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The intravenous injection of the UCB-MSCs, compared with those of other MSCs, showed superior therapeutic effects in the PH model for the (1) right ventricular function, (2) vascular remodeling, (3) immune/inflammatory profiles, and (4) classical PAH pathways.Judicious application of saline water except for critical growth stages, could be the only practical solution to meet the crop water demand in arid and semi-arid regions, due to limited access to freshwater, especially during dry winter months. A field experiment was conducted to study the effect of tillage [conventional (CT), reduced (RT), and zero (ZT)], rice straw mulch and deficit saline-water irrigation in wheat (100, 80 and 60% of wheat water requirement, CWR) followed by rainfed sorghum on soil properties and the yields of the cropping system. Yields of both the crops were comparable between RT and CT, but the wheat yield was reduced in ZT. The RT, mulching and deficit saline irrigation in wheat season (60% CWR) increased the sorghum fodder yield. Olsen's P (8.7-20.6%) and NH4OAc-K (2.5-7.5%) increased in RT and ZT, respectively, over CT under both the crops. Deficit irrigation reduced soil salinity (ECe) by 0.73-1.19 dS m-1 after each crop cycle, while soil microbial biomass C (MBC) and N (MBN), dehydrogenase, urease and alkaline phosphatase reduced with an increase in ECe. The α-glucosidase, MBC, ECe, KMnO4oxidizable N, and urease were identified as major contributors in developing the soil health index. Deficit irrigation (60% CWR) and rice straw mulching under ZT and RT showed higher values of soil health index. Overall, deficit saline-water irrigation under reduced tillage and straw mulching had the greatest potential in maintaining soil health, saving fresh irrigation water without affecting the productivity of the sorghum-wheat system in the semi-arid regions of India. Results also demonstrated that salt affected areas of arid and semiarid countries can replicate the protocol for indexing and screening of soil health indicators to assess the sustainability of a cropping system. This integrated management based on the nature of the available resources also provided a practical approach to achieve the target of land degradation neutrality and land restoration.The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6-9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.The performance of copper selenide and effectiveness of chemical catalytic reactors are dependent on an inclined magnetic field, the nature of the chemical reaction, introduction of space heat source, changes in both distributions of temperature and concentration of nanofluids. This report presents the significance of increasing radius of nanoparticles, energy flux due to the concentration gradient, and mass flux due to the temperature gradient in the dynamics of the fluid subject to inclined magnetic strength is presented. The non-dimensionalization and parameterization of the dimensional governing equation were obtained by introducing suitable similarity variables. Thereafter, the numerical solutions were obtained through shooting techniques together with 4th order Runge-Kutta Scheme and MATLAB in-built bvp4c package. It was concluded that at all the levels of energy flux due to concentration gradient, reduction in the viscosity of water-based nanofluid due to a higher radius of copper nanoparticles causes an enhancement of the velocity. The emergence of both energy flux and mass flux due to gradients in concentration and temperature affect the distribution of temperature and concentration at the free stream.Microplastics are contaminants of emerging concern; they are ingested by marine biota. About a quarter of global marine fish landings is used to produce fishmeal for animal and aquaculture feed. To provide a knowledge foundation for this matrix we reviewed the existing literature for studies of microplastics in fishmeal-relevant species. 55% of studies were deemed unsuitable due to focus on large microplastics (> 1 mm), lack of, or limited contamination control and polymer testing techniques. Overall, fishmeal-relevant species exhibit 0.72 microplastics/individual, with studies generally only assessing digestive organs. We validated a density separation method for effectiveness of microplastic extraction from this medium and assessed two commercial products for microplastics. Recovery rates of a range of dosed microplastics from whitefish fishmeal samples were 71.3 ± 1.2%. Commercial samples contained 123.9 ± 16.5 microplastics per kg of fishmeal-mainly polyethylene-including 52.0 ± 14.0 microfibres-mainly rayon. Concentrations in processed fishmeal seem higher than in captured fish, suggesting potential augmentation during the production process. Autophagy inhibitor Based on conservative estimates, over 300 million microplastic particles (mostly  less then  1 mm) could be released annually to the oceans through marine aquaculture alone. Fishmeal is both a source of microplastics to the environment, and directly exposes organisms for human consumption to these particles.Shiga toxin-producing Escherichia coli serotype O157H7 is a food and waterborne zoonotic pathogen causing gastroenteritis in humans. Rapid and simple detection in water and food is imperative to control its spread. However, traditional microbial detection approaches are time-consuming, expensive and complex to operate at the point-of-care without professional training. We present a rapid, simple, sensitive, specific and portable method for detection of E. coli O157H7 in drinking water, apple juice and milk. We evaluated the effect of gene selection in detecting E. coli O157H7 using recombinase polymerase amplification coupled with a lateral flow assay using rfbE, fliC and stx gene targets. As low as 100 ag and 1 fg DNA, 4-5 CFU/mL and 101 CFU/mL of E. coli O157H7 was detected using the stx and rfbE gene targets respectively with 100% specificity, whilst the detection limit was 10 fg DNA and 102 CFU/mL for the fliC gene target, with 72.8% specificity. The RPA-LFA can be completed within 8 min at temperatures between 37 and 42 °C with reduced handling and simple equipment requirements. The test threshold amplification of the target was achieved in 5-30 min of incubation. In conclusion, RPA-LFA represents a potential rapid and effective alternative to conventional methods for the monitoring of E. coli O157H7 in food and water.Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease whose prognosis is associated with clinical features, cell-of-origin and genetic aberrations. Recent integrative, multi-omic analyses had led to identifying overlapping genetic DLBCL subtypes. We used targeted massive sequencing to analyze 84 diagnostic samples from a multicenter cohort of patients with DLBCL treated with rituximab-containing therapies and a median follow-up of 6 years. The most frequently mutated genes were IGLL5 (43%), KMT2D (33.3%), CREBBP (28.6%), PIM1 (26.2%), and CARD11 (22.6%). Mutations in CD79B were associated with a higher risk of relapse after treatment, whereas patients with mutations in CD79B, ETS1, and CD58 had a significantly shorter survival. Based on the new genetic DLBCL classifications, we tested and validated a simplified method to classify samples in five genetic subtypes analyzing the mutational status of 26 genes and BCL2 and BCL6 translocations. We propose a two-step genetic DLBCL classifier (2-S), integrating the most significant features from previous algorithms, to classify the samples as N12-S, EZB2-S, MCD2-S, BN22-S, and ST22-S groups. We determined its sensitivity and specificity, compared with the other established algorithms, and evaluated its clinical impact. The results showed that ST22-S is the group with the best clinical outcome and N12-S, the more aggressive one. EZB2-S identified a subgroup with a worse prognosis among GCB-DLBLC cases.Image registration is a fundamental task in image analysis in which the transform that moves the coordinate system of one image to another is calculated. Registration of multi-modal medical images has important implications for clinical diagnosis, treatment planning, and image-guided surgery as it provides the means of bringing together complimentary information obtained from different image modalities. However, since different image modalities have different properties due to their different acquisition methods, it remains a challenging task to find a fast and accurate match between multi-modal images. Furthermore, due to reasons such as ethical issues and need for human expert intervention, it is difficult to collect a large database of labelled multi-modal medical images. In addition, manual input is required to determine the fixed and moving images as input to registration algorithms. In this paper, we address these issues and introduce a registration framework that (1) creates synthetic data to augment existing datasets, (2) generates ground truth data to be used in the training and testing of algorithms, (3) registers (using a combination of deep learning and conventional machine learning methods) multi-modal images in an accurate and fast manner, and (4) automatically classifies the image modality so that the process of registration can be fully automated. We validate the performance of the proposed framework on CT and MRI images of the head obtained from a publicly available registration database.Bluetongue virus (BTV) serotype 8 has been circulating in Europe since a major outbreak occurred in 2006, causing economic losses to livestock farms. The unpredictability of the biting activity of midges that transmit BTV implies difficulty in computing accurate transmission models. This study uniquely integrates field collections of midges at a range of European latitudes (in Sweden, The Netherlands, and Italy), with a multi-scale modelling approach. We inferred the environmental factors that influence the dynamics of midge catching, and then directly linked predicted midge catches to BTV transmission dynamics. Catch predictions were linked to the observed prevalence amongst sentinel cattle during the 2007 BTV outbreak in The Netherlands using a dynamic transmission model. We were able to directly infer a scaling parameter between daily midge catch predictions and the true biting rate per cow per day. Compared to biting rate per cow per day the scaling parameter was around 50% of 24 h midge catches with traps.