Computing singularities of perturbation series

Many properties of current \emph{ab initio} approaches to the quantum many-body problem, both perturbational or otherwise, are related to the singularity structure of Rayleigh--Schr\"odinger perturbation theory. A numerical procedure is presented that in principle computes the complete set of singularities, including the dominant singularity which limits the radius of convergence. The method approximates the singularities as eigenvalues of a certain generalized eigenvalue equation which is solved using iterative techniques. It relies on computation of the action of the perturbed Hamiltonian on a vector, and does not rely on the terms in the perturbation series. Some illustrative model problems are studied, including a Helium-like model with $\delta$-function interactions for which M{\o}ller--Plesset perturbation theory is considered and the radius of convergence found.Comment: 11 figures, submitte

Humoral Response Induced by Prime-Boost Vaccination with the ChAdOx1 nCoV-19 and mRNA BNT162b2 Vaccines in a Teriflunomide-Treated Multiple Sclerosis Patient.

Patients with multiple sclerosis (MS) are treated with drugs that may impact immune responses to SARS-CoV-2 vaccination. Evaluation of "prime-boost" (heterologous) vaccination regimens including a first administration of a viral vector-based vaccine and a second one of an mRNA-based vaccine in such patients has not yet been completed. Here, we present the anti-spike protein S humoral response, including the neutralizing antibody response, in a 54-year-old MS patient who had been treated with teriflunomide for the past 2 years and who received a heterologous ChAdOx1 nCoV-19/ BNT162b2 vaccination regimen. The results showed a very strong anti-S IgG response and a good neutralizing antibody response. These results show that teriflunomide did not prevent the development of a satisfactory humoral response in this MS patient after vaccination with a ChAdOx1 nCoV-19/ BNT162b2 prime-boost protocol

Impact of a Community Pharmacist-Delivered Information Program on the Follow-up of Type-2 Diabetic Patients: A Cluster Randomized Controlled Study.

Low-quality communication between patients and care providers and limited patient knowledge of the disease and the therapy are important factors associated with poor glycemic control in patients with type 2 diabetes. We conducted a multicenter study to determine whether structured and tailored information delivered by pharmacists to type 2 diabetic patients could improve patient treatment adherence, hemoglobin A1c (HbA1c) levels and knowledge about diabetes. One hundred seventy-four pharmacies were randomized to deliver an educational program on diet, drug treatment, disease and complications during three 30-min interviews over a 6-month period, or to provide no intervention, to type 2 diabetic patients treated with oral antidiabetic agents. Medication adherence was assessed by measuring the medication possession ratio and diabetes control by collecting HbA1c values. Levels of patient treatment self-management and disease knowledge were assessed using self-questionnaires. Three hundred seventy-seven patients were analyzed. The medication possession ratio, already very high at baseline in the intervention (94.8%) and control (92.3%) groups, did not vary significantly after 6 months with no difference between the two groups. Significant decreases in HbA1c were observed in both groups at 6 months (p < 0.001) and 12 months (p < 0.01), with significantly greater changes from baseline in the intervention group than in the control group at 6 months (- 0.5% vs. - 0.2%, p = 0.0047) and 12 months (- 0.6% vs. - 0.2%, p = 0.0057). Patients in the intervention group showed greater improvement in their ability to self-manage treatment (+ 4.86 vs. + 1.58, p = 0.0014) and in the extent of their knowledge about diabetes (+ 0.6 vs. + 0.2, p < 0.01) at 6 months versus baseline compared with the control group. Tailored information provided by the pharmacist to patients with type 2 diabetes did not significantly improve the already high adherence rates, but was associated with a significant decrease in HbA1c and an improvement of patient knowledge about diabetes. ISRCTN33776525. MSD France

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Metabolism and cancer:Old and new players

[No abstract available

Phytochemicals as antibiotic alternatives to promote growth and enhance host health

There are heightened concerns globally on emerging drug-resistant superbugs and the lack of new antibiotics for treating human and animal diseases. For the agricultural industry, there is an urgent need to develop strategies to replace antibiotics for food-producing animals, especially poultry and livestock. The 2nd International Symposium on Alternatives to Antibiotics was held at the World Organization for Animal Health in Paris, France, December 12-15, 2016 to discuss recent scientific developments on strategic antibiotic-free management plans, to evaluate regional differences in policies regarding the reduction of antibiotics in animal agriculture and to develop antibiotic alternatives to combat the global increase in antibiotic resistance. More than 270 participants from academia, government research institutions, regulatory agencies, and private animal industries from >25 different countries came together to discuss recent research and promising novel technologies that could provide alternatives to antibiotics for use in animal health and production; assess challenges associated with their commercialization; and devise actionable strategies to facilitate the development of alternatives to antibiotic growth promoters (AGPs) without hampering animal production. The 3-day meeting consisted of four scientific sessions including vaccines, microbial products, phytochemicals, immune-related products, and innovative drugs, chemicals and enzymes, followed by the last session on regulation and funding. Each session was followed by an expert panel discussion that included industry representatives and session speakers. The session on phytochemicals included talks describing recent research achievements, with examples of successful agricultural use of various phytochemicals as antibiotic alternatives and their mode of action in major agricultural animals (poultry, swine and ruminants). Scientists from industry and academia and government research institutes shared their experience in developing and applying potential antibiotic-alternative phytochemicals commercially to reduce AGPs and to develop a sustainable animal production system in the absence of antibiotics.Fil: Lillehoj, Hyun. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Liu, Yanhong. University of California; Estados UnidosFil: Calsamiglia, Sergio. Universitat Autònoma de Barcelona; EspañaFil: Fernandez Miyakawa, Mariano Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Patobiología; ArgentinaFil: Chi, Fang. Amlan International; Estados UnidosFil: Cravens, Ron L.. Amlan International; Estados UnidosFil: Oh, Sungtaek. United States Department of Agriculture. Agricultural Research Service; ArgentinaFil: Gay, Cyril G.. United States Department of Agriculture. Agricultural Research Service; Argentin

On reliable discovery of molecular signatures

<p>Abstract</p> <p>Background</p> <p>Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR) in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability.</p> <p>Results</p> <p>We demonstrate that with modern prediction methods, signatures that yield accurate predictions may still have a high FDR. Further, we show that even signatures with low FDR may fail to replicate in independent studies due to limited statistical power. Thus, neither stability nor predictive accuracy are relevant when FDR control is the primary goal. We therefore develop a general statistical hypothesis testing framework that for the first time provides FDR control for signature discovery. Our method is demonstrated to be correct in simulation studies. When applied to five cancer data sets, the method was able to discover molecular signatures with 5% FDR in three cases, while two data sets yielded no significant findings.</p> <p>Conclusion</p> <p>Our approach enables reliable discovery of molecular signatures from genome-wide data with current sample sizes. The statistical framework developed herein is potentially applicable to a wide range of prediction problems in bioinformatics.</p

Prediction-Based Control of Linear Systems by Compensating Input-Dependent Input Delay of Integral-Type

International audienceThis study addresses the problem of delay compensation via a predictor-based output feedback for a class of linear systems subject to input delay which itself depends on the input. The equation defining the delay is implicit and involves past values of the input through an integral relation, the kernel of which is a polynomial function of the input. This modeling represents systems where transport phenomena take place at the inlet of a system involving a nonlinearity, which frequently occurs in the processing industry. The conditions of asymptotic stabilization require the magnitude of the feedback gain to comply with the initial conditions. Arguments for the proof of this novel result include general Halanay inequalities for delay differential equations and take advantage of recent advances in backstepping techniques for uncertain or varying delay systems