CUSUM learning curves: what they can and can't tell us.

INTRODUCTION: There has been increased interest in assessing the surgeon learning curve for new skill acquisition. While there is no consensus around the best methodology, one of the most frequently used learning curve assessments in the surgical literature is the cumulative sum curve (CUSUM) of operative time. To demonstrate the limitations of this methodology, we assessed the CUSUM of console time across cohorts of surgeons with differing case acquisition rates while varying the total number of cases used to calculate the CUSUM. METHODS: We compared the CUSUM curves of the average console times of surgeons who completed their first 20 robotic-assisted (RAS) cases in 13, 26, 39, and 52 weeks, respectively, for their first 50 and 100 cases, respectively. This analysis was performed for prostatectomy (1094 surgeons), malignant hysterectomy (737 surgeons), and inguinal hernia (1486 surgeons). RESULTS: In all procedures, the CUSUM curve of the cohort of surgeons who completed their first 20 procedures in 13 weeks demonstrated a lower slope than cohorts of surgeons with slower case acquisition rates. The case number at which the peak of the CUSUM curve occurs uniformly increases when the total number of cases used in generation of the CUSUM chart changes from 50 to 100 cases. CONCLUSION: The CUSUM analyses of these three procedures suggests that surgeons with fast initial case acquisition rates have less variability in their operative times over the course of their learning curve. The peak of the CUSUM curve, which is often used in surgical learning curve literature to denote "proficiency" is predictably influenced by the total number of procedures evaluated, suggesting that defining the peak as the point at which a surgeon has overcome the learning curve is subject to routine bias. The CUSUM peak, by itself, is an insufficient measure of "conquering the learning curve."

Hotspots of riverine greenhouse gas (CH4, CO2, N2O) emissions from Qinghai Lake Basin on the northeast Tibetan Plateau.

Evasion of greenhouse gases (GHG) from fluvial systems is now recognized as a significant component of the global carbon cycle. However, the magnitudes of GHG fluxes remain uncertain due to limited research data, especially on the Tibetan Plateau. In this study Methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) concentrations were measured and their diffusive fluxes were estimated by headspace-gas chromatography in two rivers basins (Buha and Shaliu rivers) on the Northeast Tibetan Plateau during three seasons from October 2020 to August 2021. The results showed that the focal rivers on the Tibetan Plateau are potentially important sources of GHG. Both rivers have higher GHG concentrations and diffusion flux during the snowmelt period than other seasons. In general, GHG diffusion fluxes in the Buha river were higher than those in the Shaliu river and their concentrations are higher in the upstream region than in the downstream region of both basins. The salinity in water and wind spread were found to be important factors influencing in GHGs diffusion fluxes. While diffusive fluxes of GHG in rivers were a small component of watershed-scale fluvial Carbon gas efflux compared to other studies, these fluxes will likely increase as thaw slump occurrence. Overall, this study highlights that better recognition of the influence that river networks have on global warming is required-especially when it comes to high-elevation rivers across permafrost, as permafrost will continue to thaw as climate warming.

The Role of Thermokarst Lake Expansion in Altering the Microbial Community and Methane Cycling in Beiluhe Basin on Tibetan Plateau.

One of the most significant environmental changes across the Tibetan Plateau (TP) is the rapid lake expansion. The expansion of thermokarst lakes affects the global biogeochemical cycles and local climate regulation by rising levels, expanding area, and increasing water volumes. Meanwhile, microbial activity contributes greatly to the biogeochemical cycle of carbon in the thermokarst lakes, including organic matter decomposition, soil formation, and mineralization. However, the impact of lake expansion on distribution patterns of microbial communities and methane cycling, especially those of water and sediment under ice, remain unknown. This hinders our ability to assess the true impact of lake expansion on ecosystem services and our ability to accurately investigate greenhouse gas emissions and consumption in thermokarst lakes. Here, we explored the patterns of microorganisms and methane cycling by investigating sediment and water samples at an oriented direction of expansion occurred from four points under ice of a mature-developed thermokarst lake on TP. In addition, the methane concentration of each water layer was examined. Microbial diversity and network complexity were different in our shallow points (MS, SH) and deep points (CE, SH). There are differences of microbial community composition among four points, resulting in the decreased relative abundances of dominant phyla, such as Firmicutes in sediment, Proteobacteria in water, Thermoplasmatota in sediment and water, and increased relative abundance of Actinobacteriota with MS and SH points. Microbial community composition involved in methane cycling also shifted, such as increases in USCγ, Methylomonas, and Methylobacter, with higher relative abundance consistent with low dissolved methane concentration in MS and SH points. There was a strong correlation between changes in microbiota characteristics and changes in water and sediment environmental factors. Together, these results show that lake expansion has an important impact on microbial diversity and methane cycling.

High methane emissions from thermokarst lakes on the Tibetan Plateau are largely attributed to ebullition fluxes.

Ebullition has been shown to be an important pathway for methane (CH4) emissions from inland waters. However, the CH4 fluxes and their magnitudes in thermokarst lakes remain unclear due to limited research data, especially on the Tibetan Plateau (TP). The magnitude and regulation of two CH4 pathways, ebullition and diffusion, were investigated in 32 thermokarst lakes on the TP during the summer of 2020. CH4 emissions from thermokarst lakes on the TP showed significant spatiotemporal heterogeneity. Diffusion fluxes in lakes averaged 2.6 mmol m-2 d-1 (ranging from 0.003 to 48.4 mmol m-2 d-1), and ebullition fluxes in lakes averaged 6.6 mmol CH4 m-2 d-1 (ranging from 0.002 to 140.0 mmol m-2 d-1). Together, these ebullition fluxes contributed 66.1 ± 24.9% (ranging 5.4 to 100.0%) to the total (diffusion + ebullition) CH4 emissions, indicating the importance of ebullition as a major CH4 transport mechanism on the TP. In general, thermokarst lakes with higher CH4 diffusion fluxes and ebullition fluxes occurred in alpine meadows (2.5 ± 5.3 mmol m-2 d-1; 8.2 ± 20.6 mmol m-2 d-1), followed by alpine steppes (0.6 ± 5.3 mmol m-2 d-1; 0.7 ± 10.8 mmol m-2 d-1) and desert steppes (0.2 ± 0.2 mmol m-2 d-1; 0.6 ± 0.8 mmol m-2 d-1). The organic matter contents in water and sediment were found to be important factors influencing the seasonal variations in CH4 diffusion fluxes. However, the ebullition CH4 fluxes did not show a clear seasonal variation pattern. Our findings highlight the importance of considering the large spatiotemporal variations in ebullition CH4 fluxes to improve the accuracy of large-scale estimations of CH4 fluxes in thermokarst lakes on the TP. Greater insight into these aspects will increase the understanding of CH4 dynamics in thermokarst lakes on the TP, which is essential for forecasting and climate impact assessments and to better constrain feedback to climate warming.

A combined association test for rare variants using family and case-control data.

Statistical association tests for rare variants can be classified as the burden approach and the sequence kernel association test (SKAT) approach. The burden and SKAT approaches, originally developed for case-control analysis, have also been extended to family-based tests. In the presence of both case-control and family data for a study, joint analysis for the combined data set can increase the statistical power. We extended the Combined Association in the Presence of Linkage (CAPL) test, using both case-control and family data for testing common variants, to rare variant association analysis. The burden and SKAT algorithms were applied to the CAPL test. We used simulations to verify that the CAPL tests incorporating the burden and SKAT algorithms have correct type I error rates. Power studies suggested that both tests have adequate power to identify rare variants associated with the disease. We applied the tests to the Genetic Analysis Workshop 19 data set using the combined family and case-control data for hypertension. The analysis identified several candidate genes for hypertension.

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn's disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn's disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.

A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear families.

BACKGROUND: Genome-wide association studies (GWAS) have become a common approach to identifying single nucleotide polymorphisms (SNPs) associated with complex diseases. As complex diseases are caused by the joint effects of multiple genes, while the effect of individual gene or SNP is modest, a method considering the joint effects of multiple SNPs can be more powerful than testing individual SNPs. The multi-SNP analysis aims to test association based on a SNP set, usually defined based on biological knowledge such as gene or pathway, which may contain only a portion of SNPs with effects on the disease. Therefore, a challenge for the multi-SNP analysis is how to effectively select a subset of SNPs with promising association signals from the SNP set. RESULTS: We developed the Optimal P-value Threshold Pedigree Disequilibrium Test (OPTPDT). The OPTPDT uses general nuclear families. A variable p-value threshold algorithm is used to determine an optimal p-value threshold for selecting a subset of SNPs. A permutation procedure is used to assess the significance of the test. We used simulations to verify that the OPTPDT has correct type I error rates. Our power studies showed that the OPTPDT can be more powerful than the set-based test in PLINK, the multi-SNP FBAT test, and the p-value based test GATES. We applied the OPTPDT to a family-based autism GWAS dataset for gene-based association analysis and identified MACROD2-AS1 with genome-wide significance (p-value=2.5×10(-6)). CONCLUSIONS: Our simulation results suggested that the OPTPDT is a valid and powerful test. The OPTPDT will be helpful for gene-based or pathway association analysis. The method is ideal for the secondary analysis of existing GWAS datasets, which may identify a set of SNPs with joint effects on the disease.