Miracle-dz/UniFAR-FTUs huggingface dataset:
Interference with mutagenic aflatoxin B1-induced checkpoints through antagonistic action of ochratoxin A in intestinal cancer cells: a molecular explanation on potential risk of crosstalk between carcinogens

Foodborne aflatoxin B1 (AFB1) and ochratoxin A (OTA) cause genotoxic injury and subsequent tumor formation. As a biomarker of oncogenic stimulation by genotoxic mycotoxins, p53-triggered Mdm2 was assessed in intestinal cancer cells. AFB1 increased Mdm2 reporter expression in a dose-dependent manner. However, this was strongly antagonized by OTA treatment. As a positive transcription factor of Mdm2 expression, p53 levels were also increased by AFB1 alone and reduced by OTA. With marginal cell death responses, AFB1 induced p53-mediated S phase arrest and cell cycle-regulating target genes, which was completely suppressed by OTA. Although enterocyte-dominant CYP3A5 counteracted AFB1-induced DNA damage, expression of CYP3A5 was decreased by OTA or AFB1. Instead, OTA enhanced expression of another metabolic inactivating enzyme CYP3A4, attenuation of formation of AFB1-DNA adduct and p53-mediated cell cycle checking responses to the mutagens. Finally, the growth of intestinal cancer cells exposed to the mycotoxin mixture significantly exceeded the expected growth calculated from that of cells treated with each mycotoxin. Although AFB1-induced mutagen formation was decreased by OTA, interference with checkpoints through antagonistic action of OTA may contribute to the survival of tumor cells with deleterious mutations by genotoxic mycotoxins, potently increasing the risk of carcinogenesis.
Distributed Probablistic-Model Building Genetic Algorithm

In this paper, a new model of Probabilistic Model-Building Genetic Algorithms(PMBGAs), Distributed PMBGA (DPMBGA), is proposed. In the DPMBGA, the correlation among the design variables is considered by Principal Component Analysis(PCA) when the offsprings are generated. The island model is also applied in the DPMBGA for maintaining the population diversity. Through the standard test functions, the effectiveness of the DPMBGA is examined. In this paper, some models of DPMBGA are examined. The DPMBGA where PCA is executed in the half of the islands can find the good solutions in the problems whether or not the problems have the correlation among the design variables.
Regulation of tight junctions during the epithelium-mesenchyme transition: direct repression of the gene expression of claudins/occludin by Snail

Snail is a transcription repressor that plays a central role in the epithelium-mesenchyme transition (EMT), by which epithelial cells lose their polarity. Claudins and occludin are integral membrane proteins localized at tight junctions, which are responsible for establishing and maintaining epithelial cell polarity. We examined the relationship between Snail and the promoter activity of claudins and occludin. When Snail was overexpressed in cultured mouse epithelial cells, EMT was induced with concomitant repression of the expression of claudins and occludin not only at the protein but also at the mRNA level. We then isolated the promoters of genes encoding claudins and occludin, in which multiple E-boxes were identified. Transfection experiments with various promoter constructs as well as electrophoretic mobility assays revealed that Snail binds directly to the E-boxes of the promoters of claudin/occludin genes, resulting in complete repression of their promoter activity. Because the gene encoding E-cadherin was also reported to be repressed by Snail, we concluded that EMT was associated with the simultaneous repression of the genes encoding E-cadherin and claudins/occludin (i.e. the expression of adherens and tight junction adhesion molecules, respectively).
Agriculture and the Macroeconomy

Based on an economy-wide perspective, this paper begins with a discussion of the bias against exports and agriculture that characterized the economic literature and the development strategies in many developing countries after World War II. This is followed by an analysis of how the macroeconomic environment affects agricultural structural price incentives. Specifically, the paper discusses how early policies concerning industrial protection, exchange rates, and interest rates and other fiscal policies can strongly influence the economic incentives for agriculture compared with other sectors, identifying the most relevant literature and alternative approaches used on this issue. It then proceeds to examine how the real exchange rate can be affected by exogenous shocks, such as the foreign terms of trade, with emphases on the Dutch Disease phenomenon and agriculture. The paper next examines the influence of interest rates on incentives in agriculture, arguing that, surprisingly, this has been a neglected area in the literature. The paper explores the effects on agriculture of structural adjustment programs implemented since the early 1980s in developing countries. The final section surveys the literature on agriculture and the macro-economy in industrial countries, focusing on the impact of the exchange rate on export competitiveness in the United States, the cost of agricultural protection for the overall economy in Europe and Japan, and the increased importance of fluctuations in money markets for the farm sector and the additional instability they generate
Examining basic probabilistic, time-to-live, kill time and Passive Cure problems in flooding

Mobile adhoc networks are dyanamic in nature hence it is difficult to predict the toplogy of manets. basically in manets flooding is simple routjng algorithm in which every incoming packet is sent through every outgoing link except except the one it arrived on. The broadcast storm problem is so common in flooding algorithms that it has engendered a whole area of research flooding schemes over large-scale, sparse mobile networks. Traditional flooding suffers from the problem of redundant message reception, once per neighbour. Even in a reasonably connected network, the same message is received multiple times by every node, which is inefficient, wastes valuable resources, and can create contention in the transmission medium. In this paper, we focus on the evaluation of different controlled messages.
Effect of Promoter Polymorphisms on Cytokine Concentration in Preterm Breast Milk and Subsequent Infant Outcomes

Background: Breast milk concentrations of immune components are variable between women and interleukin (IL) differences may be associated with infant outcomes. Molecular mechanisms for milk variability remain unknown. Objective: The aims were to (1) examine the relationship between maternal IL genotypes and milk concentrations of IL4, IL6, and IL10, (2) describe the trajectories of milk IL change, (3) examine whether maternal IL genotypes predict IL trajectories and/or average weekly IL concentration, and (4) examine if weekly IL levels and/or IL trajectories are associated with infant outcomes. Methods: Milk aliquots were collected from each feeding of mother’s own milk and pooled weekly. DNA was extracted from 1 sample of each mother’s breast milk whey (n = 64), and single nucleotide polymorphisms (SNPs) of IL genes were genotyped. Milk IL concentrations were measured and trajectory analysis examined IL milk change over time. Multivariate breast milk IL concentration analyses controlled for gestational age and prepregnancy body mass index. Multivariate infant outcome (n = 73) analyses controlled for gestational age and the ratio of human milk to total milk. Results: Trajectory analysis resulted in linear group shapes, with 2 distinct subgroups in IL6 and 3 subgroups in IL4 and IL10. Trajectory groups trended toward significance with calprotectin, intraventricular hemorrhage, and blood transfusions. Multivariate analyses resulted in trending associations between maternal SNPs and subsequent IL6 and IL10 milk levels. There was a trending relationship between IL milk levels and both fecal calprotectin and intraventricular hemorrhage. Conclusion: Maternal IL SNPs may affect IL breast milk levels and IL milk levels may be associated with infant outcomes.
Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database

Extubation failure (EF) is an ongoing problem in the neonatal intensive care unit (NICU). Nearly 25% of neonates fail their first extubation attempt, requiring re-intubations that are associated with risk factors and financial costs. We identified 179 mechanically ventilated neonatal patients that were intubated within 24 hours of birth in the MIMIC-II intensive care database. We analyzed data from the patients 2 hours prior to their first extubation attempt, and developed a prediction algorithm to distinguish patients whose extubation attempt was successful from those that had EF. From an initial list of 57 candidate features, our machine learning approach narrowed down to six features useful for building an EF prediction model: monocyte cell count, rapid shallow breathing index, fraction of inspired oxygen (FiO2), heart rate, PaO2/FiO2 ratio where PaO2 is the partial pressure of oxygen in arterial blood, and work of breathing index. Algorithm performance had an area under the receiver operating characteristic curve (AUC) of 0.871 and sensitivity of 70.1% at 90% specificity.
Best of the Journal of Computational and Graphical Statistics ( Invited Session ) Organizer and Chair :

Risk analysis, that is the problem of estimating the probabilities of rare and damaging events, unifies the geosciences. One can mention the risks from: floods, earthquakes, forest fires, space debris. The probabilities may be fed into the computation of insurance premiums. The Poisson process often plays a prominent role, while in the talk marked point processes will have a basic function. Various ways to collect and extrapolate data will be described and examples from various fields will be presented. KDD Featured Session: Data Mining (Invited Session) Organizer and Chair: Arnold F. Goodman Thursday, 10:30 am – 10:55 am, Gold Room: Challenges for Data Miners, Statisticians, and Clients Who Depend Upon and Fund Them Arnold F. Goodman (University of California, Irvine, Center for Statistical Computing), agoodman@uci.edu
Zigbee-based Environmental Temperature Monitoring System

Environmental monitoring of environmental pollution prevention and control in humans to resolve existing or potential environmental problems and improve the living environment and the ecological environment, coordinating the relationship between human beings and the environment, and ultimately to sustainable human development plays an important role. In order to accurately and fully grasp the pollution status, early detection of abnormal changes in the environment to quickly make a pollution forecast, timely follow-up sources of pollution and so on, the establishment of pollution sources on-line monitoring system is very necessary.And communication network plays a pivotal role. In order to realize this environmental temperature monitoring system, we take a Zigbee-based wireless sensor networks.In this paper, from an architectural point of view I expound the key technologies for wireless sensor networks; wireless communication network construction and the design and implementation of network nodes.
Vesicular Zinc Regulates the Ca2+ Sensitivity of a Subpopulation of Presynaptic Vesicles at Hippocampal Mossy Fiber Terminals

Synaptic vesicles segregate into functionally diverse subpopulations within presynaptic terminals, yet there is no information about how this may occur. Here we demonstrate that a distinct subgroup of vesicles within individual glutamatergic, mossy fiber terminals contain vesicular zinc that is critical for the rapid release of a subgroup of synaptic vesicles during increased activity in mice. In particular, vesicular zinc dictates the Ca2+ sensitivity of release during high-frequency firing. Intense synaptic activity alters the subcellular distribution of zinc in presynaptic terminals and decreases the number of zinc-containing vesicles. Zinc staining also appears in endosomes, an observation that is consistent with the preferential replenishment of zinc-enriched vesicles by bulk endocytosis. We propose that functionally diverse vesicle pools with unique membrane protein composition support different modes of transmission and are generated via distinct recycling pathways.
Efficacy of Anti-TNF Agents as Adjunctive Therapy for Knee Synovitis Refractory to Disease-Modifying Antirheumatic Drugs in Patients with Peripheral Spondyloarthritis

Our aim was to evaluate the effectiveness of tumour necrosis factor (TNF) inhibitors as add-on therapy for knee synovitis that did not respond to disease-modifying antirheumatic drugs (DMARDs) and other standard treatments in patients with peripheral spondyloarthritis (SpA). We retrospectively studied 27 SpA patients, in whom an anti-TNF agent was added for active peripheral arthritis with knee synovitis refractory to DMARDs and treatment with low-dose oral corticosteroids and/or nonsteroidal anti-inflammatory drugs (NSAIDs) and intra-articular (IA) corticosteroids. As response of knee synovitis, were considered the absence of swelling, tenderness, and decreased range of movement in the clinical examination, after 4 months of anti-TNF therapy. In twenty-four (88.9%) of the patients there was response of knee synovitis. No statistical differences in gender (P = 0.53), age (P = 0.88), disease subtype (P = 0.22), and pattern of arthritis (P = 0.20) between knee synovitis responders and nonresponders were found. Fourteen patients managed to stop DMARD therapy and six, all of whom were initially on DMARDs combination, to decrease the number of DMARDs to one, maintaining simultaneously the response of knee synovitis. Our results imply a beneficial effect of adjunctive anti-TNF therapy on knee synovitis not responding to DMARDs and other standard treatments in patients with peripheral SpA.
Performance analysis of data fusion for ground target tracking using GSM networks

The Global System for Mobile Communications (GSM) networks can provide position information. However, a GSM positioning system based on current specifications faces many difficulties to yield accurate position estimate for ground target tracking. Data fusion solutions which integrate two or more types of measurements have been proposed to provide estimation with better accuracy, reliability and coverage. The performance of Mobile Station (MS) positioning has been broadly discussed, but the performance analysis of ground target tracking, i.e., dynamic position estimation, is rare. This paper focuses on the performance analysis for a data fusion approach of integrating two different types of measurements from GSM networks using posterior Cramér-Rao lower bound (PCRLB). The simulation results verify that the data fusion approach greatly improves the estimation accuracy and is more robust to noisy measurements as compared to the approaches using single kind of measurements.
Towards High-resolution Imaging from Underwater Vehicles

Large area mapping at high resolution underwater continues to be constrained by sensor-level environmental constraints and the mismatch between available navigation and sensor accuracy. In this paper, advances are presented that exploit aspects of the sensing modality, and consistency and redundancy within local sensor measurements to build high-resolution optical and acoustic maps that are a consistent representation of the environment. This work is presented in the context of real-world data acquired using autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) working in diverse applications including shallow water coral reef surveys with the Seabed AUV, a forensic survey of the RMS Titanic in the North Atlantic at a depth of 4100 m using the Hercules ROV, and a survey of the TAG hydrothermal vent area in the mid-Atlantic at a depth of 3600 m using the Jason II ROV. Specifically, the focus is on the related problems of structure from motion from underwater optical imagery assuming pose instrumented calibrated cameras. General wide baseline solutions are presented for these problems based on the extension of techniques from the simultaneous localization and mapping (SLAM), photogrammetric and the computer vision communities. It is also examined how such techniques can be extended for the very different sensing modality and scale associated with multi-beam bathymetric mapping. For both the optical and acoustic mapping cases it is also shown how the consistency in mapping can be used not only for better global mapping, but also to refine navigation estimates.
Molecular Modeling of Proteins and Mathematical Prediction of Protein Structure

This paper discusses the mathematical formulation of and solution attempts for the so-called protein folding problem. The static aspect is concerned with how to predict the folded (native, tertiary) structure of a protein given its sequence of amino acids. The dynamic aspect asks about the possible pathways to folding and unfolding, including the stability of the folded protein. From a mathematical point of view, there are several main sides to the static problem: -- the selection of an appropriate potential energy function; -- the parameter identification by fitting to experimental data; and -- the global optimization of the potential. The dynamic problem entails, in addition, the solution of (because of multiple time scales very stiff) ordinary or stochastic differential equations (molecular dynamics simulation) or (in case of constrained molecular dynamics) of differential-algebraic equations. A theme connecting the static and dynamic aspect is the determination and formation of secondary structure motifs. The present paper gives a self-contained introduction to the necessary background from physics and chemistry and surveys some of the literature. It also discusses the various mathematical problems arising, some deficiencies of the current models and algorithms, and possible (past and future) attacks to arrive at solutions to the protein-folding problem.
Efficient time synchronization for wireless sensor networks in an industrial setting

This paper outlines an efficient variation of the Flooding Time Synchronization Protocol (FTSP) on real hardware with an IEEE 802.15. 4 MAC layer. The paper compares the performance of two clock drift estimation techniques: Least Squares Linear Regression and the Kalman filter. In assessing the viability of time synchronization in an industrial setting, our reliance on real hardware gives confidence for the deployment in future products. The concrete result is a high time synchronization accuracy, with an achievable average error of 0.4 μs, and a repetition interval of a few seconds, while using a star network topology.
A Hybrid Graph Model for Unsupervised Object Segmentation

In this work, we address the problem of performing class specific unsupervised object segmentation, i.e., automatic segmentation without annotated training images. We propose a hybrid graph model (HGM) to integrate recognition and segmentation into a unified process. The vertices of a hybrid graph represent the entities associated to the object class or local image features. The vertices are connected by directed edges and/or undirected ones, which represent the dependence between the shape priors of the class (for recognition) and the similarity between the color/texture priors within an image (for segmentation), respectively. By simultaneously considering the Markov chain formed by the directed subgraph and the minimal cut of the undirected subgraph, the likelihood that the vertices belong to the underlying class can be computed. Given a set of images each containing objects of the same class, our HGM based method automatically identifies in each image the area that the objects occupy. Experiments on 14 sets of images show promising results.
Optimization of coding potentials using positional dependence of nucleotide frequencies.

We study the coding potential of human DNA sequences, using the positional asymmetry function (D(p)) and the positional information function (I(q)). Both D(p)and I(q)are based on the positional dependence of single nucleotide frequencies. We investigate the accuracy of D(p)and I(q)in distinguishing coding and non-coding DNA as a function of the parameters p and q, respectively, and explore at which parameters p(opt)and q(opt)both D(p)and I(q)distinguish coding and non-coding DNA most accurately. We compare our findings with classically used parameter values and find that optimized coding potentials yield comparable accuracies as classical frame-independent coding potentials trained on prior data. We find that p(opt)and q(opt)vary only slightly with the sequence length.
Geometry compression of 3-D mesh models using a joint prediction

Summary form only given. We address geometry coding of 3D models. Conventional geometry coding schemes quantize vertex positions using a bounding box before coding it by an entropy coder. However, in the proposed scheme, we first predict vertex positions and then quantize them differentially. Our geometry encoder consists of four stages: preprocessing, prediction, quantization, and entropy coding. Using a joint prediction, the encoder predicts vertex positions in a layer traversal order. Although the joint prediction is based on the parallelogram prediction, we use every possible preceding triangle to approximate the predicted vertex.
Physical Activity among Veterans and Nonveterans with Diabetes

Engaging in regular physical activity (PA), with or without a corresponding decrease in weight, is associated with improved health outcomes. The purpose of this study was to quantify the extent to which PA differed between veterans and nonveterans and to determine how diabetes and age influenced this association. Data from the 2009 Behavioral Risk Factor Surveillance System were used in this study. Respondents were classified as having diabetes if they reported ever being diagnosed with diabetes except during pregnancy. Respondents who reported ever serving on active military duty were classified as veterans. Based on self-report, we calculated the average minutes per week of moderate, vigorous, and total activity. After adjusting for sex, race and ethnicity, household income, education level, body mass index (BMI), and recent health checkup, veteran status was associated with a small but significantly larger amount of average weekly moderate PA (2.2 minutes, P = 0.0058) but not average weekly vigorous PA (−0.02 minutes, P = 0.98). Diabetes and prediabetes were associated with significantly lower mean levels of both moderate and vigorous intensity PA, as was increasing age. Consistent with prior research, veterans engaged in more PA than nonveterans. The association between diabetes, age, and physical activity did not differ by veteran status.