Synchrotron validation of inline coherent imaging for tracking laser keyhole depth

In situ monitoring is critical to the increasing adoption of laser powder bed fusion (LPBF) and laser welding by industry for manufacture of complex metallic components. Optical coherence tomography (OCT), an interferometric imaging technique adapted from medical applications, is now widely used for operando monitoring of morphology during high-power laser material processing. However, even in stable processing regimes, some OCT depth measurements from the keyhole (vapor cavity formed at laser beam spot) appear too shallow or too deep when compared to ex situ measurements of weld depth. It has remained unclear whether these outliers are due to imaging artifacts, multiple scattering of the imaging beam within the keyhole, or real changes in keyhole depth, making it difficult to accurately extract weld depth and determine error bounds. To provide a definitive explanation, we combine inline coherent imaging (ICI), a type of OCT, with synchrotron X-ray imaging for simultaneous, operando monitoring of the full 2-dimensional keyhole profile at high-speed (280 kHz and 140 kHz, respectively). Even in a highly turbulent pore-generation mode, the depth measured with ICI closely follows the keyhole depth extracted from radiography (>80% within ± 14 µm). Ray-tracing simulations are used to confirm that the outliers in ICI depth measurements (that significantly disagree with radiography) primarily result from multiple reflections of the imaging light (57%). Synchrotron X-ray imaging also enables tracking of bubble and pore formation events. Pores are generated during laser welding when the sidewalls of the keyhole rapidly (>10 m/s) collapse inwards, pinching off a bubble from the keyhole root and resulting in a rapid decrease in keyhole depth. Evidence of bubble formation can be found in ICI depth profiles alone, as rapid depth changes exhibit moderate correlation with bubble formation events (0.26). This work moves closer to accurate, localized defect detection during laser welding and LPBF using ICI

Code smells survival analysis in web apps

Web applications are heterogeneous, both in their target platform (split across client and server sides) and on the formalisms they are built with, usually a mixture of programming and formatting languages. This heterogeneity is perhaps an explanation why software evolution of web applications (apps) is a poorly addressed topic in the literature. In this paper we focus on web apps built with PHP, the most widely used server-side programming language. We analyzed the evolution of 6 code smells in 4 web applications, using the survival analysis technique. Since code smells are symptoms of poor design, it is relevant to study their survival, that is, how long did it take from their introduction to their removal. It is obviously desirable to minimize their survival. In our analysis we split code smells in two categories: scattered smells and localized smells, since we expect the former to be more harmful than the latter. Our results provide some evidence that the survival of PHP code smells depends on their spreadness. We have also analyzed whether the survival curve varies in the long term, for the same web application. Due to the increasing awareness on the potential harm-fulness of code smells, we expected to observe a reduction in the survival rate in the long term. The results show that there is indeed a change, for all applications except one, which lead us to consider that other factors should be analyzed in the future, to explain the phenomenon.info:eu-repo/semantics/acceptedVersio

Enrichment of clinically relevant organisms in spontaneous preterm delivered placenta and reagent contamination across all clinical groups in a large UK pregnancy cohort.

In this study differences in the placental microbiota of term and preterm deliveries from a large UK pregnancy cohort were studied using 16S targeted amplicon sequencing. The impact of contamination from DNA extraction, PCR reagents, as well as those from delivery itself were also examined. A total of 400 placental samples from 256 singleton pregnancies were analysed and differences investigated between spontaneous preterm, non-spontaneous preterm, and term delivered placenta. DNA from recently delivered placenta was extracted, and screening for bacterial DNA was carried out using targeted sequencing of the 16S rRNA gene on the Illumina MiSeq platform. Sequenced reads were analysed for presence of contaminating operational taxonomic units (OTUs) identified via sequencing of negative extraction and PCR blank samples. Differential abundance and between sample (beta) diversity metrics were then compared. A large proportion of the reads sequenced from the extracted placental samples mapped to OTUs that were also found in negative extractions. Striking differences in the composition of samples were also observed, according to whether the placenta was delivered abdominally or vaginally, providing strong circumstantial evidence for delivery contamination as an important contributor to observed microbial profiles. When OTU and genus level abundances were compared between the groups of interest, a number of organisms were enriched in the spontaneous preterm cohort, including organisms that have been previously associated with adverse pregnancy outcomes, specifically Mycoplasma spp., and Ureaplasma spp.. However, analyses of overall community structure did not reveal convincing evidence for the existence of a reproducible 'preterm placental microbiome'. IMPORTANCE: Preterm birth is associated with both psychological and physical disabilities and is the leading cause of infant morbidity and mortality worldwide. Infection is known to be an important cause of spontaneous preterm birth, and recent research has implicated variation in the 'placental microbiome' with preterm birth risk. Consistent with previous studies, the abundance of certain clinically relevant species differed between spontaneous preterm and non-spontaneous preterm or term delivered placenta. These results support the view that a proportion of spontaneous preterm births have an intra-uterine infection component. However, an additional observation from this study was that a substantial proportion of reads sequenced were contaminating reads, rather than DNA from endogenous, clinically relevant species. This observation warrants caution in the interpretation of sequencing output from such low biomass samples as the placenta

Does the biomarker search paradigm need re-booting?

The clinical problem of bladder cancer is its high recurrence and progression, and that the most sensitive and specific means of monitoring is cystoscopy, which is invasive and has poor patient compliance. Biomarkers for recurrence and progression could make a great contribution, but in spite of decades of research, no biomarkers are commercially available with the requisite sensitivity and specificity. In the post-genomic age, the means to search the entire genome for biomarkers has become available, but the conventional approaches to biomarker discovery are entirely inadequate to yield results with the new technology. Finding clinically useful biomarker panels with sensitivity and specificity equal to that of cystoscopy is a problem of systems biology

Machine learning predicts accurately mycobacterium tuberculosis drug resistance from whole genome sequencing data

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making. Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradientboosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard. Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for thirdline drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other largescale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation. Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation