An Applied Model of Interprofessional Collaboration-Assessment (AMIC-A): A Process-Based Approach to Augmentative and Alternative Communication.

Speech-language pathologists and board-certified behavior analysts both provide important support services to children who are candidates for augmentative and alternative communication. Current assessment practices neglect critical socioecological factors that are necessary to inform communication-based interventions. By leveraging the unique knowledge, research, and expertise of both disciplines, an interprofessional approach to assessment may help realize individualized or precision interventions and personalized supports that address the unique communication needs of each person.

Risk factors for treatment failure in multidrug resistant tuberculosis in Tunisia: An analytic study.

Introduction-aim: The emergence of multidrug resistant tuberculosis (MDR-TB) is a threat to global public health. The aim of our study was to determine risk factors for treatment failure in MDR-TB.

Methods: Retrospective study conducted between January 2000 and March 2019 including patients with MDR-TB.

Trade-offs between cost and information in cellular prediction.

Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal.

A systematic analysis of gene-gene interaction in multiple sclerosis.

Background: For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype. Many tools have therefore been developed to investigate the interactions between distant loci, or epistasis.

Nonlinear post-selection inference for genome-wide association studies.

To address the lack of statistical power and interpretability of genome-wide association studies (GWAS), gene-level analyses combine the p-values of individual single nucleotide polymorphisms (SNPs) into gene statistics. However, using all SNPs mapped to a gene, including those with low association scores, can mask the association signal of a gene.We therefore propose a new two-step strategy, consisting in first selecting the SNPs most associated with the phenotype within a given gene, before testing their joint effect on the phenotype.