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)

Unveiling a hidden biomarker of inflammation and tumor progression: The 65 kDa isoform of MMP-9 new horizons for therapy

Cancer metastasis is a stage of the disease where therapy is mostly ineffective; hence, the need to find reliable markers of its onset. The metalloproteinase-9 (MMP-9, gelatinase B) in its 82 kDa active form, is a good candidate, but here we show that the correspondent little known 65 kDa active MMP-9 isoform, often misrepresented with the other gelatinase MMP-2, is a more suitable marker. Sera from patients with lung and breast cancer were analyzed by bidimensional zymography to detect the activity of MMP-9 and MMP-2. Enzyme identity was confirmed by comparison with MMP-9 standards and by western blotting. The 65 kDa isoform of MMP-9 is a suitable biomarker to monitor tumor progression from tissue neoplasms to metastatic stage, as its activity begins to appear when disease severity increases and becomes very high in metastasis. Moreover, the 65 kDa MMP-9, which derives from the 82 kDa MMP-9, no longer responds to natural MMP-9 inhibitors. As its activity cannot be controlled, its appearance may warn that the pathological process is becoming irreversible. Identification and inhibition of the enzymes converting the inhibitor-sensitive 82 kDa MMP-9 into the corresponding “wild” 65 kDa MMP-9 may allow to develop therapies capable of blocking metastases

Unexpected and durable response with regorafenib in a metastatic colorectal cancer patient without KDR mutation: A case report

Regorafenib is an oral multikinase inhibitor and is approved as salvage therapy in the standard treatment of advanced colorectal cancer (CRC). Due to its limited efficacy, toxicity profile, and cost, it is necessary to identify those patients who may have the most benefit from regorafenib. In a previous case report, kinase insert domain receptor (KDR) mutation has been associated with exceptional clinical response (CR) in an elderly patient treated with a low dose of regorafenib; thus, it was hypothesized that it could represent a new predictive marker of drug response

Quality of life, compliance, safety and effectiveness in fit older metastatic colorectal patients with cancer treated in first-line with chemotherapy plus cetuximab: A restrospective analysis from the ObservEr study

Abstract Objectives The influence of age ( KRAS wild type (WT) metastatic colorectal cancer (mCRC). Methods 225 patients of the Observed study (PS 0-1) were retrieved based on age ( Results The two patient groups (141  p  = 0.002), which is likely due to higher proportions of metastatic resection (27.0% vs 8.3%; p  = 0.001) and utilization of second-line therapy in younger group (58.9% vs 42.9%; p  = 0.028). Conclusion The current data suggest that fit older patients with mCRC can be safely treated with a cetuximab-based therapy, as QoL and safety profile do not seem to be affected by age. In addition, age did not impact the choice of chemotherapy to be associated to cetuximab and treatment compliance

Peripheral blood biomarkers as prognostic factors for immunotherapy in advanced non-small cell lung cancer (aNSCLC) patients

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Demographic, tumor and clinical features of clinical trials versus clinical practice patients with HER2-positive early breast cancer: results of a prospective study

Several randomized clinical trials (RCTs) have demonstrated the efficacy of trastuzumab-based adjuvant therapy in HER2-positive breast cancer (BC). However, RCT patients may not invariably be representative of patients routinely seen in clinical practice (CP). To address this issue, we compared the clinical and tumor features of RCT and CP patients with HER2-positive BC

Survival analysis of a multicentre, randomized phase 3 study on the optimization of the combination of bevacizumab with FOLFOX/OXXEL in patients with metastatic colorectal cancer (mCRC)

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Nab-paclitaxel/gemcitabine combination is more effective than gemcitabine alone in locally advanced, unresectable pancreatic cancer - A GISCAD phase II randomized trial

The role of combination chemotherapy has not yet been established in unresectable locally advanced pancreatic cancer (LAPC) lacking dedicated randomized trials

A Monte Carlo study on learning algorithms for predicting student dropouts in higher education

The phenomenon of dropping out is one the most significant problems faced by the Italian university system. In this paper, using suitable simulation techniques, we have explored the limits and the possibilities of some Machine Learning Algorithms to predict the probability of abandonment in a timely and efficient way, using an information set that is available at the time of matriculation.Il problema dell’abbandono degli studi universitari è una rilevante difficoltà con la quale il sistema universitario italiano deve confrontarsi. In questo lavoro, utilizzando opportune tecniche di simulazione, abbiamo esplorato i limiti e le possibilità di alcuni algoritmi di Machine Learning per prevedere gli abbandoni sulla base di una serie di variabili che sono immediatamente disponibili per ciascuno studente