Miracle-dz/UniFAR-FTUs huggingface dataset:
Financial Incentives to Stimulate Integration of Care

Introduction Chronic conditions are related to 60% of disability adjusted life years and account for 75% of total health care expenditure worldwide. The economic burden of chronic conditions is much larger when considering the costs of productivity loss and informal care. The threat that chronic conditions pose to population health and economies increase because of increasing prevalence and multi-morbidity, which multiplies the burden of chronic conditions. Integrated care is seen as the means to tackle this threat by improving population health and patient experience with care, and reducing costs. It includes initiatives that seek to improve outcomes for those with (complex) chronic health problems and needs by overcoming issues of fragmentation through linkage or coordination of services of different providers along the continuum of care. In the era of personalized medicine and genomics, integrated care may be characterized by the recently suggested term ‘humanomics’ because it incorporates treatment based on personal need, preferences and capacity, it interacts with the context in which it is implemented, and its success depends highly on human behaviour [1]. Financial incentives are one of the main prerequisites for integrating care [2].
Further analysis of LSM-based unit pruning forunit selection TTS

The level of quality that can be achieved in concatenative text-to-speech synthesis is primarily governed by the inventory of units used in unit selection. This has led to the collection of ever larger corpora in the quest for ever more natural synthetic speech. As operational considerations limit the size of the unit inventory, however, pruning is critical to removing any instances that prove either spurious or superfluous. At last ICASSP we introduced an alternative pruning strategy based on a data-driven feature extraction framework separately optimized for each unit type in the inventory. This paper presents further validation of this strategy, as well as a detailed analysis of its potential benefits for concatenative synthesis.
Identifying user profile using Facebook photos

Apart from text messages, photo posting is a popular function of Facebook. The uploaded photos are of various natures, including selfie, outdoor scenes, food, etc. In this paper, we employ state-of-the-art computer vision techniques to analyze image content and establish the relationship between user profile and the type of photos posted. We collected photos from 32 Facebook users. We then applied techniques such as face detection, scene understanding and saliency map identification to gather information for automatic image tagging and classification. Grouping of users can be achieved either by tag statistics or photo classes. Characteristics of each group can be further investigated based on the results of hierarchical clustering. We wish to identify profiles of different users and respond to questions such as the type of photos most frequently posted, gender differentiation in photo posting behavior and user classification according to image content, which will promote our understanding of photo uploading activities on Facebook.
Patient-reported Outcomes in Asian Patients With Chronic Hepatitis C Treated With Ledipasvir and Sofosbuvir

AbstractPrevalence of chronic hepatitis C (CH-C) infection in patients of Asian ancestry ranges between 1% and 20%. Interferon (IFN)- and ribavirin (RBV)-containing regimens for CH-C have a negative impact on patient-reported outcomes (PROs) during treatment.The aim of this study was to assess the impact of IFN-free RBV-free sofosbuvir (SOF)-based regimens on PROs in CH-C patients of Asian ancestry.In this observational retrospective study, the PRO data from 12 multicenter multinational phase 3 clinical trials (2012–2015, conducted in Europe, North America, Australia, and New Zealand) of SOF-based regimens with and without IFN, ledipasvir (LDV), and/or RBV were used. At baseline, during treatment, and post-treatment, patients completed 4 validated PRO questionnaires (SF-36, CLDQ-HCV, FACIT-F, and WPAI:SHP). The resulting PROs in Asian patients were compared across the treatment regimens.Of 4485 of the trials’ participants, 106 patients were of Asian ancestry (55.7% male, 69.8% treatment-naïve, 17.0% cirrhotic). In comparison with other patients, the Asian CH-C cohort was younger, had lower BMI, and lower rates of pre-treatment psychiatric comorbidities (anxiety, depression, sleep disorders) (all P < .05). At baseline, Asian patients also had lower SF-36 physical functioning scores (on average, by −5.6% on a normalized 0–100% PRO scale, P = .001). During treatment, Asian CH-C patients experienced a decline in their PRO scores while receiving IFN and/or RBV-containing regimens (up to −19.6%, P < .001). In contrast, patients receiving LDV/SOF experienced no PRO decrement and improvement of some PRO scores during treatment (+9.0% in general health of SF-36, P = .03). After achieving SVR-12, some of the PRO scores in Asian patients improved regardless of the regimen (up to +9.3%, P < .001). In multivariate analysis of Asian patients, the use of LDV/SOF was independently associated with higher PRO scores during and soon after the end of treatment (betas +15.0% to +29.3%, all P < .05). Other predictors of PRO impairment included depression, type 2 diabetes mellitus, and cirrhosis.The use of IFN- and RBV-free LDV/SOF regimens leads to PRO improvement in Asian patients with CH-C during treatment. Achieving SVR-12 results in improvement of PRO scores.
The evolution of mating type switching

Predictions about the evolution of sex determination mechanisms have mainly focused on animals and plants, whereas unicellular eukaryotes such as fungi and ciliates have received little attention. Many taxa within the latter groups can stochastically switch their mating type identity during vegetative growth. Here, we investigate the hypothesis that mating type switching overcomes distortions in the distribution of mating types due to drift during asexual growth. Using a computational model, we show that smaller population size, longer vegetative periods and more mating types lead to greater distortions in the distribution of mating types. However, the impact of these parameters on optimal switching rates is not straightforward. We find that longer vegetative periods cause reductions and considerable fluctuations in the switching rate over time. Smaller population size increases the strength of selection for switching but has little impact on the switching rate itself. The number of mating types decreases switching rates when gametes can freely sample each other, but increases switching rates when there is selection for speedy mating. We discuss our results in light of empirical work and propose new experiments that could further our understanding of sexuality in isogamous eukaryotes.
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation

Previous researches have shown that learning multiple representations for polysemous words can improve the performance of word embeddings on many tasks. However, this leads to another problem. Several vectors of a word may actually point to the same meaning, namely pseudo multi-sense. In this paper, we introduce the concept of pseudo multi-sense, and then propose an algorithm to detect such cases. With the consideration of the detected pseudo multi-sense cases, we try to refine the existing word embeddings to eliminate the influence of pseudo multi-sense. Moreover, we apply our algorithm on previous released multi-sense word embeddings and tested it on artificial word similarity tasks and the analogy task. The result of the experiments shows that diminishing pseudo multi-sense can improve the quality of word representations. Thus, our method is actually an efficient way to reduce linguistic complexity.
Efficient Parallel Dictionary Encoding for RDF Data.

The SemanticWeb comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of SemanticWeb applications that perform computations over large volumes of information. A typical method for alleviating the impact of this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding for this purpose is particularly prevalent in Semantic Web database systems. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we describe a straightforward but very efficient encoding algorithm and evaluate its performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art MapReduce algorithm, we demonstrate a speedup of 2:6 - 7:4x and excellent scalability.
Black Hole Entropy and Gravity Cutoff

We study the black hole entropy as entanglement entropy and propose a resolution to the species puzzle. This resolution comes out naturally due to the fact that in the presence of $N$ species the universal gravitational cutoff is $\Lambda=M_{\rm Planck}/\sqrt{N}$, as opposed to $M_{\rm Planck}$. We demonstrate consistency of our solution by showing the equality of the two entropies in explicit examples in which the relation between $M_{\rm Planck}$ and $\Lambda$ is known from the fundamental theory.
A meta-analysis of responses of canopy photosynthetic conversion efficiency to environmental factors reveals major causes of yield gap

Improving plant energy conversion efficiency (εc) is crucial for increasing food and bioenergy crop production and yields. Using a meta-analysis, the effects of greenhouse gases, weather-related stresses projected to intensify due to climate change, and management practices including inputs, shading, and intercropping on εc were statistically quantified from 140 published studies to identify where improvements would have the largest impact on closing yield gaps. Variation in the response of εc to treatment type and dosage, plant characteristics, and growth conditions were also examined. Significant mean increases in εc were caused by elevated [CO2] (20%), shade (18%), and intercropping (15%). εc increased curvilinearly up to 55% with nitrogen additions whereas phosphorus application was most beneficial at low levels. Significant decreases in εc of –8.4% due to elevated [O3], –16.8% due to water stress, and –6.5% due to foliar damage were found. A non-significant decrease in εc of –17.3% was caused by temperature stress. These results identify the need to engineer greater stress tolerance and enhanced responses to positive factors such as [CO2] and nitrogen to improve average yields and yield potential. Optimizing management strategies will also enhance the benefits possible with intercropping, shade, and pest resilience. To determine optimal practices for εc improvement, further studies should be conducted in the field since several responses were exaggerated by non-field experimental conditions.
Road tracking for multi-hypothesis localization on navigable maps

Efficient and reliable map matching algorithms are essential for Advanced Driver Assistance Systems. While most of the existing solutions fail to provide trustworthy outputs when the situation is ambiguous (road intersections, roundabouts, parallel roads .), we present in this paper a new map-matching method based on a multi-hypothesis road tracking that takes advantage of the geographical database road connectedness to provide a reliable road-matching solution with a confidence indicator.
How can we measure awareness? An overview of current methods

It would be pushing at an open door to state that the study of consciousness is challenging because it attempts to develop an epistemically objective approach to a phenomenon that is ontologically subjective (Searle 1997). How can I objectively have access to what another person thinks or experiences? Can a person him-/herself objectively assess or report what he/she thinks? Does introspection afford privileged access, or is it merely glorified heterophenomenology? And even if introspection were truly reliable, do introspective reports reflect one’s actual phenomenological experience, or merely an interpretation thereof in light of task demands? These challenges, which present themselves in a particularly harsh light when it comes to establishing unconscious information processing, reflect the fact that the study of consciousness requires a solution to the following fundamental—and as yet unsolved—problem: How can we measure consciousness? While there has been substantial progress in measuring the level of awareness (Casali et al. 2013; Sitt et al. 2014), and we have made steady progress delineating the neural correlates of consciousness (NCC) (Boly et al. 2013), we do not know of any instrument or method that makes it possible to measure the contents of awareness directly (Seth et al. 2008). Having such an instrument (i.e. a consciousness-meter) would make it possible to establish clear relationships between an external state of affairs, people’s subjective experience of this state of affairs, and their overt behavior. However, neither does such an instrument exists nor can we conceive of any way of building it (though some are trying; Haynes and Rees 2005; Kamitani and Tong 2005; Formisano et al. 2008; Haynes 2009). Thus, today, the best we can do to find out what someone currently experiences is to ask them to produce a report about it. Verbal report is the most direct method we can use to find out if a person is aware of some knowledge. But this, as appealing as it is, is fraught with complexity: people may refrain from or simply be unable to report on vague experiences; reports are typically not obtained at the time the experience occurs; people may be biased in different ways that often interact with each other (see Newell and Shanks, 2014, for a potent list of caveats). Even introspection—first-person data per excellence—has demonstrable limits (Nisbett and Wilson 1977; Johansson et al. 2006; Carruthers 2009). For these reasons, many authors have rejected subjective methods altogether and have
Probing Nonlinear Relations in MultivariateTime

In this chapter we brieey review a method for detection and characterization of nonlinear relations in multivariate as well as in univariate time series. The method employs the technique of uni-and multivariate sur-rogate data and information-theoretic functionals called redundancies. The test for nonlinearity based on the redundancy { linear redundancy approach, combined with the surrogate data is described in detail in (Palu s, 1995), its multivariate version in (Palu s, 1996a). The univariate surrogate data have been introduced in (Theiler et al., 1992), and the multivariate surrogate data in (Prichard and Theiler, 1994). More details about the information-theoretic functionals can be found in (Cover and Thomas, 1991). (3) can be derived by simple manipulation. In addition to the redundancy (1) and the marginal redundancy (2), various types of redundancies can be deened,
Performance enhancement of galvanometer scanners using extended control structures

The galvanometer-based scanners (GS) are optomechatronic devices with a wide range of applications, from industrial to biomedical imaging. They are driven with periodical and time variable signals - especially sawtooth, triangular, sine wave or signals with special variations. We approach in this paper the rejection of the effect of disturbances in order to improve the tracking performances of the GS. The control solution proposed in the paper is based on the extension of the existing control structure with an additional loop, with an additional controller PID-Ll type, which will ensure better speed response and good immunity to constant disturbances which can affect the servo system.
Amygdala fMRI Signal as a Predictor of Reaction Time

Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing.
A Robust Mean Shift Tracking Algorithm Combined with Gray Spatial Texture

The classical Mean Shift algorithm has some limitations in target tracking of gray image sequences for it only adopts the simple gray color information. We present an improved Mean Shift tracking algorithm in this paper. Our approach employs the information of gray spatial texture which is used to characterize the object in matching models calculation. Thus, the proposed algorithm can recognize the real target from a complicated background. The experiments show that improved Mean Shift tracking algorithm has higher efficiency and reliability than previous method.
A major T cell antigen of Mycobacterium leprae is a 10-kD heat-shock cognate protein

Several mycobacterial antigens, identified by monoclonal antibodies and patient sera, have been found to be homologous to stress or heat-shock proteins (hsp) defined in Escherichia coli and yeast. A major antigen recognized by most Mycobacterium leprae-reactive human T cell lines and cell wall-reactive T cell clones is a 10-kD protein that has now been cloned and sequenced. The predicted amino acid sequence of this protein is 44% homologous to the hsp 10 (GroES) of E. coli. The purified native and recombinant 10-kD protein was found to be a stronger stimulator of peripheral blood T cell proliferation than other native and recombinant M. leprae proteins tested. The degree of reactivity paralleled the response to intact M. leprae throughout the spectrum of leprosy. Limiting-dilution analysis of peripheral blood lymphocytes from a patient contact and a tuberculoid patient indicated that approximately one third of M. leprae-reactive T cell precursors responded to the 10- kD antigen. T cell lines derived from lepromin skin tests were strongly responsive to the 10-kD protein. T cell clones reactive to both the purified native and recombinant 10-kD antigens recognized M. leprae- specific epitopes as well as epitopes crossreactive with the cognate antigen of M. tuberculosis. Further, the purified hsp 10 elicited strong delayed-type hypersensitivity reactions in guinea pigs sensitized to M. leprae. The strong T cell responses against the M. leprae 10-kD protein suggest a role for this heat-shock cognate protein in the protective/resistant responses to infection.
Hen Egg as an Antioxidant Food Commodity: A Review

Intake of antioxidants through diet is known to be important in reducing oxidative damage in cells and improving human health. Although eggs are known for their exceptional, nutritional quality, they are not generally considered as antioxidant foods. This review aims to establish the importance of eggs as an antioxidant food by summarizing the current knowledge on egg-derived antioxidants. Eggs have various natural occurring compounds including the proteins ovalbumin, ovotransferrin and lysozyme in egg white, as well as phosvitin, carotenoids and free aromatic amino acids in egg yolk. Some lipophilic antioxidants such as vitamin E, carotenoids, selenium, iodine and others can be transferred from feed into egg yolk to produce antioxidant-enriched eggs. The bioactivity of egg antioxidants can be affected by food processing, storage and gastrointestinal digestion. Generally thermal processing methods can promote loss of antioxidant properties in eggs due to oxidation and degradation, whereas gastrointestinal digestion enhances the antioxidant properties, due to the formation of new antioxidants (free amino acids and peptides). In summary, in addition to its well-known nutritional contribution to our diet, this review emphasizes the role of eggs as an important antioxidant food.
Do local employment centers modify the association between neighborhood urban form and individual obesity?

Extensive literature examines the relation between land use patterns and obesity, but less work explores whether local employment centers may reduce obesity. Local employment centers may affect obesity via providing agglomeration benefits such as increased job/transportation opportunities and closer proximity to local facilities/destinations. We utilized a large serial cross-sectional health survey from Los Angeles, California, conducted over three periods, to examine whether the presence of local employment centers modifies the association between land use patterns and individual body mass index. We retrieved individual health and sociodemographic data from the Los Angeles County Health Survey (2002, 2005, and 2007). To this Health Survey, we linked land use attributes at the census tract level collected from the Southern California Association of Governments. Our log-transformed regression models present interactions between local employment centers, neighborhood urban form features, and body mass index. Although most findings are consistent with previous studies, some of land use factors hypothesized to reduce the risk of obesity could have counterintuitive associations unless local employment centers were controlled. The unexpected results of our land use coefficients indicate that the tenet of New Urbanism may not necessarily promote health benefits. Our findings suggest that modifying the built environment may be effective in reducing obesity only in areas with high degree of local employment centers. Future studies would benefit from longitudinal and experimental, rather than cross-sectional, study designs that rigorously test the potential causal role of New Urbanism on obesity.