Spectrum of topics for world congresses and other activities of the International Society for Physical and Rehabilitation Medicine (ISPRM) : a first proposal

Background: One of the objectives of the International Society for Physical and Rehabilitation Medicine is to improve the continuity of World Congresses. This requires the development of an abstract topic list for use in congress announcements and abstract submissions. Methods: An abstract topic list was developed on the basis of the definitions of human functioning and rehabilitation research, which define 5 main areas of research (biosciences in rehabilitation, biomedical rehabilitation sciences and engineering, clinical Physical and Rehabilitation Medicine (PRM) sciences, integrative rehabilitation sciences, and human functioning sciences). For the abstract topic list, these research areas were grouped according to the proposals of congress streams. In a second step, the first version of the list was systematically compared with the topics of the 2003 ISPRM World Congress. Results: The resulting comprehensive abstract topic list contains 5 chapters according to the definition of human functioning and rehabilitation research. Due to the high significance of clinical research, clinical PRM sciences were placed at the top of the list, comprising all relevant health conditions treated in PRM services. For congress announcements a short topic list was derived. Discussion: The ISPRM topic list is sustainable and covers a full range of topics. It may be useful for congresses and elsewhere in structuring research in PRM

Spinon confinement in a quasi one dimensional anisotropic Heisenberg magnet

Confinement is a process by which particles with fractional quantum numbers bind together to form quasiparticles with integer quantum numbers. The constituent particles are confined by an attractive interaction whose strength increases with increasing particle separation and as a consequence, individual particles are not found in isolation. This phenomenon is well known in particle physics where quarks are confined in baryons and mesons. An analogous phenomenon occurs in certain magnetic insulators; weakly coupled chains of spins S=1/2. The collective excitations in these systems is spinons (S=1/2). At low temperatures weak coupling between chains can induce an attractive interaction between pairs of spinons that increases with their separation and thus leads to confinement. In this paper, we employ inelastic neutron scattering to investigate the spinon confinement in the quasi-1D S=1/2 XXZ antiferromagnet SrCo2V2O8. Spinon excitations are observed above TN in quantitative agreement with established theory. Below TN the pairs of spinons are confined and two sequences of meson-like bound states with longitudinal and transverse polarizations are observed. Several theoretical approaches are used to explain the data. A new theoretical technique based on Tangent-space Matrix Product States gives a very complete description of the data and provides good agreement not only with the energies of the bound modes but also with their intensities. We also successfully explained the effect of temperature on the excitations including the experimentally observed thermally induced resonance between longitudinal modes below TN ,and the transitions between thermally excited spinon states above TN. In summary, our work establishes SrCo2V2O8 as a beautiful paradigm for spinon confinement in a quasi-1D quantum magnet and provides a comprehensive picture of this process.Comment: 17 pages, 18 figures, submitted to PR

A novel method for radiotherapy patient identification using surface imaging

Performing a procedure on the wrong patient or site is one of the greatest errors that can occur in medicine. The addition of automation has been shown to reduce errors in many processes. In this work we explore the use of an automated patient identification process using optical surface imaging for radiotherapy treatments. Surface imaging uses visible light to align the patient to a reference surface in the treatment room. It is possible to evaluate the similarity between a daily set-up surface image and the reference image using distance to agreement between the points on the two surfaces. The higher the percentage overlapping points within a defined distance, the more similar the surfaces. This similarity metric was used to intercompare 16 left-sided breast patients. The reference surface for each patient was compared to 10 daily treatment surfaces for the same patient, and 10 surfaces from each of the other 15 patients (for a total of 160 comparisons per patient), looking at the percent of points overlapping. For each patient, the minimum same-patient similarity score was higher than the maximum different-patient score. For the group as a whole a threshold was able to classify correct and incorrect patients with high levels of accuracy. A 10-fold cross-validation using linear discriminant analysis gave cross-validation loss of 0.0074. An automated process using surface imaging is a feasible option to provide nonharmful daily patient identification verification using currently available technology

Efficient variational contraction of two dimensional tensor networks with a non trivial unit cell

Tensor network states provide an efficient class of states that faithfully capture strongly correlated quantum models and systems in classical statistical mechanics. While tensor networks can now be seen as becoming standard tools in the description of such complex many-body systems, close to optimal variational principles based on such states are less obvious to come by. In this work, we generalize a recently proposed variational uniform matrix product state algorithm for capturing one-dimensional quantum lattices in the thermodynamic limit, to the study of regular two-dimensional tensor networks with a non-trivial unit cell. A key property of the algorithm is a computational effort that scales linearly rather than exponentially in the size of the unit cell. We demonstrate the performance of our approach on the computation of the classical partition functions of the antiferromagnetic Ising model and interacting dimers on the square lattice, as well as of a quantum doped resonating valence bond state.Comment: 23 pages, 8 Figure

Nef interferes with development of thymic T cell precursors: differential mechanisms in HIV and SIV

BioMed Central Open acces

Interaction distance in the extended XXZ model

We employ the interaction distance to characterize the physics of a one-dimensional extended XXZ spin model, whose phase diagram consists of both integrable and nonintegrable regimes, with various types of ordering, e.g., a gapless Luttinger liquid and gapped crystalline phases. We numerically demonstrate that the interaction distance successfully reveals the known behavior of the model in its integrable regime. As an additional diagnostic tool, we introduce the notion of “integrability distance” and particularize it to the XXZ model to quantity how far the ground state of the extended XXZ model is from being integrable. This distance provides insight into the properties of the gapless Luttinger liquid phase in the presence of next-nearest-neighbor spin interactions which break integrability

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy.

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines