Miracle-dz/UniFAR-FTUs huggingface dataset:
Resuscitation of Salmonella enterica Serovar Typhimurium and Enterohemorrhagic Escherichia coli from the Viable but Nonculturable State by Heat-Stable Enterobacterial Autoinducer

ABSTRACT Salmonella enterica serovar Typhimurium and enterohemorrhagic Escherichia coli were stressed by prolonged incubation in water microcosms until it was no longer possible to observe colony formation when samples were plated on nonselective medium. Overnight incubation of samples in nutrient-rich broth medium supplemented with growth factors, however, allowed resuscitation of stressed and viable but nonculturable cells so that subsequent plating yielded observable colonies for significantly extended periods of time. The growth factors were (i) the trihydroxamate siderophore ferrioxamine E (for Salmonella only), (ii) the commercially available antioxidant Oxyrase, and (iii) the heat-stable autoinducer of growth secreted by enterobacterial species in response to norepinephrine. Analysis of water microcosms with the Bioscreen C apparatus confirmed that these supplements enhanced recovery of cells in stressed populations; enterobacterial autoinducer was the most effective, promoting resuscitation in populations that were so heavily stressed that ferrioxamine E or Oxyrase had no effect. Similar results were observed in Bioscreen analysis of bacterial populations stressed by heating. Patterns of resuscitation of S. enterica serovar Typhimurium rpoS mutants from water microcosms and heat stress were qualitatively similar, suggesting that the general stress response controlled by the σs subunit of RNA polymerase plays no role in autoinducer-dependent resuscitation. Enterobacterial autoinducer also resuscitated stressed populations of Citrobacter freundii and Enterobacter agglomerans.
Low complexity beamforming training method for mmWave communications

This paper introduces a low complexity method for antenna sector selection in mmWave Hybrid MIMO communication systems like the IEEE 802.11ay amendment for Wireless LANs. The method is backwards compatible to the methods already defined for the released mmWave standard IEEE 802.11ad. We introduce an extension of the 802.11ad channel model to support common Hybrid MIMO configurations. The proposed method is evaluated and compared to the theoretical limit of transmission rates found by exhaustive search. In contrast to state-of-the-art solutions, the presented method requires sparse channel information only. Numerical results show a significant complexity reduction in terms of number of necessary trainings, while approaching maximum achievable rate.
Forest-based Translation Rule Extraction

Translation rule extraction is a fundamental problem in machine translation, especially for linguistically syntax-based systems that need parse trees from either or both sides of the bi-text. The current dominant practice only uses 1-best trees, which adversely affects the rule set quality due to parsing errors. So we propose a novel approach which extracts rules from a packed forest that compactly encodes exponentially many parses. Experiments show that this method improves translation quality by over 1 BLEU point on a state-of-the-art tree-to-string system, and is 0.5 points better than (and twice as fast as) extracting on 30-best parses. When combined with our previous work on forest-based decoding, it achieves a 2.5 BLEU points improvement over the base-line, and even outperforms the hierarchical system of Hiero by 0.7 points.
Causal Consistency of Structural Equation Models

Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations between SEMs. This provides a general language to consider, for instance, the different levels of description in the following three scenarios: (a) models with large numbers of variables versus models in which the `irrelevant' or unobservable variables have been marginalised out; (b) micro-level models versus macro-level models in which the macro-variables are aggregate features of the micro-variables; (c) dynamical time series models versus models of their stationary behaviour. Our analysis stresses the importance of well specified interventions in the causal modelling process and sheds light on the interpretation of cyclic SEMs.
Federal Reserve Bank of Philadelphia Optimal Monetary Policy *

Optimal monetary policy maximizes welfare, given frictions in the economic environment. Constructing a model with two sets of frictions — the Keynesian friction of costly price adjustment by imperfectly competitive firms and the Monetarist friction of costly exchange of wealth for goods — we find optimal monetary policy is governed by two familiar principles. First, the average level of the nominal interest rate should be sufficiently low, as suggested by Milton Friedman, that there should be deflation on average. Yet, the Keynesian frictions imply that the optimal nominal interest rate is positive. Second, as various shocks occur to the real and monetary sectors, the price level should be largely stabilized, as suggested by Irving Fisher, albeit around a deflationary trend path. (In mo dern l anguage , t here is only small “base dri ft” for t he price level path.) Since expected inflation is roughly constant through time, the nominal interest rate must therefore vary with the Fisherian determinants of the real interest rate — as there is expected growth or contraction of real economic activity.
LIMSI: Learning Semantic Similarity by Selecting Random Word Subsets

We propose a semantic similarity learning method based on Random Indexing (RI) and ranking with boosting. Unlike classical RI, we use only those context vector features that are informative for the semantics modeled. Despite ignoring text preprocessing and dispensing with semantic resources, the approach was ranked as high as 22nd among 89 participants in the SemEval-2012 Task6: Semantic Textual Similarity.
Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation

This paper attacks the problem of generalized multisensor mixture estimation. A distribution mixture is said to be generalized when the exact nature of components is not known, but each of them belongs to a finite known set of families of distributions. Estimating such a mixture entails a supplementary difficulty: one must label, for each class and each sensor, the exact nature of the corresponding distribution. Such generalized mixtures have been studied assuming that the components lie in the Pearson system. We propose a more general procedure with applications to estimating generalized multisensor hidden Markov chains. Our proposed method is applied to the problem of unsupervised image segmentation. The method proposed allows one to: 1) identify the conditional distribution for each class and each sensor, 2) estimate the unknown parameters in this distribution, 3) estimate priors, and 4) estimate the "true" class image.
Approximation Algorithms for Minimizing the Number of Roles and Administrative Assignments in RBAC

In role based access control (RBAC), minimizing the descriptive set of roles (specified as Basic-RMP) and minimizing the administrative assignments for roles (specified as Edge-RMP) can greatly decrease the management costs. Both role mining problems are proved to be NP-hard. In this work, we convert Basic-RMP to set cover problem and Edge-RMP to weighted set cover problem. Hence, many well-known methods for solving the Set Cover Problems can be applied to solve these role mining problems. According to the conversion process and the greedy idea for solving set cover problems, two algorithms respectively named GABasic algorithm for Basic-RMP and GAEdge algorithm for Edge-RMP, are given in this paper. Experiment results show that the average similarity rate between role sets produced by GABasic algorithm and the original ones is above 90%. However, in the process of converting role mining into Set Cover Problem, the number of candidate role set was exponential. In order to reduce the complexity of the GABasic algorithm, this paper presents a new polynomial-time algorithm with a performance nearly the same as that of GABasic algorithm.
Putting conflict management into practice: a nursing case study.

AIM This paper is intended to put knowledge in conflict management into practice through reflecting on a nursing case study. BACKGROUND Nursing organizations are particularly vulnerable to conflict as the context of nurses' work may be difficult and stressful. Power conflict is argued to be an important source of tension within nursing units. Learning to manage conflict at an early stage is therefore crucial to the effective functioning of nursing organizations. EVALUATION A nursing case study that illustrates power conflict in an oncology nursing unit is displayed and reflection on conflict management from the case is provided. KEY ISSUES There is no appropriate or inappropriate strategy to deal with conflict. However, detecting initial symptoms of conflict and adopting the most effective behaviour to conflict resolution is essential in nursing units. CONCLUSION Further nursing education in conflict management for staff nurses and nurse managers is greatly needed.
Neural Network Based Hausa Language Speech Recognition

Speech recognition is a key element of diverse applications in communication systems, medical transcription systems, security systems etc. However, there has been very little research in the domain of speech processing for African languages, thus, the need to extend the frontier of research in order to port in, the diverse applications based on speech recognition. Hausa language is an important indigenous lingua franca in west and central Africa, spoken as a first or second language by about fifty million people. Speech recognition of Hausa Language is presented in this paper. A pattern recognition neural network was used for developing the system.
Multi-standard trellis-based FEC decoder

Nowadays wireless standards optimize their capacity thanks to techniques such as advanced Forward Error Correction (FEC) technologies like Low-Density Parity-Check and Turbo codes. Smartphone and Home automation are specific use cases where multi-standard architectures show great interest. Furthermore some standardized FEC can be interpreted as trellis codes. Trellis-based iterative algorithms are competitive to decode several codes. The goal detailed in this paper is to provide a multistandard decoder applied on 3GPP-LTE and IEEE802.11n codes. A state of the art of main decoding algorithms and on the selected codes is necessary to underline the decoding similarities and to design a trellis construction. An optimized trellis decoder followed by a common architecture are then studied and compared with existing solutions in term of performance, complexity and throughputs.
Teaching about Gender Issues in the Art Education Classroom: Myra Sadker Day1

This article is a description and analysis of the course processes and project outcomes of an an education class where feminist issues were a significant part of the course. Final projects were designed to contribute to Myra Sadker Day, established to honor Sadker's significant work to expose gender bias in U.S. classrooms and to encourage equitable practices in the classroom. Sadker Day was thought to be important in promoting education about feminist issues in classrooms by actively involving students in feminist issues. Four themes from scholarship on feminist pedagogy will be used to analyze class discussions and projects: mastery, authority, voice, and positionality.
Visibility-Based Obstacle Placing Automated Obstacle Placing based on Circularity

In this thesis the problem of placing multiple obstacles in a discrete 2D environment is considered. An example real life application for this problem is the placing of exhibition objects in museums. Previous studies by various authors showed that people’s behaviour in space is linked to the circularity measure – also called compactness – of their field of vision. This measure describes the shape of the space that can be seen from a specific point. An evolutionary algorithm (EA) is developed that places the given obstacles at the remaining unblocked space according to the compactness at the remaining unoccupied space. The EA uses different target functions to either minimise or maximise the compactness measure globally or locally. Several test series are executed in order to assess influence of the algorithm’s parameters on its performance. The solutions of the EA show emerging patterns for different target functions. From those results a faster constructive heuristic is developed that can provide several different solutions to simple placing problems within short time. In the EA a version of the Shadow Casting algorithm is used to calculate the field of vision for a point. The heuristic extends this algorithm by placing obstacles while iterating through the visible areas. The objects are placed in a way such that preset points show a high compactness value and that the architect can be inspired by the presented diverse solutions.
Enhanced nonlinear optical response of composite materials.

We present experimental results which demonstrate that the effective third-order susceptibility of a composite optical material can exceed those of the materials from which it is constructed. In particular, we have formed a composite of alternating, sub-wavelength-thick layers of titanium dioxide and the conjugated polymer poly( $p$-phenylene-benzobisthiazole), and find that its nonlinear susceptibility exceeds that of its more nonlinear constituent by 35%. The enhancement of the nonlinear susceptibility, which under more ideal but still realistic conditions can be as large as a factor of 10, can be understood as a consequence of local field corrections.
Workflow-based Services Infrastructure for Scientific Applications

The way scientists work in traditional sciences has changed drastically in recent years. Computer science is increasingly supporting them in performing and analyzing their experiments. Today, obtaining the raw data from instruments is merely the first step. No longer is the data analyzed on paper or with simple computational tools. Instead, massive amounts of data obtained raw from instruments are processed in complex and long-running computational pipelines. This trend of supporting traditional sciences with computational tools has lead to a significant speedup in executing experiments and has also enabled experiments which would not have been possible before. Scientists increasingly depend on adequate infrastructure to process experiment data using complex computational pipelines and to manage the plethora of data used and produced by them. Such computational pipelines are typically modeled as workflows and so this trend consequently challenges the current infrastructure for executing workflows as well as the infrastructure to manage the resulting data deluge. The work presented in this book addresses some of the challenges arising from this trend. A first challenge this book addresses is the sharing of scientific computations. Publishing a methodology for others to use is no longer possible with the scientific computations or workflows people develop today. Implementing such workflows and setting up the appropriate environment is far too difficult and consequently such computations or workflows must be shared. State-of-the-art approaches use proprietary interfaces to make such workflows available as services to be used by others. Proprietary interfaces, however, make it difficult to integrate such computations and we therefore use standardized interfaces. We describe in detail how we have mapped a workflow to such a standardized interface and report on an efficient implementation. The second challenge this book examines is how to provide adequate infrastructure for scientific workflows. One considerable advantage of scientific workflows is that they can be parametrized and one workflow can be run several thousand times for processing vast numbers of raw data sets obtained from instruments. Such large numbers of executions mandate the execution engine to be distributed in order to scale. However, configuring such a distributed engine is very difficult. Also, if it is configured optimally for just one type of workload, it may not be suitable for another. For this reason, we have added an autonomic controller to a distributed workflow execution engine. This controller monitors the behavior of the engine and adapts the configuration on the fly to serve the current workload best, adding self-configuration and self-optimization properties. The controller also heals the system in case of failures, thereby adding self-healing properties.
Beamforming with Finite Rate Feedback for LOS MIMO Downlink Channels

We consider a MIMO line of sight (LOS) broadcast channel where each user has perfect knowledge of its own channel and feeds back 'quantized' channel information to the transmitter with finite number of bits. The rate loss incurred due to imperfect channel information at the transmitter is quantified in terms of the number of feedback bits, assuming that low-complexity zero-forcing beamforming is used by the transmitter. Moreover, in order to maintain a constant rate loss relative to a perfect CSIT system, it is shown that the number of feedback bits per user should grow linearly with the system SNR in dB but only logarithmitcally with the number of transmit antennas. Simulation results comparing zero-forcing, MMSE and conventional beamforming with greedy user selection and finite rate feedback are also presented.
An improved evolutionary multi-objective optimization method

An improved evolutionary multi-objective optimization method is introduced to solve the general multi-objective optimization problem with the constraint functions and the different important degree of objective functions. In this method the genetic algorithm is used to search the global optimum solutions. The group is divided into unfeasible domain and feasible domain. Unfeasible solutions are sorted in order by the fitness. Feasible solutions are sorted in order by fuzzy method. Elitist preservation mechanism and the crowding distance are adopted to enhance the efficiency of searching. An example is given to demonstrate this algorithm. The results showed that the improved algorithm is an efficient method to solve the preference in optimization. The algorithm operated fast convergence speed of solution process, high accuracy, and can obtain the global optimum solutions.
Insight into Crizotinib Resistance Mechanisms Caused by Three Mutations in ALK Tyrosine Kinase using Free Energy Calculation Approaches

As a safe and efficacious drug, crizotinib was approved by the U.S. Food and Drug Administration (FDA) in 2011 for the treatment of advanced fusion-type nonsmall-cell lung cancer. Although high response ratio was detected from the patients treated with crizotinib, the cancer has eventually conferred resistance to crizotinib. Several drug resistance mutations have been found in the anaplastic lymphoma kinase (ALK) tyrosine kinase domain as the target for crizotinib, but the drug resistance mechanisms remain unclear. Therefore, in this study, the adaptive biasing force (ABF) method and two-end-state free energy calculation approaches were employed to elucidate the resistance mechanisms of crizotinib induced by the mutations L1152R, G1202R, and S1206Y. The ABF simulation results suggest that the reaction coordinates for the unbinding processes of crizotinib from the binding pockets of the mutated ALKs is different from that of the wild type ALK. The potentials of mean force for the crizotinib unbinding and the binding free energies predicted by the two-end-state free energy calculations are consistent with the experimental data. Our results indicate that the three mutations weaken the binding affinity of crizotinib obviously and lead to drug resistance. The free energy decomposition analysis illustrates the importance of the loss of two important H-bonds in the L1152R and S1206Y mutants on drug resistance. The entropy analysis shows that the entropy term plays a critical role in the substantial change of the conformational entropies of G1202R and L1152R. Our results reveal the mechanisms of drug resistance and provide vital clues for the development of new inhibitors to combat drug resistance.
Adeno-Associated Virus Vectors as Therapeutic and Investigational Tools in the Cardiovascular System

The use of vectors based on the small parvovirus adeno-associated virus has gained significant momentum during the past decade. Their high efficiency of transduction of postmitotic tissues in vivo, such as heart, brain, and retina, renders these vectors extremely attractive for several gene therapy applications affecting these organs. Besides functional correction of different monogenic diseases, the possibility to drive efficient and persistent transgene expression in the heart offers the possibility to develop innovative therapies for prevalent conditions, such as ischemic cardiomyopathy and heart failure. Therapeutic genes are not only restricted to protein-coding complementary DNAs but also include short hairpin RNAs and microRNA genes, thus broadening the spectrum of possible applications. In addition, several spontaneous or engineered variants in the virus capsid have recently improved vector efficiency and expanded their tropism. Apart from their therapeutic potential, adeno-associated virus vectors also represent outstanding investigational tools to explore the function of individual genes or gene combinations in vivo, thus providing information that is conceptually similar to that obtained from genetically modified animals. Finally, their single-stranded DNA genome can drive homology-directed gene repair at high efficiency. Here, we review the main molecular characteristics of adeno-associated virus vectors, with a particular view to their applications in the cardiovascular field.
The PB1 Domain in Auxin Response Factor and Aux/IAA Proteins: A Versatile Protein Interaction Module in the Auxin Response[OPEN]

An integral part of auxin-regulated gene expression involves the interplay of two types of transcription factors, the DNA binding auxin response factor (ARF) activators and the interacting auxin/indole acetic acid (Aux/IAA) repressors. Insight into the mechanism of how these transcription factors interact with one another has recently been revealed from crystallographic information on ARF5 and ARF7 C-terminal domains (i.e., a protein-protein interaction domain referred to as domain III/IV that is related to domain III/IV in Aux/IAA proteins). Three-dimensional structures showed that this domain in ARF5 and ARF7 conforms to a well-known PB1 (Phox and Bem1) domain that confers protein-protein interactions with other PB1 domain proteins through electrostatic contacts. Experiments verifying the importance of charged amino acids in conferring ARF and Aux/IAA interactions have confirmed the PB1 domain structure. Some in planta experiments designed to test the validity of PB1 interactions in the auxin response have led to updated models for auxin-regulated gene expression and raised many questions that will require further investigation. In addition to the PB1 domain, a second protein interaction module that functions in ARF-ARF dimerization and facilitates DNA binding has recently been revealed from crystallography studies on the ARF1 and ARF5 DNA binding domains.