Cyclospora cayetanensis is a food-borne intestinal human parasite that causes outbreaks of diarrhea. There is a need for efficient laboratory methods for strain-level characterization to assist in outbreak investigations. By using next generation sequencing, genomic sequences can be obtained and compared to identify potential genotyping markers. However, there is no method available to propagate this parasite in the laboratory. Therefore, genomic DNA must be extracted from oocysts purified from human stool. The objective of this study was to apply optimized methods to purify C. cayetanensis oocysts and extract DNA in order to obtain high-quality whole genome sequences with minimum contamination of DNA from other organisms. Oocysts from 21 human stool specimens were separated from other stool components using discontinuous density gradient centrifugation and purified further by flow cytometry. Genomic DNA was used to construct Ovation Ultralow libraries for Illumina sequencing. MiSeq sequencing reads were taxonomically profiled for contamination, de novo assembled, and mapped to a draft genome available in GenBank to assess the quality of the resulting genomic sequences. Following all purification steps, the majority (81–99%) of sequencing reads were from C. cayetanensis. They could be assembled into draft genomes of around 45 MB in length with GC-content of 52%. Density gradients performed in the presence of a detergent followed by flow cytometry sorting of oocysts yielded sufficient genomic DNA largely free from contamination and suitable for whole genome sequencing of C. cayetanensis. The methods described here will facilitate the accumulation of genomic sequences from various samples, which is a prerequisite for the development of typing tools to aid in outbreak investigations.

Fuel cell and water electrolyzer technology have been intensively investigated in the last decades toward sustainable and renewable energy conversion systems. For improved device performance and service life, nanostructured electrocatalysts on electrode have been extensively developed based on the principle of structure-activity-stability correlation. However, overall device efficiency is seriously hindered by sluggish oxygen electrocatalysis, including oxygen reduction reaction and oxygen evolution reaction. As a result, tremendous efforts have been made to construct the most active surfaces with robust durability. For knowledge-based approaches toward systematic development of highly functional nanostructures, fundamental principles within oxygen electrocatalysis should be uncovered including reaction intermediate, active site structures, and atomic dissolution from surface. However, conventional ex situ characterizations only provide a static picture of electrode surfaces without electrocatalysis. On the other hand, in situ/operando analyses allow us to directly monitor dynamics on electrode under operating conditions. In this review, we will introduce a set of in situ/operando analytical tools and summarize their contribution to fundamental researches on oxygen electrocatalysis. Taking both precious and non-precious electrocatalyst materials as examples, the most impending issues in oxygen electrocatalysis are covered with in situ/operando studies to highlight the power of in situ/operando techniques and encourage further efforts on advanced analytic techniques.

Shiga-toxin-producing E. coli (STEC) is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Whole-genome sequencing (WGS) is a powerful tool used in public health and microbiology for the detection, surveillance, and outbreak investigation of STEC. In this study, we applied three WGS-based subtyping methods, high quality single-nucleotide polymorphism (hqSNP) analysis, whole genome multi-locus sequence typing using chromosome-associated loci [wgMLST (chrom)], and core genome multi-locus sequence typing (cgMLST), to isolate sequences from 11 STEC outbreaks. For each outbreak, we evaluated the concordance between subtyping methods using pairwise genomic differences (number of SNPs or alleles), linear regression models, and tanglegrams. Pairwise genomic differences were highly concordant between methods for all but one outbreak, which was associated with international travel. The slopes of the regressions for hqSNP vs. allele differences were 0.432 (cgMLST) and 0.966 wgMLST (chrom); the slope was 1.914 for cgMLST vs. wgMLST (chrom) differences. Tanglegrams comprised of outbreak and sporadic sequences showed moderate clustering concordance between methods, where Baker’s Gamma Indices (BGIs) ranged between 0.35 and 0.99 and Cophenetic Correlation Coefficients (CCCs) were ≥0.88 across all outbreaks. The K-means analysis using the Silhouette method showed the clear separation of outbreak groups with average silhouette widths ≥0.87 across all methods. This study validates the use of cgMLST for the national surveillance of STEC illness clusters using the PulseNet 2.0 system and demonstrates that hqSNP or wgMLST can be used for further resolution.

In a future where home health care is no longer an information silo, patient information will be communicated along transitions in care to improve care. Evidence-based practice in the United States supports home health care patients to see their primary care team within the first two weeks of hospital discharge to reduce rehospitalization risk. We sought to identify a parsimonious set of home health care data to be communicated to primary care for the post-hospitalization visit. Anticipating electronic dataset communication, we investigated the completeness of the international reference terminology, Logical Observation Identifiers Names and Codes (LOINC), for coverage of the data to be communicated. We conducted deductive qualitative analysis in three steps: (1) identify home health care data available for the visit by mapping home health care to the information needed for the visit; (2) reduce the resulting home health care data set to a parsimonious set clinicians wanted for the post-hospitalization visit by eliciting primary care clinician input; and (3) map the parsimonious dataset to LOINC and assess LOINC completeness. Our study reduced the number of standardized home health care assessment questions by 40% to a parsimonious set of 33 concepts that primary care team physicians wanted for the post-hospitalization visit. Findings indicate all home health care concepts in the parsimonious dataset mapped to the information needed for the post-hospitalization visit, and 84% of the home health care concepts mapped to a LOINC term. The results indicate data flow of parsimonious home health care dataset to primary care for the post-hospitalization visit is possible using existing LOINC codes, and would require adding some codes to LOINC for communication of a complete parsimonious data set.