Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Methods for Collecting and Modeling Dissimilarity Data: Applications to Complex Sound Stimuli

Sorting procedures are frequently adopted as an alternative to dissimilarity ratings to measure the dissimilarity of large sets of stimuli in a comparatively short time. However, systematic empirical research on the consequences of this experiment-design choice is lacking. We carried out a behavioral experiment to assess the extent to which sorting procedures compare to dissimilarity ratings in terms of efficiency, reliability, and accuracy, and the extent to which data from different data-collection methods are redundant and are better fit by different distance models. Participants estimated the dissimilarity of either semantically charged environmental sounds or semantically neutral synthetic sounds. We considered free and hierarchical sorting and derived indications concerning the properties of constrained and truncated hierarchical sorting methods from hierarchical sorting data. Results show that the higher efficiency of sorting methods comes at a considerable cost in terms of data reliability and accuracy. This loss appears to be minimized with truncated hierarchical sorting methods that start from a relatively low number of groups of stimuli. Finally, variations in data-collection method differentially affect the fit of various distance models at the group-average and individual levels. On the basis of these results, we suggest adopting sorting as an alternative to dissimilarity-rating methods only when strictly necessary. We also suggest analyzing the raw behavioral dissimilarities, and avoiding modeling them with one single distance model.
Characterization of S100A11, a suppressive factor of fertilization, in the mouse female reproductive tract

We recently found that Xenopus dicalcin, present in the extracellular egg‐coating envelope, suppresses the efficiency of fertilization in vitro through binding to envelope‐constituent glycoproteins. In the present study, we explored the mouse counterpart of Xenopus dicalcin, specifically its localization in the female reproductive tract and its action on mouse fertilization. Our homology and phylogenetic analyses using known S100 proteins showed that S100A11 is most closely related to Xenopus dicalcin. S100A11 was localized in the cytosol of luteal cells, but not in the follicle, in the mouse ovary, and also in the cytosol of the oviductal epithelial cells. In addition, our quantitative analyses revealed preferential expression of S100A11 in the ampullary region of the oviduct and at the estrus stage during the mouse estrous cycle. In the cumulus cell–oocyte complex dissected from the oviduct following ovulation, S100A11 was present in the plasma membrane of cumulus cells, but not in the zona pellucida, which is comparable with Ca2+‐dependent binding of exogenously applied S100A11 to the plasma membrane of cumulus cells. Pretreatment of the cumulus cell–oocyte complex with recombinant S100A11 substantially reduced the efficiency of in vitro fertilization, but S100A10, the next closest S100 protein to Xenopus dicalcin, had no effect. These results suggested that S100A11 is the mouse counterpart of Xenopus dicalcin, suppresses the fertilization process through its action on cumulus cells, and thereby plays a key role in fertilization success in the mouse. Mol. Reprod. Dev. 78:91–103, 2011. © 2010 Wiley‐Liss, Inc.
Mass flux analysis of 137Cs plumes emitted from the Fukushima Daiichi nuclear power plant

Abstract The flow vectors of radioactive cesium-137 (137Cs) plume emitted from the Fukushima Daiichi nuclear power plant in March 2011 were quantitatively depicted by a mass flux analysis in this study. 137Cs plumes were calculated by an Eulerian dispersion model with a 3-km horizontal resolution. The vertically column-integrated mass flux was consistent with the flow approximation based on ground surface 137Cs observations, even though there were some discrepancies that were caused by differences in the wind direction between the ground surface and the dominant plume layer. These discrepancies were explained by combining the use of the ground surface horizontal mass flux with the column-integrated mass flux. The mass flux analysis clearly provided an illustration of 137Cs dominant stream locations, directions, and depositions by reducing high-dimensional model outputs into a lower-dimensional plot. Mass flux (i.e. the product of the mass density and wind velocity) has often been used in dynamic meteorology but has not been used as frequently in atmospheric chemistry or pollutant dispersion studies. However, the concept of mass flux is a robust alternative for conventional validation approaches that only utilize a time series of pollutant concentrations. Mass flux analyses can be used further in atmospheric chemistry as a quantitative visualization tool to track the emission, advection, dispersion, and deposition of atmospheric constituents.
The Use of Automated Quantitative Analysis to Evaluate Epithelial-to-Mesenchymal Transition Associated Proteins in Clear Cell Renal Cell Carcinoma

Background Epithelial-to-mesenchymal transition (EMT) has recently been implicated in the initiation and progression of renal cell carcinoma (RCC). Some mRNA gene expression studies have suggested a link between the EMT phenotype and poorer clinical outcome from RCC. This study evaluated expression of EMT-associated proteins in RCC using in situ automated quantitative analysis immunofluorescence (AQUA) and compared expression levels with clinical outcome. Methods/Principal Findings Unsupervised hierarchical cluster analysis of pre-existing RCC gene expression array data (GSE16449) from 36 patients revealed the presence of an EMT transcriptional signature in RCC [E-cadherin high/SLUG low/SNAIL low]. As automated immunofluorescence technology is dependent on accurate definition of the tumour cells in which measurements take place is critical, extensive optimisation was carried out resulting in a novel pan-cadherin based tumour mask that distinguishes renal cancer cells from stromal components. 61 patients with ccRCC and clinical follow-up were subsequently assessed for expression of EMT-associated proteins (WT1, SNAIL, SLUG, E-cadherin and phospho-β-catenin) on tissue microarrays. Using Kaplan-Meier analysis both SLUG (p = 0.029) and SNAIL (p = 0.024) (log rank Mantel-Cox) were significantly associated with prolonged progression free survival (PFS). Using Cox regression univariate and multivariate analysis none of the biomarkers were significantly correlated with outcome. 14 of the 61 patients expressed the gene expression analysis predicted EMT-protein signature [E-cadherin high/SLUG low/SNAIL low], which was not found to be associated to PFS when measured at the protein level. A combination of high expression of SNAIL and low stage was able to stratify patients with greater significance (p = 0.001) then either variable alone (high SNAIL p = 0.024, low stage p = 0.029). Conclusions AQUA has been shown to have the potential to identify EMT related protein targets in RCC allowing for stratification of patients into high and low risk groups, as well the ability to assess the association of reputed EMT signatures to progression of the disease.
Nurturing Social Networks Using Mobile Phones

Youngsters maintain contact with each other through social-networking websites. We propose new ways of nurturing contacts by monitoring users’ activity with mobile phones (i.e., by monitoring text messages, phone calls, and encounters captured by Bluetooth). We show that, based on user’s activity, one is able to recommend new friends, track health of friendships (and alert users they may be neglecting their friendship), and make users aware of their mood (so that they take action and keep negative emotions under control).
A Temperature-Scaling Approach for Projecting Changes in Short Duration Rainfall Extremes from GCM Data

Current and future urban flooding is influenced by changes in short-duration rainfall intensities. Conventional approaches to projecting rainfall extremes are based on precipitation projections taken from General Circulation Models (GCM) or Regional Climate Models (RCM). However, these and more complex and reliable climate simulations are not yet available for many locations around the world. In this work, we test an approach that projects future rainfall extremes by scaling the empirical relation between dew-point temperature and hourly rainfall and projected changes in dew-point temperature from the EC-Earth GCM. These projections are developed for the RCP 8.5 scenario and are applied to a case study in the Netherlands. The shift in intensity-duration-frequency (IDF) curves shows that a 100-year (hourly) rainfall event today could become a 73-year event (GCM), but could become as frequent as a 30-year (temperature-scaling) in the period 2071–2100. While more advanced methods can help to further constrain future changes in rainfall extremes, the temperature-scaling approach can be of use in practical applications in urban flood risk and design studies for locations where no high-resolution precipitation projections are available.
The frequency of polyploid speciation in vascular plants

Since its discovery in 1907, polyploidy has been recognized as an important phenomenon in vascular plants, and several lines of evidence indicate that most, if not all, plant species ultimately have a polyploid ancestry. However, previous estimates of the frequency of polyploid speciation suggest that the formation and establishment of neopolyploid species is rare. By combining information from the botanical community's vast cytogenetic and phylogenetic databases, we establish that 15% of angiosperm and 31% of fern speciation events are accompanied by ploidy increase. These frequency estimates are higher by a factor of four than earlier estimates and lead to a standing incidence of polyploid species within genera of 35% (n = 1,506). Despite this high incidence, we find no direct evidence that polyploid lines, once established, enjoy greater net species diversification. Thus, the widespread occurrence of polyploid taxa appears to result from the substantial contribution of polyploidy to cladogenesis, but not from subsequent increases in diversification rates of polyploid lines.
tBox: a 3d transformation widget designed for touch-screens

3D transformation widgets are commonly used in many 3D applications operated from mice and keyboards. These user interfaces allow independent control of translations, rotations, and scaling for manipulation of 3D objects. In this paper, we study how these widgets can be adapted to the tactile paradigm. We have explored an approach where users apply rotations by means of physically plausible gestures, and we have extended successful 2D tactile principles to the context of 3D interaction. These investigations led to the design of a new 3D transformation widget, tBox, that can been operated easily and efficiently from gestures on touch-screens.
Mixed chimerism as an approach to transplantation tolerance.

The induction of tolerance to transplanted organs could make transplantation safer and more uniformly successful. One of the most promising approaches currently being investigated involves the induction of deletional tolerance through the establishment of "mixed chimerism. " In this laboratory, we first studied mixed chimerism as an approach to transplantation tolerance in mice, using a nonmyeloablative preparative regimen consisting of 300 R whole-body irradiation, 700 R thymic irradiation, and treatment with monoclonal antibodies to CD4 and CD8. This approach has subsequently been extended successfully to the induction of tolerance to renal transplants in fully mismatched cynomolgus monkeys. In addition, the same approach, with minor modifications, has been found effective in producing mixed chimerism and transplantation tolerance in the concordant xenogeneic baboon to cynomolgus monkey species combination. Because pigs have many advantages as a potential xenograft donor for humans, we are also trying to extend our nonmyeloablative regimen for production of mixed chimerism to the discordant pig --> primate combination. We have used absorption of natural antibodies to prevent hyperacute rejection and then proceeded with a mixed chimerism approach. Administration of pig hematopoietic stem cells along with pig recombinant cytokines (SCF and IL-3) to primates has enabled the pig bone marrow to survive in these xenogeneic hosts for over 6 months. This chimerism has apparently been sufficient to markedly diminish T cell immunity and the induction of new T-cell-dependent responses. However, to date we have not succeeded in preventing the return of natural antibodies, which appear to be the cause of eventual loss of organ transplants and are the subject of further intensive investigations.
Generalized Coupled Dictionary Learning Approach With Applications to Cross-Modal Matching

Coupled dictionary learning (CDL) has recently emerged as a powerful technique with wide variety of applications ranging from image synthesis to classification tasks. In this paper, we extend the existing CDL approaches in two aspects to make them more suitable for the task of cross-modal matching. Data coming from different modalities may or may not be paired. For example, for image–text retrieval problem, 100 images of a class are available as opposed to only 50 samples of text data for training. Current CDL approaches are not designed to handle such scenarios, where classes of data points in one modality correspond to classes of data points in the other modality. Given the data from the two modalities, first two dictionaries are learnt for the respective modalities, so that the data have a sparse representation with respect to their own dictionaries. Then, the sparse coefficients from the two modalities are transformed in such a manner that data from the same class are maximally correlated, while that from different classes have very less correlation. This way of modeling the coupling between the sparse representations of the two modalities makes this approach work seamlessly for paired as well as unpaired data. The discriminative coupling term also makes the approach better suited for classification tasks. Experiments on different publicly available cross-modal data sets, namely, CUHK photosketch face data set, HFB visible and near-infrared facial images data set, IXMAS multiview action recognition data set, wiki image and text data set and Multiple Features data set, show that this generalized CDL approach performs better than the state-of-the-art for both paired as well as unpaired data.
Progmosis: Evaluating Risky Individual Behavior During Epidemics Using Mobile Network Data

The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%.
An Adaptive Superframe Duration Allocation Algorithm for Resource-Efficient Beacon Scheduling

Beacon scheduling is considered to be one of the most significant challenges for energy-efficient Low-Rate Wireless Personal Area Network (LR-WPAN) multi-hop networks. The emerging new standard, IEEE802.15. 4e, contains a distributed beacon scheduling functionality that utilizes a specific bitmap and multi-superframe structure. However, this new standard does not provide a critical recipe for superframe duration (SD) allocation in beacon scheduling. Therefore, in this paper, we first introduce three different SD allocation approaches, LSB first, MSB first, and random. Via experiments we show that IEEE802.15. 4e DSME beacon scheduling performs differently for different SD allocation schemes. Based on our experimental results we propose an adaptive SD allocation (ASDA) algorithm. It utilizes a single indicator, a distributed neighboring slot incrementer (DNSI). The experimental results demonstrate that the ASDA has a superior performance over other methods from the viewpoint of resource efficiency.
Optimizing Binary Code Produced by Valgrind (Project Report on Virtual Execution Environments Course - AVExe)

Valgrind is a widely used framework for dynamic binary instrumentation and its mostly known by its memcheck tool. Valgrind's code generation module is far from producing optimal code. In addition it has many backends for different CPU architectures, which difficults code optimization in an architecture independent way. Our work focused on identifying sub-optimal code produced by Valgrind and optimizing it.
A mass participatory experiment provides a rich temporal profile of temperature response in spring onions

Abstract Plants modulate their growth rates based on the environmental signals; however, it is difficult to experimentally test how natural temperature and light fluctuations affect growth, since realistic outdoor environments are difficult to replicate in controlled laboratory conditions, and it is expensive to conduct experiments in many environmentally diverse regions. In partnership with BBC Terrific Scientific, over 50 primary schools from around the UK grew spring onions outside of hydroponic growth chambers that they constructed. Over 2 weeks, students measured the height of the spring onions daily, while the hourly temperature and visibility data were determined for each school based on the UK Meteorological Office data. This rich time series data allowed us to model how plants integrate temperature and light signals to determine how much to grow, using techniques from functional data analysis. We determined that under nutrient‐poor hydroponic conditions, growth of spring onion is sensitive to even a few degrees change in temperature, and is most correlated with warm nighttime temperatures, high temperatures at the start of the experiment, and light exposure near the end of the experiment. We show that scientists can leverage schools to conduct experiments that leverage natural environmental variability to develop complex models of plant‐environment interactions.
Automated Analysis of Neurite Outgrowth in Mouse Retinal Explants

Despite intensive research efforts over the past years, regeneration of injured axons in the central nervous system remains elusive. In the quest for neurostimulatory agents that promote regeneration, well-defined models and analysis methods are required. Tissue explant cultures closely resemble the in vivo situation, making them ideal to study the effect of compounds on the neuro-glial network. This study reports the optimization of an explant culture technique using retinas of neonatal mice and the development of an analysis script that allows for rapid and automated analysis of neurite outgrowth from these explants. The key features of this script (i.e., local thresholding and form selection) allow for swift and unbiased detection of neurite outgrowth. The novel analysis method is compared with two commonly used manual methods and successfully validated by performing dose-response studies with molecules known to either inhibit (anti–β1-integrin antibody) or stimulate (brain-derived neurotrophic factor and ciliary neurotrophic factor) neurite outgrowth from retinal explants. Finally, the new analysis script is used to study whether retinal explant origin has any effect on neurite outgrowth.
Hemoglobin A1c to diagnose diabetes: why the controversy over adding a new tool?

Perhaps diabetes is a bit like obscenity: We know it when we see obvious cases, but it is difficult to develop one definition that encompasses the entire spectrum of disease. Hyperglycemia exists on a continuum, and persons destined to develop type 2 diabetes progress along this continuum over time, from having blood glucose concentrations that are physiologic, to those in some intermediate but asymptomatic range, to glucose concentrations that are frankly increased and often associated with acute symptoms and chronic complications of the disease. But at what point does an individual have blood glucose concentrations, or other measures of glycemia, that cross the line from no diabetes to diabetes? Several decades ago, the National Diabetes Data Group (NDDG)2 developed consensus diagnostic criteria for diabetes that were based on population distributions of glucose concentrations (even though for most populations there is not a bimodal distribution for glucose clearly dividing diseased from nondiseased individuals) and based on the relative risk of decompensation to overt or symptomatic diabetes. These criteria, including a fasting plasma glucose (FPG) concentration of 140 mg/dL (7.8 mmol/L) or greater and a 2-h plasma glucose concentration during a 75-g oral glucose tolerance test (OGTT) of 200 mg/dL (11.1 mmol/L) or greater, became the worldwide standard for diagnosing diabetes. Even in 1979, however, the NDDG noted that “there is no clear division between [those with diabetes] and [those without diabetes] in the FPG concentration or their response to an oral glucose load” and acknowledged that the cutpoints chosen were arbitrary (1). In 1997, the Expert Committee on Diagnosis and Classification of Diabetes Mellitus was convened to revisit the criteria for diagnosing diabetes. This group examined population data for retinopathy in 3 distinct populations and noted that for FPG, 2-h postload glucose (PG), and hemoglobin A1c (Hb A …
Node activity scheduling in wireless sensor networks

Wireless sensor networks have resources of limited capacity (e.g. bandwidth, processing power, memory and energy). That is why these resources should be efficiently used. Node activity scheduling is a technique that allows nodes to alternate sleep and awake states. This technique spares energy insofar as the sleep state is the state using the smallest power. Moreover, by allowing several nodes to transmit simultaneously without interfering, spatial reuse of the bandwidth is obtained. Furthermore with a smart schedule, data gathering can be done in a single cycle. All these reasons render node activity scheduling very attractive in wireless sensor networks. We propose in this paper a three-hop coloring algorithm for data gathering applications. Simulation results allow us to evaluate the number of colors needed to color all network nodes and hence to determine the reduced size of the activity period in each polling cycle. The complexity of our algorithm is given in terms of number of messages sent per node. We can then determine the network configurations for which coloring brings interesting benefits, namely a more efficient use of the bandwidth and the node energy, as well as a shorter delay to collect data ensuring their time consistency.