Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation and Processing of Maize Histone Deacetylase Hda1 by Limited Proteolysis Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.013995.

A maize histone deacetylase gene was identified as a homolog of yeast Hda1. The predicted protein corresponds to a previously purified maize deacetylase that is active as a protein monomer with a molecular weight of 48,000 and is expressed in all tissues of germinating embryos. Hda1 is synthesized as an enzymatically inactive protein with an apparent molecular weight of 84,000 that is processed to the active 48-kD form by proteolytic removal of the C-terminal part, presumably via a 65-kD intermediate. The enzymatically inactive 84-kD protein also is part of a 300-kD protein complex of unknown function. The proteolytic cleavage of ZmHda1 is regulated during maize embryo germination in vivo. Expression of the recombinant full-length protein and the 48-kD form confirmed that only the smaller enzyme form is active as a histone deacetylase. In line with this finding, we show that the 48-kD protein is able to repress transcription efficiently in a reporter gene assay, whereas the full-length protein, including the C-terminal part, lacks full repression activity. This report on the processing of Hda1-p84 to enzymatically active Hda1-p48 demonstrates that proteolytic cleavage is a mechanism to regulate the function of Rpd3/Hda1-type histone deacetylases.
Multicast security: a taxonomy and some efficient constructions

Multicast communication is becoming the basis for a growing number of applications. It is therefore critical to provide sound security mechanisms for multicast communication. Yet, existing security protocols for multicast offer only partial solutions. We first present a taxonomy of multicast scenarios on the Internet and point out relevant security concerns. Next we address two major security problems of multicast communication: source authentication, and key revocation. Maintaining authenticity in multicast protocols is a much more complex problem than for unicast; in particular, known solutions are prohibitively inefficient in many cases. We present a solution that is reasonable for a range of scenarios. This approach can be regarded as a 'midpoint' between traditional message authentication codes and digital signatures. We also present an improved solution to the key revocation problem.
Wireless Powered Cooperative Jamming for Secure OFDM System

This paper studies the secrecy communication in an orthogonal frequency division multiplexing system, where a source sends confidential information to a destination in the presence of a potential eavesdropper. We employ wireless powered cooperative jamming to improve the secrecy rate of this system with the assistance of a cooperative jammer, which works in the harvest-then-jam protocol over two time slots. In the first slot, the source sends dedicated energy signals to power the jammer; in the second slot, the jammer uses the harvested energy to jam the eavesdropper, in order to protect the simultaneous secrecy communication from the source to the destination. In particular, we consider two types of receivers at the destination, namely Type-I and Type-II receivers, which do not have and have the capability of canceling the (a priori known) jamming signals, respectively. For both types of receivers, we maximize the secrecy rate at the destination by jointly optimizing the transmit power allocation at the source and the jammer over sub-carriers, as well as the time allocation between the two time slots. First, we present the globally optimal solution to this problem via the Lagrange dual method, which, however, is of high implementation complexity. Next, to balance the tradeoff between the algorithm complexity and performance, we propose alternative low-complexity solutions based on the minorization maximization and heuristic successive optimization, respectively. Simulation results show that the proposed approaches significantly improve the secrecy rate, as compared to benchmark schemes without joint power and time allocation.
Experimental and numerical investigations on the size effect of moderate high-strength reinforced concrete columns under small-eccentric compression

The paper deals with an experimental investigation and numerical simulation of moderate high-strength reinforced concrete (RC) columns subjected to a small-eccentric compressive loading (e0 = 0.25h0). A series of tests on the behavior of 12 geometrically similar moderate high-strength reinforced concrete columns with two different stirrups ratios (i.e., 0% and 0.66%) were conducted. The maximum structural size of the square reinforced concrete columns was 800 mm. A 2D mesoscale method for the simulation of the behavior of reinforced concrete columns was established. The numerical tests on the reinforced concrete columns with larger stirrup ratios (1.2% and 2.4%) were carried out complementarily, based on the fact that the simulation results were consistent with the available test observations. The failure patterns, the nominal compressive stress–strain relationships, the nominal compressive strength, and the post-peak softening behavior of the reinforced concrete columns were studied. Furthermore, the influence of stirrups on failure behavior and size effect of the reinforced concrete columns was revealed. One can conclude that (1) the size effect exists in the nominal compressive strength of the eccentrically loaded high-strength reinforced concrete columns with the four different stirrup ratios; (2) comparison of the present test results and the “size effect law” proposed by Bažant shows good agreement; (3) the presence of stirrups improves the nominal strengths, makes the failure of columns less brittle, and weakens the size effect; and (4) the proposed mesoscale numerical method is capable of describing the mechanical behavior of eccentrically loaded reinforced concrete columns.
On economic heavy hitters: shapley value analysis of 95th-percentile pricing

Cost control for the Internet access providers (AP) influences not only the nominal speeds offered to the customers, but also other, more controversial, policies related to traffic shaping and discrimination. Given that the cost for the AP is determined by the peak-hour traffic (e.g. through the 95th-percentile), the individual user contribution towards the aggregate cost is not a linear function of its byte usage. In this paper we propose a metric for evaluating the contribution each individual user has on the peak demand, that is based on Shapley value, a well known game-theoretic concept. Given the computational complexity of calculating the Shapley value, we use a Monte Carlo method for approximating it with reasonable accuracy. We employ our methodology to study a dataset that logs per-subscriber temporal usage patterns over one month period for 10K broadband subscribers of a European AP and report observed results.
BoCluSt: Bootstrap Clustering Stability Algorithm for Community Detection

The identification of modules or communities in sets of related variables is a key step in the analysis and modeling of biological systems. Procedures for this identification are usually designed to allow fast analyses of very large datasets and may produce suboptimal results when these sets are of a small to moderate size. This article introduces BoCluSt, a new, somewhat more computationally intensive, community detection procedure that is based on combining a clustering algorithm with a measure of stability under bootstrap resampling. Both computer simulation and analyses of experimental data showed that BoCluSt can outperform current procedures in the identification of multiple modules in data sets with a moderate number of variables. In addition, the procedure provides users with a null distribution of results to evaluate the support for the existence of community structure in the data. BoCluSt takes individual measures for a set of variables as input, and may be a valuable and robust exploratory tool of network analysis, as it provides 1) an estimation of the best partition of variables into modules, 2) a measure of the support for the existence of modular structures, and 3) an overall description of the whole structure, which may reveal hierarchical modular situations, in which modules are composed of smaller sub-modules.
A Graphical Representation of Equivalence Classes of AMP Chain Graphs

This paper deals with chain graph models under alternative AMP interpretation. A new representative of an AMP Markov equivalence class, called the largest deflagged graph, is proposed. The representative is based on revealed internal structure of the AMP Markov equivalence class. More specifically, the AMP Markov equivalence class decomposes into finer strong equivalence classes and there exists a distinguished strong equivalence class among those forming the AMP Markov equivalence class. The largest deflagged graph is the largest chain graph in that distinguished strong equivalence class. A composed graphical procedure to get the largest deflagged graph on the basis of any AMP Markov equivalent chain graph is presented. In general, the largest deflagged graph differs from the AMP essential graph, which is another representative of the AMP Markov equivalence class.
Matrix Product State Based Algorithms

Strongly correlated systems are arguably one of the most studied systems in condensed matter physics. Yet, the (strong) interaction of many particles with each other is most of the times a strong limitation on practicable analytical methods, such that usually collective phenomenas are studied. To overcome this limitation, numerical techniques become more and more indespensable in the study of many-body systems. A fast growing field of numerical techniques which relies on the notion of entanglement in strongly correlated systems thoroughly analyzed in this thesis is usually referred to as Matrix Product States routines (in one dimension). In the following we will present such different numerical Matrix Product State routines and apply them to several interesting pehnomena in the field of condensed matter physics. Hereby, the motivation is to verify the accuracy of the implemented routines by matching them with well established results of interesting physical models. The algorithms will cover the exact computation of ground states as well as the dynamics of strongly correlated systems. Further, effort has been put in pointing out for which systems the routines work best and where possible limitations might occure. In this manner the with our routines studied physical models, will complete the picture by giving some hands-on examples for systems worth studying with Matrix Product State algorithms.
Knowledge Representation with Ontology

As a backbone of the Semantic Web, Ontologies provide a shared understanding of a domain of text. Ontologies, with their appearance, usage, and classification address for concrete ontology language which is important for the Semantic Web. They can be used to support a great variety of tasks in different domains such as knowledge representation, natural language processing, information retrieval, information exchange, collaborative systems, databases, knowledge management, database integration, digital libraries, information retrieval, or multi agent systems. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. This paper provides
Object Recognition Based on Amounts of Unlabeled Data

This paper proposes a novel semi-supervised method on object recognition. First, based on Boost Picking, a universal algorithm, Boost Picking Teaching (BPT), is proposed to train an effective binary-classifier just using a few labeled data and amounts of unlabeled data. Then, an ensemble strategy is detailed to synthesize multiple BPT-trained binary-classifiers to be a high-performance multi-classifier. The rationality of the strategy is also analyzed in theory. Finally, the proposed method is tested on two databases, CIFAR-10 and CIFAR-100. Using 2% labeled data and 98% unlabeled data, the accuracies of the proposed method on the two data sets are 78.39% and 50.77% respectively.
Perioperative Nutritional Status Changes in Gastrointestinal Cancer Patients

Purpose The presence of gastrointestinal (GI) cancer and its treatment might aggravate patient nutritional status. Malnutrition is one of the major factors affecting the postoperative course. We evaluated changes in perioperative nutritional status and risk factors of postoperative severe malnutrition in the GI cancer patients. Materials and Methods Nutritional status was prospectively evaluated using patient-generated subjective global assessment (PG-SGA) perioperatively between May and September 2011. Results A total of 435 patients were enrolled. Among them, 279 patients had been diagnosed with gastric cancer and 156 with colorectal cancer. Minimal invasive surgery was performed in 225 patients. PG-SGA score increased from 4.5 preoperatively to 10.6 postoperatively (p<0.001). Ten patients (2.3%) were severely malnourished preoperatively, increasing to 115 patients (26.3%) postoperatively. In gastric cancer patients, postoperative severe malnourishment increased significantly (p<0.006). In univariate analysis, old age (>60, p<0.001), male sex (p=0.020), preoperative weight loss (p=0.008), gastric cancer (p<0.001), and open surgery (p<0.001) were indicated as risk factors of postoperative severe malnutrition. In multivariate analysis, old age, preoperative weight loss, gastric cancer, and open surgery remained significant as risk factors of severe malnutrition. Conclusion The prevalence of severe malnutrition among GI cancer patients in this study increased from 2.3% preoperatively to 26.3% after an operation. Old age, preoperative weight loss, gastric cancer, and open surgery were shown to be risk factors of postoperative severe malnutrition. In patients at high risk of postoperative severe malnutrition, adequate nutritional support should be considered.
Resistive random access memory with high selectivity and ON/OFF ratio amplification sensing

The suppression of sneak paths in a cross-point RRAM array, one of the most challenging tasks in high density RRAM integration, has been successfully demonstrated by utilizing a Field Assisted Superlinear Threshold (FAST) selector (1, 2). The FAST selector provides a volatile switching behavior at the critical electric field. Excellent selector performance is presented, including high selectivity of > 107, sharp switching slope of <;5mV/dec, and large endurance of > 1011. Furthermore, the volatile switch based selector offers various benefits such as small operation voltage overhead in the 1S1R (1 selector 1 resistor) integration, large sensing margin and increased memory ON/OFF ratio.
The semantic augmentation of a psycholinguistically-motivated syntactic formalism

We augment an existing TAG-based incremental syntactic formalism, PLTAG, with a semantic component designed to support the simultaneous modeling effects of thematic fit as well as syntactic and semantic predictions. PLTAG is a psycholinguistically-motivated formalism which extends the standard TAG operations with a prediction and verification mechanism and has experimental support as a model of syntactic processing difficulty. We focus on the problem of formally modelling semantic role prediction in the context of an incremental parse and describe a flexible neo-Davidsonian formalism and composition procedure to accompany a PLTAG parse. To this end, we also provide a means of augmenting the PLTAG lexicon with semantic annotation. To illustrate this, we run through an experimentally-relevant model case, wherein the resolution of semantic role ambiguities influences the resolution of syntactic ambiguities and vice versa.
Philosophy in Construction: Understanding the development of expertise

Construction appears to have nothing to do with philosophy as it is a practical activity. This paper introduces a project funded by the UK, Arts and Humanities Research Council, that has supported a philosopher in residence in three construction companies. The project has taken up the challenge of the philosophy of practice focussing on the study of expertise. This philosophy has its conceptual roots in the work of Dreyfus, Heidegger and Aristotle but extends other work in the construction industry on knowledge management, experiential learning and radical education. This is of vital importance in an industry that is continually being directed theoretically to improve practice. Critical dialogue has been used as a therapeutic tool (in a Wittgensteinian sense) by seeking clarity in explanations of practice to value the actions of practitioners individually and in teams. The research has involved working in critical dialogue with trades-people, professionals and managers. The paper reports on early results exploring the development of expertise in novice through to experienced practitioners exposing issues for education to address around the handling of incomplete knowledge in contexts.
Real-time Localization of an Unmanned Ground Vehicle using a 360 Degree Range Sensor

A computer vision technique for the localization of an unman ned ground vihicle (UGV) is presented. The proposed technique is based on 3D registration of a sequence of 3 60 degree range data and a digital surface model (DSM). 3D registration of a sequence of dense range data requ ires a large computation time. For real time localization, we propose projection-based registration a nd uniform arc length sampling (UALS) techniques. UALS reduces the number of 3D sample points while maintainin g their uniformity over range data in terms of ground sample distance. The projection-based registrat ion technique reduces the time of 3D correspondence search. Experimental results from two real navigatio n paths are shown to verify the performance of the proposed method.
A novel backtracking particle filter for pattern matching indoor localization

Particle Filter (PF) techniques has been widely used in indoor localization systems. They are often used in conjunction with pattern matching based on Received Signal Strength Indication (RSSI) fingerprinting. Several variants of the particle filter within a generic framework of the Sequential Importance Sampling (SIS) algorithm have been described. The purpose of this paper is to show how a variant of PF, the so-called Backtracking Particle Filter (BPF), can be used to improve indoor localization performance. The BPF is a technique for refining state estimates based on exclusion of invalid particle trajectories. Categorization of invalid trajectory determined during importance sampling step of the PF. The BPF can also take advantage of available building plan information using the so-called Map Filtering (MF) technique. The incorporation of MF allows the BPF to exploit long-range geometrical constraints. This paper evaluates BPF with indoor localization based on WLAN RSSI fingerprinting. The filtering schema is evaluated using the propagation simulation in an office building, a typical environment for fingerprinting technique. Favorable result are obtained, showing positioning performance (1.34 m mean 2D error) superior to the PF-only no MF case (1.82 m mean 2D error), or up to 25% improvement. It is also shown that the performance is far better than the position estimates from conventional Nearest-Neighbour (NN) and Kalman Filter (KF) approaches using the same RSSI measurements.
Road Traffic Injury Trends in the City of Valledupar, Colombia. A Time Series Study from 2008 to 2012

Objective To analyze the behavior temporal of road-traffic injuries (RTI) in Valledupar, Colombia from January 2008 to December 2012. Methodology An observational study was conducted based on records from the Colombian National Legal Medicine and Forensic Sciences Institute regional office in Valledupar. Different variables were analyzed, such as the injured person’s sex, age, education level, and type of road user; the timeframe, place and circumstances of crashes and the vehicles associated with the occurrence. Furthermore, a time series analysis was conducted using an auto-regressive integrated moving average. Results There were 105 events per month on an average, 64.9% of RTI involved men; 82.3% of the persons injured were from 18 to 59 years of age; the average age was 35.4 years of age; the road users most involved in RTI were motorcyclists (69%), followed by pedestrians (12%). 70% had up to upper-secondary education. Sunday was the day with the most RTI occurrences; 93% of the RTI occurred in the urban area. The time series showed a seasonal pattern and a significant trend effect. The modeling process verified the existence of both memory and extrinsic variables related. Conclusions An RTI occurrence pattern was identified, which showed an upward trend during the period analyzed. Motorcyclists were the main road users involved in RTI, which suggests the need to design and implement specific measures for that type of road user, from regulations for graduated licensing for young drivers to monitoring road user behavior for the promotion of road safety.
An Intrinsic Criterion for the Bijectivity of Hilbert Operators Related to Friedrich' Systems

Friedrich' theory of symmetric positive systems of first-order PDE's is revisited so as to avoid invoking traces at the boundary. Two intrinsic geometric conditions are introduced to characterize admissible boundary conditions. It is shown that the space in which admissible boundary conditions can be enforced is maximal in a positive cone associated with the differential operator. The equivalence with a formalism based on boundary operators is investigated and practical means to construct these boundary operators are presented. Finally, the link with Friedrich' formalism and applications to various PDE's are discussed.
Common and Rare EGFR and KRAS Mutations in a Dutch Non-Small-Cell Lung Cancer Population and Their Clinical Outcome

Introduction In randomly assigned studies with EGFR TKI only a minor proportion of patients with NSCLC have genetically profiled biopsies. Guidelines provide evidence to perform EGFR and KRAS mutation analysis in non-squamous NSCLC. We explored tumor biopsy quality offered for mutation testing, different mutations distribution, and outcome with EGFR TKI. Patient and Methods Clinical data from 8 regional hospitals were studied for patient and tumor characteristics, treatment and overall survival. Biopsies sent to the central laboratory were evaluated for DNA quality and subsequently analyzed for mutations in exons 18–21 of EGFR and exon 2 of KRAS by bidirectional sequence analysis. Results Tumors from 442 subsequent patients were analyzed. For 74 patients (17%) tumors were unsuitable for mutation analysis. Thirty-eight patients (10.9%) had EGFR mutations with 79% known activating mutations. One hundred eight patients (30%) had functional KRAS mutations. The mutation spectrum was comparable to the Cosmic database. Following treatment in the first or second line with EGFR TKI median overall survival for patients with EGFR (n = 14), KRAS (n = 14) mutations and wild type EGFR/KRAS (n = 31) was not reached, 20 and 9 months, respectively. Conclusion One out of every 6 tumor samples was inadequate for mutation analysis. Patients with EGFR activating mutations treated with EGFR-TKI have the longest survival.