Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmentation of Overlapping Cervical Cells in Microscopic Images with Superpixel Partitioning and Cell-Wise Contour Refinement

Segmentation of cervical cells in microscopic images is an important task for computer-aided diagnosis of cervical cancer. However, their segmentation is challenging due to inhomogeneous cell cytoplasm and the overlap between the cells. In this paper, we propose an automatic segmentation method for multiple overlapping cervical cells in microscopic images using superpixel partitioning and cell-wise contour refinement. First, the cell masses are detected by superpixel generation and triangle thresholding. Then, nuclei of cells are extracted by local thresholding and outlier removal. Finally, cell cytoplasm is initially segmented by superpixel partitioning and refined by cell-wise contour refinement with graph cuts. In experiments, our method showed competitive performances in two public challenge data sets compared to the state-of-the-art methods.
Ice Age Atlantis? Exploring the Solutrean-Clovis ‘connection’

Abstract Bradley and Stanford (2004) have raised now, in several instances, the claim that European Upper Paleolithic Solutrean peoples colonized North America, and gave rise to the archaeological complex known as Clovis. They do so in the face of some obvious challenges – notably the several thousand miles of ocean and the 5000 radiocarbon years that separate the two. And yet they argue in their recent paper that the archaeological evidence in support of a historical connection is ‘overwhelming’. We are profoundly skeptical of this claim; we believe that the many differences between Solutrean and Clovis are far more significant than the few similarities, the latter being readily explained by the well-known phenomenon of technological convergence or parallelism. The origin and arrival time of the first Americans remain uncertain, but not so uncertain that we need to look elsewhere other than north-east Asia.
Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson Regression

This article is devoted to the forecast of the Africa Cup of Nations 2019 football tournament. It is based on a Poisson regression model that includes the Elo points of the participating teams as covariates and incorporates differences of team-specific skills. The proposed model allows predictions in terms of probabilities in order to quantify the chances for each team to reach a certain stage of the tournament. Monte Carlo simulations are used to estimate the outcome of each single match of the tournament and hence to simulate the whole tournament itself. The model is fitted on all football games on neutral ground of the participating teams since 2010.
Baropycnal Work: A Mechanism for Energy Transfer across Scales

The role of baroclinicity, which arises from the misalignment of pressure and density gradients, is well-known in the vorticity equation, yet its role in the kinetic energy budget has never been obvious. Here, we show that baroclinicity appears naturally in the kinetic energy budget after carrying out the appropriate scale decomposition. Strain generation by pressure and density gradients, both barotropic and baroclinic, also results from our analysis. These two processes underlie the recently identified mechanism of “baropycnal work”, which can transfer energy across scales in variable density flows. As such, baropycnal work is markedly distinct from pressure-dilatation into which the former is implicitly lumped in Large Eddy Simulations. We provide numerical evidence from 1024 3 direct numerical simulations of compressible turbulence. The data shows excellent pointwise agreement between baropycnal work and the nonlinear model we derive, supporting our interpretation of how it operates.
Ibuprofen Treatment Reduces the Neuroinflammatory Response and Associated Neuronal and White Matter Impairment in the Growth Restricted Newborn

Intrauterine growth restriction (IUGR) is a condition where the fetus does not achieve optimal growth, commonly caused by placental insufficiency. The chronic decrease in blood flow restricts oxygen and nutrient supply to the fetus, which can damage numerous organ systems, with the fetal brain being particularly vulnerable. Although white matter and neuronal injury are evident in IUGR infants, the specific mechanisms underlying these changes are poorly understood. Inflammation is considered to be a main driver in exacerbating brain injury. Using a spontaneous piglet model of IUGR, we aim to determine whether administration of the anti-inflammatory drug ibuprofen will decrease inflammation at postnatal day 4 (P4). The treatment group received ibuprofen (20 mg/kg/day on day 1 and 10 mg/kg/day on days 2 and 3) in piglet formula during the morning feed each day and brains examined on P4. Markers of inflammation, apoptosis, cell proliferation, neuronal injury, and white matter injury were examined. Ibuprofen treatment ameliorated the increase in numbers of microglia and astrocytes in the parietal cortex and white matter tracts of the IUGR piglet brain on P4 as well as decreasing proinflammatory cytokines. Ibuprofen treatment prevented the reduction in apoptosis, neuronal cell counts, and myelin index in the IUGR piglets. Our findings demonstrate ibuprofen reduces the inflammatory response in the IUGR neonatal brain and concurrently reduces neuronal and white matter impairment.
Equivariant one-parameter deformations of associative algebra morphisms

We introduce equivariant formal deformation theory of associative algebra morphisms. We also present an equivariant deformation cohomology of associative algebra morphisms and using this we study the equivariant formal deformation theory of associative algebra morphisms. We discuss some examples of equivariant deformations and use the Maurer-Cartan equation to characterize equivariant deformations.
Tumor-infiltrating mucosal-associated invariant T (MAIT) cells retain expression of cytotoxic effector molecules

Mucosal-associated invariant T (MAIT) cells all express a semi-invariable T cell receptor recognizing microbial metabolites presented on the MHC class I-like molecule MR1. Upon activation, they rapidly secrete cytokines and increase their cytotoxic potential. We showed recently that MAIT cells with Th1 phenotype accumulate in human colon adenocarcinomas. Here, we investigated the cytotoxic potential of tumor-infiltrating MAIT cells in colon adenocarcinomas, and to what extent it may be affected by the tumor microenvironment. Activation of MAIT cells from tumors induced increased Granzyme B, and to a lesser extent, perforin expression. Degranulation was assessed by surface expression of CD107a, and was also seen in response to cognate antigen recognition. The cytotoxic potential of tumor-associated MAIT cells was very similar to that of MAIT cells from unaffected colon. MAIT cells were also identified by immunofluorescence in direct contact with tumor cells in sections from colon cancer specimens. To summarize, tumor-associated MAIT cells from colon tumors have strong cytotoxic potential and are not compromised in this regard compared to MAIT cells from the unaffected colon. We conclude that MAIT cells may contribute significantly to the protective immune response to tumors, both by secretion of Th1-associated cytokines and by direct killing of tumor cells.
Non-relativistic and relativistic equations for the Kratzer potential plus a dipole in 2D systems

In this work, we study the wave equations in 2D Euclidian space for a new non-central potential consisting of a Kratzer term and a dipole term V r , θ = Qr − 1 + D r r − 2 + D θ cos ( θ ) r − 2 . For Schrödinger equation, we obtain the analytical expressions of the energies and the wave functions of the system. For Klein–Gordon and Dirac equations, we do the study in both spin and pseudo-spin symmetries to get the eigenfunctions and the eigenvalues. We also study the dependence of energies on the parameters Dr and Dθ. We find that the Dθ term tends to dissociate the system, and thus counteracts the Coulomb binding effect, and that the Dr term can either amplify or decrease this effect according to its sign.
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification

In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art. The code for key-point estimation model is available at https://github.com/Pirazh/Vehicle_Key_ Point_Orientation_Estimation
Handling imbalanced datasets: A review

Learning classifiers from imbalanced or skewed datasets is an impor- tant topic, arising very often in practice in classification problems. In such problems, almost all the instances are labelled as one class, while far fewer in- stances are labelled as the other class, usually the more important class. It is obvious that traditional classifiers seeking an accurate performance over a full range of instances are not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into the majority class, which is usually the less important class. This paper describes various techniques for handling im- balance dataset problems. Of course, a single article cannot be a complete re- view of all the methods and algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting re- search directions and suggesting possible bias combinations that have yet to be explored.
Why patients with progressing kidney disease are referred late to the nephrologist: on causes and proposals for improvement.

44: 974–981 51. Traupe T, Muenter K, Barton M. Impaired sodium and potassium excretion with aging is regulated by increased endothelin. Circulation 2002; 106 [Suppl II]: 684 52. Amaral SL, Papanek PE, Greene AS. Angiotensin II and VEGF are involved in angiogenesis induced by short-term exercise training. Am J Physiol 2001; 281: H1163–H1169 53. Shimada T, Takeshita Y, Murohara T et al. Angiogenesis and vasculogenesis are impaired in the precocious-aging klotho mouse. Circulation 2004; 110: 1148–1155 54. Zeiher AM, Drexler H, Saurbier B, Just H. Endotheliummediated coronary blood flow modulation in humans. Effects of age, atherosclerosis, hypercholesterolemia, and hypertension. J Clin Invest 1993; 92: 652–662 55. Taddei S, Virdis A, Mattei P et al. Hypertension causes premature aging of endothelial function in humans. Hypertension 1997; 29: 736–743 56. Singh N, Prasad S, Singer DR, MacAllister RJ. Ageing is associated with impairment of nitric oxide and prostanoid dilator pathways in the human forearm. Clin Sci (Lond) 2002; 102: 595–600 57. Jensen-Urstad K, Bouvier F, Jensen-Urstad M. Preserved vascular reactivity in elderly male athletes. Scand J Med Sci Sports 1999; 9: 88–91 58. Green D, Cheetham C, Mavaddat L et al. Effect of lower limb exercise on forearm vascular function: contribution of nitric oxide. Am J Physiol 2002; 283: H899–H907 59. Mazzeo RS, Tanaka H. Exercise prescription for the elderly: current recommendations. Sports Med 2001; 31: 809–818 60. Barton M, Furrer J. Cardiovascular consequences of the obesity pandemic: need for action. Exp Opin Invest Drugs 2003; 12: 1757–1759 61. Jarvisalo MJ, Harmoinen A, Hakanen M et al. Elevated serum C-reactive protein levels and early arterial changes in healthy children. Arterioscler Thromb Vasc Biol 2002; 22: 1323–1328 62. Schachinger V, Britten MB, Zeiher AM. Prognostic impact of coronary vasodilator dysfunction on adverse long-term outcome of coronary heart disease. Circulation 2000; 101: 1899–1906 63. Suwaidi JA, Hamasaki S, Higano ST, Nishimura RA, Holmes DR Jr, Lerman A. Long-term follow-up of patients with mild coronary artery disease and endothelial dysfunction. Circulation 2000; 101: 948–954
Data-driven link quality prediction using link features

As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this article, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data driven and consists of three steps: data collection, offline modeling, and online prediction. The data collection step involves gathering link quality data, and based on our analysis of the data, we propose a set of guidelines for the amount of data to be collected in our experimental scenarios. The modeling step includes offline prediction model training and selection. We present three prediction models that utilize different machine learning methods, namely, naive Bayes classifier, logistic regression, and artificial neural networks. Our models take a combination of PRR and the physical-layer information, that is, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Link Quality Indicator (LQI) as input, and output the success probability of delivering the next packet. From our analysis and experiments, we find that logistic regression works well among the three models with small computational cost. Finally, the third step involves the implementation of 4C, a receiver-initiated online link quality prediction module that computes the short temporal link quality. We conducted extensive experiments in the Motelab and our local indoor testbeds, as well as an outdoor deployment. Our results with single- and multiple-senders experiments show that with 4C, CTP improves the average cost of delivering a packet by 20% to 30%. In some cases, the improvement is larger than 45%.
An approach to Korean license plate recognition based on vertical edge matching

License plate recognition (LPR) has many applications in traffic monitoring systems. In this paper, a vertical edge matching based algorithm to recognize a Korean license plate from an input gray-scale image is proposed. The algorithm is able to recognize license plates in normal shape, as well as plates that are out of shape due to the angle of view. The proposed algorithm is fast enough and the recognition unit of the LPR system can be implemented only in software so that the cost of the system is reduced.
Influence of Temperature on Free Radical Generation in Propolis-Containing Ointments

Free radicals thermally generated in the ointments containing propolis were studied by electron paramagnetic resonance (EPR) spectroscopy. The influence of temperature on the free radical concentration in the propolis ointments was examined. Two ointment samples with different contents of propolis (5 and 7%, resp.) heated at temperatures of 30°C, 40°C, 50°C, and 60°C, for 30 min., were tested. Homogeneously broadened EPR lines and fast spin-lattice interactions characterized all the tested samples. Free radicals concentrations in the propolis samples ranged from 1018 to 1020 spin/g and were found to grow in both propolis-containing ointments along with the increasing heating temperature. Free radical concentrations in the ointments containing 5% and 7% of propolis, respectively, heated at temperatures of 30°C, 40°C, and 50°C were only slightly different. Thermal treatment at the temperature of 60°C resulted in a considerably higher free radical formation in the sample containing 7% of propolis when related to the sample with 5% of that compound. The EPR examination indicated that the propolis ointments should not be stored at temperatures of 40°C, 50°C, and 60°C. Low free radical formation at the lowest tested temperatures pointed out that both examined propolis ointments may be safely stored up to the temperature of 30°C.
Provably Secure ECC-Based Device Access Control and Key Agreement Protocol for IoT Environment

For secure communication between any two neighboring sensing devices on the Internet of Things (IoT) environment, it is essential to design a secure device access control and key agreement protocol, in which the two phases, namely, “node authentication” and “key agreement” are involved. While the node authentication allows two sensing devices to authenticate each other using their own pre-loaded secret credentials in memory, the key agreement phase permits to establish a secret key between them if the mutual authentication is successful. In this paper, we propose a new certificate-based “lightweight access control and key agreement protocol in the IoT environment, called LACKA-IoT,” that utilizes the elliptic curve cryptography (ECC) along with the “collision-resistant one-way cryptographic hash function. ” Through a detailed security analysis using the formal security under the “Real-Or-Random (ROR) model,” informal (non-mathematical) security analysis, and formal security verification using the broadly used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool, we show that the LACKA-IoT can protect various known attacks that are needed for a secure device access control mechanism in the IoT. Furthermore, through a comparative study of the LACKA-IoT and other relevant schemes, we show that there is a better tradeoff among the security and functionality features and communication and computational costs of the LACKA-IoT as compared to other schemes. Finally, the “practical demonstration using the NS2 simulation” has been carried out on the LACKA-IoT to measure various network parameters.
High accurate robotic drilling with external sensor and compliance model-based compensation

High accurate absolute robot positioning is a requirement, and still a challenge, in many applications, such as drilling in the aerospace industry. The accuracy is affected due to many sources of errors from robot model, tool calibration, sensor and product uncertainties. While model-based error compensation cannot reach the desired accuracy, sensor-based compensation appears as the practical solution to increase the robot positioning accuracy. A structured analysis of the error sources in robotic manufacturing processes can facilitate error identification and further compensation. This paper describes an error source breaking down approach for analyzing robotic manufacturing processes. Moreover, an external sensor-based compensation is proposed for error reduction and error identification. Comparison with a compliance model-based compensation is performed. The proposed approach is applied to a robotic drilling process for aircraft manufacturing, considered a general and real industrial application. Further validation through experimentation is performed. The validation revealed a clear improvement in robot positioning accuracy and the benefits of the proposed error source structure for analysis.
Nanotechnology, Science and Applications Dovepress Synthesis, Characterization, and Cytocompatibility of Potential Cockle Shell Aragonite Nanocrystals for Osteoporosis Therapy and Hormonal Delivery

you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms Abstract: Calcium carbonate is a porous inorganic nanomaterial with huge potential in biomedical applications and controlled drug delivery. This study aimed at evaluating the physicochemical properties and in vitro efficacy and safety of cockle shell aragonite calcium carbonate nanocrystals (ANC) as a potential therapeutic and hormonal delivery vehicle for osteoporosis management. Free and human recombinant parathyroid hormone 1-34 (PTH 1-34)-loaded cockle shell aragonite calcium carbonate nanocrystals (PTH-ANC) were synthesized and evaluated using standard procedures. Transmission electron microscopy and field emission scanning electron microscopy results demonstrated highly homogenized spherical-shaped aragonite nanocrystals of 30±5 nm diameter. PTH-ANC had a zeta potential of −27.6 ± 8.9 mV. The encapsulation efficiency of the formulation was found to be directly proportional to the concentrations of the drug fed. The X-ray diffraction patterns revealed strong crystal-lizations with no positional change of peaks before and after PTH-ANC synthesis. Fourier transform infrared spectroscopy demonstrated no detectable interactions between micron-sized aragonite and surfactant at molecular level. PTH-ANC formulation was stabilized at pH 7.5, enabling sustained slow release of PTH 1-34 for 168 h (1 week). A
Learning a Manifold Regression Model for Classifying High-dimensional Data

One of the most challenging classification and regression problems consists of learning relevant and meaningful relationships from high dimensional data. Given that this problem could have drastic effects on the performance, we propose a new framework for to build a regression model on locally transformed data using an optimal manifold embedding. The proposed model makes use of a non-standard family of positive definite covariance functions to fit data-domain partitions adaptively. It has the additional benefit to learn both the embedding and the dimensionality reduction, jointly within the same framework. The performance is evaluated for image retrieval using synthetic data and a public benchmark. Experimental results show that the proposed method has a better computational time and a lowest dimensionality when compared to the state-of-the-art.
Laboratory observations of mean flows under surface gravity waves

In this paper we present mean velocity distributions measured in several different wave flumes. The flows shown involve different types of mechanical wavemakers, channels of differing sizes, and two different end conditions. In all cases, when surface waves, nominally deep-water Stokes waves, are generated, counterflowing Eulerian flows appear that act to cancel locally, i.e. not in an integral sense, the mass transport associated with the Stokes drift. No existing theory of wave–current interactions explains this behaviour, although it is symptomatic of Gerstner waves, rotational waves that are exact solutions to the Euler equations. In shallow water (kH ≈ 1), this cancellation of the Stokes drift does not hold, suggesting that interactions between wave motions and the bottom boundary layer may also come into play.