Miracle-dz/UniFAR-FTUs huggingface dataset:
Illness Representation and Self-Care Ability in Older Adults with Chronic Disease

Chronic illness affects >50% of adults in the United States and accounts for >80% of healthcare spending. The purpose of this study was to determine whether beliefs about one’s chronic disease (illness representation) are associated with self-care activation, emergency department (ED) visits, or hospitalizations. Using a cross-sectional design, we recruited older adults with heart failure, chronic obstructive pulmonary disease (COPD), and chronic kidney disease. The Revised Illness Perception Questionnaire (IPQ-R) measured perceptions about disease. The Patient Activation Measure measured self-care activation. ED visits and hospitalizations were measured by self-report. IPQ-R scores were analyzed using latent profile analysis to identify subgroups. Participants included 187 adults (mean age 65 years, 54% female, 74% Black). We found three subgroups (stable, overwhelmed, and confident). Groups did not differ demographically or by disease. The stable group (few consequences, non-fluctuating pattern) had the fewest hospitalizations. The overwhelmed group (many consequences, fluctuating pattern, high negative emotion) had high hospitalizations and low self-care ability. The confident group (high disease control, well-understood) had the highest self-care ability, but also high hospitalizations. ED visits did not differ by group. We found three subgroups that differ in their illness representation and health outcomes. Findings suggest that assessing patients’ illness representations may have important implications for subgroup-specific interventions.
Identification and Doubly Robust Estimation of Data Missing Not at Random with an Ancillary Variable

We consider identification and estimation with an outcome missing not at random (MNAR). We study an identification strategy based on a so-called {\it shadow variable}. An shadow variable is assumed to be correlated with the outcome, but independent of the missingness mechanism conditional on the outcome. We give a necessary and sufficient condition for identification of the full data law given a valid shadow variable under MNAR, and also sufficient conditions which are convenient to verify in practice. The conditions are satisfied by many commonly-used models, and thus essentially state that lack of identification is not an issue in many situations. Focusing on estimation of an outcome mean, we describe three semiparametric estimation methods: inverse probability weighting, outcome regression and doubly robust estimation. We evaluate the finite sample performance of these estimators via simulations, and apply them to a China Home Pricing dataset extracted from the China Family Panel Survey (CFPS).
An Efficient Message Dissemination Scheme for Minimizing Delivery Delay in DTNs

Abstract Delay tolerant networks (DTNs) are a kind of sparse and highly mobile wireless networks, where no stable connectivity guarantee can be assumed. Most DTN users have several point of interests (PoIs), and they enjoy disseminating messages to the other users of the same PoI through WiFi. In DTNs, some time-sensitive messages (disaster warnings, and search notices, etc.) need to be rapidly propagated among specific users or areas. Therefore, finding a path from the source to the destination with the shortest delay is the key problem. Taking the dissemination cost into consideration, we propose an efficient message dissemination strategy for minimizing delivery delay (MDMD) in DTNs, which first defines the user’s activeness according to the transiting habit among different PoIs. Furthermore, depending on the activeness, an optimal user in each PoI is selected to constitute the path with the shortest delay. Finally, the MDMD with inactive state (on the way between PoIs) is further proposed to enhance the applicability. Simulation results show that, compared with other dissemination strategies, MDMD achieves the lowest average delay, and lower average hopcounts, on the premise that the delivery ratio is guaranteed to be 100% by the sufficient simulation time.
Bacteriocins from Lactic Acid Bacteria: A Review of Biosynthesis, Mode of Action, Fermentative Production, Uses, and Prospects

Bacteriocins are antimicrobial peptides that help bacteria fight competing bacteria in microecological systems. Bacteriocins of lactic acid bacteria (LAB) have attracted much interest in recent years because of their properties that make them suitable as natural food preservatives against specific food pathogens, and as possible supplement to antibiotics against drug resistant bacterial strains. LAB bacteriocins are generally classified into the lantibiotics and non-lantibiotics, the latter divided into four subgroups. To date, only nisin and to a lesser extent, pediocin are the commercially applied bacteriocins for food use. Clinical applications are still limited to animal health. One of the more exciting prospects on the use of bacteriocins is the possibility of subjecting them to bioengineering to either increase antimicrobial activity or further specify their target microorganism. The latter would make it less damaging to the natural gut microflora, which is a common drawback of conventional antibiotic therapy. This paper focuses on the nature, biology, and applications of bacteriocins based on knowledge gained abroad and in the Philippines during the last two decades. Keywords—lactic acid bacteria, antimicrobial peptides, bacteriocin, bacteriocin application, bacteriocin biosynthesis, bacteriocin prospects
A Six-Week Home Exercise Program Improves Endothelial Function and CD34+ Circulating Progenitor Cells in Patients With Pre-Diabetes

Background: Pre-diabetes is associated with endothelial dysfunction and affects endothelium-associated stem cells. Lifestyle modification has been shown to prevent the progression from pre-diabetes to overt type 2 diabetes; however, the effect of such interventions on CD34 + progenitor cells in pre-diabetes participants has not been tested. The purpose of this study was to determine whether a home-based 6-week exercise intervention improved vascular function, circulating number, function, and gene expression of circulating CD34 + progenitor cells in patients with pre-diabetes. Methods: Patients (40 - 70 years, BMI of 25 - 39.9, n = 11) were enrolled in a 16-week randomized crossover study that consisted of 6 weeks each of an exercise (150 minutes/week) and non-exercise phase with a 4-week washout between phases. Adherence to the exercise regimen was monitored by accelerometry. All participants were encouraged to follow a low fat/low calorie diet throughout the 16 weeks. Endothelial function was assessed via brachial artery flow mediated dilatation (FMD) and CFU-Hill colony formation. CD34 + cell number, migratory function, gene expression, and serum inflammatory markers were evaluated. Results: With the intervention, endothelial function improved (FMD, 5.7±0.6% to 11.2±0.9%, CFU-Hill, 3.3±0.2% to 8.2±0.6%, both P < 0.05). There was a significant reduction in cholesterol, triglyceride, insulin, leptin, interleukin-6 (IL-6), low density lipoprotein (LDL), apolipoprotein B (ApoB) and increase in apolipoprotein A1 (ApoA1) with the lifestyle intervention (all P < 0.05). An increase in circulating CD34 + cells (P < 0.005), decrease in CD34 + endothelin-1, IL-6, tumor necrosis factor (TNF)-? , p53, and p21 gene expression (all P < 0.05), and improved migration toward stromal cell derived factor (SDF)-1? occurred with the intervention. Conclusion: The improvements in endothelial function and CD34 + circulating progenitor cells in patients with pre-diabetes indicate that pre-diabetes may be a clinical window of therapeutic opportunity for lifestyle interventions. J Endocrinol Metab. 2015;5(1-2):163-171 doi: http://dx.doi. org/10.14740/jem273w
Exploring the vertical profile of atmospheric organic aerosol: comparing 17 aircraft field campaigns with a global model

Abstract. The global organic aerosol (OA) budget is highly uncertain and past studies suggest that models substantially underestimate observed concentrations. Few of these studies have examined the vertical distribution of OA. Furthermore, many model-measurement comparisons have been performed with different models for single field campaigns. We synthesize organic aerosol measurements from 17 aircraft campaigns from 2001–2009 and use these observations to consistently evaluate a GEOS-Chem model simulation. Remote, polluted and fire-influenced conditions are all represented in this extensive dataset. Mean observed OA concentrations range from 0.2–8.2 μg sm−3 and make up 15 to 70% of non-refractory aerosol. The standard GEOS-Chem simulation reproduces the observed vertical profile, although observations are underestimated in 13 of the 17 field campaigns (the median observed to simulated ratio ranges from 0.4 to 4.2), with the largest model bias in anthropogenic regions. However, the model is best able to capture the observed variability in these anthropogenically-influenced regions (R2=0.18−0.57), but has little skill in remote or fire-influenced regions. The model bias increases as a function of relative humidity for 11 of the campaigns, possibly indicative of missing aqueous phase SOA production. However, model simulations of aqueous phase SOA suggest a pronounced signature in the mid-troposphere (2–6 km) which is not supported in the observations examined here. Spracklen et al. (2011) suggest adding ~100 Tg yr−1 source of anthropogenically-controlled SOA to close the measurement-model gap, which we add as anthropogenic SOA. This eliminates the model underestimate near source, but leads to overestimates aloft in a few regions and in remote regions, suggesting either additional sinks of OA or higher volatility aerosol at colder temperatures. Sensitivity simulations indicate that fragmentation of organics upon either heterogeneous or gas-phase oxidation could be an important (missing) sink of OA in models, reducing the global SOA burden by 15% and 47% respectively. The best agreement with observations is obtained when the simulated anthropogenically-controlled SOA is increased to ~100 Tg yr−1 accompanied by either a gas-phase fragmentation process or a reduction in the temperature dependence of the organic aerosol partitioning (by decreasing the enthalpy of vaporization from 42 kJ mol−1 to 25 kJ mol−1). These results illustrate that models may require both additional sources and additional sinks to capture the observed concentrations of organic aerosol.
Oscillatory shear rheology measurements and Newtonian modeling of insoluble monolayers

Circular systems are advantageous for interfacial studies since they do not suffer from end effects, but their hydrodynamics is more complicated because their flows are not unidirectional. Here, we analyze the shear rheology of a harmonically driven knife-edge viscometer through experiments and computations based on the Navier-Stokes equations with a Newtonian interface. The measured distribution of phase lag in the surface velocity relative to the knife-edge speed is found to have a good signal-to-noise ratio and provides robust comparisons to the computations. For monomolecular films of stearic acid, the surface shear viscosity deduced from the model was found to be the same whether the film is driven steady or oscillatory, for an order of magnitude range in driving frequencies and amplitudes. Results show that increasing either the amplitude or forcing frequency steepens the phase lag next to the knife edge. In all cases, the phase lag is linearly proportional to the radial distance from the knife edge and scales with surface shear viscosity to the power −1/2.
Emotion Classification from EEG Signals Using Time-Frequency-DWT Features and ANN

This paper proposes the use of time-frequency and wavelet transform features for emotion recognition via EEG signals. The proposed experiment has been carefully designed with EEG electrodes placed at FP1 and FP2 and using images provided by the Affective Picture System (IAP), which was developed by the University of Florida. A total of two time-domain features, two frequen-cy-domain features, as well as discrete wavelet transform coefficients have been studied using Artificial Neural Network (ANN) as the classifier, and the best combination of these features has been determined. Using the data collected, the best detection accuracy achievable by the proposed schemed is about 81.8%.
The memory of what we do not recall: Dissociations and theoretical debates in the study of implicit memory

Implicit memory reflects itself on situations in which previously acquired information is expressed, without awareness or intention. The study of implicit memory has had a profound impact on how researchers have investigated the human memory. In this paper, we review the main studies which have revealed dissociations between direct and indirect tests of memory, and address the two main theoretical approaches used to explain these dissociations: the processing view and the multiple systems view. We then describe recent neuroscience studies and discuss its contributions to help clarify the debate about the mechanisms involved in explicit and implicit memory. Finally, we critically analyze some questions and controversies surrounding this literature, such as (a) the transparency assumption and the contamination issue; and (b) the theoretical utility of the dissociations. We emphasize that the biggest challenge for future research is to develop comprehensive theories that integrate behavioral and neuroscience findings.
Irradiation effect on in vitro organogenesis, callus growth and plantlet development of Gerbera jamesonii

The present work was carried out to study the effects of gamma irradiation on in vitro growth of explants, callus and the formation of shoots and plantlets. Irradiation is known to exhibit or inhibit the differentiation of cells and growth of plants in vitro, which helps in producing new plant varieties. Gamma irradiation is one of the physical mutagens that are widely used for mutation breeding. A gradual decline was observed in the number of shoots regenerated from irradiated petiole explants compared to control. Numbers of shoots regenerated from irradiated petiole explant cultured on Murashige & Skoog medium supplemented with 2.0 mg L-1 BAP and 0.5 mg L-1 NAA was reduced to 6.6±0.9 from 7.5±0.4 (control) when explants were exposed to 20 Gray of irradiation dose. Similar observation was reported on effects of gamma irradiation on in vitro propagated plantlets. Gradual decline was observed based on plant height as the dose of gamma irradiation increased. A significant decline was observed in the fresh weight of irradiated callus compared to control. In this case, growth responses of callus were strongly influenced by the radiation dose. The fresh weight of callus was reduced to 76.4±2.2% compared to 89.7±0.5% of control when callus tissues were exposed to 20 Gy.
Tree ring-based February–April temperature reconstruction for Changbai Mountain in Northeast China and its implication for East Asian winter monsoon

Abstract. Long-term climatic records are scarce in the northeast Asia for understanding the behavior of the East Asian Winter Monsoon. Here we describe a 250-year February–April temperature reconstruction (TCBM) based on tree-ring widths of Korean Pines from the Changbai Mountain area, Northeast China. The reconstruction can account for 45.7% of the temperature variance in the instrumental period (1953 to 2001). Four cold periods including 1784–1815, 1827–1851, 1878–1889 and 1911–1945, and two warm periods of 1750–1783 and 1855–1877 were identified before the instrumental period. Four shifts were also detected at 1781, 1857, 1878 and 1989. Good agreements between TCBM and other temperature records of East Asia suggest that the reconstruction is of good reliability and captures the regional cold/warm periods of East Asia. Moreover, TCBM shows negative correlations with the instrumental or proxy-based EAWM intensity records. The known weakening of the EAWM in the late 1980s is in agreement with the shift at 1989 in TCBM. These comparisons suggest that the February–April temperature reconstruction may be a good indicator of the EAWM intensity.
Embedding CP-theories as SPARQL queries to reason with preferences

Preference representation and reasoning play a central role in supporting users with complex and multi-factorial decision processes. In fact, user tastes can be used to filter information and data in a personalized way, thus maximizing their expected utility. Over the years, many frameworks and languages have been proposed to deal with user preferences. Among them, one of the most prominent formalism to represent and reason with (qualitative) conditional preferences (CPs) are conditional preference theories (CP-theories). In this paper, we show how to combine them with Semantic Web technologies in order to encode in a standard SPARQL 1.1 query the semantics of a set of CP statements representing user preferences by means of RDF triples that refer to a “preference” OWL ontology. In particular, here we focus on context-uniform conditional (cuc) acyclic CP-theories [44]. The framework that we propose allows a standard SPARQL client to query Linked Data datasets, and to order the results of such queries relative to a set of user preferences.
Spatiotemporal variability analysis of diffuse radiation in China during 1981–2010

Abstract. Solar radiation is the primary driver of terrestrial plant photosynthesis and the diffuse component can enhance canopy light use efficiency (LUE), which in turn influences the carbon balance of terrestrial ecosystems. In this study we calculated the spatial data of diffuse radiation in China from 1981 to 2010, using a radiation decomposition model and spatial interpolation method based on observational data. Furthermore, we explored the spatiotemporal characteristics of diffuse radiation using GIS and trend analysis techniques. The results show the following: (1) The spatial patterns of perennial average of annual diffuse radiation during 1981–2010 are complex and inhomogeneous in China, generally lower in the north and higher in the south and west. The perennial average ranges from 1730.20 to 3064.41 MJ m−2 yr−1 across the whole country. (2) There is an increasing trend of annual diffuse radiation in China from 1981 to 2010 on the whole, with mean increasing amplitude of 7.03 MJ m−2 yr−1 per decade. Whereas a significant downtrend was observed in the first 10 years, distinct anomalies in 1982, 1983, 1991 and 1992 occurred due to the eruptions of El Chinchon and Pinatubo. (3) The spatial distribution of the temporal variability of diffuse radiation showed significant regional heterogeneity in addition to the seasonal differences. Northwestern China has the most evident downtrend, with highest decreasing rate of 6% per decade, while the Tibetan Plateau has the most evident uptrend, with highest increasing rate of up to 9% per decade. Such quantitative spatiotemporal characteristics of diffuse radiation are essential in regional scale modeling of terrestrial carbon dynamics.
Spaces of curves with constrained curvature on hyperbolic surfaces

Let $ S $ be a hyperbolic surface. We investigate the topology of the space of all curves on $ S $ which start and end at given points in given directions, and whose curvatures are constrained to lie in a given interval $ (\kappa_1,\kappa_2) $. Such a space falls into one of four qualitatively distinct classes, according to whether $ (\kappa_1,\kappa_2) $ contains, overlaps, is disjoint from, or contained in the interval $ [-1,1] $. Its homotopy type is computed in the latter two cases. We also study the behavior of these spaces under covering maps when $ S $ is arbitrary (not necessarily hyperbolic nor orientable) and show that if $ S $ is compact then they are always nonempty.
Predicting Perceived Age: Both Language Ability and Appearance are Important

When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classifier that predicts perceived intelligence, age, and education level.
Fine-Grained Discourse Structures in Continuation Semantics

In this work, we are interested in the computation of logical representations of discourse. We argue that all discourse connectives are anaphors obeying different sets of constraints and show how this view allows one to account for the semantically parenthetical use of attitude verbs and verbs of report (e.g., think, say) and for sequences of conjunctions (A CONJ_1 B CONJ_2 C). We implement this proposal in event semantics using de Groote (2006)’s dynamic framework.
Identifying Domain Independent Update Intents in Task Based Dialogs

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs. Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance. However, in certain cases, DA’s fail to capture the actual update intent of the user. In this paper, we describe such cases and propose a new type of semantic class for user intents. This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair. We define five types of UI’s, which are independent of the domain of the conversation. We build a multi-class classification model using LSTM’s to identify the type of UI in user utterances in the Restaurant and Shopping domains. Experimental results show that our models achieve strong classification performance in terms of F-1 score.
A Unified Neural Architecture for Joint Dialog Act Segmentation and Recognition in Spoken Dialog System

In spoken dialog systems (SDSs), dialog act (DA) segmentation and recognition provide essential information for response generation. A majority of previous works assumed ground-truth segmentation of DA units, which is not available from automatic speech recognition (ASR) in SDS. We propose a unified architecture based on neural networks, which consists of a sequence tagger for segmentation and a classifier for recognition. The DA recognition model is based on hierarchical neural networks to incorporate the context of preceding sentences. We investigate sharing some layers of the two components so that they can be trained jointly and learn generalized features from both tasks. An evaluation on the Switchboard Dialog Act (SwDA) corpus shows that the jointly-trained models outperform independently-trained models, single-step models, and other reported results in DA segmentation, recognition, and joint tasks.
Identifying Explicit Discourse Connectives in German

We are working on an end-to-end Shallow Discourse Parsing system for German and in this paper focus on the first subtask: the identification of explicit connectives. Starting with the feature set from an English system and a Random Forest classifier, we evaluate our approach on a (relatively small) German annotated corpus, the Potsdam Commentary Corpus. We introduce new features and experiment with including additional training data obtained through annotation projection and achieve an f-score of 83.89.