Miracle-dz/UniFAR-FTUs huggingface dataset:
Prostate carcinogenesis in N-methyl-N-nitrosourea (NMU)-testosterone-treated rats fed tomato powder, lycopene, or energy-restricted diets.

BACKGROUND Consumption of tomato products or lycopene and energy restriction have been hypothesized to reduce the risk of human prostate cancer. We investigated the effects of these dietary variables in a rat model of prostate carcinogenesis. METHODS Male rats (n = 194) treated with N-methyl-N-nitrosourea and testosterone to induce prostate cancer were fed diets containing whole tomato powder (13 mg lycopene/kg diet), lycopene beadlets (161 mg lycopene/kg diet), or control beadlets. Rats in each group were randomly assigned to either ad libitum feeding or 20% diet restriction. Differences between Kaplan-Meier survival curves for diet composition or restriction were tested with the log-rank test. Cox proportional hazards models were developed to examine the combined effect of diet composition and restriction on survival. Statistical tests were two-sided. RESULTS Risk of death with prostate cancer was lower for rats fed the tomato powder diet than for rats fed control beadlets (hazard ratio [HR] = 0.74, 95% confidence interval [CI] = 0.59 to 0.93; P =.009). In contrast, prostate cancer-specific mortality of the control and lycopene-fed rats was similar (P =.63). The proportions of rats dying with prostate cancer in the control, lycopene, and tomato powder groups were 80% (95% CI = 68% to 89%), 72% (95% CI = 60% to 83%), and 62% (95% CI = 48% to 75%), respectively. Rats in the diet-restricted group experienced longer prostate cancer-free survival than rats in the ad libitum-fed group (HR = 0.68, 95% CI = 0.49 to 0.96; P =.029). The proportion of rats that developed prostate cancer was 79% (95% CI = 69% to 86%) for ad libitum-fed rats and 65% (95% CI = 54% to 74%) for rats fed restricted diets. No interactions were observed between diet composition and dietary restriction. CONCLUSIONS Consumption of tomato powder but not lycopene inhibited prostate carcinogenesis, suggesting that tomato products contain compounds in addition to lycopene that modify prostate carcinogenesis. Diet restriction also reduced the risk of prostate cancer. Tomato phytochemicals and diet restriction may act by independent mechanisms.
Efficient Peer-to-Peer Lookup in Multi-hop Wireless Networks

In recent years the popularity of multi-hop wireless networks has been growing. Its flexible topology and abundant routing path enables many types of applications. However, the lack of a centralized controller often makes it difficult to design a reliable service in multi-hop wireless networks. While packet routing has been the center of attention for decades, recent research focuses on data discovery such as file sharing in multi-hop wireless networks. Although there are many peer-to-peer lookup (P2P-lookup) schemes for wired networks, they have inherent limitations for multi-hop wireless networks. First, a wired P2P-lookup builds a search structure on the overlay network and disregards the underlying topology. Second, the performance guarantee often relies on specific topology models such as random graphs, which do not apply to multi-hop wireless networks. Past studies on wireless P2P-lookup either combined existing solutions with known routing algorithms or proposed tree-based routing, which is prone to traffic congestion. In this paper, we present two wireless P2P-lookup schemes that strictly build a topology-dependent structure. We first propose the Ring Interval Graph Search (RIGS) that constructs a DHT only through direct connections between the nodes. We then propose the ValleyWalk, a loosely-structured scheme that requires simple local hints for query routing. Packet-level simulations showed that RIGS can find the target with near-shortest search length and ValleyWalk can find the target with near-shortest search length when there is at least 5% object replication. We also provide an analytic bound on the search length of ValleyWalk.
A hybrid approach to improving clustering accuracy using SVM

Support Vector Machines (SVMs) have been used in many areas such as regression, classification and novelity detection due to its accuracy and generalizability. Recently SVMs have been proposed for clustering analysis as well. Support Vector Clustering (SVC) works by finding the minimum enclosing sphere of data points using SVM training. SVC is a boundary based clustering method, where the support information is used to construct cluster boundaries. In support vector-based clustering algorithms, the main computational bottle-neck is the high cluster labeling time for each data point. In addition, in many cases labeled data is not available for use with SVC. This tends to restrict the scalability of the method and results in decreased efficiency. This also decreases the applicability of the SVC method to real-life datasets most of which do not have any class labels. In this paper we present a technique that could be used to utilize SVM to improve the accuracy of clustering without the need of labeled dataset. We have used K-Means clustering algorithm to generate initial labels from the data and in the next step we have trained a Sequential Minimal Optimization (SMO) classifier on these labels. The original data set is then tested using the trained SMO classifier to improve classification accuracy. This process is continued iteratively and stops when further improvement is not possible. The proposed approach is compared against the popular Stephen winters-Hilt [1] approach and achieves 94% accuracy when applied to benchmark datasets.
COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets

The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies’ implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.
On the Complexity of Semide nite Programs

We show that the feasibility of a system of m linear inequalities over the cone of symmetric positive semideenite matrices of order n can be tested in mn O(minfm;n 2 g) arithmetic operations with ln O(minfm;n 2 g)-bit numbers, where l is the maximum binary size of the input coeecients. We also show that any feasible system of dimension (m; n) has a solution X such that log kXk ln O(minfm;n 2 g) .
Efficient search of Top-K video subvolumes for multi-instance action detection

Action detection was formulated as a subvolume mutual information maximization problem in [8], where each subvolume identifies where and when the action occurs in the video. Despite the fact that the proposed branch-and-bound algorithm can find the best subvolume efficiently for low resolution videos, it is still not efficient enough to perform multi-instance detection in videos of high spatial resolution. In this paper we develop an algorithm that further speeds up the subvolume search and targets on real-time multi-instance action detection for high resolution videos (e.g. 320 × 240 or higher). Unlike the previous branch-and-bound search technique which restarts a new search for each action instance, we find the Top-K subvolumes simultaneously with a single round of search. To handle the larger spatial resolution, we downsample the volume of videos for a more efficient upper-bound estimation. To validate our algorithm, we perform experiments on a challenging dataset of 54 video sequences where each video consists of several actions performed by different people in a crowded environment. The experiments show that our method is not only efficient, but also capable of handling action variations caused by performing speed and style changes, spatial scale changes, as well as cluttered and moving background.
Changes in Microbial Diversity, Methanogenesis and Fermentation Characteristics in the Rumen in Response to Medicinal Plant Extracts

This study evaluated the in vitro effect of medicinal plant extracts on ruminal methanogenesis, four different groups of methanogens and ruminal fermentation characteristics. A fistulated Holstein cow was used as a donor of rumen fluid. Licorice and mugwort extracts (Glycyrrhiza uralensis and Artemisia capillaris, 0.5% and 1% of total substrate DM, respectively), previously used as folk remedies, were added to an in vitro fermentation incubated with buffered-rumen fluid. Total gas production in Glycyrrhiza uralensis extract treatment was not significantly different between treatments (p<0.05) while total gas production in the Artemisia capillaris extract treatment was lower than that of the control. Artemisia capillaris extract and Glycyrrhiza uralensis extract reduced CH4 emission by 14% (p<0.05) and 8% (p<0.05), respectively. Ciliate-associated methanogens population decreased by 18% in the medicinal plant extracts treatments. Medicinal plant extracts also affected the order Methanobacteriales community. Methanobacteriales diversity decreased by 35% in the Glycyrrhiza uralensis extract treatment and 30% in the Artemisia capillaris extract treatment. The order Methanomicrobiales population decreased by 50% in the 0.5% of Glycyrrhiza uralensis extract treatment. These findings demonstrate that medicinal plant extracts have the potential to inhibit in vitro ruminal methanogenesis.
Subjective Image Quality of Low-Rate and Low-Resolution Video Sequences

For a variety of QCIF video sequences with bit rates between 30 and 64 kbit/s and frame rates between 5 and 15 fps quality related criteria like "total quality", "quality concerning blocking effects", "information value", and "smoothness of movement" were estimated by human observers on a six grades scale reaching from “1, very good”, “2, good”, “3, satisfactory”, “4, sufficient”, “5, imperfect” to “6, poor”. Presentation of the sequences was done in succession of reference video (128 kbit/s, 15 fps) and test video. Bit rates higher than 30 kbit/s led to a “total quality” better than “4, sufficient”. For the highest bit rate of 64 kbit/s and frame rates of 15 or 7 fps the criterion “total quality” as well as the dynamical criterion "smoothness of motion" are dominated by the static quality of individual coded frames. Both criteria improve with lower frame rates leading to their best estimates for the low frame rate of 7 fps. For medium bit rates of 50 kbit/s and frame rates of 7 or 5 fps we got impairments of “total quality” and “smoothness of movement” with decreasing frame rate. At a frame rate of 5 fps and bit rates of 50 and 30 kbit/s impairments of “total quality” and "smoothness of movement” were obtained for decreasing the bit rate. An appropriate metric for the subjective quality of video sequences was found as a combination of a static and a dynamic component. Mean PSNR averaged over coded frames plus a beneficial contribution from higher frame rates proves to be a good estimate for it.
A new approach to study the effect of carrier frequency offset on the BER performance of asynchronous MC-CDMA systems

In this paper, a new approach to study the effect of carrier frequency offset (CFO) due to the mismatch between the transmitter and receiver oscillators on the bit error rate (BER) performance of asynchronous multicarrier code division multiple access (MC-CDMA) systems is proposed. The closed form BER expression when the maximum ratio combining (MRC) is used is derived. The correctness and effectiveness of the proposed approach is verified through computer simulations. Numerical results show that small CFO has insignificant effect on the BER performance of MC-CDMA systems.
Analysis of Student Data for Retention Using Data Mining Techniques

At most universities, administrators and counselors are trying to devise sound methodologies to help increase student retention rates. Due to the vast amount of student data that is available, sorting through the data to extract useful knowledge is a daunting task. However, the data may be helpful in predicting future student trends - particularly as it relates to retention. In this paper, we describe data mining and machine learning techniques that can be used to predict future enrollment. In our experiments, we attempt to apply these techniques to the retention of Computer Science students - a major that traditionally has significant turnover in the first year of study. Specific algorithms are selected to classify the data in an attempt to extract relevant information. While traditional methods may focus on known issues with retention, we emphasize the importance of factors that may only be noticeable through the application of data mining. The goal of this research is to determine which newly enrolled students should be targeted as retention risks. If these students can be identified early in their academic career, this approach may help increase retention rates not only within a specific department, but across the entire university.
Journal of Internet Banking and Commerce Barriers and Profits of Distance Education in Operations Research Based Decision Analysis

Distance education and blended learning allow universities to lower costs, have more return on economies of scale, better control educational process and provide higher quality services for students. This process however requires specialized software to be used by a university.The paper tackles issues related with this approach and presents guidelines for construction of a framework for creating web-based education support software in quantitative area – the meta-framework that allows for a rapid construction of web-based educational decision analysis software. The framework design in paper covers three aspect: (1) information system modeling, (2) decision problem modeling and (3) teaching process modeling. The information system modeling includes system architecture, servers, programming languages and technical design. The decision problem modeling provides means to present an abstract decision problem (and more generally-a problem where quantitative methods can be used) and to model interaction between software and decision maker (i.e. a student learning a decision algorithm). Teaching process modeling includes support for linear teaching algorithm in the decision making process, further this can be extended into non-linear and hybrid teaching algorithms. As a proof of concept a working framework prototype named Combine! will be presented. The software's usefulness was evaluated through a survey carried out on 234 students at Warsaw School of Economics.
Why Long-term Dynamic Elasticities Differ from Cross-section Elasticities?

Data are often lacking for a good estimation of models: (1) either aggregate time-series are available (e.g. from National Accounts) and therefore the heterogeneity between individuals cannot be taken into account; or (2) a survey gives a detailed description of one point in time, but no proper information on long term dynamics. What can be derived in terms of dynamic elasticities, from the cross-sectional analysis of heterogeneous behaviors shown through only one survey? Three examples can be taken according to socio-demographic variables: age, residential location and income. Recent literature on relative income effects and social interactions can be related to the old problem of the difference between cross-section and time-series estimation. On one hand, the distribution of consumption can be estimated from the same survey by comparing individuals who are at different positions in the income distribution, in their residential location or life cycle. On the other hand, change in expenditure due to income changes, to moving home or to aging can be measured for the same agent (or the same type of agent) between two periods, thus using individual time-series (or pseudo-panel) data.
Cheap talk when interests conflict

Most evolutionary analyses of animal communication suggest that low-cost signals can evolve only when both the signaller and the recipient rank outcomes in the same order. When there is a conflict of interest between sender and receiver, honest signals must be costly. However, recent work suggests that low-cost signals can be evolutionarily stable, even when the sender and the receiver rank outcomes in different orders, as long as the interest in achieving coordination is sufficiently great. In this paper, we extend this body of work by analysing a game theory model that shows that low-cost signals can evolve when there are conflicts of interest and no interest in coordination, as long as individuals interact repeatedly. We also present an empirical example indicating that female rhesus macaques, Macaca mulatta, use honest, low-cost, vocal signals to facilitate interactions when conflicts of interest exist. Copyright 2000 The Association for the Study of Animal Behaviour.
On measures invariant under tori on quotients of semisimple groups

We classify invariant and ergodic probability measures on arithmetic homogeneous quotients of semisimple S-algebraic groups invariant under a maximal split torus in at least one simple local factor and show that the algebraic support of such a measure splits into the product of four homogeneous spaces: a torus, a homogeneous space on which the measure is (up to nite index) the Haar measure, a product of homogeneous spaces on each of which the action degenerates to a rank one action, and a homogeneous space in which every element of the action acts with zero entropy.
Task-related modulation of effective connectivity during perceptual decision making: dissociation between dorsal and ventral prefrontal cortex

The dorsal and ventral parts of the lateral prefrontal cortex have been thought to play distinct roles in decision making. Although its dorsal part such as the frontal eye field (FEF) is shown to play roles in accumulation of sensory information during perceptual decision making, the role of the ventral prefrontal cortex (PFv) is not well-documented. Previous studies have suggested that the PFv is involved in selective attention to the task-relevant information and is associated with accuracy of the behavioral performance. It is unknown, however, whether the accumulation and selection processes are anatomically dissociated between the FEF and PFv. Here we show that, by using concurrent TMS and EEG recording, the short-latency (20–40 ms) TMS-evoked potentials after stimulation of the FEF change as a function of the time to behavioral response, whereas those after stimulation of the PFv change depending on whether the response is correct or not. The potentials after stimulation of either region did not show significant interaction between time to response and performance accuracy, suggesting dissociation between the processes subserved by the FEF and PFv networks. The results are consistent with the idea that the network involving the FEF plays a role in information accumulation, whereas the network involving the PFv plays a role in selecting task relevant information. In addition, stimulation of the FEF and PFv induced activation in common regions in the dorsolateral and medial frontal cortices, suggesting convergence of information processed in the two regions. Taken together, the results suggest dissociation between the FEF and PFv networks for their computational roles in perceptual decision making. The study also highlights the advantage of TMS-EEG technique in investigating the computational processes subserved by the neural network in the human brain with a high temporal resolution.
On the Bullwhip Avoidance Phase: supply chain collaboration and order smoothing

This paper provides an assessment of the impact of collaboration and smoothing replenishment rules on supply chain operational performance and customer service level. Three supply chain configurations (i.e. Traditional, Information Exchange and Synchronised) in which orders are generated by smoothing (S, R) inventory control policies are studied for different proportional controllers. A supply chain stress test is performed through a sudden and intense change in demand. A structured and extended supply chain assessment framework is adopted. The main conclusions of this paper are the following. (i) The impact of Supply Chain Collaboration on overall supply chain performance is greater than that of order smoothing. Order smoothing mitigates the bullwhip effect, but it may have a negative impact on customer service. Supply Chain Collaboration mitigates the bullwhip effect, provides inventory stability, limits lumpy orders and enhances customer service level. (ii) The negative effect on customer service level of order smoothing is almost eliminated in synchronised supply chains.
Combining patch-based strategies and non-rigid registration-based label fusion methods

The objective of this study is to develop a patch-based labeling method that cooperates with a label fusion using non-rigid registrations. We present a novel patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances, where a previous labeling of the target image is inferred through a label fusion method using non-rigid registrations. These combined similarity measures result in better selection of the patches, and their weights are more robust, which improves the segmentation results compared to other label fusion methods, including the conventional patch-based labeling method. To evaluate the performance and the robustness of the proposed label fusion method, we employ two available databases of T1-weighted (T1W) magnetic resonance imaging (MRI) of human brains. We compare our approach with other label fusion methods in the automatic hippocampal segmentation from T1W-MRI. Our label fusion method yields mean Dice coefficients of 0.847 and 0.798 for the two databases used with mean times of approximately 180 and 320 seconds, respectively. The collaboration between the patch-based labeling method and the label fusion using non-rigid registrations is given in the several levels: (a) The pre-selection of the patches in the atlases are improved, (b) The weights of our selected patches are also more robust, (c) our approach imposes geometrical restrictions, such as shape priors, and (d) the work-flow is very efficient. We show that the proposed approach is very competitive with respect to recently reported methods.