Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Depression Care Management in a Community Setting: Main Outcomes for a Medicaid HMO Population with Multiple Medical and Psychiatric Comorbidities

The authors describe the implementation of a depression care management (DCM) program at Colorado Access, a public sector health plan, and describe the program's clinical and system outcomes for members with chronic medical conditions. High medical risk, high cost Medicaid health plan members were identified and systematically screened for depression. A total of 370 members enrolled in the DCM program. Longitudinal analyses revealed significantly reduced depression severity scores at 3, 6, and 12 months after intervention as compared to baseline depression scores. At 12 months, 56% of enrollees in the DCM program had either a 50% reduction in PHQ-9 scores or a PHQ-9 score < 10. Longitudinal economic analyses comparing 12 months before and after intervention revealed a significant but modest increase in ER visits, outpatient office visits, and overall medical and pharmacy costs when adjusted for months enrolled in DCM. Limitations and recommendations for the integrated depression care management are discussed.
Transgenic Inhibition of Synaptic Transmission Reveals Role of CA3 Output in Hippocampal Learning

The hippocampus is an area of the brain involved in learning and memory. It contains parallel excitatory pathways referred to as the trisynaptic pathway (which carries information as follows: entorhinal cortex → dentate gyrus → CA3 → CA1 → entorhinal cortex) and the monosynaptic pathway (entorhinal cortex → CA1 → entorhinal cortex). We developed a generally applicable tetanus toxin–based method for transgenic mice that permits inducible and reversible inhibition of synaptic transmission and applied it to the trisynaptic pathway while preserving transmission in the monosynaptic pathway. We found that synaptic output from CA3 in the trisynaptic pathway is dispensable and the short monosynaptic pathway is sufficient for incremental spatial learning. In contrast, the full trisynaptic pathway containing CA3 is required for rapid one-trial contextual learning, for pattern completion–based memory recall, and for spatial tuning of CA1 cells.
Age‐Specific Demography in Plantago: Uncovering Age‐Dependent Mortality in a Natural Population

Accurate measures of age‐dependent mortality are critical to life‐history analysis and measures of fitness, yet these measures are difficult to obtain in natural populations. Age‐dependent mortality patterns can be obscured not only by seasonal variation in environmental conditions and reproduction but also by changes in the heterogeneity among individuals in the population over time due to selection. This study of Plantago lanceolata uses longitudinal data from a field study with a large number of individuals to develop a model to estimate the shape of the baseline hazard function that represents the age‐dependent risk of mortality. The model developed here uses both constant (genetics, spatial location) and time‐varying (temperature, rainfall, reproduction, size) covariates not only to estimate the underlying mortality pattern but also to demonstrate that the risk of mortality associated with fitness components can change with time/age. Moreover, this analysis suggests that increasing size after reproductive maturity may allow this plant species to escape from demographic senescence.
Essays on Entrepreneurship and Economic Development

Two compelling reasons motivate the work in this dissertation. While enormous literature on developed countries has emerged identifying the determinants of entrepreneurship and its impact, very little is known about the characteristics and the role of entrepreneurship in less developed countries. Who are entrepreneurs in such contexts and who amongst them create jobs for others? What is the impact of geographic location on the initial size of new firms entering markets? What are the welfare implications of entrepreneurship in a developing country and what are the dynamics of entrepreneurial choice? Furthermore, recent methodological advances in econometrics allow rigorous analysis of occupational choice problems and the determinants of new firm formation. In this dissertation, I employ new tools of spatial analysis, Bayesian semi-parametric and non-parametric methods and some recent advances in econometrics to examine these questions.
Evaluation of head size of an interactive robot using an augmented reality

In this paper, we propose a design methodology of robots based on augmented reality (AR). While robot design based on subjective evaluation is useful, the problem of subject-based design is that the developer should prepare all variation of robots to be evaluated. Using AR, a developer can prepare robots using computer graphics. In addition, the AR technology makes it possible to evaluate a robot in an environment in which the robot is supposed to work. We conducted experiments to evaluate a robot's head size using both AR and real robots, and compared the evaluation results. As a result, similar evaluation results were obtained from both evaluation experiments, which showed a possibility of AR-based robot evaluation.
Test profiling for usage models by deriving metrics from component-dependency-models

In industry, embedded systems become complex multiple functionality devices. Research and industry have investigated the issue, how to derive test cases to cover test requirements within minimal time. For the automation of all activities related to testing tool support is available. Yet, one question is getting increasingly difficult: How to derive good, i.e. significant test cases, embracing requirements from an external perspective and incorporating knowledge about the internal design and architecture of the system under test (SUT). In this paper we present the use of internal dependences and implementation metrics in combination with an exterior view on the basis of requirements with the aim to profile the test model for the generation of good, i.e. significant test cases. The incorporation of different sources of knowledge of the SUT into test generation reduces the effort to determine test cases for different test goals independently while recreating the knowledge of the SUT in each test process step. We applied the method to a real implementation of automotive functionality in the thermal control unit of a German OEM, that was designed in Matlab Simulink and architectured in AUTOSAR 3.2. The existing test suites were linked with the internal dependences of the Matlab Code, as to assess the reachability of the existing test cases already on the implementation model. In a next step, model metrics were calculated for the implementation modules. They were set into relation with the test cases and code coverage. In doing so, we profiled the test cases and assessed the significance and expressiveness of the model metrics.
Classical and quantum gravity in dimensions: I. A unifying approach

We provide a concise approach to generalized dilaton theories with and without torsion and coupling to Yang - Mills fields. Transformations on the space of fields are used to trivialize the field equations locally. In this way their solution becomes accessible within a few lines of calculation only. In this first of a series of papers we set the stage for a thorough global investigation of classical and quantum aspects of more or less all available 2D gravity - Yang - Mills models.
Spread dynamics with resistances of computer virus on complex networks

One open problem in computer virus research is that the prevalence of computer virus has always been much lower in reality than that in theories. Instead of solving the problem from diverse virus mechanisms, this paper focuses on effects of spread environments, including complex network platforms from macroscopic perspective and spread resistances from microscopic perspective. By classifying virus platforms and resistances, and evaluating their respective impacts to virus propagation with virus spread dynamics, the paper reveals answers to the open problem comprehensively for the first time. Conclusions show that different network platforms curb the scale of virus propagation, and various resistances limit both speed and scale of virus propagation.
Abundance of the Fanconi anaemia core complex is regulated by the RuvBL1 and RuvBL2 AAA+ ATPases

Fanconi anaemia (FA) is a genome instability disease caused by defects in the FA DNA repair pathway that senses and repairs damage caused by DNA interstrand crosslinks. At least 8 of the 16 genes found mutated in FA encode proteins that assemble into the FA core complex, a multisubunit monoubiquitin E3 ligase. Here, we show that the RuvBL1 and RuvBL2 AAA+ ATPases co-purify with FA core complex isolated under stringent but native conditions from a vertebrate cell line. Depletion of the RuvBL1-RuvBL2 complex in human cells causes hallmark features of FA including DNA crosslinker sensitivity, chromosomal instability and defective FA pathway activation. Genetic knockout of RuvBL1 in a murine model is embryonic lethal while conditional inactivation in the haematopoietic stem cell pool confers profound aplastic anaemia. Together these findings reveal a function for RuvBL1-RuvBL2 in DNA repair through a physical and functional association with the FA core complex. Surprisingly, depletion of RuvBL1-RuvBL2 leads to co-depletion of the FA core complex in human cells. This suggests that a potential mechanism for the role of RuvBL1-RuvBL2 in maintaining genome integrity is through controlling the cellular abundance of FA core complex.
Black Politics in New Deal Atlanta

When Franklin Roosevelt was elected president in 1932, Atlanta had the South's largest population of college-educated African Americans. The dictates of Jim Crow meant that these men and women were almost entirely excluded from public life, but as Karen Ferguson demonstrates, Roosevelt's New Deal opened unprecedented opportunities for black Atlantans struggling to achieve full citizenship. Black reformers, often working within federal agencies as social workers and administrators, saw the inclusion of African Americans in New Deal social welfare programs as a chance to prepare black Atlantans to take their rightful place in the political and social mainstream. They also worked to build a constituency they could mobilize for civil rights, in the process facilitating a shift from elite reform to the mass mobilization that marked the postwar black freedom struggle. Although these reformers' efforts were an essential prelude to civil rights activism, Ferguson argues that they also had lasting negative repercussions, embedded as they were in the politics of respectability. By attempting to impose bourgeois behavioral standards on the black community, elite reformers stratified it into those they determined deserving to participate in federal social welfare programs and those they consigned to remain at the margins of civic life. |Ferguson looks at how black reformers in Atlanta used New Deal federal programs to advance their struggle for citizenship--and how they used their authority as agents of the state to impose a bourgeois "politics of respectability" that effectively stratified the black community.
Represent Image Contents Using Curves and Chain Code

This paper presents a new approach to extracting and representing structural features of images. The approach is based on both a region-based analysis and a contour-based analysis. An image is first segmented into regions, after which an area of each region is computed using a number of bound pixels. A contour of the region is approximated by a B-spline curve to get its control polygon. A convex hull of each region is then computed from the obtained control polygon. Ellipses are used to intuitively represent structural components in the image. Each ellipse is centered at its corresponding convex hull’s centroid and is approximated from the convex hull. Color and spatial information of each region is also used. In a multi-scale setting, the Chaikin’s algorithm is iteratively applied to the obtained B-spline control polygon in order to get finer control polygons, which in the limit results in the B-spline curve. To compare similarity between the control polygons and their higher-level refined control polygons, we use the chain code representation in concert with the Chaikin’s algorithm. Our method could be applied in many applications including image retrieval, image classification, image clustering, image understanding, image manipulation, pattern recognition, and definitely in machine vision.
An Integrated Approach to Predictive Analytics for Stock Indexes and Commodity Trading Using Computational Intelligence

— Investment prediction is a method to decide the future values of stock indexes and commodity exchanges or trading of financial services. The aim of the model is to perform optimized prediction on commodities and stock market indexes. The investment prediction is an important task for an investor to maximize his or her return on investment. The purpose of the paper is to propose an optimized model using computational intelligence and it is a step by step method that follows an integrated approach which can solve several complex problems in predictive analytics. The integrated approach in this paper uses genetic algorithm, pearson’s correlation coefficient and multilayer perceptron adaline feed forward neural network to predict the next business day high values of stock indexes and commodities trading. As an integrated method, the model uses genetic algorithm as first step to check the data optimization, since the data is considered as an important element in data analytics. The optimized data is extracted using correlation coefficient and the classifier prediction is done with multilayer perceptron adaline feed forward neural network for making the prediction. The proposed model was implemented continuously on three months data to evaluate the performance and to check the accuracy on the NSEindia. The predicted values were checked against the next business day of original values, the predicted result is very close to the original values. The model is evaluated with the statistical parameter MRE, MMRE and the accuracy rate. In comparison with other existing methods, the current method outperforms other testing patterns.
Diversity of freshwater Epsilonproteobacteria and dark inorganic carbon fixation in the sulphidic redoxcline of a meromictic karstic lake.

Sulfidic redoxclines are a suitable niche for the growth and activity of different chemo- and photolithotrophic sulphide-oxidizing microbial groups such as the Epsilonproteobacteria and the green sulfur bacteria (GSB). We have investigated the diversity, abundance and contribution to inorganic carbon uptake of Epsilonproteobacteria in a meromictic basin of Lake Banyoles. CARD-FISH counts revealed that Epsilonproteobacteria were prevalent at the redoxcline in winter (maximum abundance of 2 × 10(6) cells mL(-1), ≈60% of total cells) but they were nearly absent in summer, when GSB bloomed. This seasonal trend was supported by 16S rRNA gene pyrotag datasets, which revealed that the epsilonproteobacterial community was mainly composed of a member of the genus Arcobacter. In situ incubations using NaH(14)CO3 and MAR-CARD-FISH observations showed that this population assimilated CO2 in the dark, likely being mainly responsible for the autotrophic activity at the redoxcline in winter. Clone libraries targeting the aclB gene provided additional evidence of the potential capacity of these epsilonproteobacteria to fix carbon via rTCA cycle. Our data reinforce the key role of Epsilonproteobacteria in linking carbon and sulphur cycles, extend their influence to freshwater karstic lakes and raise questions about the actual contribution of chemolithotrophy at their redoxcline and euxinic water compartments.
Multimodal sensor fusion for foot state estimation in bipedal robots using the Extended Kalman Filter

Towards enhancing the dynamic locomotion and manipulation abilities of bipedal robots in real-world scenarios, a key problem lies in the accurate estimation of the dynamic state of the feet of the robot. In this paper, an approach is presented for estimating the dynamic pose and the internal (body) and external (ground contact) wrenches acting on the individual feet of a bipedal robot fusing haptic (compliant skin), inertial, and force/torque (F/T) measurements. Assuming rigid body dynamics on an individual foot, an Extended Kalman Filter (EKF) is used to combine ankle F/T sensor readings, contact forces computed from a compliant tactile array on the foot sole and accelerometer plus gyroscope measurements, thereby estimating both the state and the external wrenches affecting a foot through a method of state augmentation. Moreover, covariance estimation of the measurement noise was carried out for all sensors, in particular, for the skin, a bayesian-network-based regression method was chosen. The framework was implemented with the iCub humanoid robot under a toppling scenario; the estimated augmented foot state was then used to compute the Foot Rotation Indicator (FRI) trajectory as a validation through prediction of the onset of toppling and instability.
The effect of gaze-contingent stimulus elimination on preference judgments

This study examined how stimulus elimination (SE) in a preference judgment task affects observers’ choices. Previous research suggests that biasing gaze toward one alternative can increase preference for it; this preference reciprocally promotes gaze bias. Shimojo et al. (2003) called this phenomenon the Gaze Cascade Effect. They showed that the likelihood that an observer’s gaze was directed toward their chosen alternative increased steadily until the moment of choosing. Therefore, we tested whether observers would prefer an alternative at which they had been gazing last if both alternatives were removed prior to the start of this rising gaze likelihood. To test this, we used a preference judgment task and controlled stimulus presentation based on gaze using an eye-tracking system. A pair of non-sensical figures was presented on the computer screen and both stimuli were eliminated while participants were still making their preference decision. The timing of the elimination differed between two experiments. In Experiment 1, after gazing at both stimuli one or more times, stimuli were removed when the participant’s gaze fell on one alternative, pre-selected as the target stimulus. There was no significant difference in the preference of the two alternatives. In Experiment 2, we did not predefine any target stimulus. After the participant gazed at both stimuli one or more times, both stimuli were eliminated when the participant next fixated on either. The likelihood of choosing the stimulus that was gazed at last (at the moment of elimination) was greater than chance. Results showed that controlling participants’ choices using gaze-contingent SE was impossible, but the different results between these two experiments suggest that participants decided which stimulus to choose during their first period of gazing at each alternative. Thus, we could predict participants’ choices by analyzing eye movement patterns at the moment of SE.
Social Preferences in the Public Arena: Evidence from a Prisoner's Dilemma Game on a TV Show

We analyze a large stakes prisoner's dilemma game played on a TV show. Players cooperate 40% of the time, demonstrating that social preferences are important; however, cooperation is significantly below the 50% threshold that is required for inequity aversion to sustain cooperation. Women cooperate significantly more than men, while players who have "earned" more of the stake cooperate less. A player's promise to cooperate is also a good predictor of his decision. Surprisingly, a player's probability of cooperation is unrelated to the opponent's characteristics or promise. We argue that inequity aversion alone cannot adequately explain these results; reputational concerns in a public setting might be more important.
Modular Shape Analysis for View-Serializable Libraries

We present novel modular staticshapeanalysis algorithms for concurrent libraries. Our analyses conservativelyverify the absence of certain memory and concurrency errors, verify a certain class of progra m assertions, and infer shape (heap) module invariants. The key idea is to focus on a c lass of concurrent programs that follow certain standard locking policie s which ensureviewserializability. This allows our analyses not to consider interleaving betw e n the low-level instructions implementinghigh-leveloperations on thread-shared data structures. Technically, we employ existing sequentialshape analysis algorithms to performinterproceduralandmodularanalyses that can establish sufficient conditions for view-serializabilityfor concurrentlibraries.
A Versatile ΦC31 Based Reporter System for Measuring AP-1 and Nrf2 Signaling in Drosophila and in Tissue Culture

This paper describes the construction and characterization of a system of transcriptional reporter genes for monitoring the activity of signaling pathways and gene regulation mechanisms in intact Drosophila, dissected tissues or cultured cells. Transgenic integration of the reporters into the Drosophila germline was performed in a site-directed manner, using ΦC31 integrase. This strategy avoids variable position effects and assures low base level activity and high signal responsiveness. Defined integration sites furthermore enable the experimenter to compare the activity of different reporters in one organism. The reporter constructs have a modular design to facilitate the combination of promoter elements (synthetic transcription factor binding sites or natural regulatory sequences), reporter genes (eGFP, or DsRed.T4), and genomic integration sites. The system was used to analyze and compare the activity and signal response profiles of two stress inducible transcription factors, AP-1 and Nrf2. To complement the transgenic reporter fly lines, tissue culture assays were developed in which the same synthetic ARE and TRE elements control the expression of firefly luciferase.
Detection of Rare Objects in Massive Astronomical Datasets Using Innovative Knowledge Discovery Technology

Our work presents an application of knowledge discovery technology aimed to help scientists in the detection of rare types of astro- physical objects. Our main idea is that while computer have the power to search huge amounts of data, an expert has the domain knowledge to e-ciently lead this search. Our system builds upon two main compo- nents: a probabilistic model able to scale to large datasets and a set of modules to interact with the scientist. Here, we focus on the probabilis- tic model used to represent the joint uncertainty among the attributes of the objects registered in a sky survey catalog. This model consists of a combination of a Bayesian network and a set of Gaussian mixture models (GMMs) trained with an accelerated version of the expectation maximization (EM) algorithm. The model is currently being tested us- ing data from the release 1 of the Sloan Digital Sky Survey. The results indicate that the system is able to accurately detect a set of simulated rare objects, but it also provides a large number of false positives.