Diagnostic Point-of-Care Ultrasound: Recommendations From an Expert Panel.

Diagnostic point-of-care ultrasound (PoCUS) has emerged as a powerful tool to help anesthesiologists guide patient care in both the perioperative setting and the subspecialty arenas. Although anesthesiologists can turn to guideline statements pertaining to other aspects of ultrasound use, to date there remains little in the way of published guidance regarding diagnostic PoCUS. To this end, in 2018, the American Society of Anesthesiologists chartered an ad hoc committee consisting of 23 American Society of Anesthesiologists members to provide recommendations on this topic. The ad hoc committee convened and developed a committee work product. This work product was updated in 2021 by an expert panel of the ad hoc committee to produce the document presented herein. The document, which represents the consensus opinion of a group of practicing anesthesiologists with established expertise in diagnostic ultrasound, addresses the following issues: (1) affirms the practice of diagnostic PoCUS by adequately trained anesthesiologists, (2) identifies the scope of practice of diagnostic PoCUS relevant to anesthesiologists, (3) suggests the minimum level of training needed to achieve competence, (4) provides recommendations for how diagnostic PoCUS can be used safely and ethically, and (5) provides broad guidance about diagnostic ultrasound billing.

The Phenotype Paradox: Lessons From Natural Transcriptome Evolution on How to Engineer Plants.

Plants have evolved genome complexity through iterative rounds of single gene and whole genome duplication. This has led to substantial expansion in transcription factor numbers following preferential retention and subsequent functional divergence of these regulatory genes. Here we review how this simple evolutionary network rewiring process, regulatory gene duplication followed by functional divergence, can be used to inspire synthetic biology approaches that seek to develop novel phenotypic variation for future trait based breeding programs in plants.

Miniaturized Echocardiography in the Cardiac Intensive Care Unit.

Miniaturized transesophageal echocardiography has become more common in cardiac intensive care units. There are potential benefits to this mode of technology, many of which have been described in the literature. However, image acquisition and quality have been cited as being less optimal when compared to traditional transesophageal echocardiography. This review will discuss the current options available for miniaturized transesophageal echocardiography along with a literature review of this emerging assessment modality.

Engineering microbial phenotypes through rewiring of genetic networks.

The ability to program cellular behaviour is a major goal of synthetic biology, with applications in health, agriculture and chemicals production. Despite efforts to build 'orthogonal' systems, interactions between engineered genetic circuits and the endogenous regulatory network of a host cell can have a significant impact on desired functionality. We have developed a strategy to rewire the endogenous cellular regulatory network of yeast to enhance compatibility with synthetic protein and metabolite production. We found that introducing novel connections in the cellular regulatory network enabled us to increase the production of heterologous proteins and metabolites. This strategy is demonstrated in yeast strains that show significantly enhanced heterologous protein expression and higher titers of terpenoid production. Specifically, we found that the addition of transcriptional regulation between free radical induced signalling and nitrogen regulation provided robust improvement of protein production. Assessment of rewired networks revealed the importance of key topological features such as high betweenness centrality. The generation of rewired transcriptional networks, selection for specific phenotypes, and analysis of resulting library members is a powerful tool for engineering cellular behavior and may enable improved integration of heterologous protein and metabolite pathways.

Building synthetic systems to learn nature's design principles.

Evolution undoubtedly shapes the architecture of biological systems, yet it is unclear which features of regulatory, metabolic, and signalling circuits have adaptive significance and how the architecture of these circuits constrains or promotes evolutionary processes, such as adaptation to new environments. Experimentally rewiring circuits using genetic engineering and constructing novel circuits in living cells allows direct testing and validation of hypotheses in evolutionary systems biology. Building synthetic genetic systems enables researchers to explore regions of the genotype-phenotype and fitness landscapes that may be inaccessible to more traditional analysis. Here, we review the strategies that allow synthetic systems to be constructed and how evolutionary design principles have advanced these technologies. We also describe how building small genetic regulatory systems can provide insight on the trade-offs that constrain adaptation and can shape the structure of biological networks. In the future, the possibility of building biology de novo at the genome scale means that increasingly sophisticated models of the evolutionary dynamics of networks can be proposed and validated, and will allow us to recreate ancestral systems in the lab. This interplay between evolutionary systems theory and engineering design may illuminate the fundamental limits of performance, robustness, and evolvability of living systems.

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana.

MOTIVATION: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here. RESULTS: Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor-target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings. AVAILABILITY: Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.