On the Definition of Energy Flux in One-Dimensional Chains of Particles

We review two well-known definitions present in the literature, which are used to define the heat or energy flux in one dimensional chains. One definition equates the energy variation per particle to a discretized flux difference, which we here show it also corresponds to the flux of energy in the zero wavenumber limit in Fourier space, concurrently providing a general formula valid for all wavelengths. The other relies somewhat elaborately on a definition of the flux, which is a function of every coordinate in the line. We try to shed further light on their significance by introducing a novel integral operator, acting over movable boundaries represented by the neighboring particles’ positions, or some combinations thereof. By specializing to the case of chains with the particles’ order conserved, we show that the first definition corresponds to applying the differential continuity-equation operator after the application of the integral operator. Conversely, the second definition corresponds to applying the introduced integral operator to the energy flux. It is, therefore, an integral quantity and not a local quantity. More worryingly, it does not satisfy in any obvious way an equation of continuity. We show that in stationary states, the first definition is resilient to several formally legitimate modifications of the (models of) energy density distribution, while the second is not. On the other hand, it seems peculiar that this integral definition appears to capture a transport contribution, which may be called of convective nature, which is altogether missed by the former definition. In an attempt to connect the dots, we propose that the locally integrated flux divided by the inter-particle distance is a good measure of the energy flux. We show that the proposition can be explicitly constructed analytically by an ad hoc modification of the chosen model for the energy density

Contact-dependent growth inhibition systems in Acinetobacter

In bacterial contact-dependent growth inhibition (CDI) systems, CdiA proteins are exported to the outer membrane by cognate CdiB proteins. CdiA binds to receptors on susceptible bacteria and subsequently delivers its C-terminal toxin domain (CdiA-CT) into neighbouring target cells. Whereas self bacteria produce CdiI antitoxins, non-self bacteria lack antitoxins and are therefore inhibited in their growth by CdiA. In silico surveys of pathogenic Acinetobacter genomes have enabled us to identify >40 different CDI systems, which we sorted into two distinct groups. Type-II CdiAs are giant proteins (3711 to 5733 residues) with long arrays of 20-mer repeats. Type-I CdiAs are smaller (1900-2400 residues), lack repeats and feature central heterogeneity (HET) regions, that vary in size and sequence and can be exchanged between CdiA proteins. HET regions in most type-I proteins confer the ability to adopt a coiled-coil conformation. CdiA-CT and pretoxin modules differ significantly between type-I and type-II CdiAs. Moreover, type-II genes only have remnants of genes in their 3' end regions that have been displaced by the insertion of novel cdi sequences. Type-I and type-II CDI systems are equally abundant in A. baumannii, whereas A. pittii and A. nosocomialis predominantly feature type-I and type-II systems, respectively

Geometry of Empty Space is the Key to Near-Arrest Dynamics

We study several examples of kinetically constrained lattice models using dynamically accessible volume as an order parameter. Thereby we identify two distinct regimes exhibiting dynamical slowing, with a sharp threshold between them. These regimes are identified both by a new response function in dynamically available volume, as well as directly in the dynamics. Results for the selfdiffusion constant in terms of the connected hole density are presented, and some evidence is given for scaling in the limit of dynamical arrest.Comment: 11 page

Single cell classification of macrophage subtypes by label-free cell signatures and machine learning

Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time- and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using single-cell, label-free and high throughput light scattering pattern analyses combined with a straightforward machine learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad hoc developed wide-angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage—Rather than seasonal weather forecasting—Could be key for improving our predictive capacity.JRC.H.1-Water Resource

Efficient characterisation of large deviations using population dynamics

We consider population dynamics as implemented by the cloning algorithm for analysis of large deviations of time-averaged quantities. We use the simple symmetric exclusion process with periodic boundary conditions as a prototypical example and investigate the convergence of the results with respect to the algorithmic parameters, focussing on the dynamical phase transition between homogeneous and inhomogeneous states, where convergence is relatively difficult to achieve. We discuss how the performance of the algorithm can be optimised, and how it can be efficiently exploited on parallel computing platforms

A Plea for Surgery in Pancreatic Metastases from Renal Cell Carcinoma: Indications and Outcome from a Multicenter Surgical Experience

Pancreatic metastases from renal-cell carcinoma (RCC-PMs) are rare. Surgery may play a role in improving overall (OS) and disease-free survival (DFS)

Whole-exome analysis in osteosarcoma to identify a personalized therapy

Osteosarcoma is the most common pediatric primary non-hematopoietic bone tumor. Survival of these young patients is related to the response to chemotherapy and development of metastases. Despite many advances in cancer research, chemotherapy regimens for osteosarcoma are still based on non-selective cytotoxic drugs. It is essential to investigate new specific molecular therapies for osteosarcoma to increase the survival rate of these patients. We performed exomic sequence analyses of 8 diagnostic biopsies of patients with conventional high grade osteosarcoma to advance our understanding of their genetic underpinnings and to correlate the genetic alteration with the clinical and pathological features of each patient to identify a personalized therapy. We identified 18,275 somatic variations in 8,247 genes and we found three mutated genes in 7/8 (87%) samples (KIF1B, NEB and KMT2C). KMT2C showed the highest number of variations; it is an important component of a histone H3 lysine 4 methyltransferase complex and it is one of the histone modifiers previously implicated in carcinogenesis, never studied in osteosarcoma. Moreover, we found a group of 15 genes that showed variations only in patients that did not respond to therapy and developed metastasis and some of these genes are involved in carcinogenesis and tumor progression in other tumors. These data could offer the opportunity to get a key molecular target to identify possible new strategies for early diagnosis and new therapeutic approaches for osteosarcoma and to provide a tailored treatment for each patient based on their genetic profile

Clarification of the Bootstrap Percolation Paradox

We study the onset of the bootstrap percolation transition as a model of generalized dynamical arrest. We develop a new importance-sampling procedure in simulation, based on rare events around "holes", that enables us to access bootstrap lengths beyond those previously studied. By framing a new theory in terms of paths or processes that lead to emptying of the lattice we are able to develop systematic corrections to the existing theory, and compare them to simulations. Thereby, for the first time in the literature, it is possible to obtain credible comparisons between theory and simulation in the accessible density range.Comment: 4 pages with 3 figure