Electron Acceleration by Multi-Island Coalescence

Energetic electrons of up to tens of MeV are created during explosive phenomena in the solar corona. While many theoretical models consider magnetic reconnection as a possible way of generating energetic electrons, the precise roles of magnetic reconnection during acceleration and heating of electrons still remain unclear. Here we show from 2D particle-in-cell simulations that coalescence of magnetic islands that naturally form as a consequence of tearing mode instability and associated magnetic reconnection leads to efficient energization of electrons. The key process is the secondary magnetic reconnection at the merging points, or the `anti-reconnection', which is, in a sense, driven by the converging outflows from the initial magnetic reconnection regions. By following the trajectories of the most energetic electrons, we found a variety of different acceleration mechanisms but the energization at the anti-reconnection is found to be the most important process. We discuss possible applications to the energetic electrons observed in the solar flares. We anticipate our results to be a starting point for more sophisticated models of particle acceleration during the explosive energy release phenomena.Comment: 14 pages, 12 figures (degraded figure quality), 1 table. Accepted for publication in ApJ

The Role of Inverse Compton Scattering in Solar Coronal Hard X-ray and Gamma-ray Sources

Coronal hard X-ray (HXR) and continuum gamma-ray sources associated with the impulsive phase of solar flares have been the subject of renewed interest in recent years. They have been interpreted in terms of thin-target, nonthermal bremsstrahlung emission. This interpretation has led to rather extreme physical requirements in some cases. For example, in one case, essentially all of the electrons in the source must be accelerated to nonthermal energies to account for the coronal HXR source. In other cases, the extremely hard photon spectra of the coronal continuum gamma-ray emission suggest that the low energy cutoff of the electron energy distribution lies in the MeV energy range. Here we consider the role of inverse Compton scattering (ICS) as an alternate emission mechanism in both the ultra- and mildly relativistic regimes. It is known that relativistic electrons are produced during powerful flares; these are capable of up-scattering soft photospheric photons to HXR and gamma-ray energies. Previously overlooked is the fact that mildly relativistic electrons, generally produced in much greater numbers in flares of all sizes, can up-scatter EUV/SXR photons to HXR energies. We also explore ICS on anisotropic electron distributions and show that the resulting emission can be significantly enhanced over an isotropic electron distribution for favorable viewing geometries. We briefly review results from bremsstrahlung emission and reconsider circumstances under which nonthermal bremsstrahlung or ICS would be favored. Finally, we consider a selection of coronal HXR and gamma-ray events and find that in some cases the ICS is a viable alternative emission mechanism

Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study.

Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches

Predicting Gonadal Germ Cell Cancer in People with Disorders of Sex Development; Insights from Developmental Biology

The risk of gonadal germ cell cancer (GGCC) is increased in selective subgroups, amongst others, defined patients with disorders of sex development (DSD). The increased risk is due to the presence of part of the Y chromosome, i.e., GonadoBlastoma on Y chromosome GBY region, as well as anatomical localization and degree of testicularization and maturation of the gonad. The latter specifically relates to the germ cells present being at risk when blocked in an embryonic stage of development. GGCC originates from either germ cell neoplasia in situ (testicular environment) or gonadoblastoma (ovarian-like environment). These precursors are characterized by presence of the markers OCT3/4 (POU5F1), SOX17, NANOG, as well as TSPY, and cKIT and its ligand KITLG. One of the aims is to stratify individuals with an increased risk based on other parameters than histological investigation of a gonadal biopsy. These might include evaluation of defined susceptibility alleles, as identified by Genome Wide Association Studies, and detailed evaluation of the molecular mechanism underlying the DSD in the individual patient, combined with DNA, mRNA, and microRNA profiling of liquid biopsies. This review will discuss the current opportunities as well as limitations of available knowledge in the context of predicting the risk of GGCC in individual patients

An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects

[EN] This paper addresses a sequence dependent setup times no-wait flowshop with learning and forgetting effects to minimize total flowtime. This problem is NP-hard and has never been considered before. A position-based learning and forgetting effects model is constructed. Processing times of operations change with the positions of corresponding jobs in a schedule. Objective increment properties are deduced and based on them three accelerated neighbourhood construction heuristics are presented. Because of the simplicity and excellent performance shown in flowshop scheduling problems, an iterated greedy heuristic is proposed. The proposed iterated greedy algorithm is compared with some existing algorithms for related problems on benchmark instances. Comprehensive computational and statistical tests show that the presented method obtains the best performance among the compared methods. (C) 2018 Elsevier Inc. All rights reserved.This work is supported by the National Natural Science Foundation of China (Nos. 61572127, 61272377), the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 12KJA630001). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness(MINECO), under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" with reference DPI2015-65895-R.Li, X.; Yang, Z.; Ruiz García, R.; Chen, T.; Sui, S. (2018). An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects. Information Sciences. 453:408-425. https://doi.org/10.1016/j.ins.2018.04.038S40842545

Variant morphology in upper urinary tract urothelial carcinoma: a fourteen-year case series of biopsy and resection specimens

Upper urinary tract urothelial carcinoma exhibiting variant morphology, especially in higher-grade tumors, is a recognized phenomenon but has not been comparatively studied in biopsy versus resection material. We studied the morphologic patterns and clinicopathological features, and provide a comparison between biopsy and resection specimens. Consultation cases were evaluated separately to investigate for possible consultation bias. A total of 383 in-house cases from 352 patients including 314 resection specimens and 69 biopsies from 2001–2014 were reviewed from a single institution. Histologic type, tumor grade, invasion, pathologic stage, nodal status, metastasis, and the presence and type of variant morphology for each case were evaluated. Variant morphology was identified in 5 biopsy specimens (7.2%) and 42 resection specimens (13.4%). The most common variant morphologic pattern was squamous differentiation (16 cases, 4.5%) followed by an inverted growth pattern (8 cases, 2.2%). The presence of variant morphology in resection specimens had a significant association with higher tumor grade, higher pT stage, and non-papillary configuration. Out of 69 patients with biopsies, 31 had a subsequent resection. In comparison, 181 consultation cases from 168 patients showed variant morphology in six biopsies (7.1%) and twenty-seven resections (28.1%). In conclusion, the frequency of recognizing variant morphology in biopsies is about one-half of that in resections. The inclusion of consultation cases can inflate the incidence of variant morphology. As a result, the frequency of variant morphology in our in-house cases is lower than the percentage reported in the literature, most likely secondary to a consultation bias

Novel in situ multi-level analysis of structural-mechanical relations in a bioinspired polyurethane-based tissue model

In this manuscript, we elucidated, for the first time, the substructural mechanisms present in our recently developed bioinspired polyurethane-based pancreatic tissue models. Different protein coatings of the model, i.e., collagen and fibronectin were examined. More specifically, analysis took place by combined real-time synchrotron X-ray scattering techniques and confocal laser scanning microscopy, to quantify the structural alteration of uncoated-polyurethane (PU) and protein-coated PU as well as the time-resolved structural reorganisation occurring at the micro-, nano- and lattice length scales during in situ micromechanical testing. We demonstrate that a clear increase of stiffness at the lamellar level following the fibronectin-PU modification, which is linked to the changes in the mechanics of the lamellae and interlamellar cohesion. This multi-level analysis of structural-mechanical relations in this polyurethane-based pancreatic cancer tissue model opens an opportunity in designing mechanically robust cost-effective tissue models not only for fundamental research but also for treatment screening

Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Discrepancies among transcriptome studies are frequently reported, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks normalizes data against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using an in-house printed chip and GeneChip. The framework is based on a common statistical characteristic of microarray data, and each data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other methods