A machine learning and directed network optimization approach to uncover TP53 regulatory patterns.

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

Intensive-Dose Tinzaparin in Hospitalized COVID-19 Patients: The INTERACT Study.

(1) Background: It is well-established that coronavirus disease-2019 (COVID-19) is highly pro-inflammatory, leading to activation of the coagulation cascade. COVID-19-induced hypercoagulability is associated with adverse outcomes and mortality. Current guidelines recommend that hospitalized COVID-19 patients should receive pharmacological prophylaxis against venous thromboembolism (VTE). (2) INTERACT is a retrospective, phase IV, observational cohort study aiming to evaluate the overall clinical effectiveness and safety of a higher than conventionally used prophylactic dose of anticoagulation with tinzaparin administered for VTE prevention in non-critically ill COVID-19 patients with moderate disease severity. (3) Results: A total of 705 patients from 13 hospitals in Greece participated in the study (55% men, median age 62 years). Anticoagulation with tinzaparin was initiated immediately after admission. A full therapeutic dose was received by 36.3% of the participants (mean ± SD 166 ± 33 IU/Kgr/day) and the remaining patients (63.9%) received an intermediate dose (mean ± SD 114 ± 22 IU/Kgr/day). The median treatment duration was 13 days (Q1−Q3: 8−20 days). During the study (April 2020 to November 2021), 14 thrombotic events (2.0%) were diagnosed (i.e., three cases of pulmonary embolism (PE) and 11 cases of deep venous thrombosis, DVT). Four bleeding events were recorded (0.6%). In-hospital death occurred in 12 patients (1.7%). Thrombosis was associated with increasing age (median: 74.5 years, Q1−Q3: 62−79, for patients with thrombosis vs. 61.9 years, Q1−Q3: 49−72, p = 0.0149), increased D-dimer levels for all three evaluation time points (at admission: 2490, Q1−Q3: 1580−6480 vs. 700, Q1−Q3: 400−1475, p < 0.0001), one week ± two days after admission (3510, Q1−Q3: 1458−9500 vs. 619, Q1−Q3: 352−1054.5, p < 0.0001), as well as upon discharge (1618.5, Q1−Q3: 1010−2255 vs. 500, Q1−Q3: 294−918, p < 0.0001). Clinical and laboratory improvement was affirmed by decreasing D-dimer and CRP levels, increasing platelet numbers and oxygen saturation measurements, and a drop in the World Health Organization (WHO) progression scale. (4) Conclusions: The findings of our study are in favor of prophylactic anticoagulation with an intermediate to full therapeutic dose of tinzaparin among non-critically ill patients hospitalized with COVID-19.

A new non-monotonic infeasible simplex-type algorithm for Linear Programming.

This paper presents a new simplex-type algorithm for Linear Programming with the following two main characteristics: (i) the algorithm computes basic solutions which are neither primal or dual feasible, nor monotonically improving and (ii) the sequence of these basic solutions is connected with a sequence of monotonically improving interior points to construct a feasible direction at each iteration. We compare the proposed algorithm with the state-of-the-art commercial CPLEX and Gurobi Primal-Simplex optimizers on a collection of 93 well known benchmarks. The results are promising, showing that the new algorithm competes versus the state-of-the-art solvers in the total number of iterations required to converge.

A Nexus Approach for Sustainable Urban Energy-Water-Waste Systems Planning and Operation.

Energy, water, and waste systems analyzed at a nexus level are important to move toward more sustainable cities. In this paper, the "resilience.io" platform is developed and applied to emphasize on waste-to-energy pathways, along with the water and energy sectors, aiming to develop waste treatment capacity and energy recovery with the lowest economic and environmental cost. Three categories of waste including wastewater (WW), municipal solid waste (MSW), and agriculture waste are tested as the feedstock for thermochemical treatment via incineration, gasification, or pyrolysis for combined heat and power generation, or biological treatment such as anaerobic digestion (AD) and aerobic treatment. A case study is presented for Ghana in sub-Saharan Africa, considering a combination of waste treatment technologies and infrastructure, depending on local characteristics for supply and demand. The results indicate that the biogas generated from waste treatment turns out to be a promising renewable energy source in the analyzed region, while more distributed energy resources can be integrated. A series of scenarios including the business-as-usual, base case, naturally constrained, policy interventions, and environmental and climate change impacts demonstrate how simulation with optimization models can provide new insights in the design of sustainable value chains, with particular emphasis on whole-system analysis and integration.

An integrated platform for intuitive mathematical programming modeling using LaTeX.

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.