Predicting flow reversals in chaotic natural convection using data assimilation

A simplified model of natural convection, similar to the Lorenz (1963) system, is compared to computational fluid dynamics simulations in order to test data assimilation methods and better understand the dynamics of convection. The thermosyphon is represented by a long time flow simulation, which serves as a reference "truth". Forecasts are then made using the Lorenz-like model and synchronized to noisy and limited observations of the truth using data assimilation. The resulting analysis is observed to infer dynamics absent from the model when using short assimilation windows. Furthermore, chaotic flow reversal occurrence and residency times in each rotational state are forecast using analysis data. Flow reversals have been successfully forecast in the related Lorenz system, as part of a perfect model experiment, but never in the presence of significant model error or unobserved variables. Finally, we provide new details concerning the fluid dynamical processes present in the thermosyphon during these flow reversals

Update on HER-2 as a target for cancer therapy: HER2/neu peptides as tumour vaccines for T cell recognition

During the past decade there has been renewed interest in the use of vaccine immunotherapy for the treatment of cancer. This review focuses on HER2/neu, a tumour-associated antigen that is overexpressed in 10–40% of breast cancers and other carcinomata. Several immunogenic HER2/neu peptides recognized by T lymphocytes have been identified to be included in cancer vaccines. Some of these peptides have been assessed in clinical trials of patients with breast and ovarian cancer. Although it has been possible to detect immunological responses against the peptides in the immunized patients, no clinical responses have so far been described. Immunological tolerance to self-antigens like HER2/neu may limit the functional immune responses against them. It will be of interest to determine whether immune responses against HER2/neu epitopes can be of relevance to cancer treatment

Efficacy and Safety of Duvelisib Following Disease Progression on Ofatumumab in Patients with Relapsed/Refractory CLL or SLL in the DUO Crossover Extension Study

Purpose: In the phase 3 DUO trial, duvelisib, an oral dual PI3K-δ,γ inhibitor, demonstrated significantly improved efficacy vs ofatumumab (median [m]PFS, 13.3 vs 9.9 months [HR, 0.52; P < .0001]; ORR, 74% vs 45% [P < .0001]), with a manageable safety profile in patients with relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL). We report results from patients with progressive disease (PD) after ofatumumab who crossed over to duvelisib in the DUO trial. Experimental Design: Patients with radiographically confirmed PD after ofatumumab received duvelisib 25 mg twice daily in 28-day cycles until PD, intolerance, death, or study withdrawal. The primary endpoint was ORR per investigator. Secondary endpoints included duration of response (DOR), PFS, and safety. Results: As of December 14, 2018, 90 ofatumumab-treated patients in the DUO trial prior to crossover had an ORR of 29%, mDOR of 10.4 months, and mPFS of 9.4 months. After crossover, 77% of patients (69/90) achieved a response, with an mDOR of 14.9 months and mPFS of 15.7 months. Patients with del(17p) and/or TP53 mutations had similar outcomes (ORR, 77% [20/26]; mPFS, 14.7 months). Notably, 73% of patients (47/64) with disease previously refractory to ofatumumab achieved a response. The most frequent any-grade/grade 3/4 treatment-emergent adverse events were diarrhea (47%/23%), neutropenia (26%/23%), pyrexia (24%/4%), cutaneous reactions (23%/4%), and thrombocytopenia (10%/6%). Conclusions: Duvelisib demonstrated high response rates with good durability and a manageable safety profile in patients with R/R CLL/SLL who progressed on ofatumumab, including patients with high-risk disease and disease previously refractory to ofatumumab

Cytotoxic T-cell precursor frequencies to HER-2 (369 – 377) in patients with HER-2/neu-positive epithelial tumours

HER-2/neu oncoprotein contains several major histocompatibility complex class I-restricted epitopes, which are recognised by cytotoxic T lymphocyte (CTL) on autologous tumours and therefore can be used in immune-based cancer therapies. Of these, the most extensively studied is HER-2(9(369)). In the present report, we used dendritic cells pulsed with HER-2(9(369)) to stimulate, in the presence of IL-7 and IL-12, the production of IFN-gamma by patients' CTL detected by the enzyme-linked immunosorbent spot-assay. Frequencies of peptide-specific precursors were estimated in HLA-A2, HLA-A3 and HLA-A26 patients with HER-2/neu-positive (+) breast, ovarian, lung, colorectal and prostate cancers and healthy individuals. We found increased percentages of such precursors in HLA-A2 (25%) and HLA-A26 (30%) patients, which were significantly higher (60%) in HLA-A3 patients. Our results demonstrate for the first time that pre-existing immunity to HER-2(9(369)) occurs in patients with colorectal, lung and prostate cancer. They also suggest that HER-2(9(369)) can be recognised by CTL, besides HLA-A2, also in the context of HLA-A3 and HLA-A26, thus increasing the applicability of HER-2(9(369))-based vaccinations in a considerably broader patients' population.Journal ArticleResearch Support, Non-U.S. Gov'tinfo:eu-repo/semantics/publishe

Firm-size distribution and price-cost margins in Dutch manufacturing

Industrial economists surmise a relation between the size distribution of firms and performance. Usually, attention is focused on the high end of the size distribution. The widely used 4-firm seller concentration, C4, ignores what happens at the low end of the size distribution. An investigation is presented of the extent to which the level and the growth of small business presence influence price-cost margins in Dutch manufacturing. A large data set of 66 industries for a 13-year period is used. This allows the investigation of both small business influences within a framework in which that of many other market structure variables is also studied. Evidence is shown that price-cost margins are influenced by large firm dominance, growth in small business presence, capital intensity, business cycle, international trade, and buyer concentration

Impact of hypoxia on chemoresistance of mesothelioma mediated by the proton-coupled folate transporter, and preclinical activity of new anti-LDH-A compounds

BACKGROUND: Expression of proton-coupled folate transporter (PCFT) is associated with survival of mesothelioma patients treated with pemetrexed, and is reduced by hypoxia, prompting studies to elucidate their correlation. METHODS: Modulation of glycolytic gene expression was evaluated by PCR arrays in tumour cells and primary cultures growing under hypoxia, in spheroids and after PCFT silencing. Inhibitors of lactate dehydrogenase (LDH-A) were tested in vitro and in vivo. LDH-A expression was determined in tissue microarrays of radically resected malignant pleural mesothelioma (MPM, N = 33) and diffuse peritoneal mesothelioma (DMPM, N = 56) patients. RESULTS: Overexpression of hypoxia marker CAIX was associated with low PCFT expression and decreased MPM cell growth inhibition by pemetrexed. Through integration of PCR arrays in hypoxic cells and spheroids and following PCFT silencing, we identified the upregulation of LDH-A, which correlated with shorter survival of MPM and DMPM patients. Novel LDH-A inhibitors enhanced spheroid disintegration and displayed synergistic effects with pemetrexed in MPM and gemcitabine in DMPM cells. Studies with bioluminescent hypoxic orthotopic and subcutaneous DMPM athymic-mice models revealed the marked antitumour activity of the LDH-A inhibitor NHI-Glc-2, alone or combined with gemcitabine. CONCLUSIONS: This study provides novel insights into hypoxia/PCFT-dependent chemoresistance, unravelling the potential prognostic value of LDH-A, and demonstrating the preclinical activity of LDH-A inhibitors

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

Cause of Death and Predictors of All-Cause Mortality in Anticoagulated Patients With Nonvalvular Atrial Fibrillation : Data From ROCKET AF

M. Kaste on työryhmän ROCKET AF Steering Comm jäsen.Background-Atrial fibrillation is associated with higher mortality. Identification of causes of death and contemporary risk factors for all-cause mortality may guide interventions. Methods and Results-In the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) study, patients with nonvalvular atrial fibrillation were randomized to rivaroxaban or dose-adjusted warfarin. Cox proportional hazards regression with backward elimination identified factors at randomization that were independently associated with all-cause mortality in the 14 171 participants in the intention-to-treat population. The median age was 73 years, and the mean CHADS(2) score was 3.5. Over 1.9 years of median follow-up, 1214 (8.6%) patients died. Kaplan-Meier mortality rates were 4.2% at 1 year and 8.9% at 2 years. The majority of classified deaths (1081) were cardiovascular (72%), whereas only 6% were nonhemorrhagic stroke or systemic embolism. No significant difference in all-cause mortality was observed between the rivaroxaban and warfarin arms (P=0.15). Heart failure (hazard ratio 1.51, 95% CI 1.33-1.70, P= 75 years (hazard ratio 1.69, 95% CI 1.51-1.90, P Conclusions-In a large population of patients anticoagulated for nonvalvular atrial fibrillation, approximate to 7 in 10 deaths were cardiovascular, whereasPeer reviewe

Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins

Using dietary biomarkers in nutritional epidemiological studies may better capture exposure and improve the level at which diet-disease associations can be established and explored. Here, we aimed to identify and evaluate reproducibility of novel biomarkers of reported habitual food intake using targeted and non-targeted metabolomic blood profiling in a large twin cohort. Reported intakes of 71 food groups, determined by FFQ, were assessed against 601 fasting blood metabolites in over 3500 adult female twins from the TwinsUK cohort. For each metabolite, linear regression analysis was undertaken in the discovery group (excluding MZ twin pairs discordant [≥1 SD apart] for food group intake) with each food group as a predictor adjusting for age, batch effects, BMI, family relatedness and multiple testing (1.17x10-6 = 0.05/[71 food groups x 601 detected metabolites]). Significant results were then replicated (non-targeted: P<0.05; targeted: same direction) in the MZ discordant twin group and results from both analyses meta-analyzed. We identified and replicated 180 significant associations with 39 food groups (P<1.17x10-6), overall consisting of 106 different metabolites (74 known and 32 unknown), including 73 novel associations. In particular we identified trans-4-hydroxyproline as a potential marker of red meat intake (0.075[0.009]; P = 1.08x10-17), ergothioneine as a marker of mushroom consumption (0.181[0.019]; P = 5.93x10-22), and three potential markers of fruit consumption (top association: apple and pears): including metabolites derived from gut bacterial transformation of phenolic compounds, 3-phenylpropionate (0.024[0.004]; P = 1.24x10-8) and indolepropionate (0.026[0.004]; P = 2.39x10-9), and threitol (0.033[0.003]; P = 1.69x10-21). With the largest nutritional metabolomics dataset to date, we have identified 73 novel candidate biomarkers of food intake for potential use in nutritional epidemiological studies. We compiled our findings into the DietMetab database (http://www.twinsuk.ac.uk/dietmetab-data/), an online tool to investigate our top associations