Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

The purpose of this study was to evaluate the performance of artificial intelligence tools for the prediction of Salmonella presence and absence in agricultural surface waters based on the population of microbiological indicators (total coliform, generic Escherichia coli, and enterococci) and physicochemical attributes of water (air and water temperature, conductivity, ORP, pH, and turbidity). Previously collected data set from six agricultural ponds monitored for two growing seasons were used for analysis. Classification algorithms including artificial neural networks (ANNs), the nearest neighborhood algorithm (kNN), and support vector machines (SVM) were trained and tested with a 539-point data set for optimum prediction accuracy. Classification accuracy performances were validated with data set (400 samples) collected from different agricultural surface water sources. All tested algorithms yielded the highest accuracy around 75 ± 1% for generic E. coli followed by enterococci (65 ± 5%) and total coliform (60 ± 10%). Classifiers calculated 6–15% higher accuracy ranging from 62 to 66% for turbidity than all other tested physicochemical attributes. Based on E. coli populations measured in other water sources, trained algorithms predicted the presence and absence of Salmonella with an accuracy between 58.15 and 59.23%. The classification performance of ANN, kNN, and SVM algorithms are encouraging for the prediction of Salmonella in agricultural surface waters.     

Layer by layer surface engineering of poly(lactide-co-glycolide) nanoparticles for plasmid DNA delivery

The administration of exogenous DNA has been proposed as a promising therapeutic approach for a variety of diseases. Unfortunately, exogenous DNA is unable to spontaneously penetrate mammalian cells. Although viral vectors facilitate DNA delivery at high transfection efficiency, they are restricted for in vivo applications as they could potentially induce immunogenicity and mutagenesis. To overcome the clinical challenge of viral delivery, a strategy for the encapsulation of plasmid DNA on the surface of poly(lactide-co-glycolide) nanoparticles (PLGA NPs) is shown. Plasmid green fluorescence protein (pEF-GFP) or piggybac transposon (PBCAG-eGFP) are assembled on the surface of PLGA NPs through layer by layer technique. The assembly of pEF-GFP with biopolyelectrolytes is monitored on a planar support using a quartz crystal microbalance with dissipation. The assembly of the biopolymer multilayers on PLGA NPs is followed by ζ-potential measurements. Encapsulation of plasmid DNA within the multilayers coating is confirmed by gel electrophoresis. Cellular uptake studies on HEK293 cells revealed that PLGA NPs are taken up by cells within the first 5 hr of co-culturing. Intracellular release of cargo is confirmed by GFP expression in HEK293 cells. PLGA NPs encapsulating pEF-GFP on their surface are able to transfect ~20% of HEK293 cells, while those encapsulating PBCAG-eGFP can transfect up to 75% of cells after 72 hr, causing minimum to non-cytotoxic effects.     

Additive Manufacturing of Catalyst Substrates for Steam–Methane Reforming

Steam–methane reforming is a highly endothermic reaction, which is carried out at temperatures up to 1100 °C and pressures up to 3000 kPa, typically with a Ni-based catalyst distributed over a substrate of discrete alumina pellets or beads. Standard pellet geometries (spheres, hollow cylinders) limit the degree of mass transfer between gaseous reactants and catalyst. Further, heat is supplied to the exterior of the reactor wall, and heat transfer is limited due to the nature of point contacts between the reactor wall and the substrate pellets. This limits the degree to which the process can be intensified, as well as limiting the diameter of the reactor wall. Additive manufacturing now gives us the capability to design structures with tailored heat and mass transfer properties, not only within the packed bed of the reactor, but also at the interface between the reactor wall and the packed bed. In this work, the use of additive manufacturing to produce monolithic-structured catalyst substrate models, made from acrylonitrile–butadiene–styrene, with enhanced conductive heat transfer is described. By integrating the reactor wall into the catalyst substrate structure, the effective thermal conductivity increased by 34% from 0.122 to 0.164 W/(m K).     

Mitochondrial Transfer Improves Cardiomyocyte Bioenergetics and Viability in Male Rats Exposed to Pregestational Diabetes

Offspring born to diabetic or obese mothers have a higher lifetime risk of heart disease. Previously, we found that rat offspring exposed to late-gestational diabetes mellitus (LGDM) and maternal high-fat (HF) diet develop mitochondrial dysfunction, impaired cardiomyocyte bioenergetics, and cardiac dysfunction at birth and again during aging. Here, we compared echocardiography, cardiomyocyte bioenergetics, oxidative damage, and mitochondria-mediated cell death among control, pregestational diabetes mellitus (PGDM)-exposed, HF-diet-exposed, and combination-exposed newborn offspring. We hypothesized that PGDM exposure, similar to LGDM, causes mitochondrial dysfunction to play a central, pathogenic role in neonatal cardiomyopathy. We found that PGDM-exposed offspring, similar to LGDM-exposed offspring, have cardiac dysfunction at birth, but their isolated cardiomyocytes have seemingly less bioenergetics impairment. This finding was due to confounding by impaired viability related to poorer ATP generation, more lipid peroxidation, and faster apoptosis under metabolic stress. To mechanistically isolate and test the role of mitochondria, we transferred mitochondria from normal rat myocardium to control and exposed neonatal rat cardiomyocytes. As expected, transfer provides a respiratory boost to cardiomyocytes from all groups. They also reduce apoptosis in PGDM-exposed males, but not in females. Findings highlight sex-specific differences in mitochondria-mediated mechanisms of developmentally programmed heart disease and underscore potential caveats of therapeutic mitochondrial transfer.     

A Review of Murine Cytomegalovirus as a Model for Human Cytomegalovirus Disease—Do Mice Lie?

Since murine cytomegalovirus (MCMV) was first described in 1954, it has been used to model human cytomegalovirus (HCMV) diseases. MCMV is a natural pathogen of mice that is present in wild mice populations and has been associated with diseases such as myocarditis. The species-specific nature of HCMV restricts most research to cell culture-based studies or to the investigation of non-invasive clinical samples, which may not be ideal for the study of disseminated disease. Initial MCMV research used a salivary gland-propagated virus administered via different routes of inoculation into a variety of mouse strains. This revealed that the genetic background of the laboratory mice affected the severity of disease and altered the extent of subsequent pathology. The advent of genetically modified mice and viruses has allowed new aspects of disease to be modeled and the opportunistic nature of HCMV infection to be confirmed. This review describes the different ways that MCMV has been used to model HCMV diseases and explores the continuing difficulty faced by researchers attempting to model HCMV congenital cytomegalovirus disease using the mouse model.     

Plasma Amyloid-Beta Levels in a Pre-Symptomatic Dutch-Type Hereditary Cerebral Amyloid Angiopathy Pedigree: A Cross-Sectional and Longitudinal Investigation

Plasma amyloid-beta (Aβ) has long been investigated as a blood biomarker candidate for Cerebral Amyloid Angiopathy (CAA), however previous findings have been inconsistent which could be attributed to the use of less sensitive assays. This study investigates plasma Aβ alterations between pre-symptomatic Dutch-type hereditary CAA (D-CAA) mutation-carriers (MC) and non-carriers (NC) using two Aβ measurement platforms. Seventeen pre-symptomatic members of a D-CAA pedigree were assembled and followed up 3–4 years later (NC = 8; MC = 9). Plasma Aβ1-40 and Aβ1-42 were cross-sectionally and longitudinally analysed at baseline (T1) and follow-up (T2) and were found to be lower in MCs compared to NCs, cross-sectionally after adjusting for covariates, at both T1(Aβ1-40: p = 0.001; Aβ1-42: p = 0.0004) and T2 (Aβ1-40: p = 0.001; Aβ1-42: p = 0.016) employing the Single Molecule Array (Simoa) platform, however no significant differences were observed using the xMAP platform. Further, pairwise longitudinal analyses of plasma Aβ1-40 revealed decreased levels in MCs using data from the Simoa platform (p = 0.041) and pairwise longitudinal analyses of plasma Aβ1-42 revealed decreased levels in MCs using data from the xMAP platform (p = 0.041). Findings from the Simoa platform suggest that plasma Aβ may add value to a panel of biomarkers for the diagnosis of pre-symptomatic CAA, however, further validation studies in larger sample sets are required.     

Exploiting a Single-Crystal Environment to Minimize the Charge Noise on Qubits in Silicon

Electron spins in silicon offer a competitive, scalable quantum-computing platform with excellent single-qubit properties. However, the two-qubit gate fidelities achieved so far have fallen short of the 99% threshold required for large-scale error-corrected quantum computing architectures. In the past few years, there has been a growing realization that the critical obstacle in meeting this threshold in semiconductor qubits is charge noise arising from the qubit environment. In this work, a notably low level of charge noise of S₀ = 0.0088 ± 0.0004 μeV² Hz⁻¹ is demonstrated using atom qubits in crystalline silicon, achieved by separating the qubits from surfaces and interface states. The charge noise is measured using both a single electron transistor and an exchange-coupled qubit pair that collectively provide a consistent charge noise spectrum over four frequency decades, with the noise level S₀ being an order of magnitude lower than previously reported. Low-frequency detuning noise, set by the total measurement time, is shown to be the dominant dephasing source of two-qubit exchange oscillations. With recent advances in fast (≈μs) single-shot readout, it is shown that by reducing the total measurement time to ≈1 s, 99.99% two-qubit $\text{◂√▸}\sqrt{SWAP}$ gate fidelities can be achieved in single-crystal atom qubits in silicon.