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The optimal combinations of the eligible functions in multiple property assets enhancement.

ABSTRACT: The present research concerns the procedures of concessions for the improvement and exploitation of public property assets. We propose a model that helps to define the optimal combination of novel uses in public properties. This model is an answer to the need for effective strategies to find new functions for disused buildings or abandoned areas. The model integrates the logic of the Discounted Cash-Flow Analysis (DCFA) and the goal programming for determining the new functions, to identify the macro-solution that maximizes the financial conveniences of the parties involved (Public Administration and private investor), in terms of monetary compensation for the Public Administration and return on investment for the private operator. The algorithm of the model has been applied to five existing public properties located in the city of Rome (Italy); for each property a specific procedure of concessions for the improvement of public assets can be activated and different eligible enhancement projects can be realized. The outputs generated by the optimization model are a valid decision support in all the phases of the concession procedure to identify the (public and private) strengths and weaknesses concerning the redevelopment initiatives on public properties. The effective re-use of these properties can actually help to prevent further soil consumption.

SUBMITTER: Tajani F

PROVIDER: S-EPMC7467029 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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The optimal combinations of the eligible functions in multiple property assets enhancement.

Tajani Francesco F Morano Pierluigi P Di Liddo Felicia F

Land use policy 20200902

The present research concerns the procedures of concessions for the improvement and exploitation of public property assets. We propose a model that helps to define the optimal combination of novel uses in public properties. This model is an answer to the need for effective strategies to find new functions for disused buildings or abandoned areas. The model integrates the logic of the Discounted Cash-Flow Analysis (DCFA) and the goal programming for determining the new functions, to identify the ...[more]

PMID: 32895589

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Project description:The FITExP profiling approach was used to identify protein hits from cells incubated with RAPTA-T or RAPTA-EA. Validation experiments with paclitaxel and cisplatin underlined the reliability of the method and hit lists for both test compounds (and?) gave physiologically viable anti-cancer mechanisms. The hit proteins for paclitaxel match with those published and the ones obtained for cisplatin were related to DNA repair, which is in line with the principal mechanism of cisplatin cytotoxicity involving the formation of nuclear DNA lesions. RAPTA-T treatment leads to upregulation of multiple hits suggesting a broad mechanism of action involving both metastasis and tumorigenicity. In contrast, hit proteins identified after incubation with RAPTA-EA are linked to regulation of the oxidative stress response and are therefore thought to be conferred by the EA moiety in the drug. From a therapeutic standpoint, RAPTA-EA could be explored in cancers where EA alone has shown potency, such as chronic lymphocytic leukemias,91 or where EA combined with another agent shows synergy, such as the combination of EA with afatinib, an irreversible epidermal growth factor receptor tyrosine kinase inhibitors for breast cancers.92 On the other hand, due to its broad mechanism of action, RAPTA-T could potentially be more useful if used concomitantly with drugs that target specific cancer pathways and could also play a role in therapies for later stage cancers due to its anti-metastatic properties. Here, application of the FITExP allowed us to gauge the mechanistic pathways involved in the action of two novel ruthenium(II) metallodrugs with very different phenotypic characteristics and from this knowledge infer their role in therapy. Potentially, future application of the FITExP approach in this manner could be useful for identification of cancer chemotherapy drug combinations, where commonly cancer clinical trials fail at phase II where drug combinations are first tried and tested.

Project description:Biomarkers associated with treatment-effect heterogeneity can be used to make treatment recommendations that optimize individual clinical outcomes. To accomplish this, statistical methods are needed to generate marker-based treatment-selection rules that can most effectively reduce the population burden due to disease and treatment. Compared to the standard approach of risk modeling to derive treatment-selection rules, a more robust approach is to directly minimize an unbiased estimate of total disease and treatment burden among a pre-specified class of rules. This problem is one of minimizing a weighted sum of 0-1 loss function, which is computationally challenging to solve due to the nonsmoothness of 0-1 loss. Huang and Fong, among others, proposed a method that uses the Ramp loss to approximate the 0-1 loss and solves the minimization problem through repetitive constrained optimizations. The algorithm was shown to have comparable or better performance than other comparative estimators in various settings. Our aim in this article is to further extend the algorithm to allow for variable selection in the presence of a large number of candidate markers.We develop an alternative method to derive marker combinations to minimize the weighted sum of Ramp loss in Huang and Fong, based on data from randomized trials. The new algorithm estimates treatment-selection rules by repetitively minimizing a smooth and differentiable objective function. Through the use of an L1 penalty, we expand the method to allow for feature selection and develop an algorithm based on the coordinate descent method to build the treatment-selection rule.Through extensive simulation studies, we compared performance of the proposed estimator to four existing approaches: (1) a logistic regression risk modeling approach, and three other "direct optimizing" approaches including (2) the estimator in Huang and Fong, (3) the weighted support vector machine, and (4) the weighted logistic regression. The proposed estimator performs comparably to that of Huang and Fong, and comparably or better than other estimators. Allowing for variable selection using the proposed estimator in the presence of a large number of markers further improves treatment-selection performance. The proposed estimator is also advantageous for selecting variables relevant to treatment selection compared to L1 penalized logistic regression and weighted logistic regression. We illustrate the application of the proposed methods in host-genetics data from an HIV vaccine trial.The proposed estimator is appealing considering its effectiveness and conceptual simplicity. It has significant potential to contribute to the selection and combination of biomarkers for treatment selection in clinical practice.

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The optimal combinations of the eligible functions in multiple property assets enhancement.

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The optimal combinations of the eligible functions in multiple property assets enhancement.

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