Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements. Incremental gradient algorithms offer ...inexpensive iterations by sampling a subset of the terms in the sum; these methods can make great progress initially, but often slow as they approach a solution. In contrast, full-gradient methods achieve steady convergence at the expense of evaluating the full objective and gradient on each iteration. We explore hybrid methods that exhibit the benefits of both approaches. Rate-of-convergence analysis shows that by controlling the sample size in an incremental-gradient algorithm, it is possible to maintain the steady convergence rates of full-gradient methods. We detail a practical quasi-Newton implementation based on this approach. Numerical experiments illustrate its potential benefits. PUBLICATION ABSTRACT
In 2014, FIGO’s Committee for Gynecologic Oncology revised the staging of ovarian cancer, incorporating ovarian, fallopian tube, and peritoneal cancer into the same system. Most of these malignancies ...are high‐grade serous carcinomas (HGSC). Stage IC is now divided into three categories: IC1 (surgical spill); IC2 (capsule ruptured before surgery or tumor on ovarian or fallopian tube surface); and IC3 (malignant cells in the ascites or peritoneal washings). The updated staging includes a revision of Stage IIIC based on spread to the retroperitoneal lymph nodes alone without intraperitoneal dissemination. This category is now subdivided into IIIA1(i) (metastasis ≤10 mm in greatest dimension), and IIIA1(ii) (metastasis >10 mm in greatest dimension). Stage IIIA2 is now “microscopic extrapelvic peritoneal involvement with or without positive retroperitoneal lymph node” metastasis. This review summarizes the genetics, surgical management, chemotherapy, and targeted therapies for epithelial cancers, and the treatment of ovarian germ cell and stromal malignancies.
Synopsis
The 2014 FIGO staging incorporates ovarian, fallopian tube, and peritoneal cancer into the same system. This review summarizes the updated staging and treatment of these malignancies.
The basis pursuit problem seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis pursuit denoise (BPDN) fits the least-squares problem only approximately, and a single ...parameter determines a curve that traces the optimal trade-off between the least-squares fit and the one-norm of the solution. We prove that this curve is convex and continuously differentiable over all points of interest, and show that it gives an explicit relationship to two other optimization problems closely related to BPDN. We describe a root-finding algorithm for finding arbitrary points on this curve; the algorithm is suitable for problems that are large scale and for those that are in the complex domain. At each iteration, a spectral gradient-projection method approximately minimizes a least-squares problem with an explicit one-norm constraint. Only matrix-vector operations are required. The primal-dual solution of this problem gives function and derivative information needed for the root-finding method. Numerical experiments on a comprehensive set of test problems demonstrate that the method scales well to large problems.
The scientific organizing committee of the 12th International Symposium on Advanced Ovarian Cancer: Optimal Therapy. Update proposed the question regarding whether all patients with recurrent ovarian ...cancer (ROC) need systemic therapy. This article has addressed this question and focused on the clinical scenarios in which the benefits of systemic therapy in patients with ROC are limited, including the frail elderly and patients with multiple medical comorbidities, as well as a subset of patients with platinum‐resistant ovarian cancer who have a particularly poor prognosis with a short survival. The challenges of identifying and selecting which patients are unlikely to benefit from systemic therapy were addressed. The benefit of systemic therapy also can be questioned in specific histological subtypes of ROC such as low‐grade serous cancers as well as clear cell and mucinous cancers in view of low response rates. Finally, the contentious question regarding the timing of chemotherapy in asymptomatic patients with CA 125 disease progression after response to first‐line chemotherapy was addressed and an argument made challenging the current treatment paradigm. Clearly, not all patients with ROC need or should be offered systemic therapy, and ultimately the recommendations need to be based on evidence and communicated in a clear and sensitive manner to patients and their families.
There is complex interplay between patient‐associated and tumor‐associated factors that impacts the likelihood of a response to systemic therapies and prognosis in patients with advanced ovarian cancer. These factors should be considered to help avoid futile treatment, particularly in the final weeks of life.
Cancer of the ovary, fallopian tube, and peritoneum Berek, Jonathan S.; Kehoe, Sean T.; Kumar, Lalit ...
International journal of gynecology and obstetrics,
October 2018, 2018-Oct, 2018-10-00, 20181001, Letnik:
143, Številka:
S2
Journal Article
Recenzirano
Odprti dostop
The Gynecologic Oncology Committee of FIGO in 2014 revised the staging of ovarian cancer, incorporating ovarian, fallopian tube, and peritoneal cancer into the same system. Most of these malignancies ...are high‐grade serous carcinomas (HGSC). Stage IC is now divided into three categories: IC1 (surgical spill); IC2 (capsule ruptured before surgery or tumor on ovarian or fallopian tube surface); and IC3 (malignant cells in the ascites or peritoneal washings). The updated staging includes a revision of Stage IIIC based on spread to the retroperitoneal lymph nodes alone without intraperitoneal dissemination. This category is now subdivided into IIIA1(i) (metastasis ≤10 mm in greatest dimension), and IIIA1(ii) (metastasis >10 mm in greatest dimension). Stage IIIA2 is now “microscopic extrapelvic peritoneal involvement with or without positive retroperitoneal lymph node” metastasis. This review summarizes the genetics, surgical management, chemotherapy, and targeted therapies for epithelial cancers, and the treatment of ovarian germ cell and stromal malignancies.
The revised FIGO staging incorporates ovarian, fallopian tube, and peritoneal cancer into the same system. This review summarizes the updated staging and treatment of these malignancies.
The use of convex optimization for the recovery of sparse signals from incomplete or compressed data is now common practice. Motivated by the success of basis pursuit in recovering sparse vectors, ...new formulations have been proposed that take advantage of different types of sparsity. In this paper we propose an efficient algorithm for solving a general class of sparsifying formulations. For several common types of sparsity we provide applications, along with details on how to apply the algorithm, and experimental results. PUBLICATION ABSTRACT
The projection onto the epigraph or a level set of a closed proper convex function can be achieved by finding a root of a scalar equation that involves the proximal operator as a function of the ...proximal parameter. This paper develops the variational analysis of this scalar equation. The approach is based on a study of the variational-analytic properties of general convex optimization problems that are (partial) infimal projections of the sum of the function in question and the perspective map of a convex kernel. When the kernel is the Euclidean norm squared, the solution map corresponds to the proximal map, and thus, the variational properties derived for the general case apply to the proximal case. Properties of the value function and the corresponding solution map—including local Lipschitz continuity, directional differentiability, and semismoothness—are derived. An SC
1
optimization framework for computing epigraphical and level-set projections is, thus, established. Numerical experiments on one-norm projection illustrate the effectiveness of the approach as compared with specialized algorithms.
Funding:
This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC). M.P. Friedlander was supported by NSERC discovery grant Grant RGPIN-2017-04461. T. Hoheisel was supported by NSERC discovery grant Grant RGPIN-2017-04035. A. Goodwin’s work was partially supported by an NSERC summer research stipend.
The double-stranded RNA-activated protein kinase (PKR) was originally identified as a sensor of virus infection, but its function in the brain remains unknown. Here, we report that the lack of PKR ...enhances learning and memory in several behavioral tasks while increasing network excitability. In addition, loss of PKR increases the late phase of long-lasting synaptic potentiation (L-LTP) in hippocampal slices. These effects are caused by an interferon-γ (IFN-γ)-mediated selective reduction in GABAergic synaptic action. Together, our results reveal that PKR finely tunes the network activity that must be maintained while storing a given episode during learning. Because PKR activity is altered in several neurological disorders, this kinase presents a promising new target for the treatment of cognitive dysfunction. As a first step in this direction, we show that a selective PKR inhibitor replicates the
Pkr
−/− phenotype in WT mice, enhancing long-term memory storage and L-LTP.
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► The dsRNA-sensing kinase, PKR, modulates network excitability in the neocortex ► Suppression of PKR activity selectively impairs GABAergic inhibitory signaling ► PKR deficiency enhances long-term memory and late long-term potentiation ► PKR regulates IFN-γ-mediated depression of GABA release to influence cognition
To modulate network activity in the neocortex, neurons repurpose an immunoregulatory pathway that is controlled by the RNA-responsive kinase PKR. Elevated activity of this kinase is associated with age-related memory loss in humans, and inhibition of PKR promotes enhanced learning and memory in mice.
We study recovery conditions of weighted l 1 minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that if at least 50% of ...the (partial) support information is accurate, then weighted l 1 minimization is stable and robust under weaker sufficient conditions than the analogous conditions for standard l 1 minimization. Moreover, weighted l 1 minimization provides better upper bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal to be recovered. We illustrate our results with extensive numerical experiments on synthetic data and real audio and video signals.
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