Delivery of ENaC siRNA to epithelial cells mediated by a targeted nanocomplex: a therapeutic strategy for cystic fibrosis

The inhibition of ENaC may have therapeutic potential in CF airways by reducing sodium hyperabsorption, restoring lung epithelial surface fluid levels, airway hydration and mucociliary function. The challenge has been to deliver siRNA to the lung with sufficient efficacy for a sustained therapeutic effect. We have developed a self-assembling nanocomplex formulation for siRNA delivery to the airways that consists of a liposome (DOTMA/DOPE; L), an epithelial targeting peptide (P) and siRNA (R). LPR formulations were assessed for their ability to silence expression of the transcript of the gene encoding the α-subunit of the sodium channel ENaC in cell lines and primary epithelial cells, in submerged cultures or grown in air-liquid interface conditions. LPRs, containing 50 nM or 100 nM siRNA, showed high levels of silencing, particularly in primary airway epithelial cells. When nebulised these nanocomplexes still retained their biophysical properties and transfection efficiencies. The silencing ability was determined at protein level by confocal microscopy and western blotting. In vivo data demonstrated that these nanoparticles had the ability to silence expression of the α-ENaC subunit gene. In conclusion, these findings show that LPRs can modulate the activity of ENaC and this approach might be promising as co-adjuvant therapy for cystic fibrosis

Bioavailability in soils

The consumption of locally-produced vegetables by humans may be an important exposure pathway for soil contaminants in many urban settings and for agricultural land use. Hence, prediction of metal and metalloid uptake by vegetables from contaminated soils is an important part of the Human Health Risk Assessment procedure. The behaviour of metals (cadmium, chromium, cobalt, copper, mercury, molybdenum, nickel, lead and zinc) and metalloids (arsenic, boron and selenium) in contaminated soils depends to a large extent on the intrinsic charge, valence and speciation of the contaminant ion, and soil properties such as pH, redox status and contents of clay and/or organic matter. However, chemistry and behaviour of the contaminant in soil alone cannot predict soil-to-plant transfer. Root uptake, root selectivity, ion interactions, rhizosphere processes, leaf uptake from the atmosphere, and plant partitioning are important processes that ultimately govern the accumulation ofmetals and metalloids in edible vegetable tissues. Mechanistic models to accurately describe all these processes have not yet been developed, let alone validated under field conditions. Hence, to estimate risks by vegetable consumption, empirical models have been used to correlate concentrations of metals and metalloids in contaminated soils, soil physico-chemical characteristics, and concentrations of elements in vegetable tissues. These models should only be used within the bounds of their calibration, and often need to be re-calibrated or validated using local soil and environmental conditions on a regional or site-specific basis.Mike J. McLaughlin, Erik Smolders, Fien Degryse, and Rene Rietr

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

[EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/).Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry. Lecture Notes in Computer Science. 131-142. https://doi.org/10.1007/978-3-030-64580-9_11S131142Silva, P.C.L., Sadaei, H.J., Ballini, R., Guimaraes, F.G.: Probabilistic forecasting with fuzzy time series. IEEE Trans. Fuzzy Syst. (2019). https://doi.org/10.1109/TFUZZ.2019.2922152Lorente-Leyva, L.L., et al.: Optimization of the master production scheduling in a textile industry using genetic algorithm. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 674–685. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_57Seifert, M., Siemsen, E., Hadida, A.L., Eisingerich, A.B.: Effective judgmental forecasting in the context of fashion products. J. Oper. Manag. 36, 33–45 (2015). https://doi.org/10.1016/j.jom.2015.02.001Tratar, L.F., Strmčnik, E.: Forecasting methods in engineering. IOP Conf. Ser. Mater. Sci. Eng. 657, 012027 (2019). https://doi.org/10.1088/1757-899X/657/1/012027Prak, D., Teunter, R.: A general method for addressing forecasting uncertainty in inventory models. Int. J. Forecast. 35, 224–238 (2019). https://doi.org/10.1016/j.ijforecast.2017.11.004Gaba, A., Tsetlin, I., Winkler, R.L.: Combining interval forecasts. Decis. Anal. 14, 1–20 (2017). https://doi.org/10.1287/deca.2016.0340Zhang, B., Duan, D., Ma, Y.: Multi-product expedited ordering with demand forecast updates. Int. J. Prod. Econ. 206, 196–208 (2018). https://doi.org/10.1016/j.ijpe.2018.09.034Januschowski, T., et al.: Criteria for classifying forecasting methods. Int. J. Forecast. 36, 167–177 (2020). https://doi.org/10.1016/j.ijforecast.2019.05.008Box, G.E., Jenkins, G.M., Reinsel, C., Ljung, M.: Time Series Analysis: Forecasting and Control, 5th edn. Wiley, Hoboken (2015)Murray, P.W., Agard, B., Barajas, M.A.: Forecast of individual customer’s demand from a large and noisy dataset. Comput. Ind. Eng. 118, 33–43 (2018). https://doi.org/10.1016/j.cie.2018.02.007Bruzda, J.: Quantile smoothing in supply chain and logistics forecasting. Int. J. Prod. Econ. 208, 122–139 (2019). https://doi.org/10.1016/j.ijpe.2018.11.015Bajari, P., Nekipelov, D., Ryan, S.P., Yang, M.: Machine learning methods for demand estimation. Am. Econ. Rev. 105, 481–485 (2015). https://doi.org/10.1257/aer.p20151021Villegas, M.A., Pedregal, D.J., Trapero, J.R.: A support vector machine for model selection in demand forecasting applications. Comput. Ind. Eng. 121, 1–7 (2018). https://doi.org/10.1016/j.cie.2018.04.042Herrera-Granda, I.D., et al.: Artificial neural networks for bottled water demand forecasting: a small business case study. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11507, pp. 362–373. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20518-8_31Dudek, G.: Multilayer perceptron for short-term load forecasting: from global to local approach. Neural Comput. Appl. 32(8), 3695–3707 (2019). https://doi.org/10.1007/s00521-019-04130-ySalinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. (2019). https://doi.org/10.1016/j.ijforecast.2019.07.001Weng, Y., Wang, X., Hua, J., Wang, H., Kang, M., Wang, F.Y.: Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. IEEE Trans. Comput. Soc. Syst. 6, 547–553 (2019). https://doi.org/10.1109/TCSS.2019.2914499Zhang, X., Zheng, Y., Wang, S.: A demand forecasting method based on stochastic frontier analysis and model average: an application in air travel demand forecasting. J. Syst. Sci. Complexity 32(2), 615–633 (2019). https://doi.org/10.1007/s11424-018-7093-0Lorente-Leyva, L.L., et al.: Artificial neural networks for urban water demand forecasting: a case study. J. Phys: Conf. Ser. 1284(1), 012004 (2019). https://doi.org/10.1088/1742-6596/1284/1/012004Scott, S.L., Varian, H.R.: Predicting the present with Bayesian structural time series. Int. J. Math. Model. Numer. Optim. 5, 4–23 (2014). https://doi.org/10.1504/IJMMNO.2014.059942Gallego, V., Suárez-García, P., Angulo, P., Gómez-Ullate, D.: Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model. Appl. Stoch. Model. Bus. Ind. 35, 479–491 (2019). https://doi.org/10.1002/asmb.2460Han, S., Ko, Y., Kim, J., Hong, T.: Housing market trend forecasts through statistical comparisons based on big data analytic methods. J. Manag. Eng. 34 (2018). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000583Lee, J.: A neural network method for nonlinear time series analysis. J. Time Ser. Econom. 11, 1–18 (2019). https://doi.org/10.1515/jtse-2016-0011Trull, O., García-Díaz, J.C., Troncoso, A.: Initialization methods for multiple seasonal holt-winters forecasting models. Mathematics 8, 1–16 (2020). https://doi.org/10.3390/math8020268Biau, G., Scornet, E.: A random forest guided tour. Test 25(2), 197–227 (2016). https://doi.org/10.1007/s11749-016-0481-

IFN-γ-inducible protein of 10 kDa upregulates the effector functions of eosinophils through β2 integrin and CXCR3

<p>Abstract</p> <p>Background</p> <p>Eosinophils play an important role in the pathogenesis of bronchial asthma and its exacerbation. Recent reports suggest the involvement of IFN-γ-inducible protein of 10 kDa (IP-10) in virus-induced asthma exacerbation. The objective of this study was to examine whether CXCR3 ligands including IP-10 modify the effector functions of eosinophils.</p> <p>Methods</p> <p>Eosinophils isolated from the blood of healthy donors were stimulated with CXCR3 ligands and their adhesion to rh-ICAM-1 was then measured using eosinophil peroxidase assays. The generation of eosinophil superoxide anion (O₂^-) was examined based on the superoxide dismutase-inhibitable reduction of cytochrome C. Eosinophil-derived neurotoxin (EDN) release was evaluated to determine whether CXCR3 ligands induced eosinophil degranulation. Cytokine and chemokine production by eosinophils was examined using a Bio-plex assay.</p> <p>Results</p> <p>Eosinophil adhesion to ICAM-1 was significantly enhanced by IP-10, which also significantly induced eosinophil O₂^-generation in the presence of ICAM-1. Both the enhanced adhesion and O₂^-generation were inhibited by an anti-β₂integrin mAb or an anti-CXCR3 mAb. Other CXCR3 ligands, such as monokine induced by IFN-γ (Mig) and IFN-inducible T cell α chemoattractant (I-TAC), also induced eosinophil adhesion and O₂^-generation in the presence of ICAM-1. IP-10, but not Mig or I-TAC, increased the release of EDN. IP-10 increased the production of a number of cytokines and chemokines by eosinophils.</p> <p>Conclusions</p> <p>These findings suggest that CXCR3 ligands such as IP-10 can directly upregulate the effector functions of eosinophils. These effects might be involved in the activation and infiltration of eosinophils in the airway of asthma, especially in virus-induced asthma exacerbation.</p

The design, evaluation, and reporting on non- pharmacological, cognition- oriented treatments for older adults: Results of a survey of experts

IntroductionCognitive decline and dementia significantly affect independence and quality of life in older adults; therefore, it is critical to identify effective cognition- oriented treatments (COTs; eg, cognitive training, rehabilitation) that can help maintain or enhance cognitive functioning in older adults, as well as reduce dementia risk or alleviate symptoms associated with pathological processes.MethodsThe Cognitive Intervention Design Evaluation and Reporting (CIDER), a working group from the Non- Pharmacological Interventions Professional Interest Area (NPI- PIA) of the Alzheimer’s Association conducted as survey in 2017 with experts in COTs worldwide. The survey’s aims were three- fold: (1) determine the common attitudes, beliefs, and practices of experts involved in the COTs research targeting older people; (2) identify areas of relative agreement and disagreement among experts in the field; and (3) offer a critical review of the literature, including recommendations for future research.ResultsThe survey identified several areas of agreements among experts on critical features of COTs, and on study design and outcome measures. Nevertheless, there were some areas with relative disagreement. Critically, expert opinions were not always supported by scientific evidence, suggesting that methodologic improvements are needed regarding design, implementation, and reporting of COTs. There was a clear consensus that COTs provide benefits and should be offered to cognitively unimpaired older adults, mild cognitive impairment (MCI), and mild dementia, but opinions differed for moderate and severe dementia. In addition, there is no consensus on the potential role of COTs in dementia prevention, indicating that future research should prioritize this aspect.DiscussionEvidence of COTs in older adults is encouraging, but additional evidence is needed to enhance dementia prevention. Consensus building and guidelines in the field are critical to improve and accelerate the development of high- quality evidence for COTs in cognitively unimpaired older adults, and those with MCI and dementia.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155935/1/trc212024_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155935/2/trc212024.pd

Enhanced Luminescence of Eu-Doped TiO2Nanodots

Monodisperse and spherical Eu-doped TiO2nanodots were prepared on substrate by phase-separation-induced self-assembly. The average diameters of the nanodots can be 50 and 70 nm by changing the preparation condition. The calcined nanodots consist of an amorphous TiO2matrix with Eu3+ions highly dispersed in it. The Eu-doped TiO2nanodots exhibit intense luminescence due to effective energy transfer from amorphous TiO2matrix to Eu3+ions. The luminescence intensity is about 12.5 times of that of Eu-doped TiO2film and the luminescence lifetime can be as long as 960 μs

Vertebral Bomb Radiocarbon Suggests Extreme Longevity in White Sharks

Conservation and management efforts for white sharks (Carcharodon carcharias) remain hampered by a lack of basic demographic information including age and growth rates. Sharks are typically aged by counting growth bands sequentially deposited in their vertebrae, but the assumption of annual deposition of these band pairs requires testing. We compared radiocarbon (Δ14C) values in vertebrae from four female and four male white sharks from the northwestern Atlantic Ocean (NWA) with reference chronologies documenting the marine uptake of 14C produced by atmospheric testing of thermonuclear devices to generate the first radiocarbon age estimates for adult white sharks. Age estimates were up to 40 years old for the largest female (fork length [FL]: 526 cm) and 73 years old for the largest male (FL: 493 cm). Our results dramatically extend the maximum age and longevity of white sharks compared to earlier studies, hint at possible sexual dimorphism in growth rates, and raise concerns that white shark populations are considerably more sensitive to human-induced mortality than previously thought

Moving towards a population health approach to the primary prevention of common mental disorders

There is a need for the development of effective universal preventive approaches to the common mental disorders, depression and anxiety, at a population level. Poor diet, physical inactivity and smoking have long been recognized as key contributors to the high prevalence noncommunicable diseases. However, there are now an increasing number of studies suggesting that the same modifiable lifestyle behaviors are also risk factors for common mental disorders. In this paper we point to the emerging data regarding lifestyle risk factors for common mental disorders, with a particular focus on and critique of the newest evidence regarding diet quality. On the basis of this most recent evidence, we consequently argue for the inclusion of depression and anxiety in the ranks of the high prevalence noncommunicable diseases influenced by habitual lifestyle practices. We believe that it is both feasible and timely to begin to develop effective, sustainable, population-level prevention initiatives for the common mental illnesses that build on the established and developing approaches to the noncommunicable somatic diseases.<br /

Identification of a 4-microRNA Signature for Clear Cell Renal Cell Carcinoma Metastasis and Prognosis

Renal cell carcinoma (RCC) metastasis portends a poor prognosis and cannot be reliably predicted. Early determination of the metastatic potential of RCC may help guide proper treatment. We analyzed microRNA (miRNA) expression in clear cell RCC (ccRCC) for the purpose of developing a miRNA expression signature to determine the risk of metastasis and prognosis. We used the microarray technology to profile miRNA expression of 78 benign kidney and ccRCC samples. Using 28 localized and metastatic ccRCC specimens as the training cohort and the univariate logistic regression and risk score methods, we developed a miRNA signature model in which the expression levels of miR-10b, miR-139-5p, miR-130b and miR-199b-5p were used to determine the status of ccRCC metastasis. We validated the signature in an independent 40-sample testing cohort of different stages of primary ccRCCs using the microarray data. Within the testing cohort patients who had at least 5 years follow-up if no metastasis developed, the signature showed a high sensitivity and specificity. The risk status was proven to be associated with the cancer-specific survival. Using the most stably expressed miRNA among benign and tumorous kidney tissue as the internal reference for normalization, we successfully converted his signature to be a quantitative PCR (qPCR)-based assay, which showed the same high sensitivity and specificity. The 4-miRNA is associated with ccRCC metastasis and prognosis. The signature is ready for and will benefit from further large clinical cohort validation and has the potential for clinical application