Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety of intravitreal voriconazole: electroretinographic and histopathologic studies.

PURPOSE Voriconazole, a novel triazole antifungal agent, presents potent activity against a broad spectrum of yeast and molds. To determine whether it could be safely used as an intravitreal agent in the treatment of fungal endophthalmitis, retinal toxicity of voriconazole was examined in a rodent animal model. METHODS Voriconazole solutions were serially diluted and injected intravitreally into eyes of normal adult Sprague-Dawley rats so that the final intravitreal concentrations were 5 microg, 10 microg, 25 microg, 50 microg, and 500 microg/mL (N = 3 for each concentration group). Saline was injected into the fellow eyes of each animal as controls. Three weeks after injections, electroretinograms (ERGs) were measured, and eyes were subsequently enucleated for histologic examination. RESULTS In ERG studies, maximum scotopic b-wave, bmax intensity needed for half saturation, I0.5, and saturated a-wave amplitude were measured. There was no statistically significant difference in these parameters recorded between control eyes and voriconazole-injected eyes in any concentration groups. Histologic examination with light microscopy did not reveal any retinal abnormality in the eyes with 5 to 25 microg/mL intravitreal voriconazole. In the eyes with 50 microg/mL and 500 microg/mL voriconazole, small foci of retinal necrosis were occasionally observed in the outer retina, especially in the eyes with 500 mg/mL voriconazole. CONCLUSION Our results demonstrate that intravitreal voriconazole of up to 25 mg/mL causes no ERG change or histologic abnormality in rat retina. This indicates that voriconazole is a safe antifungal agent that may be employed by intravitreal injection in the treatment of fungal endophthalmitis.
Perceived Intergroup Stereotypes, Threats, and Emotions Toward Asian Americans

The purpose of this study was to apply the theoretical assumptions of the stereotype content model and the integrated threat theory to explore the effects of perceived intergroup stereotypes and threats on intergroup emotions toward Asian Americans. Three major findings were reported. First, Asian Americans were perceived as more competent than warm, more of a realistic threat than of a symbolic threat, and elicited more admiration than envy, contempt, and pity. Second, perceived competence and warmth of Asian Americans have differing associations with perceived threats and emotions toward them. Third, perceived competence toward Asian Americans has direct effects on emotions, whereas perceived warmth has indirect effects on emotions mediated through perceived threats.
‘A real rollercoaster of confidence and emotions’: learning to be a university student

Accounts of emotion and affect have gained popularity in studies of learning. This article draws on qualitative research with a group of non‐traditional students entering an elite university in the UK to illustrate how being and becoming a university student is an intrinsically emotional process. It argues that feelings of loss and dislocation are inherent to the students’ experiences of entering university, and that ‘coming to know’ a new community of practice is an emotional process that can incorporate feelings of alienation and exclusion, as well as of excitement and exhilaration. A broader understanding of how students learn then depends not just upon the individual’s emotional commitment to developing a new learning identity, but on the emotional interaction between the student and the learning environment of the university.
Infrared-Guided Laser Stimulation of Neurons in Brain Slices

Infrared-guided laser stimulation is a new technique that allows precise and rapid stimulation of visualized neurons in brain slices. Infrared imaging of neurons with a new contrast system is combined with the photolytic release of caged neurotransmitters by an ultraviolet (UV) laser. Addition of caged neurotransmitters to the superfusion medium of neurons in brain slices allows local excitation in the micrometer range with a focused spot of UV light. In this way, the distribution of glutamate or γ-aminobutyric acid (GABA) receptors on neuronal dendrites can be mapped. Furthermore, this technique can be used to map the connectivity of neuronal networks through the controlled stimulation of neighboring neurons. Because the laser stimulation can be performed much faster than can paired recording, it is also possible to search for synaptic connections between distant neurons that have a low probability of connectivity.
A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement

Nestedness has been widely reported for both metacommunities and networks of interacting species. Even though the concept of this ecological pattern has been well-defined, there are several metrics by which it can be quantified. We noted that current metrics do not correctly quantify two major properties of nestedness: (1) whether marginal totals (i.e. fills) differ among columns and/or among rows, and (2) whether the presences (1’s) in less-filled columns and rows coincide, respectively, with those found in the more-filled columns and rows. We propose a new metric directly based on these properties and compare its behavior with that of the most used metrics, using a set of model matrices ranging from highly-nested to alternative structures in which no nestedness should be detected. We also used an empirical dataset to explore possible biases generated by the metrics as well as to evaluate correlations between metrics. We found that nestedness has been quantified by metrics that inappropriately detect this pattern, even for matrices in which there is no nestedness. In addition, the most used metrics are prone to type I statistical errors while our new metric has better statistical properties and consistently rejects a nested pattern for different types of random matrices. The analysis of the empirical data showed that two nestedness metrics, matrix temperature and the discrepancy measure, tend to overestimate the degrees of nestedness in metacommunities. We emphasize and discuss some implications of these biases for the theoretical understanding of the processes shaping species interaction networks and metacommunity structure.
Geographical and temporal similarity measurement in location-based social networks

Using "check-in" data gathered from location-based social networks, this paper proposes to measure the similarity of users by considering the geographical and the temporal aspect of their geographical and temporal aspects of their "check-ins". Temporal neighborhood is added to support the time dimension on the basis of the traditional DBSCAN clustering algorithm, which determines the similarity among users at different scales using the classical Vector Space Model (VSM) with vectors composed of the amount of visits in different cluster area. The spatio-temporal similarity of the user behaviors are obtained through overlapping the different weighted user similarity values. The experimental results show that the proposed approach is effective in measuring user similarity in location-based social networks.
Using version control data to evaluate the impact of software tools

Software tools can improve the quality and maintainability of software, but are expensive to acquire, deploy and maintain, especially in large organizations. We explore how to quantify the effects of a software tool once it has been deployed in a development environment. We present a simple methodology for tool evaluation that correlates tool usage statistics with estimates of developer effort, as derived from a project's change history (version control system). Our work complements controlled experiments on software tools, which usually take place outside the industrial setting, and tool assessment studies that predict the impact of software tools before deployment. Our analysis is inexpensive, non-intrusive and can be applied to an entire software project in its actual setting. A key part of our analysis is how to control confounding variables such as developer work-style and experience in order accurately to quantify the impact of a tool on developer effort. We demonstrate our method in a case study of a software tool called VE, a version-sensitive editor used in BellLabs. VE aids software developers in coping with the rampant use of preprocessor directives (such as if/ endif) in C source files. Our analysis found that developers were approximately 36% more productive when using VE than when using standard text editors.
Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses

Motivation: Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global ‘best guess’ reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations. Results: We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data. We evaluated our method by measuring the performance of two summary statistics-based methods: imputation and joint-testing. When using our method as opposed to the current standard of ‘best guess’ reference panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease in mean-squared error for joint-testing. Availability and implementation: Our method is publicly available in a software package called ADAPT-Mix available at https://github.com/dpark27/adapt_mix. Contact: noah.zaitlen@ucsf.edu
The validity of examination essays in higher education: Issues and responses

The use of timed, essay examinations is a well-established means of evaluating student learning in higher education. The reliability of essay scoring is highly problematic and it appears that essay examination grades are highly dependent on language and organisational components of writing. Computer-assisted scoring of essays makes use of language features and has demonstrated strong similarity to human ratings. Studies of examiner behaviour show that attention to content and language features contributes to grading decisions. However, given the time constraints on essay examinations, an overemphasis on language aspects may weaken the validity of essay examination grades. This article suggests alternative approaches to the standard essay prompt which should raise the validity of essay tasks and scoring in higher education. Suggested options include redesigning tasks so that organisational and language features are less influential in scoring and the use of content maps.
Exchange Rate Regimes and Supply Shocks Asymmetry: The Case of the Accession Countries

This paper reviews the pros and cons of an early EU enlargement towards Central and Eastern European Countries (CEECs hereafter). Firstly, the Maastricht criteria, which cannot be literally assessed during the catching up process, but that nevertheless mirror the huge efforts undertaken in order to (i) stabilise the economies, (ii) converge towards the EU, and then (iii) participate in the EMU, are analysed. Secondly, real convergence is observed to occur at different rates, depending upon the initial conditions faced and the productivity gains realised by each country. Thirdly, computing the correlation of demand and supply shocks in a sample of Euro countries, such as Ireland, Portugal and Spain, at the time when they were entering the EU and the CEECs, gives some indication of the similarity of the business cycles and economic structures of the CEECs on the one hand, and the EU on the other. However, we argue that looking at static correlation only (averaged over the last decade) is too simplistic, as these averages will be blurred by the transition process. Using the Kalman filter, we are able to compute time varying correlation, hence differentiating between the transition and the most recent period. Our results emphasise an ongoing process of demand shocks convergence, but supply shocks divergence. Various exchange rate strategies are then discussed.
A deep learning approach for web service interactions

Predicting Web service interactions such as composition and substitution provides support for developers during mashup design. In this paper, we propose a deep-learning approach for predicting compositions and substitutions. To the best of our knowledge, this work is the first to adopt deep learning for interactions prediction. We use stacked autoencoders to learn latent service features. A deep feed forward neural network leverages the learned features and the history of previous interactions to predict new ones. We conducted extensive experiments on real-world Web services to illustrate the performance of our approach. We show that the use of deep learning achieves a high accuracy level and outperforms existing models such as multi-layer perceptron and support vector machine.
How golf players’ satisfaction from golf experience predicts their loyalty intentions? Mediating role of perceived value

Purpose – This study aims to evaluate the perceptions and behavioral intentions of golf players by means of the relationship between customer satisfaction, perceived value and loyalty concepts. Design/methodology/approach – The data used in testing the hypothesis developed were gathered by face-to-face survey method from the golf players using four golf courses in Antalya, Turkey during the peak season (March, April, May, October). At the end of the data screening period, 351 usable questionnaires were gathered. The proposed model was estimated through a mediation analysis with parallel multiple mediator model. Findings – The roles of perceived value dimensions between satisfaction and loyalty were revealed within the context of the proposed model. Moreover, the relative importance of perceived value dimensions in the causal relationship between satisfaction and loyalty were determined. Research limitations/implications – The research was limited to a specific topic in service industry. The possible effec.
Distinguishing Catholics and Protestants among Irish Immigrants to Clydeside: A New Approach to Immigration and Ethnicity in Victorian Britain

During the 1840s Britain became host to upwards of half a million economic refugees from Ireland. This  ow of very poor and usually rural immigrants continued at a reduced rate for the rest of the century. Historians have long recognised that this immigration has the potential to reveal a great deal about the dynamics of occupational groupings and community structures within Britain, and in particular about the changing character of political and class identities. There is general agreement that the in ux from Ireland accompanied a fairly basic shift in the character of mass politics in the middle years of the century, in which divisions along the lines of religion, local community and ethnicity became more marked [2]. It is also generally agreed that the scale of the in ux transformed the pro le of the labour force. The continued  ow of Irish immigrants, while not directly depressing wages, did systematically deepen local markets for unskilled labour. To this extent Irish immigration would appear to be quite strongly associated with the fateful predisposition of late nineteenth-century entrepreneurs towards the continued use of labour-intensive technologies [3]. With this, however, the limits of even modi ed agreement are reached. There is no consensus on how far the host community was actively discriminatory. The failure of the Irish to move up the occupational ladder is variously ascribed to their own lack of skill, to their wider cultural characteristics or, conversely, to ethnic stereotyping and discrimination by the host community [4]. There is equally little agreement on why the incidence of ethnic con ict within Britain was so uneven. Frank Neal and Graham Davis have argued that it was simply a product of the relative scale of the immigration: the more Irish, the more con ict [5]. Neville Kirk and Martin Mitchell have claimed that it re ects differing values among host communities, including the relative level of class cohesion [6]. Others, such as Robert Miles and the Checklands, have suggested that ethnic con ict in Scotland was deliberately perpetuated to enhance employer control over the workforce [7]. Mary Hickman proposes a conscious project by the
Efficient Multichannel NLMS Implementation for Acoustic Echo Cancellation

An acoustic echo cancellation structure with a single loudspeaker and multiple microphones is, from a system identification perspective, generally modelled as a single-input multiple-output system. Such a system thus implies specific echo-path models (adaptive filter) for every loudspeaker to microphone path. Due to the often large dimensionality of the filters, which is required to model rooms with standard reverberation time, the adaptation process can be computationally demanding. This paper presents a selective updating normalized least mean square (NLMS)-based method which reduces complexity to nearly half in practical situations, while showing superior convergence speed performance as compared to conventional complexity reduction schemes. Moreover, the method concentrates the filter adaptation to the filter which is most misadjusted, which is a typically desired feature.
Improvement in Thrombolytic Therapy Administration in Acute Stroke with Feedback

Background: The benefits of intravenous recombinant tissue plasminogen activator (rt-PA) in acute ischemic stroke is time dependent. Guidelines recommend a door-to-needle (DTN) time of less than 60 minutes. Methods: A retrospective audit of 730 stroke charts from 2008 - 2011 was conducted at Health Sciences Centre. 158 patients treated with IV rt-PA were identified. The time intervals between Emergency Department (ED) arrival, administration of rt-PA and uninfused brain computed axial tomographic scan (CT) were recorded. From this, CT to needle times were calculated. During November 2010 to January 2011 feedback was given to neurologists, ED physicians, ED nurses, and CT technologists. This raised awareness and emphasized the importance of this time driven protocol. Results: The median DTN times for 2008, 2009, and 2010 were 69, 71 and 76 minutes respectively. The median CT-to-needle time for this time period was 47 minutes. In 2011 (n =58) the median DTN time was 49 minutes and the median CT-to-needle was 18 minutes, which were marked improvements (p<0.00005 and p<0.005, respectively). In 2008-2010 only 31% of treated patients (n=100) received rt-PA within 60 minutes, whereas in 2011 this increased to 64%. Conclusions: Dramatic improvements in DTN times and in the percentage of patients receiving rt-PA treatment within 60 minutes were observed in 2011 after feedback was provided regarding the suboptimal performance. Prior to receiving feedback, DTN times were similar to national median DTN times. All centres administering rt-PA for acute ischemic stroke should monitor their clinical performance and give feedback on a regular basis.
On-line simultaneous learning and recognition of everyday activities from virtual reality performances

Capturing realistic human behaviors is essential to learn human models that can later be transferred to robots. Recent improvements in virtual reality (VR) head-mounted displays provide a viable way to collect natural examples of human behavior without the difficulties often associated with capturing performances in a physical environment. We present a realistic, cluttered, VR environment for experimentation with household tasks paired with a semantic extraction and reasoning system able to utilize data collected in real-time and apply ontology-based reasoning to learn and classify activities performed in VR. The system performs continuous segmentation of the motions of users' hands and simultaneously classifies known actions while learning new ones on demand. The system then constructs a graph of all related activities in the environment through its observations, extracting the task space utilized by observed users during their performance. The action recognition and learning system was able to maintain a high degree of accuracy of around 92% while dealing with a more complex and realistic environment compared to earlier work in both physical and virtual spaces.
Pointy: A hybrid pointer prefetcher for managed runtime systems

This work proposes Pointy, a software assisted hardware pointer prefetcher for Java applications. Pointy exploits the strengths of both software and hardware. Its runtime software component communicates points-to relationships between objects to the underlying hardware. This points-to information is maintained and tracked in any managed runtime that implements automatic garbage collection. Pointy stores the object connectivity information in a separate hardware structure and uses it to generate timely pointer prefetches. To achieve a low-cost hardware implementation, Pointy spills the object metadata to the conventional memory hierarchy and retrieves it only when needed. Taking advantage of its hybrid design, Pointy can selectively communicate points-to metadata to the hardware based on class profiling that is readily available at the runtime level, while impractical to extract at the hardware level. Experimental results show that Pointy improves performance for pointer intensive benchmarks even in the presence of conventional prefetchers. When used in conjunction with traditional prefetchers, such as striding and next line, Pointy improves application performance by 53% for SpecJBB 2005 and by 72% on average1, which represents a speedup of 19% and 18%, respectively, compared to traditional prefetchers.
Smuggling around the world: evidence from a structural equation model

This article uses a Multiple Indicators Multiple Causes (MIMIC) model to analyse the determinants of smuggling. The analysis reveals that higher corruption and a lower rule of law encourage smuggling. Tariffs and trade restrictions are important push factors, while a higher Black Market Premium (BMP) discourages smugglers. Based on the MIMIC estimates, we calculate an index of smuggling which provides a ranking for 54 countries. We find that smuggling is rampant in Cameroon, Pakistan and Kenya while it is least prevalent in Switzerland, Finland and Sweden.