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The analysis of non-metallic inclusions is crucial for the assessment of steel properties. Scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS) is one of the most prominent methods for inclusion analysis. This study utilizes the output generated from SEM/EDS analysis to predict inclusion types from BSE images. Prediction models were generated using two different algorithms, Random Forest (RF) and convolutional neural networks (CNN), for comparison. For each method, three separate models were developed. Starting with a simple binary model to differentiate between inclusions and non-inclusions, then developing to more complex four and five class models. For the 4-class model, inclusions were split into oxides, sulfides, and oxy-sulfides, in addition to the non-inclusion class. The 5-class model included specific types of inclusions only, namely alumina, calcium aluminates, calcium sulfides, complex calcium-manganese sulfides, and oxy-sulfide inclusions. CNN achieved better accuracy for the binary (92%) and 4-class (78%) models, compared to RF (binary 87%, 4-class 75%). For the 5-class model, the results were similar, 60% accuracy for RF and 59% for CNN. 

Abstract Background Lactobacillus rhamnosus GG (LGG) is the most widely used probiotic, but the mechanisms underlying its beneficial effects remain unresolved. Previous studies typically inoculated LGG in hosts with established gut microbiota, limiting the understanding of specific impacts of LGG on host due to numerous interactions among LGG, commensal microbes, and the host. There has been a scarcity of studies that used gnotobiotic animals to elucidate LGG-host interaction, in particular for gaining specific insights about how it modifies the metabolome. To evaluate whether LGG affects the metabolite output of pathobionts, we inoculated with LGG gnotobiotic mice containing Propionibacterium acnes, Turicibacter sanguinis, and Staphylococcus aureus (PTS). Results 16S rRNA sequencing of fecal samples by Ion Torrent and MinION platforms showed colonization of germ-free mice by PTS or by PTS plus LGG (LTS). Although the body weights and feeding rates of mice remained similar between PTS and LTS groups, co-associating LGG with PTS led to a pronounced reduction in abundance of P. acnes in the gut. Addition of LGG or its secretome inhibited P. acnes growth in culture. After optimizing procedures for fecal metabolite extraction and metabolomic liquid chromatography-mass spectrometry analysis, unsupervised and supervised multivariate analyses revealed a distinct separation among fecal metabolites of PTS, LTS, and germ-free groups. Variables-important-in-projection scores showed that LGG colonization robustly diminished guanine, ornitihine, and sorbitol while significantly elevating acetylated amino acids, ribitol, indolelactic acid, and histamine. In addition, carnitine, betaine, and glutamate increased while thymidine, quinic acid and biotin were reduced in both PTS and LTS groups. Furthermore, LGG association reduced intestinal mucosal expression levels of inflammatory cytokines, such as IL-1α, IL-1β and TNF-α. Conclusions LGG co-association had a negative impact on colonization of P. acnes , and markedly altered the metabolic output and inflammatory response elicited by pathobionts. 

ABSTRACT Microsporidia are a large phylum of obligate intracellular parasites. Approximately a dozen species of microsporidia infect humans, where they are responsible for a variety of diseases and occasionally death, especially in immunocompromised individuals. To better understand the impact of microsporidia on human cells, we infected human colonic Caco2 cells with Encephalitozoon intestinalis , and showed that these enterocyte cultures can be used to recapitulate the life cycle of the parasite, including the spread of infection with infective spores. Using transmission electron microscopy, we describe this lifecycle and demonstrate nuclear, mitochondrial and microvillar alterations by this pathogen. We also analyzed the transcriptome of infected cells to reveal host cell signaling alterations upon infection. These high-resolution imaging and transcriptional profiling analysis shed light on the impact of the microsporidial infection on its primary human target cell type. This article has an associated First Person interview with the first authors of the paper. 

ABSTRACT Although Wnt signaling is clearly important for the intestinal epithelial homeostasis, the relevance of various sources of Wnt ligands themselves remains incompletely understood. Blocking the release of Wnt in distinct stromal cell types suggests obligatory functions of several stromal cell sources and yields different observations. The physiological contribution of epithelial Wnt to tissue homeostasis remains unclear. We show here that blocking epithelial Wnts affects colonic Reg4+ epithelial cell differentiation and impairs colonic epithelial regeneration after injury in mice. Single-cell RNA analysis of intestinal stroma showed that the majority of Wnt-producing cells were contained in transgelin (Tagln+) and smooth muscle actin α2 (Acta2+) expressing populations. We genetically attenuated Wnt production from these stromal cells using Tagln-Cre and Acta2-CreER drivers, and found that blockage of Wnt release from either epithelium or Tagln+ and Acta2+ stromal cells impaired colonic epithelial healing after chemical-induced injury. Aggregated blockage of Wnt release from both epithelium and Tagln+ or Acta2+ stromal cells drastically diminished epithelial repair, increasing morbidity and mortality. These results from two uncharacterized stromal populations suggested that colonic recovery from colitis-like injury depends on multiple Wnt-producing sources. 

Computer vision and machine learning systems for microstructural characterization and analysis are used for a variety of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation, leading to accurate, autonomous, objective, repeatable results in an indefatigable and permanently available manner. 

RAB11 small GTPases and associated recycling endosome have been localized to mitotic spindles and implicated in regulating mitosis. However, the physiological significance of such regulation has not been observed in mammalian tissues. We have used newly engineered mouse models to investigate intestinal epithelial renewal in the absence of single or double isoforms of RAB11 family members: Rab11a and Rab11b. Comparing with single knockouts, mice with compound ablation demonstrate a defective cell cycle entry and robust mitotic arrest followed by apoptosis, leading to a total penetrance of lethality within 3 days of gene ablation. UponRab11deletionex vivo, enteroids show abnormal mitotic spindle formation and cell death. Untargeted proteomic profiling of Rab11a and Rab11b immunoprecipitates has uncovered a shared interactome containing mitotic spindle microtubule regulators. Disrupting Rab11 alters kinesin motor KIF11 function and impairs bipolar spindle formation and cell division. These data demonstrate that RAB11A and RAB11B redundantly control mitotic spindle function and intestinal progenitor cell division, a mechanism that may be utilized to govern the homeostasis and renewal of other mammalian tissues.

 

Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.