In our implementation of the procedure, all parameters are recovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posterior density (HPD) intervals for the marginal posterior distributions are used to determine whether differences in protein expression are due to differences in mean expression intensities, and/or differences in the probabilities of expression. CONCLUSIONS: Simulation analyses showed that the global model is able to accurately recover the underlying global distributions, and identify more differentially expressed proteins than the simple application of a LRT. Additionally, simulations also indicate that the probability of incorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) is very low. The source code is available at https://github.com/stevenhwu/BIDE-2D.

Academic interventional nephrology: a model for training, research, and patient care.

Dialysis vascular access dysfunction is currently a huge clinical problem. We believe that comprehensive academic-based dialysis vascular access programs that go all the way from basic and translational science investigation to clinical research to a dedicated curriculum and opportunities in vascular access for nephrologists in training are essential for improving dialysis vascular access care. This paper reviews the fundamental concepts and requirements for us to move toward this vision.

Successful models of interventional nephrology at academic medical centers.

The foundation of endovascular procedures by nephrologists was laid in the private practice arena. Because of political issues such as training, credentialing, space and equipment expenses, and co-management concerns surrounding the performance of dialysis-access procedures, the majority of these programs provided care in an outpatient vascular access center. On the basis of the improvement of patient care demonstrated by these centers, several nephrology programs at academic medical centers have also embraced this approach. In addition to providing interventional care on an outpatient basis, academic medical centers have taken a step further to expand collaboration with other specialties with similar expertise (such as with interventional radiologists and cardiologists) to enhance patient care and research. The enthusiastic initiative, cooperative, and mutually collaborative efforts used by academic medical centers have resulted in the successful establishment of interventional nephrology programs. This article describes various models of interventional nephrology programs at academic medical centers across the United States.