Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperthermia Sensitizes Rhizopus oryzae to Posaconazole and Itraconazole Action through Apoptosis

ABSTRACT The high mortality rate of mucormycosis with currently available monotherapy has created interest in studying novel strategies for antifungal agents. With the exception of amphotericin B (AMB), the triazoles (posaconazole [PCZ] and itraconazole [ICZ]) are fungistatic in vitro against Rhizopus oryzae . We hypothesized that growth at a high temperature (42°C) results in fungicidal activity of PCZ and ICZ that is mediated through apoptosis. R. oryzae had high MIC values for PCZ and ICZ (16 to 64 μg/ml) at 25°C; in contrast, the MICs for PCZ and ICZ were significantly lower at 37°C (8 to 16 μg/ml) and 42°C (0.25 to 1 μg/ml). Furthermore, PCZ and ICZ dose-dependent inhibition of germination was more pronounced at 42°C than at 37°C. In addition, intracellular reactive oxygen species (ROS) increased significantly when fungi were exposed to antifungals at 42°C. Characteristic cellular changes of apoptosis in R. oryzae were induced by the accumulation of intracellular reactive oxygen species. Cells treated with PCZ or ICZ in combination with hyperthermia (42°C) exhibited characteristic markers of early apoptosis: phosphatidylserine externalization visualized by annexin V staining, membrane depolarization visualized by bis-[1,3-dibutylbarbituric acid] trimethine oxonol (DiBAC) staining, and increased metacaspase activity. Moreover, terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling (TUNEL) assay and DAPI (4′,6-diamidino-2-phenylindole) staining demonstrated DNA fragmentation and condensation, respectively. The addition of N-acetylcysteine increased fungal survival, prevented apoptosis, reduced ROS accumulation, and decreased metacaspase activation. We concluded that hyperthermia, either alone or in the presence of PCZ or ICZ, induces apoptosis in R. oryzae. Local thermal delivery could be a therapeutically useful adjunct strategy for these refractory infections.
Promoting social plasticity in developmental disorders with non-invasive brain stimulation techniques

Being socially connected directly impacts our basic needs and survival. People with deficits in social cognition might exhibit abnormal behaviors and face many challenges in our highly social-dependent world. These challenges and limitations are associated with a substantial economical and subjective impact. As many conditions where social cognition is affected are highly prevalent, more treatments have to be developed. Based on recent research, we review studies where non-invasive neuromodulatory techniques have been used to promote Social Plasticity in developmental disorders. We focused on three populations where non-invasive brain stimulation seems to be a promising approach in inducing social plasticity: Schizophrenia, Autism Spectrum Disorder (ASD) and Williams Syndrome (WS). There are still very few studies directly evaluating the effects of transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) in the social cognition of these populations. However, when considering the promising preliminary evidences presented in this review and the limited amount of clinical interventions available for treating social cognition deficits in these populations today, it is clear that the social neuroscientist arsenal may profit from non-invasive brain stimulation techniques for rehabilitation and promotion of social plasticity.
Nurturing wearable and mHealth technologies for self-care: Mindset, tool set and skill set

This study aimed to understand whether and how wearable technologies and mHealth services could be nurtured for self-care at the individual-level. We launched a self-care online survey in 2015, and a total of 188 participants (66% female; mean age = 30 years) completed the survey. Following the survey, we also conducted a qualitative study with 12 Fitbit users. In both studies, we focused on understanding three prerequisite elements for behavior change: mindset, tool set and skill set. The results showed that most people had the mindset that self-care was important for preventing chronic diseases, and their top health concerns were sleep quality, body weight, mood, skin conditions and chronic fatigue. As for tool set, users acknowledge the potentially positive impact and efficacy of the technologies in facilitating self-care. However, current technologies have several usability issues such as low accuracy, low technology transparency, and limited feedback. As for skill set, two major obstacles were identified: difficulty in sustaining the usage of the technologies, and lacking domain knowledge and data analysis skills to gain insights from personal data. Different from existing studies which mainly focused on understanding the tool set, i.e., the technologies and services per se, this study produced new insights on the current landscape of people's mindset and skill set on adopting the technologies for health behavior change. We also summarized the opportunities and challenges to guide researchers in designing new wearable technologies and mHealth services for self-care.
A comparative study of pre-image techniques: The kernel autoregressive case

The autoregressive (AR) model is one of the most used techniques for time series analysis, applied to study stationary as well as non-stationary processes. However, being a linear technique, it is not adapted for nonlinear systems. Recently, we introduced the kernel AR model, a straightforward extension of the AR model to the nonlinear case. It is based on the concept of kernel machines, where data are nonlinearly mapped from the input space to a feature space. The AR model can thus be applied on the mapped data. Nevertheless, in order to predict future samples, one needs to go back to the input space, by solving the pre-image problem. The prediction performance highly depends on the considered pre-image technique. In this paper, a comparative study of several state-of-the-art pre-image techniques is conducted for the kernel AR model, investigating the prediction error with the optimal model parameters, as well as the computational complexity. The conformal map approach presents results as good as the well known fixed-point iterative method, with less computational time. This is shown on unidimensional and multidimensional chaotic time series.
The influence of national competition policy in reshaping human service delivery

This thesis sets out to look at the influence of Australia’s National Competition Policy reform in reshaping human services. In trying to understand this influence, the research explores the main threads of National Competition Policy – including concepts of contestable markets, Competitive Neutrality and Competitive Tendering and Contracting. It also notes the dominance of neoclassical economics and public choice theories underpinning the framing of this reform. More specifically, the work tracks down the way in which National Competition Policy is impacting on human service delivery through using particular contractual arrangements with non-government providers in a bid to foster value for money through competition and market choice arrangements. This part of the research rests on two case studies, an employment assistance service (Labour Market Programs) known as Job Network and a crisis accommodation service (Supported Accommodation Assistance Program - SAAP). Through them the thesis explores and locates the parameters and the terms under which federal and state funding is accessed and the new organisational arrangements which regulate the terms of client treatment and the scope of professional practice. In theoretical terms, the thesis draws on mainly a governmentality literature to illuminate the emerging modes of neo-liberal governance, particular attention is given to patterns of self regulation and the conditions governing distant actors.
A meta-analysis for efficacy and safety evaluation of transcatheter left atrial appendage occlusion in patients with nonvalvular atrial fibrillation

Objectives: This meta-analysis was conducted to evaluate the efficacy and safety of transcatheter left atrial appendage (LAA) occlusion in patients with nonvalvular atrial fibrillation. Methods: The randomized controlled trials (RCT) or observational studies with any transcatheter LAA occlusion devices were searched in PubMed, Embase, and Cochrane library from inception to November 2015. The incidence rates from individual studies were combined to evaluate the procedural efficacy and safety, including all-cause death, cardiac/neurological death, stroke, transient ischemic attack (TIA), thrombosis, hemorrhagic complications, and pericardial effusion/tamponade. Results: Thirty-eight studies involving 3585 patients and 6 different occlusion devices were eligible for our inclusion criteria. The procedural failure rate for LAA closure was 0.02 (95% CI: 0.02–0.03). The all-cause mortality was 0.03 (95% CI: 0.02–0.03) and cardiac/neurological mortality was 0 (95% CI: 0.00–0.01). The stroke/TIA rate was estimated only 0.01 (95% CI: 0.01–0.01). The incidence of thrombus on devices was 0.01 (95% CI: 0.01–0.02). The major hemorrhagic complication rate was estimated 0.01 (95% CI: 0.00–0.01). Pericardial effusion/tamponade was estimated 0.02 (95% CI: 0.02–0.03). No heterogeneity was observed for above pooled estimates (I 2 = 0). In devices subgroups analysis, the all-cause mortality and cardiac/neurological mortality of PLAATO group were the highest (P = 0.01 and P < 0.01 respectively), whereas the incidence of thrombus on devices in the ACP group was the highest (P < 0.01). In follow-up period subgroups analysis, there were significant differences in all-cause death, stroke/TIA, major hemorrhage, and pericardial effusion/tamponade events between the shorter and longer follow-up period subgroups (P < 0.05). However, the differences among the subgroups were numerically small. Conclusions: the pooled data demonstrated that transcatheter LAA occlusion was effective and safe in the patients with nonvalvular atrial fibrillation who were not suitable for lifelong antithrombotic therapy.
Deflect Routing for Throughput Improvement in Multi-Hop Millimeter-Wave WPAN System

In this paper, we propose a cross-layer deflect routing scheme that improves effective throughput of multi-hop millimeter-wave wireless personal area network (mmWave WPAN) system. The upcoming mmWave WPAN is designed to provide Gbps-order transmission capability, targeting applications like high definition TV (HDTV) transmission, high speed wireless docking and gaming etc. Adopting multi-hop relay offers solutions to the issues existing in mmWave WPAN system such as limited coverage range caused by the significant path loss, dramatic communication link change due to the unexpected blockage of the line-of-sight (LOS) path etc. However, multi-hop relay on the other hand decreases the effective throughput because of the required extra time. As a result, the compromised throughput may not be able to support the above applications which are greedy for data rate. Inspired by the fact that the significant path loss of millimeter-wave environment can provide good space isolation, our proposed scheme manages to improve the multi-hop throughput by sharing time slots for relay path with direct path for other transmissions. We also propose two algorithms, namely random fit deflect routing (RFDR) and best fit deflect routing (BFDR), to find the relay path on which the interference due to time slot sharing is low enough to guarantee the concurrent transmissions. Computer simulations show that, in realistic 60GHz environment, the effective multi-hop throughput can be improved up to 45%.
Capturing Reasoning Process through User Interaction

In recent years, visual analytics has taken an important role in solving many types of complex analytical problems that require deep and specific domain knowledge from users. While the analysis products generated by these expert users are of great importance, how these users apply their domain expertise in using the visualization to validate their hypotheses and arrive at conclusions is often just as invaluable. Recent research efforts in capturing an expert’s reasoning process using a visualization have shown that some of a user’s analysis process is indeed recoverable. However, there does not exist a generalizable principle that explains the success of these domainspecific systems in capturing the user’s reasoning process. In this paper, we present a framework that examines two aspects of the capturing process. First, we inspect how a user’s reasoning process can be captured by utilizing van Wijk’s operational model of visualization. Second, we evaluate the likelihood of success in capturing a user’s interactions in a visualization by introducing three criteria designed for disambiguating the meanings behind the interactions. Various visualization systems in the visualization and HCI communities are examined for the purpose of demonstrating the impact of the three criteria.
Classical simulation of quantum computation, the gottesman-Knill theorem, and slightly beyond

We study classical simulation of quantum computation, taking the Gottesman-Knilltheorem as a starting point. We show how each Clifford circuit can be reduced to anequivalent, manifestly simulatable circuit (normal form). This provides a simple proofof the Gottesman-Knill theorem without resorting to stabilizer techniques. The normalform highlights why Clifford circuits have such limited computational power in spiteof their high entangling power. At the same time, the normal form shows how theclassical simulation of Clifford circuits fits into the standard way of embedding classicalcomputation into the quantum circuit model. This leads to simple extensions of Cliffordcircuits which are classically simulatable. These circuits can be efficiently simulated byclassical sampling ("weak simulation") even though the problem of exactly computingthe outcomes of measurements for these circuits ("strong simulation") is proved to be#P-complete-thus showing that there is a separation between weak and strong classicalsimulation of quantum computation.
Dynamics computation of structure-varying kinematic chains for motion synthesis of humanoid

Discusses the dynamics computation of structure-varying kinematic chains which imply mechanical link systems whose structure may change from open kinematic chain to closed one and vice versa. The proposed algorithm can handle structure changes in a seamless manner without switching among algorithms for different kinematic chains. The structure-varying kinematic chains are commonly found in computing human motions. The developed computation will provide the general algorithm for the computation of motion and control of humanoid robots and computer graphic human figures.
The Financial Cycle Indexes for Early Warning Exercise

This paper introduces financial cycle indexes and uses them in an early warning exercise. The indexes are based on the traditional theory of business cycles. Juglar cycles are deduced from a number of financial indicators, categorized as leading and lagging indicators, and aggregated into leading and lagging indexes. We constructed Japanese financial cycle indexes and found that they warn of the current global financial crisis about a year in advance. However, the result should be interpreted with caution, since the indexes fail to take into consideration the uncertainty surrounding real-time problems and the potential delays that may be caused by policy judgment. As a solution, we propose a scheme to forecast financial cycle indexes. According to the test results based on the Japanese data, our scheme gives advance warning of the ongoing global financial crisis.
Grounding social interactions with a ective intelligence in the iterated prisoner ' s dilemma

Symbolic interactionist principles of sociology are based on the idea that human action is guided by culturally shared symbolic representations of identities, behaviours, situations and emotions. Shared linguistic, paralinguistic, or kinesic elements allow humans to coordinate action by enacting identities in social situations. Structures of identity-based interactions can lead to the enactment of social orders that solve social dilemmas (e.g., by promoting cooperation). Our goal is to build an arti cial agent that mimics the identity-based interactions of humans, and to compare networks of such agents to human networks. In this paper, we take a rst step in this direction, and describe a study in which humans played a repeated prisoner's dilemma game against other humans, or against one of three arti cial agents (bots). One of the bots has an explicit representation of identity (for self and other), and attempts to optimise with respect to this representation. We compare the human play against bots to human play against humans, and show how the identity-based bot exhibits the most human-like behaviour.
Direct power control of shunt active filter

This paper presents a new control method entitled direct power control (DPC) for shunt active power filtering, which is applied to eliminate line current harmonics and compensate reactive power. Its main goal is to rebuild active and reactive powers to be compared to references values using hysteresis control. The outputs of hysteresis controllers associated with a switching table, control the instantaneous active and reactive power by selecting the optimum switching state of the voltage source inverter (VSI). A theoretical analysis with a complete simulation of the system and experimental results are presented to prove the excellent performance of the proposed technique.
Estimation of hand force from surface Electromyography signals using Artificial Neural Network

Haptic technology has many real world applications such as rehabilitation robotics, telepresence surgery, gaming, virtual reality and human-robot interaction. Force plays an important role in the above mentioned haptic applications. In this paper, we propose a method to estimate force from surface Electromyography (SEMG) signals using Artificial Neural Network (ANN). The haptic device is modeled to act as a virtual spring. The neural network is trained with EMG data from wrist flexion action as input and force values from the haptic device as target. The results shown in this paper illustrate the neural network performance in estimating the force values in real-time.
RT / 52 / 2009 RASM : A Reliable Algorithm for Scalable Multicast

Recently there has been an effort to build scalable and reliable application-level multicast solutions that combine the resilience of pure gossip-based with the efficiency of tree-based schemes. However, such solutions assume that participants have unlimited resources, for instance, that they can send an unbounded number of messages to mask network omissions. Such scenario is not realistic, specially for streaming protocols, where messages can be transmitted at a very high rate and have a small temporal validity. In this paper, we propose RASM, a scalable distributed protocol for application-level multicast. Our protocol is based on the combination of gossip-based and tree-based multicast schemes. Unlike previous approaches, which strive to combine gossip-based and tree-based schemes, our solution takes into consideration the reliability of components: nodes and communication links can fail, unexpectedly, ceasing their operation and dropping messages, respectively. Experimental results show that our scheme offers better reliability than previous solutions with low overhead.
A Region-based Approach for Sparse Parallel Computing

This paper introduces a technique for parallel sparse compu tation by extending the array-language concept ofregions—regular programmer-specified index sets used for specifyin g array computations. We introduce the notion of sparse regionswhich can represent an arbitrary set of indices. Sparse regi ons inherit the benefits of regular regions, including concisen ess, a direct encapsulation of parallelism, and support for language performance models that highlight par allel overheads. We show that region-based array languages can benefit from the use of sparse regions, bo th in terms of the semantic richness available to the programmer and the execution times of the resulti ng program. We also demonstrate that regions result in efficient implementations as compared to a rray-based approachs, due to their role in amortizing sparse overheads and enabling optimizations.
The spectrum of a magnetic Schrödinger operator with randomly located delta impurities

We consider a single band approximation to the random Schrodinger operator in an external magnetic field. The spectrum of such an operator has been characterized in the case where delta impurities are located on the sites of a lattice. In this paper we generalize these results by letting the delta impurities have random positions as well as strengths; they are located in squares of a lattice with a general bounded distribution. We characterize the entire spectrum of this operator when the magnetic field is sufficiently high. We show that the whole spectrum is pure point, the energy coinciding with the first Landau level is infinitely degenerate, and that the eigenfunctions corresponding to other Landau band energies are exponentially localized.
Evaluation Complexity for Nonlinear Constrained Optimization Using Unscaled KKT Conditions and High-Order Models

The evaluation complexity of general nonlinear, possibly nonconvex, constrained optimization is analyzed. It is shown that, under suitable smoothness conditions, an $\epsilon$-approximate first-order critical point of the problem can be computed in order $O(\epsilon^{1-2(p+1)/p})$ evaluations of the problem's functions and their first $p$ derivatives. This is achieved by using a two-phase algorithm inspired by Cartis, Gould, and Toint [SIAM J. Optim. , 21 (2011), pp. 1721--1739; SIAM J. Optim. , 23 (2013), pp. 1553--1574]. It is also shown that strong guarantees (in terms of handling degeneracies) on the possible limit points of the sequence of iterates generated by this algorithm can be obtained at the cost of increased complexity. At variance with previous results, the $\epsilon$-approximate first-order criticality is defined by satisfying a version of the KKT conditions with an accuracy that does not depend on the size of the Lagrange multipliers.
Does the Choice of Model or Benchmark Affect Inference in Measuring Mutual Fund Performance?

We address the practical question of whether investors and researchers are likely to make invalid inferences about fund manager performance when using the wrong model and/or benchmark. We consider three well-known models, those of Jensen (1968), Treynor and Mazuy (1966), and Henriksson and Merton (1981), and two commonly used timing benchmarks, the SP (2) but biases in measures of overall performance are economically insignificant; (3) benchmark misspecification results in qualitatively similar difficulties, with the addition that overall performance as well can be biased; and (4) model and benchmark misspecification do not appreciably alter the power to detect ability and distinguish a good fund from a bad fund. These results are robust to alternative asset pricing specifications, alternative simulation schemes, varying length of the return series, and periodicity of the simulated series. The use of daily fund returns amplifies our conclusions about the biases induced by model misspecifications. Moreover, the biases we identify appear to be difficult to correct by using standard model selection criteria and misspecification tests. If the benchmark is known but the timing model is not, investors should use measures of overall performance to evaluate funds and managers.