A Multiple Imputation Strategy for Eddy Covariance Data

Half-hourly time series of net ecosystem exchange (NEE) of CO2, latent heat flux (LE) and sensible heat flux (H) measured through the micro-meteorological eddy covariance (EC) technique are noisy and show a high percentage of missing data. By using EC measurements that are part of the FLUXNET2015 dataset, we evaluate the performance of a multiple imputation (MI) strategy based on an efficient computational strategy introduced in Honaker and King (2010), combining the classic Expectation-Maximization (EM) algorithm with a bootstrap approach, in order to take draws from a suitable approximation of posterior distribution of model parameters. Armed with these instruments, we are able to introduce three new multiple imputation models, characterized by an increasing level of complexity, and built on top of multivariate normality assumption: 1) MLR, which imputes EC missing values using a static multiple linear regression of observed values of suitable input variables; 2) ADL, which enriches with dynamic properties the static specification of MLR, by considering an autoregressive distributed lag specification; 3) PADL, which adds further complexity by embedding the ADL model in a panel-data perspective. Under several artificial gap scenarios, we show that PADL has a better ability in modeling the complex dynamics of ecosystem fluxes and reconstructing missing data points, thus providing unbiased imputations and preserving the original sampling distribution. The added flexibility arising from the time series cross section structure of PADL warrants improved performances, outperforming those of other imputation methods, as well as of the marginal distribution sampling algorithm (MDS), a widely used gap- filling approach introduced by Reichstein et al. (2005), especially in the case of nighttime flux data. It is expected that the strategy proposed in this paper will become useful in creating multiple imputations for a variety of EC datasets, providing valid inferences for a broad range of scientific estimands (such as annual budgets)

A Systems Biology Overview on Human Diabetic Nephropathy: From Genetic Susceptibility to Post-Transcriptional and Post-Translational Modifications

Diabetic nephropathy (DN), a microvascular complication occurring in approximately 20-40% of patients with type 2 diabetes mellitus (T2DM), is characterized by the progressive impairment of glomerular filtration and the development of KimmelstielWilson lesions leading to end-stage renal failure (ESRD). The causes and molecular mechanisms mediating the onset of T2DM chronic complications are yet sketchy and it is not clear why disease progression occurs only in some patients. We performed a systematic analysis of the most relevant studies investigating genetic susceptibility and specific transcriptomic, epigenetic, proteomic, and metabolomic patterns in order to summarize the most significant traits associated with the disease onset and progression. The picture that emerges is complex and fascinating as it includes the regulation/dysregulation of numerous biological processes, converging toward the activation of inflammatory processes, oxidative stress, remodeling of cellular function and morphology, and disturbance of metabolic pathways. The growing interest in the characterization of protein post-translational modifications and the importance of handling large datasets using a systems biology approach are also discussed

Erratum to: Proteomics and diabetic nephropathy: what have we learned from a decade of clinical proteomics studies?

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Lysine 63 ubiquitination is involved in the progression of tubular damage in diabetic nephropathy

The purpose of our study was to evaluate how hyperglycemia (HG)influences Lys63 protein ubiquitination and its involvement in tubular damage and fibrosis in diabetic nephropathy (DN). Gene and protein expression of UBE2v1, a ubiquitin-conjugating E2-enzyme variant that mediates Lys63-linked ubiquitination, and Lys63-ubiquitinated proteins increased in HK2 tubular cells under HG. Matrix-assisted laser desorption/ionization-time of flight/tandemmass spectrometry identified 30 Lys63-ubiquitinated proteins, mainly involved in cellular organization, such as β-actin, whose Lys63 ubiquitination increased under HG, leading to cytoskeleton disorganization. This effect was reversed by the inhibitor of the Ubc13/UBE2v1 complexNSC697923. Western blot analysis confirmed that UBE2v1 silencing in HK2 under HG, restored Lys63-β-actin ubiquitination levels tothebasal condition. Immunohistochemistry on patients with type 2diabetic (T2D) revealed an increase in UBE2v1-and Lys63-ubiquitinatedproteins, particularly in kidneys of patients with DN compared with control kidneys and other non diabetic renal diseases, such as membranous nephropathy. Increased Lys63 ubiquitination both in vivo in patients with DN and in vitro, correlated with a-SMA expression, whereas UBE2v1 silencing reduced HG-induced a-SMA protein levels, returning them to basal expression. In conclusion, UBE2v1- and Lys63-ubiquitinated proteins increase in vitro under HG, as well as in vivo in T2D, is augmented in patients with DN, and may affect cytoskeleton organization and influence epithelial-to-mesenchymal transition. This process may drive the progression of tubular damage and interstitial fibrosis in patients with DN

An Italian Multicenter Study on the Epidemiology of Respiratory Syncytial Virus During SARS-CoV-2 Pandemic in Hospitalized Children

Since the beginning of 2020, a remarkably low incidence of respiratory virus hospitalizations has been reported worldwide. We prospectively evaluated 587 children, aged <12 years, admitted for respiratory tract infections from 1 September 2021 to 15 March 2022 in four Italian pediatric hospitals to assess the burden of respiratory viruses during the COVID-19 pandemic in Italy. At admission, a Clinical Respiratory Score was assigned and nasopharyngeal or nasal washing samples were collected and tested for respiratory viruses. Total admissions increased from the second half of October 2021 to the first half of December 2021 with a peak in early November 2021. The respiratory syncytial virus (RSV) incidence curve coincided with the total hospitalizations curve, occurred earlier than in the pre-pandemic years, and showed an opposite trend with respect to the incidence rate of SARS-CoV-2. Our results demonstrated an early peak in pediatric hospitalizations for RSV. SARS-CoV-2 may exhibit a competitive pressure on other respiratory viruses, most notably RSV

Prognostic imaging biomarkers for diabetic kidney disease (iBEAt):study protocol

Background: Diabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim). Methods: iBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes. Discussion: iBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D. Trial registration: Clinicaltrials.gov (NCT03716401)

Modelling random uncertainty of eddy covariance flux measurements

The eddy-covariance (EC) technique is considered the most direct and reliable method to calculate flux exchanges of the main greenhouse gases over natural ecosystems and agricultural fields. The resulting measurements are extremely important to characterize ecosystem exchanges of carbon, water, energy and other trace gases, and are widely used to validate or constrain parameter of land surface models via data assimilation techniques. For this purpose, the availability of both complete half-hourly flux time series and its associated uncertainty is mandatory. However, uncertainty estimation for EC data is challenging because the standard procedures based on repeated sampling are not suitable for this kind of measurements, and the presence of missing data makes it difficult to build any sensible time series model with time-varying second-order moments that can provide estimates of total random uncertainty. To overcome such limitations, this paper describes a new method in the context of the strategy based on the model residual approach proposed by Richardson et al. (Agric For Meteorol 148(1): 38–50, 2008). The proposed approach consists in (1) estimating the conditional mean process as representative of the true signal underlying observed data and (2) estimating the conditional variance process as representative of the total random uncertainty affecting EC data. The conditional mean process is estimated through the multiple imputation algorithm recently proposed by Vitale et al. (J Environ Inform https://doi.org/10.3808/jei.201800391, 2018). The conditional variance process is estimated through the stochastic volatility model introduced by Beltratti and Morana (Econ Notes 30(2): 205–234, 2001). This strategy is applied to ten sites that are part of FLUXNET2015 dataset, selected in such a way to cover various ecosystem types under different climatic regimes around the world. The estimated uncertainty is compared with estimates by other well-established methods, and it is demonstrated that the scaling relationship between uncertainty and flux magnitude is preserved. Additionally, the proposed strategy allows obtaining a complete half-hourly time series of uncertainty estimates, which are expected to be useful for many users of EC flux data.3n

A Systems Biology Overview on Human Diabetic Nephropathy: From Genetic Susceptibility to Post-Transcriptional and Post-Translational Modifications

Diabetic nephropathy (DN), a microvascular complication occurring in approximately 20–40% of patients with type 2 diabetes mellitus (T2DM), is characterized by the progressive impairment of glomerular filtration and the development of Kimmelstiel-Wilson lesions leading to end-stage renal failure (ESRD). The causes and molecular mechanisms mediating the onset of T2DM chronic complications are yet sketchy and it is not clear why disease progression occurs only in some patients. We performed a systematic analysis of the most relevant studies investigating genetic susceptibility and specific transcriptomic, epigenetic, proteomic, and metabolomic patterns in order to summarize the most significant traits associated with the disease onset and progression. The picture that emerges is complex and fascinating as it includes the regulation/dysregulation of numerous biological processes, converging toward the activation of inflammatory processes, oxidative stress, remodeling of cellular function and morphology, and disturbance of metabolic pathways. The growing interest in the characterization of protein post-translational modifications and the importance of handling large datasets using a systems biology approach are also discussed

Retinite pigmentosa e glaucoma: momento causale o correlazione statisticamente positiva?

Gli Autori presentano due casi di R.P. associata a glaucoma. Essi colgono l'occasione per una disamina degli altri casi descritti in letteratura e delle possibili connessioni etiologiche tra le due malattie proposte da diversi Autori. Gli Autori ritengono che l'analisi dei rapporti intercorrenti tra R.P. e glaucoma non può prescindere dalla disamina dello stato idrodinamico dei soggetti