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It is unclear whether the combined effects of exercise and nutrition are better than those of exercise alone or nutrition alone in older adults with sarcopenia. This paper assesses the comparative effects of exercise, nutrition, and the combination of exercise and nutrition on muscle mass, muscle strength, and physical performance in older adults with sarcopenia. In this systematic review and network meta-analysis, 11 electronic databases were searched up to November 2020. Inclusion criteria were: (1) adults aged 65 years and older with sarcopenia; (2) interventions of exercise, nutrition, or the combination of both; (3) the outcomes include at least one of the following: muscle mass, muscle strength (hand grip and knee extension strength), or physical performance (gait speed and dynamic balance); and (4) randomized control trials. For each outcome measurement, a network meta-analysis was conducted to determine the direct and indirect effects of each intervention compared with each of the other interventions. A total of 26 studies were included in the network meta-analysis. Compared with the control group, exercise alone and the combination of exercise and nutrition significantly increased handgrip strength (1.12 kg, 95% CI: 0.12, 2.11; 2.03 kg, 95% CI: 1.10, 2.97) and improved dynamic balance (-1.76 seconds, 95% CI: -2.24, -1.28; -1.02 seconds, 95% CI: -1.64, -0.39). Both exercise alone and the combination of exercise and nutrition have beneficial effects on muscle strength and physical performance in older adults with sarcopenia. [ABSTRACT FROM AUTHOR]

Indoor radon represents a health hazard for occupants. However, the indoor radon measurement rate is low in Sweden because of no mandatory requirements. Measuring indoor radon on an urban scale is complicated, machine learning exploiting existing data for pattern identification provides a cost-efficient approach to estimate indoor radon exposure in the building stock. Extreme gradient boosting (XGBoost) models and deep neural network (DNN) models were developed based on indoor radon measurement records, property registers, and geogenic information. The XGBoost models showed promising results in predicting indoor radon intervals for different types of buildings with macro-F1 between 0.93 and 0.96, whereas the DNN models attained macro-F1 between 0.64 and 0.74. After that, the XGBoost models trained on the national indoor radon dataset were transferred to fit building registers in metropolitan regions to estimate the indoor radon intervals in non-measured and measured buildings by regions and building classes. By comparing the prediction results and the statistical summary of indoor radon intervals in measured buildings, the model uncertainty and validity were determined. The study ascertains the prediction performance of machine learning models in classifying indoor radon intervals and discusses the benefits and limitations of the data-driven approach. The research outcomes can assist preliminary large-scale indoor radon distribution estimation for relevant authorities and guide onsite measurements for prioritized building stock prone to indoor radon exposure. • Status-quo of indoor radon in Sweden is investigated by building types and regions. • Machine learning is promising for indoor radon interval prediction in building stock. • Large post-war buildings with basements and natural ventilation are prone to radon. • XGBoost models outperform DNN models with high performances in all building types. • Indoor radon distribution in non-measured buildings is estimated and verified. [ABSTRACT FROM AUTHOR]

[Display omitted] • Learning Bayesian networks identify the presence pattern of in situ blue concrete. • Indoor radon measurements and environmental inventories are used for modeling. • Conditional probability and causal relationship of radioactive concrete are computed. • Predictive inference estimates the presence of radioactive concrete in buildings. • The method can advise risk-based inspection in potentially contaminated buildings. The undesirable legacy of radioactive concrete (blue concrete) in post-war dwellings contributes to increased indoor radon levels and health threats to occupants. Despite continuous decontamination efforts, blue concrete still remains in the Swedish building stock due to low traceability as the consequence of lacking systematic documentation in technical descriptions and drawings and resource-demanding large-scaled radiation screening. The paper aims to explore the predictive inference potential of learning Bayesian networks for evaluating the presence probability of blue concrete. By integrating blue concrete records from indoor radon measurements, pre-demolition audit inventories, and building registers, it is possible to estimate buildings with high probabilities of containing blue concrete and encode the dependent relationships between variables. The findings show that blue concrete is estimated to be present in more than 30% of existing buildings, more than the current expert assumptions of 18–20%. The probability of detecting blue concrete depends on the distance to historical blue concrete manufacturing plants, building class, and construction year, but it is independent of floor area and basements. Multifamily houses and buildings built between 1960 and 1968 or nearby manufacturing plants are more likely to contain blue concrete. Despite heuristic, the data-driven approach offers an overview of the extent and the probability distribution of blue concrete-prone buildings in the regional building stock. The paper contributes to method development for pattern identification for hazardous building materials, i.e., blue concrete, and the trained models can be used for risk-based inspection planning before renovation and selective demolition. [ABSTRACT FROM AUTHOR]

Background: Diabetic cardiomyopathy (DC) is one of the major lethal complications in patients with diabetes mellitus (DM); however, no specific strategy for preventing or treating DC has been identified.Purpose: This study aimed to investigate the effects of β-lapachone (Lap), a natural compound that increases antioxidant activity in various tissues, on DC and explore the underlying mechanisms.Study Design and Methods: As an in vivo model, C57BL/6 mice were fed with the high-fat diet (HF) for 10 weeks to induce type 2 DM. Mice were fed Lap with the HF or after 5 weeks of HF treatment to investigate the protective effects of Lap against DC.Results: In the two in vivo models, Lap decreased heart weight, increased heart function, reduced oxidative stress, and elevated mitochondrial content under the HF. In the in vitro model, palmitic acid (PA) was used to mimic the effects of an HF on the differentiated-cardiomyoblast cell line H9c2. The results demonstrated that Lap reduced PA-induced ROS production by increasing the expression of antioxidant regulators and enzymes, inhibiting inflammation, increasing mitochondrial activity, and thus reducing cell damage. Via the use of specific inhibitors and siRNA, the protective effects of Lap were determined to be mediated mainly by NQO1, Sirt1 and mitochondrial activity.Conclusion: Heart damage in DM is usually caused by excessive oxidative stress. This study showed that Lap can protect the heart from DC by upregulating antioxidant ability and mitochondrial activity in cardiomyocytes. Lap has the potential to serve as a novel therapeutic agent for both the prevention and treatment of DC. [ABSTRACT FROM AUTHOR]

Identifying in situ hazardous materials can improve demolition waste recyclability and reduce project uncertainties concerning cost overrun and delay. With the attempt to characterize their detection patterns in buildings, the study investigates the prediction potential of machine learning techniques with hazardous waste inventories and building registers as input data. By matching, validating, and assuring the quality of empirical data, a hazardous material dataset for training, testing, and validation was created. The objectives of the explorative study are to highlight the challenges in machine learning pipeline development and verify two prediction hypotheses. Our findings show an average of 74% and 83% accuracy rates in predicting asbestos pipe insulation in multifamily houses and PCB joints or sealants in school buildings in two major Swedish cities Gothenburg and Stockholm. Similarly, 78% and 83% of recall rates were obtained for imbalanced classification. By correlating the training sample size and cross-validation accuracy, the bias and variance issues were assessed in learning curves. In general, the models perform well on the limited dataset, yet collecting more training data can improve the model's generalizability to other building stocks, meanwhile decreasing the chance of overfitting. Furthermore, the average impact on the model output magnitude of each feature was illustrated. The proposed applied machine learning approach is promising for in situ hazardous material management and could support decision-making regarding risk evaluation in selective demolition work. [Display omitted] • The potential of machine learning in predicting hazardous materials is verified. • High accuracies are obtained for asbestos pipe insulation and PCB joint prediction. • Tree-ensambled classifiers perform well in imbalanced classification. • The impact of limited input data on models' performance is investigated. • Influential features to hazardous material risk assessment are pinpointed. [ABSTRACT FROM AUTHOR]

Abstract: Neuro-2a (N2a) cells are derived from spontaneous neuroblastoma of mouse and capable to differentiate into neuronal-like cells. Recently, P2X7 receptor has been shown to sustain growth of human neuroblastoma cells but its role during neuronal differentiation remains unexamined. We characterized the role of P2X7 receptors in the retinoic acid (RA)-differentiated N2a cells. RA induced N2a cells differentiation into neurite bearing and neuronal specific proteins, microtubule-associated protein 2 (MAP2) and neuronal specific nuclear protein (NeuN), expressing neuronal-like cells. Interestingly, the RA-induced neuronal differentiation was associated with decreases in the expression and function of P2X7 receptors. Functional inhibition of P2X7 receptors by P2X7 receptor selective antagonists, 5′-triphosphate, periodate-oxidized 2′,3′-dialdehyde ATP (oATP), brilliant blue G (BBG) or A438079 induced neurite outgrowth. In addition, RA and oATP treatment stimulated the expression of neuron-specific class III beta-tubulin (TuJ1), and knockdown of P2X7 receptor expression by siRNA induced neurite outgrowth. To elucidate the possible mechanism, we found the levels of basal intracellular Ca2+ concentrations ([Ca2+]i) were decreased in either RA- or oATP-differentiated or P2X7 receptor knockdown N2a cells. Simply cultured N2a cells in low Ca2+ medium induced a 2-fold increase in neurite length. Treatment of N2a cells with ATP hydrolase apyrase and the P2X7 receptors selective antagonist oATP or BBG decreased cell viability and cell number. Nevertheless, oATP but not BBG decreased cell proliferation and cell cycle progression. These results suggest for the first time that decreases in expression/function of P2X7 receptors are involved in neuronal differentiation. We provide additional evidence shown that the ATP release-activated P2X7 receptor is important in maintaining cell survival of N2a neuroblastoma cells. [Copyright &y& Elsevier]

• Smooth electrodes excluded the effects of surface morphology on exoelectrogenesis. • Activities of MR-1 electron transfer pathways varied based on the anode materials. • The behaviors of soluble mediators depended on the anode materials. • Mutants missing an MHC showed reduced biofilm formation and current generation. • The extent of the losses depended on the mutant strain and anode material. The development of anodes supporting a high rate of current generation stands as a crucial and challenging aspect in microbial electrochemical systems. A well-defined electrode surface morphology is essential for comprehending the impact of material intrinsic properties on microbial current generation, offering molecular insights into the respiration of exoelectrogens with electrodes made of diverse materials. We fabricated smooth electrodes (root mean square roughness below 10 nm) using four materials, each representing a distinct class of materials commonly utilized to construct bioanodes. These electrodes were employed to investigate current generation, electrochemical characteristics, and biofilm formation by Shewanella oneidensis MR-1 and mutants lacking cytochromes MtrC (Δ mtrC), MtrA (Δ mtrA), or CymA (Δ cymA). In reactors inoculated with MR-1, the poly(3,4-ethylenedioxythiophene) polystyrene sulfonate electrodes demonstrated a current density of 12.5 ± 1.1 μA cm-2, surpassing the 3.57 ± 0.50 μA cm-2 observed with indium tin oxide electrodes, while current generation levels by gold and glassy carbon electrodes fell in between. This disparity was not attributed to distinct biofilm formations by MR-1, as it formed stable monolayer biofilms on all types of working electrodes. At least five electron transfer pathways were identified in the MR-1 Vammograms, with the activities of these pathways depending on the electrode materials. The effectiveness of both endogenous and exogenous electron mediators varied with the electrode materials involved. Additionally, a phenazine compound exhibited diverse formal potentials when interacting with the biofilm and different types of electrodes. The mutants displayed varying degrees of impaired current generation and biofilm formation capabilities, with Δ mtrA and Δ cymA being the most affected regardless of the electrode used. However, for a specific mutant, the overall current generation, relative contribution by individual pathways, and biofilm formations underwent significant changes based on the electrode materials employed. [Display omitted] [ABSTRACT FROM AUTHOR]

The quality and stability of X-ray flat panel detectors and fingerprint identifiers are crucial requirements for high-resolution applications and to ensure successful manufacture and commercially applicability. To achieve higher resolutions, the areas of the photodiodes must be reduced, which results in an increase in the leakage current owing to sidewall defects caused by the photodiodes. This study introduces a supercritical fluid low-temperature defect passivation (SCF-LTDP) treatment to repair the dangling bonds of amorphous silicon (a-Si) PIN-diode-based photo sensors. Unlike traditional atmospheric annealing processes, the hydrogen achieves a low-temperature and high-pressure fluid state in this technique, which offers the advantages of deeper penetration, higher surface tension, and greater viscous force. This technique helps suppress the leakage current generated in the reverse bias voltage by passivating the defects under illumination. The recombination current caused by the defects is, therefore, reduced, resulting in an overall increase of the photocurrent in the X-ray photodiode. The results obtained in this study are beneficial for the development of PIN diodes for panel detector applications. [ABSTRACT FROM AUTHOR]

Ta–N–Al thin films were produced by reactive sputtering with two different TaAl alloys (Al 10 at.% and 20 at.%) for use as resistive materials for thin film chip resistors. Changing the N 2 gas flow of the N 2 /Ar plasma and the Al ratio in TaAl alloy resulted in different microstructures and electrical properties of the Ta–N–Al thin film. The resistivity of the thin film sputtered by TaAl (Al 20 at.%) alloy was 1.23 times that sputtered by Ta under 20% N 2 /Ar. After deposition, rapid thermal processing was applied to anneal the thin films for 5 min at 600 °C, 650 °C, and 700 °C under N 2 (99.995%) atmosphere, causing the Ta 2 N and TaN crystalline phase formation. Al in Ta–N–Al was observed to hinder Ta 2 N and TaN crystalline phase formation during annealing. It is crucial to dope Al ith Ta–N appropriately to obtain a near-zero temperature coefficient of resistance (TCR) after annealing. The TCR of Ta–N–Al thin film, sputtered by TaAl (Al 20 at.%) alloy, was substantially affected by the annealing temperature. However, the thin film sputtered by TaAl (Al 10 at.%) alloy under 16% N 2 /Ar can achieve a TCR of −1.8 ppm/°C and 1.4 ppm/°C after annealing at 600 °C and 650 °C, respectively. • Ta-N-Al with adequate Al has a higher resistivity than Ta-N. • Get near-zero TCR of Ta-N-Al by annealing within a specific temperature range. • Al in Ta-N-Al hinders Ta 2 N and TaN grain growth during annealing. [ABSTRACT FROM AUTHOR]

In this paper, we investigate two different resistive switching mechanisms in sputtered nickel oxide thin film which result from using the forming compliances of 15 μA and 15 mA. X-ray photoelectron spectroscopy material analysis results verify the nickel and oxygen chemical shifts, while energy-dispersive X-ray spectroscopy results confirm oxygen content changes after the forming process. Moreover, two markedly different resistive switching mechanisms, those of unipolar and bipolar, are accomplished in a single nickel oxide thin film. To clarify the two resistance switching mechanisms in the Pt/NiO/TiN device, a size effect measurement was conducted to determine the filament-type conduction. Moreover, the device capability to act as a memory device utilizing these two switching behaviors was determined, and a retention test for reliability was also carried out over 125 °C. Finally, the switching mechanism was confirmed by current fitting and sampling reset methods, and an appropriate physical model is presented. We anticipate this model can provide an insight into further future applications of RRAM devices. • We have examined the unipolar and bipolar resistive switching (RS) characteristics using different forming compliance. • The physical mechanisms were confirmed by current fitting and constant voltage sampling results. • The physical models were established to explain two switching characteristics. [ABSTRACT FROM AUTHOR]