In flooded soils, soil–water interface (SWI) is the key zone controlling biogeochemical dynamics. Chemical species and concentrations vary greatly at micro- to cm-scales. Techniques able to track these changing element profiles both in space and over time with appropriate resolution are rare. Here, we report a patent-pending technique, the Integrated Porewater Injection (IPI) sampler, which is designed for soil porewater sampling with minimum disturbance to saturated soil environment. IPI sampler employs a single hollow fiber membrane tube to passively sample porewater surrounding the tube. When working, it can be integrated into the sample introduction system, thus the sample preparation procedure is dramatically simplified. In this study, IPI samplers were coupled to ICP-MS at data-only mode. The limits of detection of IPI-ICP-MS for Ni, As, Cd, Sb, and Pb were 0.12, 0.67, 0.027, 0.029, and 0.074 μg·L–1, respectively. Furthermore, 25 IPI samplers were assembled into an SWI profiler using 3D printing in a one-dimensional array. The SWI profiler is able to analyze element profiles at high spatial resolution (∼2 mm) every ≥24 h. When deployed in arsenic-contaminated paddy soils, it depicted the distributions and dynamics of multiple elements at anoxic–oxic transition. The results show that the SWI profiler is a powerful and robust technique in monitoring dynamics of element profile in soil porewater at high spatial resolution. The method will greatly facilitate studies of elements behaviors in sediments of wetland, rivers, lakes, and oceans.

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Background: Mask ventilation (MV) is an essential component of airway management. Difficult mask ventilation (DMV) is a major cause for perioperative hypoxic brain injury; however, predicting DMV remains a challenge. This study aimed to determine the potential value of voice parameters as novel predictors of DMV in patients scheduled for general anesthesia. Methods: We included 1,160 adult patients scheduled for elective surgery under general anesthesia. The clinical variables usually reported as predictors of DMV were collected before surgery. Voice sample of phonemes ([a], [o], [e], [i], [u], [ü], [ci], [qi], [chi], [le], [ke], and [en]) were recorded and their formants (f1-f4) and bandwidths (bw1-bw4) were extracted. The definition of DMV was the inability of an unassisted anesthesiologist to ensure adequate ventilation during MV under general anesthesia. Univariate and multivariate logistic regression analyses were used to explore the association between voice parameters and DMV. The predictive value of the voice parameters was evaluated by assessment of area under the curve (AUC) of receiver operating characteristic (ROC) curves of a stepwise forward model. Results:The prevalence of DMV was 218/1,160 (18.8%). The AUC of the stepwise forward model (including o_f4, e_bw2, i_f3, u_pitch, u_f1, u_f4, ü_bw4, ci_f1, qi_f1, qi_f4, qi_bw4, chi_f1, chi_bw2, chi_ bw4, le_pitch, le_bw3, ke_bw2, en_pitch, and en_f2, en_bw4) attained a value of 0.779. The sensitivity and specificity of the model were 75.0% and 71.0%, respectively.Conclusions: Voice parameters may be considered as alternative predictors of DMV, but additional studies are needed to confirm the initial findings.

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicabil- ity of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia