Miracle-dz/UniFAR-FTUs huggingface dataset:
An investigation of the relationship between anxiety and depression and urge incontinence : development of a psychological model

An investigation of the relationship between anxiety and depression and urge incontinence: development of a psychological model Dr Sarah I Perry Aims: The study investigated the association between anxiety and depression and urge incontinence and the direction of causal pathways between these variables. The objective was to produce a psychological model that described the contribution of emotional factors in the development and maintenance of urge incontinence. Methods: In this prospective longitudinal study, a random sample of women aged 40 years or more, registered with a general practitioner in Leicestershire or Rutland, was mailed a postal questionnaire. The questionnaire included questions on general health, urinary symptoms and the Hospital Anxiety and Depression Scale (HADS). In total, 12, 568 women responded to the baseline postal survey (65.3% response rate) and 9,596 to the first annual follow-up (79.8% response rate). The prevalence and one-year incident rates of these symptoms were compared and contrasted, whilst controlling for confounding variables (e.g. long-term illness, age and other urinary symptoms). Results: A significant proportion of women with urge incontinence reported symptoms of anxiety (56.6%) and depression (37.6%). These symptoms were positively associated with the severity of incontinence and the presence of other urinary symptoms. Anxiety and depression were associated with a number of urinary symptoms and were not exclusive to urge incontinence. Incident cases of anxiety and depression were predicted by the presence of urge incontinence at baseline. Incident cases of urge incontinence were predicted by anxiety at baseline, but not depression. Anxiety, urge incontinence and frequency appeared to interact and exacerbate each other. Conclusion: The findings demonstrated the relevance of emotional factors in the development and maintenance of urge incontinence. Currently, assessment and treatment protocols for urge incontinence concentrate on physical symptoms and toilet behaviours. A more integrated psychological model of urge incontinence is proposed along with how this might be implemented and evaluated in clinical practice.
2B4 Is Dispensable for T-Dependent B Cell Immune Responses, but Its Deficiency Leads to Enhanced T-Independent Responses Due to an Increase in Peritoneal Cavity B1b Cells

The signaling lymphocyte activation molecule (SLAM) family plays important roles in adaptive immune responses. Herein, we evaluated whether the SLAM family member 2B4 (CD244) plays a role in immune cell development, homeostasis and antibody responses. We found that the splenic cellularity in Cd244 -/- mice was significantly reduced due to a reduction in both CD4 T cells and follicular (Fo) B cells; whereas, the number of peritoneal cavity B cells was increased. These findings led us to examine whether 2B4 modulates B cell immune responses. When we examined T-dependent B cell responses, while there was no difference in the kinetics or magnitude of the antigen-specific IgM and IgG1 antibody response there was a reduction in bone marrow (BM) memory, but not plasma cells in Cd244 -/- mice. When we evaluated T-independent immune responses, we found that antigen-specific IgM and IgG3 were elevated in the serum following immunization. These data indicate that 2B4 dampens T-independent B cell responses due to a reduction in peritoneal cavity B cells, but has minimal impact on T-dependent B cell responses.
Observation support system of an ROV for underwater archaeology

In this paper, we propose an observation support system of a Remotely Operated Vehicle (ROV) for underwater archaeology. In general, it is difficult for unskilled users to drive an ROV for underwater observation because a camera mounted on an ROV always moves due to external disturbances and objects easily move out of the field of view (FOV) of the camera. Once an object is out of the FOV, users cannot easily find out it again. The observation support system is used to help unskilled users to drive an ROV. To realize the proposed system, object detection based on image processing is a key component. We apply the SURF (Speeded Up Robust Features) algorithm to detect archaeological objects, and investigate the performance of the algorithm using video images recorded by our ROV in an underwater archaeological site.
Modified k-mean clustering method of HMM states for initialization of Baum-Welch training algorithm

Hidden Markov models are widely used for recognition algorithms (speech, writing, gesture, .). In this paper, a classical set of models is considered: state space of hidden variable is discrete and observation probabilities are modeled as Gaussian distributions. The models parameters are generally estimated with training sequences and the Baum-Welch algorithm, i.e. an expectation maximization algorithm. However this kind of algorithm is well known to be sensitive to its initialization point. The problem of this initialization point choice is addressed in this paper: a model with a very large number of states which describe training sequences with accuracy is first constructed. The number of states is then reduced using a k-mean algorithm on the state. This algorithm is compared to other methods based on a k-mean algorithm on the data with numerical simulations.
XCS with stack-based genetic programming

We present an extension of the learning classifier system XCS in which classifier conditions are represented by RPN expressions and stack-based genetic programming is used to recombine and mutate classifiers. In contrast with other extensions of XCS involving tree-based genetic programming, the representation we apply here produces conditions that are linear programs, interpreted by a virtual stack machine (similar to a pushdown automaton), and recombined through standard genetic operators. We test the version of XCS extended with stack-based conditions on a set of problems of different complexity.
CURRENT CHALLENGES IN THE DESIGN OF ENERGY EFFICIENT AD HOC WIRELESS NETWORKS

In this paper, we have discussed the current challenges in designing of energy efficient ad hoc mobile networks. Mobile ad-hoc networking involves peer-to-peer communication in a network with a dynamically changing topology. Achieving energy efficient communication in such a network is more challenging than in cellular networks since there is no centralized arbiter such as a base station that can administer power management. A major focus of research in ad-hoc wireless networking is to reduce energy consumption because the wireless devices are envisioned to have small batteries and be incapable of energy scavenging. Hence, power consumption becomes an important issue. To maximize the lifetime of ad hoc mobile networks, the power consumption rate of each node must be evenly distributed, and the overall transmission power for each connection request must be minimized.
Wiener Filtering Using Neighboring Coefficients

In order to provide a more clean and better denoising result, a novel denoising method called wiener filtering using neighboring coefficients (WNC) is proposed in this paper. It combined both the advantages of neighboring coefficients (NC) and wiener filtering. Experimental results show that WNC can effectively suppress the noise components of different kinds of signals. It is better than the traditional Wiener filtering, NC scheme and hard thresholding. Comparing other 3 methods, the average SNR improvement of the signals denoised by the WNC scheme was increased by 0.199dB, 1.438dB and 2.345 dB, respectively.
Different real-time PCR formats compared for the quantitative detection of human cytomegalovirus DNA.

BACKGROUND The aim of this study was to compare the ABI PRISM 7700 Sequence Detection System and the LightCycler to develop a quantitative real-time PCR assay for the detection of human cytomegalovirus (HCMV) DNA suitable for routine hospital application. METHODS We used one exonuclease probe and five different hybridization probe sets as sequence-specific fluorescence detection formats. For the exonuclease assay and two hybridization probe sets, reproducibility and the detection limit were determined. To keep the total assay time to a minimum, we gradually shortened individual reaction steps on both instruments. RESULTS The exonuclease assay can be interchangeably performed on the 7700 and the LightCycler. No change of reaction conditions is required, except for the addition of bovine serum albumin to the LightCycler reaction. The shortest possible total assay time is 80 min for the ABI PRISM 7700 Sequence Detection System and 20 min for the LightCycler. When the LightCycler is used, the exonuclease probe can be replaced by a set of hybridization probes. All assays presented here detected HCMV DNA in a linear range from 10(1) to 10(7) HCMV genome equivalents/assay (r >0.995) with low intraassay (<5%) and interassay (<10%) variation. CONCLUSIONS The ABI PRISM 7700 Sequence Detection System as well as the LightCycler are useful instruments for rapid and precise online PCR detection. Moreover, the two principles of fluorescence signal production allow HCMV quantification with the same accuracy.
An Improved Profile-Based CF Scheme with Privacy

Traditional collaborative filtering (CF) systems widely employing k-nearest neighbor (kNN) algorithms mostly attempt to alleviate the contemporary problem of information overload by generating personalized predictions for items that users might like. Unlike their popularity and extensive usage, they suffer from several problems. First, with increasing number of users and/or items, scalability becomes a challenge. Second, as the number of ratable items increases and number of ratings provided by each individual remains as a tiny fraction, CF systems suffer from sparsity problem. Third, many schemes fail to protect private data referred to as privacy problem. Due to such problems, accuracy and online performance become worse. In this paper, we propose two preprocessing schemes to overcome scalability and sparsity problems. First, we suggest using a novel content-based profiling of users to estimate similarities on a reduced data for better performance. Second, we propose pseudo-prediction protocol to help CF systems surmount sparsity. We finally propose to use randomization methods to preserve individual users' confidential data, where we show that our proposed preprocessing schemes can be applied to perturbed data. We analyze our schemes in terms of privacy. To investigate their effects on accuracy and performance, we perform real databased experiments. Empirical results demonstrate that our preprocessing schemes improve both performance and accuracy.
Realistic Simulation for Body Area and Body-To-Body Networks

In this paper, we present an accurate and realistic simulation for body area networks (BAN) and body-to-body networks (BBN) using deterministic and semi-deterministic approaches. First, in the semi-deterministic approach, a real-time measurement campaign is performed, which is further characterized through statistical analysis. It is able to generate link-correlated and time-varying realistic traces (i.e., with consistent mobility patterns) for on-body and body-to-body shadowing and fading, including body orientations and rotations, by means of stochastic channel models. The full deterministic approach is particularly targeted to enhance IEEE 802.15. 6 proposed channel models by introducing space and time variations (i.e., dynamic distances) through biomechanical modeling. In addition, it helps to accurately model the radio link by identifying the link types and corresponding path loss factors for line of sight (LOS) and non-line of sight (NLOS). This approach is particularly important for links that vary over time due to mobility. It is also important to add that the communication and protocol stack, including the physical (PHY), medium access control (MAC) and networking models, is developed for BAN and BBN, and the IEEE 802.15. 6 compliance standard is provided as a benchmark for future research works of the community. Finally, the two approaches are compared in terms of the successful packet delivery ratio, packet delay and energy efficiency. The results show that the semi-deterministic approach is the best option; however, for the diversity of the mobility patterns and scenarios applicable, biomechanical modeling and the deterministic approach are better choices.
How Much Do Metamemory Beliefs Contribute to the Font-Size Effect in Judgments of Learning?

Evidence shows that the font size of study items significantly influences judgments of learning (JOLs) and that people’s JOLs are generally higher for larger words than for smaller words. Previous studies have suggested that font size influences JOLs in a belief-based way. However, few studies have directly examined how much people’s beliefs contribute to the font-size effect in JOLs. This study investigated the degree to which font size influenced JOLs in a belief-based way. In Experiment 1, one group of participants (learners) studied words with different font sizes and made JOLs, whereas another group of participants (observers) viewed the learners' study phase and made JOLs for the learners. In Experiment 2, participants made both JOLs and belief-based recall predictions for large and small words. Our results suggest that metamemory beliefs play an important role in the font-size effect in JOLs.
Vegetation height derivation from Shuttle Radar Topography Mission data in southeast Georgia, USA

A study was conducted to determine the extent to which data from the 2000 Shuttle Radar Topography Mission (SRTM) can be used to estimate vegetation canopy height in conjunction with an existing bald Earth DEM as provided by the National Elevation Dataset (NED). Intensively managed slash pine stands with canopy heights ranging from 11 to 21 m were biometrically surveyed within the general mission timeframe in early 2000. Results indicate that SRTM data can be successfully correlated via linear regression modeling with ground-measured mean vegetation canopy height at the stand level when mean SRTM-NED height difference measures are extracted by averaging pixels within the stands. Regression analysis using 20 and 50 pixels as stand size thresholds yielded adjusted r2 values of 0.79 and 0.86 with rms errors of 1.1 m and 1.0 m respectively. Thus a minimum SRTM mapping unit of approximately 1.8 hectares can be postulated which allows vegetation canopy height retrieval at the stand scale
Econometrics of Panel Data: Methods and Applications

Panel data is a data type increasingly used in research in economics, social sciences, and medicine. Its primary characteristic is that the data variation goes jointly over space (across individuals, firms, countries, etc.) and time (over years, months, etc.). Panel data allow examination of problems that cannot be handled by cross-section data or time-series data. Panel data analysis is a core field in modern econometrics and multivariate statistics, and studies based on such data occupy a growing part of the field in many other disciplines. The book is intended as a text for master and advanced undergraduate courses. It may also be useful for PhD-students writing theses in empirical and applied economics and readers conducting empirical work on their own. The book attempts to take the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation. A distinctive feature is that more attention is given to unbalanced panel data, the measurement error problem, random coefficient approaches, the interface between panel data and aggregation, and the interface between unbalanced panels and truncated and censored data sets. The 12 chapters are intended to be largely self-contained, although there is also natural progression. Most of the chapters contain commented examples based on genuine data, mainly taken from panel data applications to economics. Although the book, inter alia, through its use of examples, is aimed primarily at students of economics and econometrics, it may also be useful for readers in social sciences, psychology, and medicine, provided they have a sufficient background in statistics, notably basic regression analysis and elementary linear algebra.
Exploiting Domain Knowledge in Collaborative Missions

In collaborative missions, coalition members depend on one another to deliver on different aspects of shared goals. In such settings, task delegation decisions are complicated because activities of coalition members may be regulated by policies. Especially when such policies are private, learning these policies become crucial to estimate the outcome of delegation decisions. In this paper, we present an approach that utilises domain knowledge in aiding the learning of policies. Our approach combines ontological reasoning, machine learning and argumentation in a novel way to accomplish this. Using our approach, decision makers can reason about the policies that others are operating with, and make informed decisions about to whom to delegate a task. In a series of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that more accurate models of others’ policies can be developed more rapidly using various forms of domain knowledge.
Quantitative Error Analysis for the Reconstruction of Derivatives

We present a general Fourier-based method which provides an accurate prediction of the approximation error, when the derivative of a signal s(t) is continuously reconstructed from uniform point samples or generalized measurements on s. This formalism applies to a wide class of convolution-based techniques. It provides a key tool, the frequency error kernel, for designing computationally efficient reconstruction schemes which are near optimal in the least-squares sense.
Cross-Currency and Hybrid Markov-Functional Models

In this paper we consider cross-currency Markov-functional models and their calibration under the spot measure. Hunt, Kennedy and Pelsser introduced a single-currency Markov-functional interest rate model in the terminal measure and showed how to efficiently calibrate it to LIBOR or swaprate options. Building upon their work we will present a multi-factor cross-currency LIBOR model under different measures. We see zero correlated FX spot and LIBOR rates as a natural starting point. Under this assumption we don't need a change of numeraire drift correction and the functionals of the foreign currency rates under the domestic numeraire are identical to the functionals of the foreign currency model under its (foreign) spot measure. This provides the motivation for first deriving a spot measure version of single-currency Markov-functional model. We will show that in the spot measure it is possible to formulate and implement a very efficient calibration procedure comparable to that provided by Hunt, Kennedy and Pelsser for the terminal measure. Combining single-currency Markov-functional interest rate models with a Markov-functional FX spot model we build two and three factor cross-currency models. Relaxing the zero correlation assumption is technically quite simple, but it entails considerable additional computational costs, mainly for the calibration of the model to FX options. To circumvent this problem we suggest a more efficient approximate procedure, which seems to work quite well for low correlations.
The transcription factor HNF3beta is required in visceral endoderm for normal primitive streak morphogenesis.

During early embryogenesis, the transcription factor HNF3beta is expressed in visceral and definitive endoderm, node, notochord and floorplate. A targeted mutation in the HNF3&bgr ; gene results in the lack of a definitive node and notochord. Furthermore, lack of HNF3beta results in failure of proper primitive streak elongation. To address whether HNF3beta is required in visceral endoderm, we have used tetraploid embryo-ES cell aggregations to generate chimeric mouse embryos with wild-type visceral endoderm and homozygous mutant HNF3beta embryonic ectoderm or vice versa. Replacing the visceral endoderm of mutant HNF3beta embryos rescued proper primitive streak elongation and, conversely, mutant visceral endoderm imposed a severe embryonic-extraembryonic constriction on wild-type embryonic ectoderm. Restoration of normal streak morphogenesis was not sufficient to allow formation of the node and notochord in HNF3beta mutant embryos. Thus, our results demonstrate that HNF3beta has two separate roles in primitive streak formation. One is to act within the visceral endoderm to promote proper streak morphogenesis. The second is autonomous to the node and its precursors and involves specification of node and notochord cell fates. HNF3beta mutant embryos rescued for the embryonic-extraembryonic constriction developed further than mutant embryos, allowing examination of later roles for HNF3beta. We show that such mutant embryos lack foregut and midgut endoderm. In addition, left-right asymmetry is affected in the mutant embryos.
LinkedPPI: Enabling Intuitive, Integrative Protein-Protein Interaction Discovery

Understanding the dynamics of protein-protein interactions (PPIs) is a cardinal step for studying human diseases at the molecular level. Advances in "sequencing" technologies have resulted in a deluge of biological data related to gene and protein expression, yet our knowledge of PPI networks is far from complete. The lack of an integrated vocabulary makes querying this data difficult for domain users, whereas the large volume makes it difficult for intuitive exploration. In this paper we employ Linked Data technologies to develop a framework 'LinkedPPI' to facilitate domain researchers in integrative PPI discovery. We demonstrate the semantic integration of various data sources pertaining to biological interactions, expression and functions using a domain-specific model. We deploy a platform which enables search and aggregative visualization in real-time. We finally showcase three user scenarios to depict how our approach can help identify potential interactions between proteins, domains and genomic segments.
The Client's and Therapist's Vocal Qualities in CBT and PE-EFT for Depression

The psychotherapy client’s vocal quality indicates the quality of and shifts in the client’s engagement in treatment. In contrast, the therapist’s vocal quality is a treatment intervention, either facilitating or hindering the client’s progress. The Client Vocal Quality (CVQ) and Therapist Vocal Quality (TVQ) measures (Rice & Kerr, 1986) were applied to responses in the middle 20 minutes of low and high change sessions for 61 clients who received either cognitive behavioural therapy (CBT) or process-experiential emotion focused therapy (PE-EFT) for depression (Watson, Gordon, Stermac, Kalogerakos, & Steckley, 2003). Sessions were selected using the Client Task Specific Change Measure-Revised (CTSC-R; Watson, Greenberg, Rice, & Gordon, 1996). Outcome measures included the Beck Depression Inventory, Dysfunctional Attitude Scale, Global Severity Index of the Symptom Checklist-90-Revised, Inventory of Interpersonal Problems, Problem-Focused Style of Coping Scale, and Rosenberg Self-Esteem Scale. The CVQ categories differentiated between treatment types, with PE-EFT clients expressing a significantly higher proportion of Emotional vocal quality and CBT clients expressing a significantly higher proportion of Externalizing vocal quality. There was no difference between the treatment types for Focused or Limited vocal quality. The CVQ categories predicted outcome. This relationship was particularly evident in the session in which clients first reported moderate to high change on the CTSC-R. The combination of Emotional and Focused categories was a stronger predictor than either category alone. In contrast, a higher