Updating constraint preconditioners for KKT systems in quadratic programming via low-rank corrections

This work focuses on the iterative solution of sequences of KKT linear systems arising in interior point methods applied to large convex quadratic programming problems. This task is the computational core of the interior point procedure and an efficient preconditioning strategy is crucial for the efficiency of the overall method. Constraint preconditioners are very effective in this context; nevertheless, their computation may be very expensive for large-scale problems, and resorting to approximations of them may be convenient. Here we propose a procedure for building inexact constraint preconditioners by updating a "seed" constraint preconditioner computed for a KKT matrix at a previous interior point iteration. These updates are obtained through low-rank corrections of the Schur complement of the (1,1) block of the seed preconditioner. The updated preconditioners are analyzed both theoretically and computationally. The results obtained show that our updating procedure, coupled with an adaptive strategy for determining whether to reinitialize or update the preconditioner, can enhance the performance of interior point methods on large problems.Comment: 22 page

TTF-1/p63-positive poorly differentiated NSCLC: A histogenetic hypothesis from the basal reserve cell of the terminal respiratory unit

TTF-1 is expressed in the alveolar epithelium and in the basal cells of distal terminal bronchioles. It is considered the most sensitive and specific marker to define the adenocarcinoma arising from the terminal respiratory unit (TRU). TTF-1, CK7, CK5/6, p63 and p40 are useful for typifying the majority of non-small-cell lung cancers, with TTF and CK7 being typically expressed in adenocarcinomas and the latter three being expressed in squamous cell carcinoma. As tumors with coexpression of both TTF-1 and p63 in the same cells are rare, we describe different cases that coexpress them, suggesting a histogenetic hypothesis of their origin. We report 10 cases of poorly differentiated non-small-cell lung carcinoma (PD-NSCLC). Immunohistochemistry was performed by using TTF-1, p63, p40 (∆Np63), CK5/6 and CK7. EGFR and BRAF gene mutational analysis was performed by using real-time PCR. All the cases showed coexpression of p63 and TTF-1. Six of them showing CK7+ and CK5/6− immunostaining were diagnosed as “TTF-1+ p63+ adenocarcinoma”. The other cases of PD-NSCLC, despite the positivity for CK5/6, were diagnosed as “adenocarcinoma, solid variant”, in keeping with the presence of TTF-1 expression and p40 negativity. A “wild type” genotype of EGFR was evidenced in all cases. TTF1 stained positively the alveolar epithelium and the basal reserve cells of TRU, with the latter also being positive for p63. The coexpression of p63 and TTF-1 could suggest the origin from the basal reserve cells of TRU and represent the capability to differentiate towards different histogenetic lines. More aggressive clinical and morphological features could characterize these “basal-type tumors” like those in the better known “basal-like” cancer of the breast

A dynamic role of mastermind-like 1. A journey through the main (path)ways between development and cancer

Major signaling pathways, such as Notch, Hedgehog (Hh), Wnt/β-catenin and Hippo, are targeted by a plethora of physiological and pathological stimuli, ultimately resulting in the modulation of genes that act coordinately to establish specific biological processes. Many biological programs are strictly controlled by the assembly of multiprotein complexes into the nucleus, where a regulated recruitment of specific transcription factors and coactivators on gene promoter region leads to different transcriptional outcomes. MAML1 results to be a versatile coactivator, able to set up synergistic interlinking with pivotal signaling cascades and able to coordinate the network of cross-talking pathways. Accordingly, despite its original identification as a component of the Notch signaling pathway, several recent reports suggest a more articulated role for MAML1 protein, showing that it is able to sustain/empower Wnt/β-catenin, Hh and Hippo pathways, in a Notch-independent manner. For this reason, MAML1 may be associated to a molecular “switch”, with the function to control the activation of major signaling pathways, triggering in this way critical biological processes during embryonic and post-natal life. In this review, we summarize the current knowledge about the pleiotropic role played by MAML proteins, in particular MAML1, and we recapitulate how it takes part actively in physiological and pathological signaling networks. On this point, we also discuss the contribution of MAML proteins to malignant transformation. Accordingly, genetic alterations or impaired expression of MAML proteins may lead to a deregulated crosstalk among the pathways, culminating in a series of pathological disorders, including cancer development. Given their central role, a better knowledge of the molecular mechanisms that regulate the interplay of MAML proteins with several signaling pathways involved in tumorigenesis may open up novel opportunities for an attractive molecular targeted anticancer therapy

Constrained dogleg methods for nonlinear systems with simple bounds

We focus on the numerical solution of medium scale bound-constrained systems of nonlinear equations. In this context, we consider an affine-scaling trust region approach that allows a great flexibility in choosing the scaling matrix used to handle the bounds. The method is based on a dogleg procedure tailored for constrained problems and so, it is named Constrained Dogleg method. It generates only strictly feasible iterates. Global and locally fast convergence is ensured under standard assumptions. The method has been implemented in the Matlab solver CoDoSol that supports several diagonal scalings in both spherical and elliptical trust region frameworks. We give a brief account of CoDoSol and report on the computational experience performed on a number of representative test problem

Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: A machine learning approach

Background: Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms. Methods: Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute–right ventricular failure (N = 8, 11%) or chronic–right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an “all-subsets” approach. Results: Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular–free wall were the most significant predictors of acute–right ventricular failure (maximum receiver operating characteristic–area under the curve = 0.95, 95% confidence interval = 0.91–1.00, by the naïve Bayes), while the right ventricular–free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic–area under the curve = 0.97, 95% confidence interval = 0.91–1.00, according to naïve Bayes). Conclusion: Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute–right ventricular failure and chronic–right ventricular failure, respectively

MSR32 COVID-19 Beds’ Occupancy and Hospital Complaints: A Predictive Model

Objectives COVID-19 pandemic limited the number of patients that could be promptly and adequately taken in charge. The proposed research aims at predicting the number of patients requiring any type of hospitalizations, considering not only patients affected by COVID-19, but also other severe viral diseases, including untreated chronic and frail patients, and also oncological ones, to estimate potential hospital lawsuits and complaints. Methods An unsupervised learning approach of artificial neural network’s called Self-Organizing Maps (SOM), grounding on the prediction of the existence of specific clusters and useful to predict hospital behavioral changes, has been designed to forecast the hospital beds’ occupancy, using pre and post COVID-19 time-series, and supporting the prompt prediction of litigations and potential lawsuits, so that hospital managers and public institutions could perform an impacts’ analysis to decide whether to invest resources to increase or allocate differentially hospital beds and humans capacity. Data came from the UK National Health Service (NHS) statistic and digital portals, concerning a 4-year time horizon, related to 2 pre and 2 post COVID-19 years. Results Clusters revealed two principal behaviors in selecting the resources allocation. In case of increase of non-COVID hospitalized patients, a reduction in the number of complaints (-55%) emerged. A higher number of complaints was registered (+17%) against a considerable reduction in the number of beds occupied (-26%). Based on the above, the management of hospital beds is a crucial factor which can influence the complaints trend. Conclusions The model could significantly support in the management of hospital capacity, helping decision-makers in taking rational decisions under conditions of uncertainty. In addition, this model is highly replicable also in the estimation of current hospital beds, healthcare professionals, equipment, and other resources, extremely scarce during emergency or pandemic crises, being able to be adapted for different local and national settings

In Silico Shear and Intramural Stresses are Linked to Aortic Valve Morphology in Dilated Ascending Aorta

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Potential impact of a nonavalent HPV vaccine on HPV related low-and high-grade cervical intraepithelial lesions: A referral hospital-based study in Sicily

While bivalent and quadrivalent HPV vaccines have been used for about 10 years, a nonavalent vaccine against HPV types 6/11/16/18/31/33/45/52 and 58 has been recently approved by FDA and EMA and is now commercially available. The objective of our study was to evaluate the potential impact of the nonavalent vaccine on HPV infection and related low- and high-grade squamous intraepithelial lesions (LSIL, HSIL), compared to the impact of the quadrivalent vaccine, in a female population living in Sicily (Italy). Low estimates of HPV vaccine impact were calculated as prevalence of HPV 6/11/16/18/31/33/45/52 and 58 genotypes, alone or in association, but excluding presence of other HPV types; high estimates were calculated as prevalence of HPV 6/11/16/18/31/33/45/52 and 58 genotypes alone or in association, in the presence of other HPV types. The nonavalent HPV vaccine showed increased impact, compared to the quadrivalent vaccine. Estimates of potential impact varied from 30.9% (low estimate) to 53.3% (high estimate) for LSIL, and from 56.9% to 81,0% for HSIL. The proportion of additional cases potentially prevented by the nonavalent vaccine was 14.4%\u201323.8% for LSIL, and 19.0%\u201332.8% for HSIL. The benefit of the nonavalent vaccine compared to the quadrivalent vaccine was more than 80% for both low and high impact estimates for LSIL and more than 50% for both low and high impact estimates for HSIL. The present study confirms that the switch from a first generation HPV vaccines to a nonavalent vaccine would increase the prevention of cervical HSIL in up to 90% of cases

Loss of ATP2C1 function promotes trafficking and degradation of NOTCH1: Implications for Hailey-Hailey disease

Hailey-Hailey disease (HHD) is a rare autosomal dominantly inherited disorder caused by mutations in the ATP2C1 gene that encodes an adenosine triphosphate (ATP)-powered calcium channel pump. HHD is characterized by impaired epidermal cell-to-cell adhesion and defective keratinocyte growth/differentiation. The mechanism by which mutant ATP2C1 causes HHD is unknown and current treatments for affected individuals do not address the underlying defects and are ineffective. Notch signalling is a direct determinant of keratinocyte growth and differentiation. We found that loss of ATP2C1 leads to impaired Notch1 signalling, thus deregulation of the Notch signalling response is therefore likely to contribute to HHD manifestation. NOTCH1 is a transmembrane receptor and upon ligand binding, the intracellular domain (NICD) translocates to the nucleus activating its target genes. In the context of HHD, we found that loss of ATP2C1 function promotes upregulation of the active NOTCH1 protein (NICD-Val1744). Here, deeply exploring this aspect, we observed that NOTCH1 activation is not associated with the transcriptional enhancement of its targets. Moreover, in agreement with these results, we found a cytoplasmic localization of NICD-Val1744. We have also observed that ATP2C1-loss is associated with the degradation of NICD-Val1744 through the lysosomal/proteasome pathway. These results show that ATP2C1-loss could promote a mechanism by which NOTCH1 is endocytosed and degraded by the cell membrane. The deregulation of this phenomenon, finely regulated in physiological conditions, could in HHD lead to the deregulation of NOTCH1 with alteration of skin homeostasis and disease manifestation