Miracle-dz/UniFAR-FTUs huggingface dataset:
The Structure of MESSI Biological Systems

We introduce a general framework for biological systems, called MESSI systems, that describe Modifications of type Enzyme-Substrate or Swap with Intermediates, and we prove general results based on the network structure. Many post-translational modification networks are MESSI systems. For example: the motifs in [Feliu and Wiuf (2012a)], sequential distributive and processive multisite phosphorylation networks, most of the examples in [Angeli et al. (2007)], phosphorylation cascades, two component systems as in [Kothamachu et al. (2015)], the bacterial EnvZ/OmpR network in [Shinar and Feinberg (2010)], and all linear networks. We show that, under mass-action kinetics, MESSI systems are conservative. We simplify the study of steady states of these systems by explicit elimination of intermediate complexes and we give conditions to ensure an explicit rational parametrization of the variety of steady states (inspired by [Feliu and Wiuf (2013a, 2013b), Thomson and Gunawardena (2009)]). We define an important subclass of MESSI systems with toric steady states [P\'erez Mill\'an et al. (2012)] and we give for MESSI systems with toric steady states an easy algorithm to determine the capacity for multistationarity. In this case, the algorithm provides rate constants for which multistationarity takes place, based on the theory of oriented matroids.
Clinical comparison of new monoclonal antibody-based nephelometric assays for free light chain kappa and lambda to polyclonal antibody-based assays and immunofixation electrophoresis

Abstract Background: New monoclonal antibody-based assays for serum-free light chains (FLC) have become available. Methods: In a clinical study with 541 patients, the new N Latex FLC assays were compared with the Freelite™ FLC assays and immunofixation electrophoresis (IF). Results: Comparison of the different FLC kappa (κ) assays showed a slope of 0.99 with a deviation of 5.0%, rs=0.92, for FLC lambda (λ) a slope of 1.22, deviation 13.8%, rs=0.90 and for the κ/λ ratio a slope of 0.72, deviation –4.6%, rs=0.72. The concordance for the FLC κ assays was 91%, for FLC λ 85% and κ/λ ratio 95%. The clinical sensitivity and specificity of the κ/λ ratios in the study were comparable: 60% and 99% for the N Latex FLC assay and 61% and 97% for the Freelite™ assay. In IF-FLC positive samples, the N Latex FLC κ/λ ratio scored 20/23 (87%) samples outside the reference range and Freelite™ 21/23 (91%). For IF-FLC negative samples, N Latex FLC assay κ/λ ratio scored 338/350 (97%) within the reference range and Freelite™ scored 332/350 (95%). Conclusions: The concordance scores and the clinical sensitivity and specificity of the new N Latex FLC assays and Freelite™ assays appeared comparable, but there are some differences in measurement of concentrations between the methods.
Fighting Bandits with a New Kind of Smoothness

We provide a new analysis framework for the adversarial multi-armed bandit problem. Using the notion of convex smoothing, we define a novel family of algorithms with minimax optimal regret guarantees. First, we show that regularization via the Tsallis entropy, which includes EXP3 as a special case, matches the O(√NT) minimax regret with a smaller constant factor. Second, we show that a wide class of perturbation methods achieve a near-optimal regret as low as O(√NT log N), as long as the perturbation distribution has a bounded hazard function. For example, the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this key property and lead to near-optimal algorithms.
Scalable packet classification

Packet classification is important for applications such as firewalls, intrusion detection, and differentiated services. Existing algorithms for packet classification reported in the literature scale poorly in either time or space as filter databases grow in size. Hardware solutions such as TCAMs do not scale to large classifiers. However, even for large classifiers (say 100,000 rules), any packet is likely to match a few (say 10) rules. Our paper seeks to exploit this observation to produce a scalable packet classification scheme called Aggregated Bit Vector (ABV). Our paper takes the bit vector search algorithm (BV) described in [11] (which takes linear time) and adds two new ideas, recursive aggregation of bit maps and filter rearrangement, to create ABV (which can take logarithmic time for many databases). We show that ABV outperforms BV by an order of magnitude using simulations on both industrial firewall databases and synthetically generated databases.
Making sense of low-level usage data to understand user activities

Empirical studies of user activity, based on data collected from the systems with which users interact, present technical challenges related to the transformation of data streams to a form suitable for analysis. In this paper we discuss the particular challenges confronted during a study of user interruption behaviour based on low-level "keystroke" data and the ways in which these challenges were addressed. We also report on a method of data cleaning and analytical preparation that was developed and consider its effectiveness and potential applicability for similar studies.
Dependency-Topic-Affects-Sentiment-LDA Model for Sentiment Analysis

Sentiment analysis tends to use automated approaches to mine the sentiment information expressed in text, such as reviews, blogs and forum discussions. As most traditional approaches for sentiment analysis are based on supervised learning models and need many labeled corpora as their training data which are not always easily obtained, various unsupervised models based on Latent Dirichlet Allocation (LDA) have been proposed for sentiment classification. In this paper, we propose a novel probabilistic modeling framework based on LDA, called Dependency-Topic-Affects-Sentiment-LDA (DTAS) model, which drops the "bag of words" assumption and assumes that the topics of sentences in a document form a Markov chain, and the sentiment of one sentence is affected by its corresponding topic and its previous sentence's topic. We applied DTAS to reviews of books and hotels. The experiment results of sentiment classification shows that DTAS outperforms other unsupervised generative models and gets high and stable accuracy.
Do all foam dressings have the same efficacy in the treatment of chronic wounds

Janice Bianchi is a Medical Education Specialist, Glasgow: David Gray is a Clinical Nurse Specialist; John Timmons is a Tissue Viability Nurse both at the Department of Tissue Viability, NHS Grampian, Aberdeen; Sylvie Meaume is Dermatologist-Geriatrician, Head of Wound Healing Unit, Geriatric Department, APHP Rothschild Hospital, Paris, France The challenge of chronic wounds remains significant both in terms of clinical management, impact on patients and cost to the NHS. Foam dressings are largely used in the treatment of chronic wounds and manufacturers claim to have different product characteristics. However, is there any foam which is better than another in terms of results? This paper analyses some of the studies which have compared the efficacy of different foam dressings in chronic wounds to see if there is any real difference.
Zinc Mapping and Density Imaging of Rabbit Pancreas Endocrine Tissue Sections Using Nuclear Microscopy

Abstract Nuclear microscopy is a suite of techniques based on a focused beam of MeV protons. These techniques have the unique ability to image density and structural variations in relatively thick tissue sections, map trace elements at the cellular level to the microgram per gram (dry weight) level, and extract quantitative information on these elements. The trace elemental studies can be carried out on unstained freeze-dried tissue sections, thereby minimizing any problems of contamination or redistribution of elements during conventional staining and fixing procedures. The pancreas is a gland with different specialized cells and a complex hormonal activity where trace elements play an important role. For example, zinc has an active role in insulin production, and calcium ions participate in the stimulation and secretion process of insulin. Using nuclear microscopy with a spatial resolution of 1 μm, we have located, using zinc mapping, the islets of Langerhans in freeze-dried normal rabbit tissue sections. The islets of Langerhans contain β-cells responsible for insulin production. Subsequent quantitative analyses have indicated elevations in most elements within the islets of Langerhans, and significantly so for the concentrations of Zn [3,300 compared to 90 μg/g (dry weight)] and Ca [1,100 compared to 390 μg/g (dry weight)].
GTube: geo-predictive video streaming over HTTP in mobile environments

Mobile video streaming sometimes suffers from playback interruptions which are typically due to considerable network bandwidth variations that a user may experience when s/he travels along a route. Segmented adaptive HTTP streaming that switches between video streams encoded at different bitrates -- and hence different quality levels -- can be used to alleviate the issues of variable bandwidth. An important issue with respect to users' perceived experience is how the system schedules the quality levels to match the end-to-end network bandwidth capacity. It is very beneficial if the application can estimate the future network conditions in advance, and therefore perform quality control and buffer control wisely. This work describes GTube, a video streaming system for receivers equipped with a GPS positioning sensor in mobile environments. The available network bandwidth for each location is collected by mobile users and then this data along with the measured locations are uploaded and recorded in a server. Mobile devices that stream video can send queries to the server in order to better predict the near-future bandwidth availability and plan quality adaptations accordingly.
Discriminative Learning of Contour Fragments for Object Detection

The goal of this work is to discriminatively learn contour fragment descriptors for the task of object detection. Unlike previous methods that incorporate learning techniques only for object model generation or for verification after detection, we present a holistic object detection system using solely shape as underlying cue. In the learning phase, we interrelate local shape descriptions (fragments) of the object contour with the corresponding spatial location of the object centroid. We introduce a novel shape fragment descriptor that abstracts spatially connected edge points into a matrix consisting of angular relations between the points. Our proposed descriptor fulfills important properties like distinctiveness, robustness and insensitivity to clutter. During detection, we hypothesize object locations in a generalized Hough voting scheme. The back-projected votes from the fragments allow to approximately delineate the object contour. We evaluate our method e.g. on the well-known ETHZ shape data base, where we achieve an average detection score of 87:5% at 1:0 FPPI only from Hough voting, outperforming the highest scoring Hough voting approaches by almost 8%.
Modified model predictive control in a virtual satellite

In a virtual satellite, communications between any two spacecraft modules may be established on weak wireless inter-satellite using limited resources. The challenges for the attitude determination and control module include maintaining the performance with large data loss and time delay, limited on-board computational resources and hard constraints due to the limited torques that can be provided by actuators on slave satellite sides. In this paper, a modified constrained model predictive controller is developed for the virtual satellite attitude mission. The performance and stability of the MPC controller has been analyzed in this work and the simulation results showed good adoptability of MPC controller for the proposed situation.
Variacin, a new lanthionine-containing bacteriocin produced by Micrococcus varians: comparison to lacticin 481 of Lactococcus lactis

A new lanthionine-containing bacteriocin, variacin, displaying a broad host range of inhibition against gram-positive food spoilage bacteria, has been identified from two strains of Micrococcus varians isolated from meat fermentations. The new bacteriocin was purified, and its amino-terminal end and total amino acid composition were determined. The structural gene was isolated and analyzed. Variacin is resistant to heat and pH conditions from 2 to 10. Its primary sequence shows significant homology to lacticin 481 to Lactococcus lactis, which is more pronounced for the probacteriocin than for the leader sequence. Variacin, like lacticin 481, contains lanthionine and beta-methyllanthionine residues, but its leader sequence clearly resembles nonlantibiotic leader sequences. In particular, the prepeptide contains glycine residues at positions -1 and -2 of the processing site.
A quality of service framework for adaptive and dependable large scale system-of-systems

There is growing recognition within industry that for system growth to be sustainable, the way in which existing assets are used must be improved. Future systems are being developed with a desire for dynamic behaviour and a requirement for dependability at mission critical and safety critical levels. These levels of criticality require predictable performance and as such have traditionally not been associated with adaptive systems. The software architecture proposed for such systems is based around a publish/subscribe model, an approach that, while adaptive, does not typically support critical levels of performance. There is, however, the scope for dependability within such architectures through the use of Quality of Service (QoS) methods. QoS is used in systems where the distribution of resources cannot be decided at design time. A QoS based framework is proposed for providing adaptive and dependable behaviour for future large-scale system-of-systems. Initial simulation results are presented to demonstrate the benefits of QoS.
Near2me: an authentic and personalized social media-based recommender for travel destinations

This paper presents Near2me, a prototype system implementing a travel recommender concept that generates recommendations that are not only personalized, but also authentic. Exploitation of implicit situational knowledge makes it possible for Near2me to recommend places that are not necessarily touristic or famous, but rather are genuinely representative of place and also match users' personal interests. The system allows users to explore, evaluate, and understand recommendations, control recommendation direction and discover informative supporting material. This functionality makes it possible for users to assess recommendations and confirm their suitability and authentic nature. The recommendation system makes use of user photos from the image sharing community Flickr. We take the position that a social media-based environment incorporating multimedia content items, user-contributed annotations and social network connections is uniquely suited to providing users with authentic, personalized recommendations. First results of a user study allow us to conclude that users are interested in exploring locations, topics, and people from different perspectives and confirm authenticity as a relevance criterion.
Channel estimation for OFDM with fast fading channels by modified Kalman filter

In OFDM systems, time-varying channels destroy the orthogonality between subchannels and cause interchannel interference (ICI) in conventional frequency-domain channel estimation approaches. To compensate for this ICI, we propose a new channel estimator for OFDM systems in a fast and frequency-selective Rayleigh fading channel. The proposed method is based on a new modified Kalman filter (MKF). The time-varying channel is modeled as an autoregressive (AR) process and the proposed MKF is used to estimate this AR parameter. In addition, to track the time-varying channel, a channel predictor using regression analysis and the minimum mean-squared error (MMSE) time-domain equalizer are also proposed. The simulation results show that the proposed channel estimator can effectively compensate for ICI.
On the Degrees-of-Freedom of the K-user Gaussian interference channel

The degrees-of-freedom of a K-user Gaussian interference channel (GIFC) has been defined to be the multiple of (1/2) log2 P at which the maximum sum of achievable rates grows with increasing P. In this paper, we establish that the degrees-of-freedom of three or more user, real, scalar GIFCs, viewed as a function of the channel coefficients, is discontinuous at points where all of the coefficients are non-zero rational numbers. More specifically, for all K ≫ 2, we find a class of K-user GIFCs that is dense in the GIFC parameter space for which K/2 degrees-of-freedom are exactly achievable, and we show that the degrees-of-freedom for any GIFC with non-zero rational coefficients is strictly smaller than K/2. These results are proved using new connections with number theory and additive combinatorics.
A new simple method for labeling field crops with stable isotope tracers

Numerous systems have been used to label plants with 13C, which differ in design and complexity depending upon the desired experimental goals. However, most of these systems have generally been applied to greenhouse grown plants. Here, we report on a relatively simple 13C labeling system designed to label crops such as switchgrass (Panicum virgatum L.) grown in the greenhouse or small field plots. The main goals of this study were to validate the system and establish performance benchmarks. We constructed and field tested a simple design plexiglass chamber that was sealed at the soil level with a buried rubber apron. Chamber air was circulated through an infrared gas analyzer to monitor CO2 levels within the chamber. Provisions were made to control temperatures and minimize condensation inside the chamber during labeling. Control experiments using the empty chamber both under greenhouse and field settings showed that decline in CO2 levels was only due to plant CO2 absorption and not due to leakage. Results indicated that the system had generally suitable performance characteristics in both greenhouse and field settings. Isotope enrichment data from our studies revealed that switchgrass fixed 13CO2 that was injected into the chamber within 15-20 min labeling periods. The mean and standard deviation of leaf ? 13C values across nine plants enclosed in the chamber was 34 +_ 8.9 and 96.1 +_ 23.6 for the single and double labeling experiments, respectively. Results indicate that a chamber of this construction type can be effectively used also for labeling other crop plants.
Underwater Self‐Cleaning PEDOT‐PSS Hydrogel Mesh for Effective Separation of Corrosive and Hot Oil/Water Mixtures

Oil‐polluted water is a worldwide problem due to the increasing industry oily wastewater and the frequent oil‐spill pollution. Here, PEDOT‐PSS hydrogel meshes are successfully prepared by using in‐situ chemical polymerization on Ti mesh substrate, which are composed of hierarchical porous structures and present superhydrophilicity in air and superoleophobicity underwater. And PEDOT‐PSS hydrogel meshes exhibit excellent environmental stability under a series of harsh conditions, which are used for the separation of the mixtures of oil and various corrosive and active aqueous solutions, including strong acidic, alkaline, or salt aqueous solutions, even hot‐water. The hydrogel meshes offer high separation efficiency of up to 99.9%. Importantly, the mesh still reveals 99.5% separation efficiency even after 50 times separation operation, demonstrating its excellent durability that shows attractive potential for practical oil‐water separation in industry and everyday life.
Approximation algorithms for restless bandit problems

The restless bandit problem is one of the most well-studied generalizations of the celebrated stochastic multi-armed bandit (MAB) problem in decision theory. In its ultimate generality, the restless bandit problem is known to be PSPACE-Hard to approximate to any nontrivial factor, and little progress has been made on this problem despite its significance in modeling activity allocation under uncertainty. In this article, we consider the Feedback MAB problem, where the reward obtained by playing each of n independent arms varies according to an underlying on/off Markov process whose exact state is only revealed when the arm is played. The goal is to design a policy for playing the arms in order to maximize the infinite horizon time average expected reward. This problem is also an instance of a Partially Observable Markov Decision Process (POMDP), and is widely studied in wireless scheduling and unmanned aerial vehicle (UAV) routing. Unlike the stochastic MAB problem, the Feedback MAB problem does not admit to greedy index-based optimal policies. We develop a novel duality-based algorithmic technique that yields a surprisingly simple and intuitive (2+&epsis;)-approximate greedy policy to this problem. We show that both in terms of approximation factor and computational efficiency, our policy is closely related to the Whittle index, which is widely used for its simplicity and efficiency of computation. Subsequently we define a multi-state generalization, that we term Monotone bandits, which remains subclass of the restless bandit problem. We show that our policy remains a 2-approximation in this setting, and further, our technique is robust enough to incorporate various side-constraints such as blocking plays, switching costs, and even models where determining the state of an arm is a separate operation from playing it. Our technique is also of independent interest for other restless bandit problems, and we provide an example in nonpreemptive machine replenishment. Interestingly, in this case, our policy provides a constant factor guarantee, whereas the Whittle index is provably polynomially worse. By presenting the first O(1) approximations for nontrivial instances of restless bandits as well as of POMDPs, our work initiates the study of approximation algorithms in both these contexts.
Production by planetary ball milling and phase transition of Fe–Nd–Nb nanocrystalline alloys

The main aim of the present work is to study the synthesis and phase transition of Fe77Nd10Nb7B4Cu1Ag1 magnetic nanostructured materials under high temperature conditions. Fe77Nd10Nb7B4Cu1Ag1 alloy powders were prepared by high energy ball milling using milling times of up to 50 h. The as milled alloys consist of an iron based nanocrystalline solid solution with an average grain size of 12·5 nm (after 50 h milling time). The evolution of grain size during the ball milling process and investigations under external temperatures up to 875°C were evaluated using synchrotron radiation X-ray diffraction and α-Fe→γ-Fe phase transition (onset transition temperature Tx1 = 825°C), and the formation of Fe17Nd2B compound (onset temperature Tx2 = 875°C) was observed. The X-ray experiments were performed at the B2 instrument beamline at DESY/HASYLAB, Hamburg, Germany.