Space-Time Mixed System Formulation of Phase-Field Fracture Optimal Control Problems

In this work, space-time formulations and Galerkin discretizations for phase-field fracture optimal control problems are considered. The fracture irreversibility constraint is formulated on the time-continuous level and is regularized by means of penalization. The optimization scheme is formulated in terms of the reduced approach and then solved with a Newton method. To this end, the state, adjoint, tangent, and adjoint Hessian equations are derived. The key focus is on the design of appropriate function spaces and the rigorous justification of all Fréchet derivatives that require fourth-order regularizations. Therein, a second-order time derivative on the phase-field variable appears, which is reformulated as a mixed first-order-in-time system. These derivations are carefully established for all four equations. Finally, the corresponding time-stepping schemes are derived by employing a dG(r) discretization in time

SABMIS: sparse approximation based blind multi-image steganography scheme

We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stegoimage as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the length and the width of the secret images to be half of the length and the width of cover image, respectively, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now (3 times and 6 times than the existing best, respectively). For the case of hiding four secret images, although our capacity is slightly lower than one work (about 2/3rd), we do better on the other two goals (quality of stego-image & extracted secret image as well as resistance to steganographic attacks). For our experiments, there is very little deterioration in the quality of the stego-images as compared to their corresponding cover images. Like all other competing works, this is supported visually as well as over 30 dB of Peak Signal-to-Noise Ratio (PSNR) values. The good quality of the stego-images is further validated by multiple numerical measures. None of the existing works perform this exhaustive validation. When using SABMIS, the quality of the extracted secret images is almost same as that of the corresponding original secret images. This aspect is also not demonstrated in all competing literature. SABMIS further improves the security of the inherently steganographic attack resistant transform based schemes. Thus, it is one of the most secure schemes among the existing ones. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on the real-life problems of securely transmitting medical images over the internet

Algorithmisches Programmieren (Numerische Algorithmen mit C++)

Dieser Kurs führt in die Programmiersprache C++ ein. Es werden die Grundlagen von C++, Kontrollstrukturen, Zahldarstellungen und Datentypen, Funktionen, Zeiger, objekt-orientierte Programmierung, Operatoren und deren Überladung, bishin zu Grundlagen der Vererbung und Klassentemplates, behandelt. Dieses Skriptum ist durch langjährige Erfahrungen der Autoren im Rahmen der gleichnamigen Vorlesung an der Leibniz Universität Hannover entstanden

Adjuvant and concurrent temozolomide for 1p/19q non-co-deleted anaplastic glioma (CATNON; EORTC study 26053-22054): second interim analysis of a randomised, open-label, phase 3 study

BACKGROUND The CATNON trial investigated the addition of concurrent, adjuvant, and both current and adjuvant temozolomide to radiotherapy in adults with newly diagnosed 1p/19q non-co-deleted anaplastic gliomas. The benefit of concurrent temozolomide chemotherapy and relevance of mutations in the IDH1 and IDH2 genes remain unclear. METHODS This randomised, open-label, phase 3 study done in 137 institutions across Australia, Europe, and North America included patients aged 18 years or older with newly diagnosed 1p/19q non-co-deleted anaplastic gliomas and a WHO performance status of 0-2. Patients were randomly assigned (1:1:1:1) centrally using a minimisation technique to radiotherapy alone (59·4 Gy in 33 fractions; three-dimensional conformal radiotherapy or intensity-modulated radiotherapy), radiotherapy with concurrent oral temozolomide (75 mg/m$^{2}$ per day), radiotherapy with adjuvant oral temozolomide (12 4-week cycles of 150-200 mg/m$^{2}$ temozolomide given on days 1-5), or radiotherapy with both concurrent and adjuvant temozolomide. Patients were stratified by institution, WHO performance status score, age, 1p loss of heterozygosity, the presence of oligodendroglial elements on microscopy, and MGMT promoter methylation status. The primary endpoint was overall survival adjusted by stratification factors at randomisation in the intention-to-treat population. A second interim analysis requested by the independent data monitoring committee was planned when two-thirds of total required events were observed to test superiority or futility of concurrent temozolomide. This study is registered with ClinicalTrials.gov, NCT00626990. FINDINGS Between Dec 4, 2007, and Sept 11, 2015, 751 patients were randomly assigned (189 to radiotherapy alone, 188 to radiotherapy with concurrent temozolomide, 186 to radiotherapy and adjuvant temozolomide, and 188 to radiotherapy with concurrent and adjuvant temozolomide). Median follow-up was 55·7 months (IQR 41·0-77·3). The second interim analysis declared futility of concurrent temozolomide (median overall survival was 66·9 months [95% CI 45·7-82·3] with concurrent temozolomide vs 60·4 months [45·7-71·5] without concurrent temozolomide; hazard ratio [HR] 0·97 [99·1% CI 0·73-1·28], p=0·76). By contrast, adjuvant temozolomide improved overall survival compared with no adjuvant temozolomide (median overall survival 82·3 months [95% CI 67·2-116·6] vs 46·9 months [37·9-56·9]; HR 0·64 [95% CI 0·52-0·79], p<0·0001). The most frequent grade 3 and 4 toxicities were haematological, occurring in no patients in the radiotherapy only group, 16 (9%) of 185 patients in the concurrent temozolomide group, and 55 (15%) of 368 patients in both groups with adjuvant temozolomide. No treatment-related deaths were reported. INTERPRETATION Adjuvant temozolomide chemotherapy, but not concurrent temozolomide chemotherapy, was associated with a survival benefit in patients with 1p/19q non-co-deleted anaplastic glioma. Clinical benefit was dependent on IDH1 and IDH2 mutational status. FUNDING Merck Sharpe & Dohme

Tumor heterogeneity and tumor-microglia interactions in primary and recurrent IDH1-mutant gliomas

The isocitrate dehydrogenase (IDH) gene is recurrently mutated in adult diffuse gliomas. IDH-mutant gliomas are categorized into oligodendrogliomas and astrocytomas, each with unique pathological features. Here, we use single-nucleus RNA and ATAC sequencing to compare the molecular heterogeneity of these glioma subtypes. In addition to astrocyte-like, oligodendrocyte progenitor-like, and cycling tumor subpopulations, a tumor population enriched for ribosomal genes and translation elongation factors is primarily present in oligodendrogliomas. Longitudinal analysis of astrocytomas indicates that the proportion of tumor subpopulations remains stable in recurrent tumors. Analysis of tumor-associated microglia/macrophages (TAMs) reveals significant differences between oligodendrogliomas, with astrocytomas harboring inflammatory TAMs expressing phosphorylated STAT1, as confirmed by immunohistochemistry. Furthermore, inferred receptor-ligand interactions between tumor subpopulations and TAMs may contribute to TAM state diversity. Overall, our study sheds light on distinct tumor populations, TAM heterogeneity, TAM-tumor interactions in IDH-mutant glioma subtypes, and the relative stability of tumor subpopulations in recurrent astrocytomas

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

ARIA 2016 : Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA-disseminated and implemented in over 70 countries globally-is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.Peer reviewe