Miracle-dz/UniFAR-FTUs huggingface dataset:
Persistence of apoptotic cells without autoimmune disease or inflammation in CD14−/− mice

Interaction of macrophages with apoptotic cells involves multiple steps including recognition, tethering, phagocytosis, and anti-inflammatory macrophage responses. Defective apoptotic cell clearance is associated with pathogenesis of autoimmune disease. CD14 is a surface receptor that functions in vitro in the removal of apoptotic cells by human and murine macrophages, but its mechanism of action has not been defined. Here, we demonstrate that CD14 functions as a macrophage tethering receptor for apoptotic cells. Significantly, CD14−/− macrophages in vivo are defective in clearing apoptotic cells in multiple tissues, suggesting a broad role for CD14 in the clearance process. However, the resultant persistence of apoptotic cells does not lead to inflammation or increased autoantibody production, most likely because, as we show, CD14−/− macrophages retain the ability to generate anti-inflammatory signals in response to apoptotic cells. We conclude that CD14 plays a broad tethering role in apoptotic cell clearance in vivo and that apoptotic cells can persist in the absence of proinflammatory consequences.
A kernel level composition of multiple local classifiers for nonlinear classification

Kernel functions based machine learning algorithms have been extensively studied over the past decades with successful applications in a variety of real-world tasks. In this paper, we formulate a kernel level composition method to embed multiple local classifiers (kernels) into one kernel function, so as to obtain a more flexible data-dependent kernel. Since such composite kernels are composed by multiple local classifiers interpolated with several localizing gating functions, a specific learning process is also introduced in this paper to pre-determine their parameters. Experimental results are provided to validate two major perspectives of this paper. Firstly, the introduced learning process is effective to detect local information, which is essential for the parameter pre-determination of the localizing gating functions. Secondly, the proposed composite kernel has a capacity to improve classification performance.
Finger: An efficient policy system for body sensor networks

Body sensor networks (BSNs) for healthcare put more emphasis on security and adaptation to changes in context and application requirement. Policy-based management enables flexible adaptive behaviour by supporting dynamic loading, enabling and disabling of policies without shutting down nodes. This overcomes many of the limitations of sensor operating systems, such as TinyOS, which do not support dynamic modification of code. Alternative schemes for network adaptation, such as networking programming, suffer from high communication cost and operational interruption. In addition, the policy-driven approach enables fine-grained access control through specifying authorization policies. This paper presents an efficient policy system called finger which enables policy interpretation and enforcement on distributed sensors to support sensor level adaptation and fine-grained access control. It features support for dynamic management of policies, minimization of resources usage, high responsiveness and node autonomy. The policy system is integrated as a TinyOS component, exposing simple, well-defined interfaces which can easily be used by application developers. The system performance in terms of processing latency and resource usage is evaluated.
Inflammation Aggravates Disease Severity in Marfan Syndrome Patients

Background Marfan syndrome (MFS) is a pleiotropic genetic disorder with major features in cardiovascular, ocular and skeletal systems, associated with large clinical variability. Numerous studies reveal an involvement of TGF-β signaling. However, the contribution of tissue inflammation is not addressed so far. Methodology/Principal Findings Here we showed that both TGF-β and inflammation are up-regulated in patients with MFS. We analyzed transcriptome-wide gene expression in 55 MFS patients using Affymetrix Human Exon 1.0 ST Array and levels of TGF-β and various cytokines in their plasma. Within our MFS population, increased plasma levels of TGF-β were found especially in MFS patients with aortic root dilatation (124 pg/ml), when compared to MFS patients with normal aorta (10 pg/ml; p = 8×10−6, 95% CI: 70–159 pg/ml). Interestingly, our microarray data show that increased expression of inflammatory genes was associated with major clinical features within the MFS patients group; namely severity of the aortic root dilatation (HLA-DRB1 and HLA-DRB5 genes; r = 0.56 for both; False Discovery Rate(FDR) = 0%), ocular lens dislocation (RAET1L, CCL19 and HLA-DQB2; Fold Change (FC) = 1.8; 1.4; 1.5, FDR = 0%) and specific skeletal features (HLA-DRB1, HLA-DRB5, GZMK; FC = 8.8, 7.1, 1.3; FDR = 0%). Patients with progressive aortic disease had higher levels of Macrophage Colony Stimulating Factor (M-CSF) in blood. When comparing MFS aortic root vessel wall with non-MFS aortic root, increased numbers of CD4+ T-cells were found in the media (p = 0.02) and increased number of CD8+ T-cells (p = 0.003) in the adventitia of the MFS patients. Conclusion/Significance In conclusion, our results imply a modifying role of inflammation in MFS. Inflammation might be a novel therapeutic target in these patients.
Intelligent feature selection for regions of interest identification in retinal images

In this paper we developed an intelligent method for the selection of statistical, textural and fractal features that characterize different regions of interest in eye-fundus images. Because the regions like optic disc, macula, exudates and hemorrhages are difficult to detect, an intelligent scheme for feature detection and classification is necessary. The method is based on a voting scheme that takes into account the values on the main diagonal of different confusion matrices. These matrices are generated based on clusters from sorted data-sets of feature values. Both the sliding box and fixed box techniques where used to divide the image into patches, in order to highlight the regions of interest and to obtain the unique signature of features for each of them. Two algorithms are proposed, one for the intelligent selection of features and the second for the testing of the accuracy of selected features. The results obtained on 100 test images proved the efficiency of the proposed method compared to other algorithms.
Cardiovascular Morbidity and Mortality in Finnish Men and Women Separated Temporarily From Their Parents in Childhood—A Life Course Study

Objective Early-life stress may influence health later in life. We examined morbidity and mortality from cardiovascular disease over 60 years in individuals separated temporarily from their parents in childhood due to World War II. Methods We studied 12,915 members of the Helsinki Birth Cohort Study born from 1934 to 1944, of whom 1726 (13.4%) had been evacuated aboard without their parents to temporary foster families for an average of 1.8 (standard deviation = 1.1) years at an average age of 4.6 (standard deviation = 2.4) years. Data on parental separations were extracted from the Finnish National Archives. Information on use of medication for coronary heart disease and hypertension was derived from the National Register of Medication Reimbursement, and information on coronary events, stroke, and cardiovascular deaths was derived from Finnish Hospital Discharge Register and Causes of Death Register between Years 1971 and 2003. Results Participants who were separated in childhood used medications for coronary heart disease more frequently than those who were not separated (7.2% versus 4.5%, respectively; hazard ratio [HR] = 1.29, 95% confidence interval [CI] = 1.04–1.59; p = .02). No associations between separation and all-cause mortality (HR = 1.04, 95% CI = 0.90–1.20) or cardiovascular mortality (HR = 0.94, 95% CI = 0.72–1.21) or hospitalizations for cardiovascular disease or stroke were observed. Conclusions Early-life stress may possibly be a factor predisposing to coronary heart disease decades later, but no evidence was found for increased risk of hospitalizations or mortality. Abbreviations CDR = Causes of Death Register CHD = coronary heart disease CI = confidence interval CVD = cardiovascular disease ELS = early-life stress HBCS = Helsinki Birth Cohort Study HDR = Hospital Discharge Register HPA = hypothalamic-pituitary-adrenal HR = hazard ratio ICD = International Classification of Diseases WWII = World War II
Estrogen Receptor (cid:1) (ER (cid:1) ) Mediates Stimulatory Effects of Estrogen on Aromatase ( CYP19 ) Gene Expression in Human Placenta

A 246-bp region upstream of placenta-specific exon I.1 of the human aromatase ( hCYP19 ) gene mediates placenta-specific, developmental, and O 2 regulation of expression. In this study, trophoblast differentiation and associated induction of CYP19 expression were prevented when cytotrophoblasts were cultured in phenol red-free medium containing charcoal-stripped serum or with the estrogen receptor (ER) antagonist, ICI 182,780, suggesting a stimulatory role of estrogen/ER. ER (cid:1) protein was expressed in human trophoblasts and increased during syncytiotrophoblast differentiation, whereas ER (cid:2) was undetectable. Mutational analysis revealed that an estrogen response element-like sequence (ERE-LS) at (cid:1) 208 bp is required for inductive effects of estradiol/ER (cid:1) on hCYP19I.1 promoter activity in transfected COS-7 cells. Increased binding of syncytiotrophoblast compared with cytotrophoblast nuclear proteins to the ERE-LS was observed in vitro ; how-ever, ER (cid:1) antibodies failed to supershift the complex and in vitro -transcribed/translated ER (cid:1) did not bind. Nonetheless, chromatin immunoprecipitation assays in cultured trophoblasts revealed recruitment of endogenous ER (cid:1)
HEART DISEASE PREDICTION SYSTEM USING WEIGHT OPTIMIZED NEURAL NETWORK

Development of medical domain application has been one of the most active research areas. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network includes various factors such as selection of the optimal number of hidden nodes, selection of the relevant input variables and selection of optimal connection weights, automatically obtaining Knowledge from the patient’s clinical data. This paper presents the application of Multi Layer Feed Forward Neural Network that integrates Genetic Algorithm and Back Propagation network (BPN) for heart attack prediction. GA is used to initialize and optimize the connection weights of MLFFNN. The genetic optimized NN is trained and tested using 270 patient data. Keyword: Multi Layer Feed Forward Neural Network,, Genetic Algorithm, Back propagation, Heart Disease
Listeria species in a California coast estuarine environment

Listeria species and L. monocytogenes were found in 81 and 62%, respectively, of fresh or low-salinity waters (37 samples) in tributaries draining into Humboldt-Arcata Bay, Calif. , during a winter (January-February) sampling period. The incidence of Listeria species and L. monocytogenes in sediment (46 samples) from the same sites where water was sampled was 30.4 and 17.4%, respectively. One of three bay water samples contained Listeria species (including L. monocytogenes), while of 35 samples of oysters examined, only 1 was found positive for Listeria species (L. innocua). A given species or L. monocytogenes serogroup appeared to predominate in fresh water when domesticated animals (cows, horses) were nearby, whereas greater variety with no species predominance was observed in areas with no direct animal influence.
Leanness, Smoking, and Enhanced Oxidative DNA Damage

An increased risk of some forms of cancer, including lung cancer, among lean individuals has been consistent; however, there is a paucity of biological evidence supporting this relation. Subjects analyzed were 177 healthy Japanese workers who participated in a lifestyle intervention study. The levels of urinary 8-hydroxydeoxyguanosine (8-OHdG), a marker of oxidative DNA damage, were measured using an automated high-pressure liquid chromatography and urinary creatinine levels were adjusted for before statistical analysis. A clear inverse association was found between body mass index (BMI) and 8-OHdG levels among smokers [Pearson correlation coefficient (r) = −0.48], and the association did not materially change after adjustment for potential confounding factors. In contrast, no apparent relation was observed between BMI and 8-OHdG levels among nonsmokers (r = −0.12), although lean nonsmokers had a slightly higher mean of 8-OHdG levels compared with nonlean nonsmokers. The interaction of smoking and BMI reached statistical significance (P = 0.04). Leanness may enhance oxidative DNA damage induced by smoking and thus serve as a marker of host susceptibility to smoking-related cancers. (Cancer Epidemiol Biomarkers Prev 2006;15(3):582–5)
Effect of Powder Particle Size and Substrate Hardening on the Formation of Nanostructured TiC Coating on AISI-D2 Steel by Mechanical Milling

In this study TiC coating was formed on AISI-D2 steel by mechanical milling. In this regard, steel sample, balls and the powder were placed within a milling vial. Ball milling were carried out with annealed and quench-tempered samples using TiC powder having particle sizes of 44 and 200 μm for 5, 10, 15, 20, 50 and 100 h. During milling treatment, sample surface was exposed to high energy collisions and powder particles trapped between balls and sample adhered to the surface through cold welding. It was shown that the thickness and the structure of the coating depended on powder particle size; hardness and milling time. The thickness of the coating increased at first and decreased thereafter with milling time. The results showed that the substrate hardening decreased the thickness of the coating. Scanning Electron Microscopy (SEM) was employed to investigate the structural characteristics of the coatings. X-ray Diffraction (XRD) analysis was also conducted to determine the kind of phases in the coating. SEM investigations showed that the greatest thickness of the coating was reached after 20 h of milling. Furthermore, no new phases were detected in the XRD results after 100 h. It was shown that the thickness and hardness of coatings with coarse particle size were lower than that of the other. It was revealed that lattice parameter of TiC coating increased with milling time.
Synthesis and characterization of single- and few-layer mica nanosheets by the microwave-assisted solvothermal approach

We have successfully fabricated single- and few-layer mica nanosheets, by means of using a solvothermal method in conjunction with a microwave irradiated expansion process. In the solvothermal process, dissolved potassium ions were intercalated onto the interlayer space of the mica. Following this, microwave irradiation facilitates the exfoliation of individual nanosheets. The synthesized products were characterized by field emission scanning electron microscopy, transmission electron microscopy (TEM), atomic force microscopy (AFM), x-ray diffraction, x-ray photoelectron spectroscopy (XPS), and Raman spectroscopy measurements. AFM and TEM studies claimed the existence of single-layer mica. High-resolution TEM (HR-TEM) investigations revealed that the exfoliated product corresponded to a crystalline mica structure, being comprised of Si, Al, O, and K elements. XPS spectra exhibited the major constituent peaks, including O 1s, Si 2p, Al 2p, and K 2p. In addition, C atomic concentration has been slightly increased by the contamination during exfoliation, presumably due to the increase of the exposed mica surface. The C 1s XPS spectra revealed that the C–C bonding in organic surface contaminants was broken, whereas the Si–C bonding was enhanced, by the exfoliation process. The O 1s XPS spectra revealed that the Si–O bonding in mica was broken, generating the O–Si–C bonding. This study paves the way towards the fabrication of single- or few-layer inorganic nanosheets of desired materials, via a convenient and efficient route.
Affine Invariant Representation and Classification of Object Contours for Image and Video Retrieval

: - Recent literature comprises a large number of papers on the query and retrieval of visual information based on its content. At the same time, a number of prototype systems have been implemented enabling searching through on-line image databases and still image retrieval. However, it has been often pointed out that meaningful/semantic information should be extracted from visual information in order to improve the efficiency and functionality of a content-based retrieval tool. In this context, present work focuses on the extraction of objects from images and video clips and modeling of the resulting object contours using B-splines. Affine-invariant curve representation is obtained through Normalized Fourier descriptors (NFD), curve moments, as well as a novel curve normalization algorithm that leads to major preservation of object shape information. A neural network approach is employed for supervised classification of video objects into prototype object classes. Experiments on several real-life and simulated video sequences are included to evaluate the classification results for all affine-invariant representations used.
Maxout filter networks referencing morphological filters

This paper presents nonlinear filters that are obtained from extensions of morphological filters. The proposed nonlinear filter consists of a convex and concave filter that are extensions of the dilation and erosion of morphological filter with the maxout activation function. Maxout can approximate arbitrary convex functions as piecewise linear functions, including the max function of the morphological filters. The class of the convex function hence includes the morphological dilation and can be trained for specific image processing tasks. In this paper, the closing filter is extended to a convex—concave filter with maxout. The convex-concave filter is trained for noise and mask removal with a training set. The examples of noise and mask removal show that the convex-concave filter can obtain a recovered image, whose quality is comparable to in-painting by using the total variation minimization with reduced computational cost without mask information of the corrupted pixels.
Effect of Bursty Impulsive Noise on the Performance of Multi-Relay DF Cooperative Relaying Scheme

In this paper, we consider the performance analysis of a decode-and-forward (DF) cooperative relaying (CR) scheme over bursty impulsive noise channel. As compared to existing literature, here, we generalize the performance analysis to the multi-relay scenario with and without considering error propagation from the relays. For this scheme, we evaluate the bit error rate (BER) performance in the presence of Rayleigh fading with a maximum a posteriori (MAP) receiver. From the obtained results it is seen that, similar to single relay scheme, the proposed MAP receiver attains the lower bound derived for multi-relay DF CR scheme also, and performs significantly better than the conventional schemes developed for additive white Gaussian noise (AWGN) channel and memoryless impulsive noise channel. Moreover, the performance improvement of optimal MAP receiver over the memoryless receivers is substantial with increasing the number of relays.
A Disturbance-Adaptive Design for VANET-Enabled Vehicle Platoon

In highway systems, grouping vehicles into platoons can improve road capacity and energy efficiency. With the advance of technologies, the performance of platoons can be further enhanced by vehicular ad hoc networks (VANETs). In the past few years, many studies have been conducted on the dynamics of a VANET-enabled platoon under traffic disturbance, which is a common scenario on a highway. However, most of them do not consider the impact of platoon dynamics on the behaviors of VANETs. Moreover, most existing studies focus on how to maintain the stability of a platoon and do not address how to mitigate negative effects of traffic disturbance, such as uncomfortable passenger experience, increased fuel consumption, and increased exhaust emission. In this paper, we will investigate the dynamics of the VANET-enabled platoon from an integrated perspective. In particular, we first propose a novel disturbance-adaptive platoon (DA-Platoon) architecture, in which a platoon controller shall adapt to the disturbance scenario and shall consider both VANET and platoon dynamics requirements. Based on a specific realization of the DA-Platoon architecture, we then analyze the traffic dynamics inside a platoon and derive desired parameters, including intraplatoon spacing and platoon size, so as to satisfy VANET constraints under traffic disturbance. To mitigate the adverse effects of traffic disturbance, we also design a novel driving strategy for the leading vehicle of a platoon, with which we can determine the desired interplatoon spacing. Finally, we conduct extensive simulation experiments, which not only validate our analysis but also demonstrate the effectiveness of the proposed driving strategy.