Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical activity and breast cancer risk in women aged 20-54 years.

BACKGROUND/METHODS Although several studies have suggested that physical activity is associated with a decreased risk of breast cancer, such a decrease has not been found consistently, perhaps because physical activity was assessed in different ways and for restricted periods. Few studies have assessed the risk of breast cancer in relation to lifetime physical activity. We used data from a population-based, case-control study, including 918 case subjects (aged 20-54 years) and 918 age-matched population control subjects, to examine associations between breast cancer risk and physical activity at ages 10-12 years and 13-15 years, lifetime recreational activity, and title of longest held job. RESULTS Women who were more active than their peers at ages 10-12 years had a lower risk of breast cancer (odds ratio [OR] = 0.68; 95% confidence interval [CI] = 0.49-0.94). Women who had ever engaged in recreational physical activity had a reduced risk of breast cancer compared with inactive women (OR = 0.70; 95% CI = 0.56-0.88). Neither very early recreational activity (before age 20 years) nor recent activity (last 5 years) was associated with a greater reduction in risk than recreational activity in the intermediate period. Furthermore, women who started recreational activities after age 20 years and women who started earlier and continued their activities throughout adult life experienced a similar reduction in risk. Lean women, i.e., women with a body mass index (weight in kg/[height in m](2)) less than 21. 8 kg/m(2), appeared to have a lower risk associated with recreational physical activity than women with a body mass index greater than 24.5 kg/m(2) (OR = 0.57 [95% CI = 0.40-0.82] and OR = 0. 92 [95% CI = 0.65-1.29], respectively). CONCLUSIONS Our findings support the hypothesis that recreational physical activity is associated with a decreased risk of breast cancer. Physical activity in early or recent life does not appear to be associated with additional beneficial effects.
Unsupervised Simplification of Image Hierarchies via Evolution Analysis in Scale-Sets Framework

Region-based hierarchical image representation is crucial in many computer vision applications. However, in practice, an image hierarchy is usually dense, and contains many less informative branches. It is expected that a hierarchy should be accurate and simplified, which is not only desirable for different applications, but also saves considerable computational load for the further analysis. To achieve this target, this paper proposes a novel approach for unsupervised simplification of region-based image hierarchies, which employs the global and local evolution analyses of a hierarchy. First, we introduce a global evolution analysis in the scale-sets framework, which provides clues for eliminating less informative branches. Moreover, a hybrid unsupervised simplification method is designed, utilizing the information from global and local evolution functions. A number of experiments on various images have shown that the proposed approach is effective and efficient in removing less informative nodes (averagely about 90% of the whole nodes), while preserving salient image details and retaining the accuracy.
An Approach to Heterogeneous Data Translation based on XML Conversion

In this paper, we illustrate a preliminary approach to the translation of Web data between heterogeneous formats. This work fits into a larger project whose aim is the development of a tool for the management of data described according to a large variety of formats used on the Web and the (semi)automatic translation of schemes and instances from one model to another. Data translations operate over XML representations of instances and rely on a uniform representation of models that we call metamodel. The metamodel shows structural diversities and dictates the needed transformations. Complex translation can be derived by combining a number of predefined basic functions performing XML transformations expressed in XQuery. Practical examples are provided to show the effectiveness of the approach.
Towards the Modeling of Personal Privacy in Ubiquitous Computing Environments

Privacy is a known barrier to the acceptance of ubiquitous computing technologies because they require individuals to trade control of their personal information and personal spaces for improved quality of life and assistance with daily activities. Previous work has been done to analyze and protect privacy in ubiquitous computing environments, but such efforts do not include a formal underlying model. We seek to approach the privacy problem from a different perspective. Namely, we seek to propose, verify, and analyze a formal model of privacy for these environments. In this paper, we discuss the beginning stages of our model, namely the resources that need to be protected, the guidelines for constructing the model, and the high-level components of our personal privacy model.
Interferon-Gamma-Induced Nitric Oxide Inhibits the Proliferation of Murine Renal Cell Carcinoma Cells

Renal cell carcinoma (RCC) remains one of the most resistant tumors to systemic chemotherapy, radiotherapy, and immunotherapy. Despite great progress in understanding the basic biology of RCC, the rate of responses in animal models and clinical trials using interferons (IFNs) has not improved significantly. It is likely that the lack of responses can be due to the tumor's ability to develop tumor escape strategies. Currently, the use of targeted therapies has improved the clinical outcomes of patients with RCC and is associated with an increase of Th1-cytokine responses (IFNγ), indicating the importance of IFNγ in inhibiting tumor proliferation. Thus, the present study was designed to investigate a new mechanism by which IFNγ mediates direct anti-proliferative effects against murine renal cell carcinoma cell lines. When cultured RCC cell lines were exposed to murine recombinant IFNγ, a dose dependent growth inhibition in CL-2 and CL-19 cells was observed; this effect was not observed in Renca cells. Growth inhibition in CL-2 and CL-19 cell lines was associated with the intracellular induction of nitric oxide synthase (iNOS) protein, resulting in a sustained elevation of nitric oxide (NO) and citrulline, and a decrease in arginase activity. The inhibition of cell proliferation appears to be due to an arrest in the cell cycle. The results indicate that in certain RCC cell lines, IFNγ modulates L-arginine metabolism by shifting from arginase to iNOS activity, thereby developing a potent inhibitory mechanism to encumber tumor cell proliferation and survival. Elucidating the cellular events triggered by IFNγ in murine RCC cell lines will permit anti-tumor effects to be exploited in the development of new combination therapies that interfere with L-arginine metabolism to effectively combat RCC in patients.
Combining connectionist multi-band and full-band probability streams for speech recognition of natural numbers

Multi-band automatic speech recognition is a new and ex-ploratory area of speech recognition which has been getting much attention in the research community. It has been shown that multi-band ASR reduces word error in noisy conditions, particularly in the case of narrow band noise. In this work we show that multi-band ASR could be used to improve the speech recognition accuracy of natural numbers for clean speech when the multi-band (MB) information stream is used in addition to the full-band (FB) one. We also observe that a similar combination method signiﬁcantly reduces the error rate on reverberant speech. Finally, we analyze the error patterns of the full-band and multi-band paradigms to understand why the combination of the two streams is effective.
MDS-Based Localization Algorithm for RFID Systems

In radio frequency identification (RFID) systems, location information is of great importance to provide location-aware services combined with identification. Conventional RFID systems can only provide coarse localization information. In this paper, we propose a novel approach named MDS-RFID to locate active RFID tags based on multidimensional scaling (MDS), an efficient data analysis technique. The approach has the advantage of fully utilizing the distance information in the network simultaneously, and thus can achieve better localization results than previous multilateration-based methods. The MDS-RFID algorithm first infers the tag-to-reader distances from the received signal strength (RSS). To obtain the distance matrix, the inter-tag distances are estimated using triangular inequalities. Then, classical MDS algorithms can be applied to determine the estimated locations of the tags. An optional refinement step can be added to further improve the accuracy using maximum likelihood estimate (MLE) when enough readers are deployed at the expense of additional computation costs. Simulation results show that the MDS-RFID algorithm can achieve a significant gain in accuracy over the previous localization schemes based on multilateration.
Stochastic growth in one dimension and Gaussian multi-matrix models

We discuss the space-time determinantal random field which arises for the PNG model in one dimension and resembles the one for Dyson’s Brownian motion. The information of interest for growth processes is carried by the edge statistics of the random field and therefore their universal scaling is related to the edge properties of Gaussian multi-matrix models.
Dynamic and Multimodal Responses of Gustatory Cortical Neurons in Awake Rats

To investigate the dynamic aspects of gustatory activity, we recorded the responses of small ensembles of cortical neurons to tastants administered to awake rats. Multiple trials of each tastant were delivered during recordings made in oral somatosensory (SI) and gustatory cortex (GC). When integrated tastant responses (firing rates averaged across 2.5 sec) were compared with water responses, 14.4% (13/90) of the GC neurons responded in a taste-specific manner. When time was considered as a source of information, however, the incidence of taste-specific firing increased: as many as 41% (37/90) of the recorded GC neurons exhibited taste-specific patterns of response. For 17% of the neurons identified as responding with taste-specific patterns, the stimulus that caused the most significant response was a function of the time since stimulus delivery. That is, a single neuron might respond most strongly to one tastant in the first 500 msec of a response and then respond most strongly to another tastant later in the response. Further analysis of the time courses of GC and SI cortical neural responses revealed that modulations of GC firing rate arose from three separable processes: early somatosensory input (less than ∼0.2 sec post-stimulus), later chemosensory input (∼0.2–1 sec), and delayed somatosensory input related to orofacial responses (more than ∼1.0 sec). These data demonstrate that sensory information is available in the time course of GC responses and suggest the viability of views of gustatory processing that treat the temporal structure of cortical responses as an integral part of the neural code.
Robust H∞ control for discrete singular systems with time-varying delays and uncertainties

This paper concentrates on the H∞ control problem of discrete singular systems with time-varying delay and norm-bounded uncertainties. Firstly, a novel bounded real lemma is established for discrete-time singular systems with time-varying delay. Then, with Lyapunov function, an improved reciprocally convex combination approach and linear matrix inequality (LMI) technique, a sufficient criterion for the existence of the H∞ state feedback controller is given in the form of matrix inequalities with nonlinear constraints. The cone complementarity linearization (CCL) algorithm is adopted to solve the nonlinear constraints. Finally, a numerical example is presented to illustrate the advantage of the proposed approach.
Comparison and analysis of transformation models on UAV image mosaic

In the precondition of getting the same feature points, the selection of different transformation models can greatly affect the speed and effectiveness of image mosaic. In this paper, the focus was on the determination of a transformation model from a given set of feature points. The high-resolution images captured by UAV (unmanned aerial vehicle) in the water-resource-investigation field were selected as source data. Four different transformation models-Affine Transformation, Quadratic Polynomial Transformation, Delaunay Triangulation and Homography Matrix were compared and analyzed for the optimal mosaic model of stereo pair in this situation. The experiment results indicated that Homography Matrix was the best in this case. Finally, it was given some useful advice for the selection of transformation models for the entire air strip mosaic.
Janus Nematic Colloids with Designable Valance

Generalized Janus nematic colloids based on various morphologies of particle surface patches imposing homeotropic and planar surface anchoring are demonstrated. By using mesoscopic numerical modeling, multiple types of Janus particles are explored, demonstrating a variety of novel complex colloidal structures. We also show binding of Janus particles to a fixed Janus post in the nematic cell, which acts as a seed and a micro-anchor for the colloidal structure. Janus colloidal structures reveal diverse topological defect configurations, which are effectively combinations of surface boojum and bulk defects. Topological analysis is applied to defects, importantly showing that topological charge is not a well determined topological invariant in such patchy nematic Janus colloids. Finally, this work demonstrates colloidal structures with designable valence, which could allow for targeted and valence-conditioned self-assembly at micro- and nano-scale.
Effects of climate change on maize production, and potential adaptation measures: a case study in Jilin Province, China

Jilin is among the most important grain-producing provinces in China. Its maize produc- tion plays an important role in local and national food security. In this study, we developed a new approach to assess the vulnerability and adaptation options for Jilin maize yields with respect to climate change by modifying a site-based biophysical model to a spatial grid-based application. An ensemble approach that used a combination of 20 general circulation model results and 6 scenarios from the Special Report on Emissions Scenarios was adopted in order to reflect the high uncertainties in future climate projections. The results show that the yield is highly likely to decline in the western and central regions of Jilin but to increase in the east, where maize is not currently grown as the main crop. Phenologically, the growing season will be reduced in the central and western parts, leading to a shortened grain-filling period. The average maize yield in the west and central regions is thus pro- jected to decrease 15% or more by 2050 as predicted by 90% of 120 projected scenarios. In addition, CO2 fertilization was investigated and demonstrated a noticeable compensation effect on the yield deduction. However, further field work and/or laboratory-based experiments are required to validate the modeled CO2 fertilization effects. Two potential adaptation strategies, i.e. improving irrigation facilities and introducing cultivars, were identified from the vulnerability assessment and were fur- ther tested for the reduction areas. The results revealed that the increase in effective irrigation by upgrading the irrigation system would help to maintain the current production level, but in the long run, the maize cultivars need to be introduced in line with the future warming climate.
Research on Strategic Behavior of Real Estate Firms Based on Option Game Under Asymmetric Competition Interaction

This paper illustrates the use of real options valuation and game theory principles to analyze strategic behavior of the real estate firms under asymmetric competition interaction. Symmetrical option game model assumes the leader and the follower share market equally. So the demands and profits flow are equal for competitors. In fact, due to the competitive advantages and negative externality, the interaction between competitors is unequal. That is to say, the competitors won't share markets equally under asymmetric competition interaction. It brings the leaders positive or negative effects. In order to analyze the asymmetrical competition interaction, this paper introduces a demand coefficient to change the demands when the real estate firm preempting. It makes the demands and profits flow unequal. Then we discuss its effect on the threshold and option value of leaders and followers. From the numerical analysis, we can see, to real estate firms, the more competitive advantages the firm has, the earlier it will invest. It also brings more negative externalities and lowers the follower's investment threshold.
A Public School Model of CS Education

The New York City Foundation for Computer Science Education was launched in the summer of 2013 to ensure that all children in New York City public schools have access to computer science education. Over the past year and a half the foundation has worked with eight (8) programs to expand access to computer science, reaching more than 10,000 students in the 2014-2015 school year alone. In this paper we present a brief history of computer science education in K12 schools, focused primarily on New York City. We detail the philosophy of the foundation and highlight the programs we support and the impact they are having on New York City public school students. It is our hope that the unique approach of CSNYC in supporting research proven initiatives and connecting teachers in communities of practice will inspire other advocates of computer science education to adopt our successful model. At the end of the paper we present upcoming expansions for the 20152016 school year and discuss the research challenges and opportunities available.
Phylogenetic Relationships and Evolution of the Androecia in Ruteae (Rutaceae)

Ruta, which belongs to tribe Ruteae, is the type genus of the subfamily Rutoideae and the family Rutaceae. Molecular systematic studies have shown that the genera in Ruteae are closer related to Aurantioideae than to most other genera of Rutoideae, some of the genera traditionally placed in Ruteae have been shown to be nested within the Aurantioideae clade, but the diagnostic characters for determining new patterns in the relationship are poor. In this study, we investigated the floral development of Boenninghausenia in Ruteae (sensu stricto), Haplophyllum in the basal position of Aurantioideae and Murraya in traditional Aurantioideae using scanning electron microscopy. The androecium of Boenninghausenia is obdiplostemony. As androecia in other genera within Ruteae (s.s.) are also obdiplostemonous, reconstruction of the ancestral state indicates that obdiplostemony is an ancestral character in this clade. Because the androecia of Haplophyllum and Murraya are also obdiplostemonous, obdiplostemony is also an ancestral character in Aurantioideae clade. The ancestral state reconstruction indicates this character can serve as a synapomorphy of the Ruteae-Aurantioideae clade. The results of our work also shed light on the evolution of the androecium in Rutaceae, as the obdiplostemony of this group is clearly derived from haplostemony in the ancestral genera in Rutaceae and has develop into polyandry by increasing antepetalous stamens.
Evaluation of ad hoc routing over a channel switching MAC protocol

Several recent works on wireless ad hoc networks have looked at increasing bandwidth by utilizing multiple radio channels. One such scheme is SSCH, which uses a distributed coordination mechanism to dynamically distribute overlapping flows on separate channels. While the simulation data from the original SSCH paper show substantial performance gains through channel hopping, they also suggest an adverse effect on ad hoc routing when running over SSCH. In this paper we investigate further the interaction between routing and SSCH. We argue that the potential problems are significant, and build a simulation to quantify these effects. Unfortunately, due to problems with the simulation, we were for the most part unable to collect meaningful data on our question.
Antiobesity Effect of Codonopsis lanceolata in High-Calorie/High-Fat-Diet-Induced Obese Rats

The antiobesity effects of Codonopsis lanceolata (CL) were evaluated in a high-calorie/high-fat-diet (HFD-) induced obesity rat model and 3T3-L1 cells. The Sprague-Dawley male rats were fed a normal diet (ND) or a HFD for a period of 12 weeks. The rats were subdivided into groups: ND, ND + wild Codonopsis lanceolata (wCL) (900 mg/kg/day, p.o.), ND + cultivated Codonopsis lanceolata (cCL) (900 mg/kg/day, p.o.), HFD, HFD + wCL (100, 300, or 900 mg/kg/day, p.o.), HFD + cCL (100, 300, or 900 mg/kg/day, p.o.), and HFD + sibutramine. The body weight gains of the administered HFD + CL (wCL or CCL) were lower than those of the rats fed with only the HFD group. Moreover, the weight of adipose pads and the serum levels of triglycerides, total cholesterol, and low density lipoprotein cholesterol in the group administered HDL + CL were significantly lower than in the HFD group. The inhibitory effect of lipid accumulation in 3T3-L1 cells was measured by Oil Red O staining and reverse transcription-polymerase chain reaction (RT-PCR). Treatment of 3T3-L1 cells with wCL inhibited lipid accumulation and expression of C/EBPα and PPARγ. These results suggest that CL has a great potential as a functional food with anti-obesity effects and as a therapeutic alternative in the treatment of obesity.
Wire reconnections based on implication flow graph

Global Flow Optimization (GFO) can perform the fanout/fanin wire re-connections by modeling the problem of the wire reconnections by a flow graph and then solving the problem using the maxflow-mincut algorithm on the flow graph. However, the flow graph cannot fully characterize the wire re-connections which causes GFO to lose optimality on several obvious cases. In addition, we find that the fanin re-connection can have more optimization power than the fanout re-connection but requires more sophisticated modeling. In this paper we re-formulate the problem of the fanout/fanin re-connections by a new graph called the implication flow graph. We show that the problem of wire re-connections on the implication flow graph is NP complete and also propose an efficient heuristic on the new graph. Our experimental results are very exciting.
Computing a language-based guarantee for timing properties of cyber-physical systems

Real-time systems are often guaranteed in terms of schedulability, which verifies whether or not all jobs meet their deadlines. However, such a guarantee can be insufficient in certain applications. In this paper, we propose a method to compute a language-based guarantee which provides a more detailed description of the deadline miss patterns of an observed task. The only requirement of our method is that the timing behavior of the real-time system be modelled by a network of timed automata. We compute the language-based guarantee by constructing an equivalent finite state automaton in an iterative manner, using a counter-example guided procedure. We illustrate the language-based guarantee for two applications: design of a networked control system and scheduling in a mixed criticality system. In both cases, we show that the language-based guarantee leads to a more efficient design than the schedulability guarantee.