Miracle-dz/UniFAR-FTUs huggingface dataset:
Weight Gain following Pallidal Deep Brain Stimulation: A PET Study

The mechanisms behind weight gain following deep brain stimulation (DBS) surgery seem to be multifactorial and suspected depending on the target, either the subthalamic nucleus (STN) or the globus pallidus internus (GPi). Decreased energy expenditure following motor improvement and behavioral and/or metabolic changes are possible explanations. Focusing on GPi target, our objective was to analyze correlations between changes in brain metabolism (measured with PET) and weight gain following GPi-DBS in patients with Parkinson’s disease (PD). Body mass index was calculated and brain activity prospectively measured using 2-deoxy-2[18F]fluoro-D-glucose PET four months before and four months after the start of GPi-DBS in 19 PD patients. Dopaminergic medication was included in the analysis to control for its possible influence on brain metabolism. Body mass index increased significantly by 0.66 ± 1.3 kg/m2 (p = 0.040). There were correlations between weight gain and changes in brain metabolism in premotor areas, including the left and right superior gyri (Brodmann area, BA 6), left superior gyrus (BA 8), the dorsolateral prefrontal cortex (right middle gyrus, BAs 9 and 46), and the left and right somatosensory association cortices (BA 7). However, we found no correlation between weight gain and metabolic changes in limbic and associative areas. Additionally, there was a trend toward a correlation between reduced dyskinesia and weight gain (r = 0.428, p = 0.067). These findings suggest that, unlike STN-DBS, motor improvement is the major contributing factor for weight gain following GPi-DBS PD, confirming the motor selectivity of this target.
In Silico Simulation of Corticosteroids Effect on an NFkB- Dependent Physicochemical Model of Systemic Inflammation

Background During the onset of an inflammatory response signaling pathways are activated for “translating” extracellular signals into intracellular responses converging to the activation of nuclear factor (NF)-kB, a central transcription factor in driving the inflammatory response. An inadequate control of its transcriptional activity is associated with the culmination of a hyper-inflammatory response making it a desired therapeutic target. Predicated upon the nature of the response, a systems level analysis might provide rational leads for the development of strategies that promote the resolution of the response. Methodology and Findings A physicochemical host response model is proposed to integrate biological information in the form of kinetic rules and signaling cascades with pharmacokinetic models of drug action for the modulation of the response. The unifying hypothesis is that the response is triggered by the activation of the NFkB signaling module and corticosteroids serve as a template for assessing anti-inflammatory strategies. The proposed in silico model is evaluated through its ability to predict and modulate uncontrolled responses. The pre-exposure of the system to hypercortisolemia, i.e. 6 hr before or simultaneously with the infectious challenge “reprograms” the dynamics of the host towards a balanced inflammatory response. However, if such an intervention occurs long before the inflammatory insult a symptomatic effect is observed instead of a protective relief while a steroid infusion after inducing inflammation requires much higher drug doses. Conclusions and Significance We propose a reversed engineered inflammation model that seeks to describe how the system responds to a multitude of external signals. Timing of intervention and dosage regimes appears to be key determinants for the protective or symptomatic effect of exogenous corticosteroids. Such results lie in qualitative agreement with in vivo human studies exposed both to LPS and corticosteroids under various time intervals thus improving our understanding of how interacting modules generate a behavior.
A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior

This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.
Limnological conditions in Subglacial Lake Vostok, Antarctica

Subglacial Lake Vostok is located ~4 km beneath the surface of the East Antarctic Ice Sheet and has been isolated from the atmosphere for ≫15 million yr. Concerns for environmental protection have prevented direct sampling of the lake water thus far. However, an ice core has been retrieved from above the lake in which the bottom ,85 m represents lake water that has accreted (i.e., frozen) to the bottom of the ice sheet. We measured selected constituents within the accretion ice core to predict geomicrobiological conditions within the surface waters of the lake. Bacterial density is two‐ to sevenfold higher in accretion ice than the overlying glacial ice, implying that Lake Vostok is a source of bacterial carbon beneath the ice sheet. Phylogenetic analysis of amplified small subunit ribosomal ribonucleic acid (rRNA) gene sequences in accretion ice formed over a deep portion of the lake revealed phylotypes that classify within the β‐, γ‐, and Δ‐Proteobacteria. Cellular, major ion, and dissolved organic carbon levels all decreased with depth in the accretion ice (depth is a proxy for increasing distance from the shoreline), implying a greater potential for biological activity in the shallow shoreline waters of the lake. Although the exact nature of the biology within Lake Vostok awaits direct sampling of the lake water, our data from the accretion ice support the working hypothesis that a sustained microbial ecosystem is present in this subglacial lake environment, despite high pressure, constant cold, low nutrient input, potentially high oxygen concentrations, and an absence of sunlight.
Discussion of Shared Information Can Increase the Influence of Divergent Members

Based on the theoretical frameworks of information-sharing in groups and the linear discrepancy model, this study highlights the importance of communicating shared information for a divergent member to influence a group. Participants received information concerning whether “under God” should be in the Pledge of Allegiance. After stating individual opinions, they discussed the issue in small groups and came to a group decision on a continuous, ordered scale. Low divergent members, who had opinions closer to the average of other group members, had more influence than high divergent members. Group members with high divergence were more confident and talked more than others. However, there was no relationship between the amount divergent members talked or their confidence level and their amount of influence. Highly divergent group members who mentioned more shared information were more influential and came across as more knowledgeable.
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control

This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.
Blue Variable Stars from the MACHO Database. I. Photometry and Spectroscopy of the Large Magellanic Cloud Sample

We present the photometric properties of 1279 blue variable stars within the Large Megallanic Cloud. Photometry is derived from the MACHO database. The light curves of the sample exhibit a variety of quasi-periodic and aperiodic outburst behavior. A characteristic feature of the photometric variation is that the objects are reddest when at maximum outburst. A subset of 102 objects were examined spectroscopically. Within this subset, 91% exhibited Balmer emission in at least one epoch, in some cases with spectacular spectral variability. The variability observed in the sample is consistent with the establishment and maintenance of the Be phenomenon.
Neural Generative Question Answering

This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.
Comparison between Manual and Semi-automatic Segmentation of Nasal Cavity and Paranasal Sinuses from CT Images

Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic segmentation of paranasal sinuses and nasal cavity is needed. We present a comparison of manual and semi-automatic segmentation of paranasal sinuses and the nasal cavity. Manual segmentation is performed by custom software whereas semi-automatic segmentation is realized by a commercial product (Amira). For this study we used a CT dataset of the paranasal sinuses which consists of 98 transversal slices, each 1.0 mm thick, with a resolution of 512 x 512 pixels. For the analysis of both segmentation procedures we used volume, extension (width, length and height), segmentation time and 3D-reconstruction. The segmentation time was reduced from 960 minutes with manual to 215 minutes with semi-automatic segmentation. We found highest variances segmenting nasal cavity. For the paranasal sinuses manual and semiautomatic volume differences are not significant. Dependent on the segmentation accuracy both approaches deliver useful results and could be used for e.g. robot-assisted systems. Nevertheless both procedures are not useful for everyday surgical workflow, because they take too much time. Fully automatic and reproducible segmentation algorithms are needed for segmentation of paranasal sinuses and nasal cavity.
Maximizing Text-Mining Performance

WITH THE ADVENT OF CENTRALized data warehouses, where data might be stored as electronic documents or as text fields in databases, text mining has increased in importance and economic value. One important goal in text mining is automatic classification of electronic documents. Computer programs scan text in a document and apply a model that assigns the document to one or more prespecified topics. Researchers have used benchmark data, such as the Reuters-21578 test collection, to measure advances in automated text categorization. Conventional methods such as decision trees have had competitive, but not optimal, predictive performance. Using the Reuters collection, we show that adaptive resampling techniques can improve decision-tree performance and that relatively small, pooled local dictionaries are effective. We’ve applied these techniques to online banking applications to enhance automated e-mail routing.
Manuel, Recovering the Sense of the Democratic Movement Through Living Solidarity in Dialogic Literary Gatherings

Manuel’s biography shows a valuable process of re-enchantment and belief in the possibility of social change. Manuel was strongly disenchanted with the bureaucratization established in Spain during the transition from the Franco regime to democracy in the mid-70s. He recovered his enchantment with democratic movements through contact with the social and educational movement of adult education in La Verneda (Barcelona) and especially through his participation in the Dialogic Literary Gatherings held in the school for adults. In Manuel’s words, the gathering was the best expression of democracy in the school; it was the democratic experience that he had always imagined. In this outstanding space, he could listen to his friends, who, just like him, had lowly academic backgrounds, and share in the reading of the best universal classic literature. For Manuel, this gathering held the germ of a great debate on social, cultural, and democratic transformation.
Mapping transparency through metaphor: towards more expressive musical instruments

We define a two-axis transparency framework that can be used as a predictor of the expressivity of a musical device. One axis is the player's transparency scale, while the other is the audience's transparency scale. Through consideration of both traditional instruments and new technology-driven interfaces, we explore the role that metaphor plays in developing expressive devices. Metaphor depends on a literature, which forms the basis for making transparent device mappings. We examine four examples of systems that use metaphor: Iamascope, Sound Sculpting, MetaMuse and Glove-TalkII; and discuss implications on transparency and expressivity. We believe this theory provides a framework for design and evaluation of new human–machine and human–human interactions, including musical instruments.
Scheduling and Production & Control: MA 27. Scheduling and Production & Control: MA

This chapter addresses three main problems of the production planning area: Single Machine Scheduling (SMS), Parallel Machine Scheduling (PMS) and Flowshop Scheduling (FS). Many situations in production environments found in industries around the world can be modelled as one or a set of them (Baker 1974). It is easy to find dozens of problem variants in the literature due to different production constraints and objective functions. Due to the overwhelming number of combinations, we focused our discussion on only one of each problem class. They are:
Implementation of the AeroRP and AeroNP Protocols in Python

The domain-specific ANTP protocol suite consisting of AeroTP, AeroRP, and AeroNP has been developed to cope with the challenges in highly-dynamic airborne telemetry networks. These protocols have been designed and modelled through simulation methodology. In this paper, we present an implementation of the AeroRP and AeroNP components in Python. Initially, we implement and test through an emulated wireless environment on the PlanetLab testbed. Further, we present our prototype implementation that is deployed in a real-world wireless environment using radio-controlled vehicles.
Patient costs for paediatric hospital admissions in Tanzania: a neglected burden?

OBJECTIVE Tanzania has a policy of free provision of inpatient care for young children in order to promote timely access and thus reduce the current levels of mortality. However, little is known about out-of-pocket costs that may be incurred by families in seeking care for sick children. We conducted this study to identify the magnitude of these costs in relation to family income. METHODS Five hundred and ten caretakers were interviewed on the day of discharge of their child from 11 hospitals in north-east Tanzania. Caretakers were asked to report expenditure related to hospitalization in various categories and family wealth was assessed through reported expenditure in the previous month. RESULTS Food (mean US$2.2, median US$1.6), transport (mean US$1.7, median US$0) and medicines (mean US$1.0, median US$0.4) were the leading categories of expenditure, and overall the mean out-of-pocket expenditure was US$5.5 (median US$3.7) per admission. Mean out-of-pocket expenditure was more than 1.5 times higher for households in the highest monthly expenditure quintile compared with those in the lowest. However, this differential was reversed when expenditure was considered as a proportion of family expenditure in the previous month; for the lowest quintile, families spent more than three-quarters of their total monthly expenditure on a single paediatric admission. CONCLUSION Out-of-pocket expenditure on child hospitalization places a considerable burden on poor families. Our findings justify a closer scrutiny of how this expenditure could be reduced, particularly through the provision of adequate food for both children and caretakers and through reducing stock-outs of essential medicines.
Use of a Short Fragment of the C-Terminal E Gene for Detection and Characterization of Two New Lineages of Dengue Virus 1 in India

ABSTRACT Here we propose the use of a 216-nucleotide fragment located in the carboxyl terminus of the E gene (E-COOH) and a pairwise-based comparison method for genotyping of dengue virus 1 (DENV-1) strains. We have applied this method to the detection and characterization of DENV-1 in serum samples from travelers returning from the tropics. The results obtained with the typing system correlate with the results obtained by comparison of the sequences of the entire E gene of the strains. The approach demonstrates utility in plotting the distribution and circulation of different genotypes of DENV-1 and also suggests the presence of two new clades of Indian strains. The integration of the method with an online database and a typing characterization tool enhances its strength. Additionally, the analysis of the complete E gene of DENV-1 strains suggested the occurrence of a nondescribed recombination event in the China GD23-95 strain. We propose the use of this methodology as a tool for real-time epidemiological surveillance of dengue virus infections and their pathogenesis.
Sixteen-Month-Old Infants’ Segment Words from Infant- and Adult-Directed Speech

ABSTRACT One of the first challenges facing the young language learner is the task of segmenting words from a natural language speech stream, without prior knowledge of how these words sound. Studies with younger children find that children find it easier to segment words from fluent speech when the words are presented in infant-directed speech, i.e., the kind of speech typically directed toward infants, compared to adult-directed speech. The current study examines whether infants continue to display similar differences in their segmentation of infant- and adult-directed speech later in development. We show that 16-month-old infants successfully segment words from a natural language speech stream presented in the adult-directed register and recognize these words later when presented in isolation. Furthermore, there were no differences in infants’ ability to segment words from infant- and adult-directed speech at this age, although infants’ success at segmenting words from adult-directed speech correlated with their vocabulary size.
Digital Story Mapping to Advance Educational Atlas Design and Enable Student Engagement

Storytelling is recognized as a valid and important method of communicating information and knowledge gleaned from volumes of ever-accumulating data. Practices of data-driven storytelling in journalism and geovisual analytics have contributed to the development of geovisual stories; also called story maps. The benefits of student-focused multi-thematic atlases and digital storytelling methods in education can also be realized in story maps. An online, interactive version of the original paper version of the Wyoming Student Atlas was developed using story mapping technology. Studies on best practices for data-driven storytelling and web map interaction were used to inform the transition of the atlas from a traditional paper format to a collection of story maps. Evaluation of the atlas story maps for educational purposes was conducted by observing students from multiple classrooms as they used the story maps in a lesson. The students and educators responded to a survey after using the story maps. Results of the survey show positive responses to the atlas story maps, including ease of use and preference over a traditional paper atlas. However, certain types of interaction with the map resulted in increased negative or uncertain responses from students concerning their perception of the atlas story maps.
Uncertainty, Inflation, and Welfare

This paper studies the welfare costs and the redistributive effects of inflation in the presence of idiosyncratic liquidity risk, in a micro-founded search-theoretical monetary model. We calibrate the model to match the empirical aggregate money demand and the distribution of money holdings across households, and study the effects of inflation under the implied degree of market incompleteness. We show that in the presence of imperfect insurance the estimated long-run welfare costs of inflation are on average 40% smaller compared to a complete markets, representative agent economy, and that inflation induces important redistributive effects across households. For example, the welfare gains of reducing inflation from 10% to 0% is 0.59% of income. Furthermore, we estimate that the long-run welfare gains of reducing the typical current inflation target of 2 to 1 percent to be 0.06% of income.
Path decomposition under a new cost measure with applications to optical network design

We introduce a problem directly inspired by its application to DWDM (dense wavelength division multiplexing) network design. We are given a set of demands to be carried over a network. Our goal is to choose a route for each demand and to decompose the network into a collection of edge-disjoint simple paths. These paths are called optical line systems. The cost of routing one unit of demand is the number of line systems with which the demand route overlaps; our design objective is to minimize the total cost over all demands. This cost metric is motivated by the need to minimize O-E-O (optical-electrical-optical) conversions in optical transmission. For given line systems, it is easy to find the optimal demand routes. On the other hand, for given demand routes designing the optimal line systems can be NP-hard. We first present a 2-approximation for general network topologies. As optical networks often have low node degrees, we offer an algorithm that finds the optimal solution for the special case in which the node degree is at most 3. Our solution is based on a local greedy approach. If neither demand routes nor line systems are fixed, the situation becomes much harder. Even for a restricted scenario on a 3-regular Hamiltonian network, no efficient algorithm can guarantee a constant approximation better than 2. For general topologies, we offer a simple algorithm with an O(log K)- and an O(log n)-approximation, where K is the number of demands and n the number of nodes. This approximation ratio is almost tight. For rings, a common special topology, we offer a more complex 3/2-approximation algorithm.