Machine Learning-Based Estimation of Soil’s True Air-Entry Value from GSD Curves

The application of machine learning (ML) methods has proven to be promising in dealing with a wide range of geotechnical engineering problems in recent years. ML methods have already been used for the prediction of soil water retention curves (SWRC) and estimation of air-entry values (AEV). However, the reported works in the literature are generally based on limited data and conventional, less accurate approaches for AEV estimation. In this paper, a large database, known as UNsaturated SOil hydraulic DAtabase (UNSODA), is studied and the conventional and true AEVs of 790 soil samples are estimated based on determination methods reported in the literature. A ML approach is then employed for the development of a predictive model for the estimation of true AEV from water content-based SWRCs of a wide range of soil types taking into account the impact of bulk density and grain size distribution parameters. The obtained results reveal an enhanced accuracy in AEV determination, featuring R2 values of 0.964, 0.901 and 0.851 for training, validation, and testing data, respectively, which confirm the marked performance of the developed ML model. Based on the results of a sensitivity analysis, the particle sizes of 50 and 250 µm are found to have the highest impact on the AEV estimation

Microplastic in angling baits as a cryptic source of contamination in European freshwaters.

High environmental microplastic pollution, and its largely unquantified impacts on organisms, are driving studies to assess their potential entry pathways into freshwaters. Recreational angling, where many anglers release manufactured baits into freshwater ecosystems, is a widespread activity with important socio-economic implications in Europe. It also represents a potential microplastic pathway into freshwaters that has yet to be quantified. Correspondingly, we analysed three different categories of industrially-produced baits ('groundbait', 'boilies' and 'pellets') for their microplastic contamination (particles 700 µm to 5 mm). From 160 samples, 28 microplastics were identified in groundbait and boilies, with a mean concentration of 17.4 (± 48.1 SD) MP kg-1 and 6.78 (± 29.8 SD) mg kg-1, yet no microplastics within this size range were recorded in the pellets. Microplastic concentrations significantly differed between bait categories and companies, but microplastic characteristics did not vary. There was no correlation between microplastic contamination and the number of bait ingredients, but it was positively correlated with C:N ratio, indicating a higher contamination in baits with higher proportion of plant-based ingredients. We thus reveal that bait microplastics introduced accidentally during manufacturing and/or those originating from contaminated raw ingredients might be transferred into freshwaters. However, further studies are needed to quantify the relative importance of this cryptic source of contamination and how it influences microplastic levels in wild fish

Machine learning-based prediction of the seismic bearing capacity of a shallow strip footing over a void in heterogeneous soils

The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to predict the seismic bearing capacity of a shallow strip footing located over a single unsupported rectangular void in heterogeneous soil. A dataset consisting of 38,000 finite element limit analysis simulations has been created, and the mean value between the upper and lower bounds of the bearing capacity has been computed at the varying undrained shear strength and internal friction angle of the soil, horizontal earthquake accelerations, and position, shape, and size of the void. Three machine learning techniques have been adopted to learn the link between the aforementioned parameters and the bearing capacity: multilayer perceptron neural networks; a group method of data handling; and a combined adap-tive-network-based fuzzy inference system and particle swarm optimization. The performances of these ML techniques have been compared with each other, in terms of the following statistical performance indices: coefficient of determination (R2); root mean square error (RMSE); mean absolute percentage error; scatter index; and standard bias. Results have shown that all the ML techniques perform well, though the multilayer perceptron has a slightly superior accuracy featuring notewor-thy results (R2 = 0.9955 and RMSE = 0.0158)

Assessment of working interactions of emergency team members using social network analysis

Introduction: Emergency situations are of the major challenges in industries. Understading the status of inter-team interaction is effective in improvement of emergency response team (ERT). The purpose of this study was to investigate the interaction space of ERP in a refinery, using the social network analysis (SNA). .Methods and Materials: In the present case study, the density indicator was used to examine the interaction space in the ERT. The obtained data were analyzed, employing UCINET 6.0 social network analysis program. .Results: The findings showed that the ERT has the relatively low concentration with the density of 0.2 overall, the result reflect a low level of interactions among response reams at emergency situations management. .Conclusion: The presented approach provided an appropriate image of interactions network among the emergency response teams. The social network analysis can be used for assessing the interactions of the emergency response teams

Disruption of blood-aqueous barrier in dry eye disease

Purpose: To evaluate level of flare in aqueous humor of dry eye disease (DED) and compare it with normal controls. Methods: In this cross-sectional study, we compared the anterior chamber flare between 28 patients with DED (the DED group) and 27 normal age- and gender-matched controls (the control group). DED group was divided in Sjӧgren's syndrome dry eye (SDE, n = 10) and non- Sjӧgren's syndrome dry eye (non-SDE, n = 18) groups. Results: This study enrolled 55 participants including 28 patients with DED and 27 normal controls. The mean age was 53.4 ± 14.7 years in the DED group and 48.5 ± 14.7 years in the control group (P = 0.086). Mean flare was significantly higher in DED group (12.1 ± 10.2 ph/ms, range 2.7�68.3) compared to the control group (5.0 ± 3.9 ph/ms, range 1.30�30.0, P < 0.001). There was no statistically significant difference in the flare intensity between the Sjӧgren syndrome dry eye (SDE) group (14.5 ± 14.4 ph/ms) and the non-Sjӧgren dry eye (non-SDE) group (10.8 ± 6.9 ph/ms, P = 0.330). A significant correlation was observed between the flare intensity and the ocular surface staining in the SDE group (r = 0.62, P = 0.018). Conclusion: There is a significant increase in aqueous humor flare in patients with DED. Such finding, which is a marker of disruption of blood-aqueous barrier, demonstrates deeper tissue involvement than ocular surface in these patients. © 202

Tracking and capturing of bioorthogonal labelled RNA carried by extracellular vesicles during maternal–embryo communication

Background: During implantation window, the uterine epithelium acquires a receptive phenotype and is being prepared for the initial blastocyst attachment. This unique phenomenon may stem from embryonic–maternal crosstalk utilizing an intricate language. Extracellular vesicles (EV) could be a logical mean for maternal–embryo communication. The current investigation was aimed at deciphering the main signals exchanged between the mother and the baby. Methods: The 5-ethynyl uridine (EU)-labelled trophoblast spheroids were cultivated with an endometrial cell line in a non-contact co-culturesystem.ThetrophoblastEU-labelledRNAwastrackedandcaptured in endometrial cells. The transferred labelled RNA was affinity-precipitated and purified using biotin-azide click chemistry. Total RNA-sequencing was conducted with synthesized cDNA from captured labelled and non-EU labelled RNA (background) (n=4).Differential expression analysis of RNA-seq data was performed using edgeR and limma packages to identify the transferred transcripts using differential enrichment as a proxy. The Integrative Genomics Viewer was used to validate the coverage of differentially enriched transcripts. The results were confirmed by quantitative PCR (qPCR).To establish the route of candidate RNA transfer, EVs were isolated from co-culture media using size-exclusion chromatography. Total RNA was extracted from EVs, EU-labelled RNA was affinity-precipitated and the absolute copy number of putatively transferred RNA sequences was quantified. Results: Differential enrichment analysis demonstrated that the majority of putatively transferred transcripts were non-coding RNAs derived from the mir99alet7c cluster (Chromosome 21: LINC00478). The presence of non-coding sequences from this chromosomal region in the RNA extracted from EVs was confirmed by qPCR. This suggests that these sequences are carried by throphoblast EVs. Summary/Conclusion: In this study, we showed that biorthogonal RNA labelling chemistry can be used for the deciphering trophoblast–endometrial communications. These are the initial steps towards decoding the earliest stages of the mother–offspring language/crosstalk