Miracle-dz/UniFAR-FTUs huggingface dataset:
RING EDGE WAVES AND THE MASSES OF NEARBY SATELLITES

Moons embedded in gaps within Saturn's main rings generate waves on the gap edges due to their gravitational disturbances. These edge waves can serve as diagnostics for the masses and, in some cases, orbital characteristics of the embedded moons. Although N-body simulations of the edges are far better in inferring masses from edge morphology, the long run-times of this technique often make it impractical. In this paper, we describe a faster approach to narrow the range of masses to explore with N-body simulations, to explore the multidimensional parameter space of edge/moon interactions, and to guide the planning of spacecraft observations. Using numerical, test-particle models and neglecting particle–particle interactions, we demonstrate that the simple analytic theory of the edge waves applies well to Pan in the Encke Gap but breaks down for smaller moons/gaps like Daphnis in the Keeler Gap. Fitting an analytic model to our simulation results allows us to suggest an improved relationship between moon-mass and edge wave amplitude. Numerical methods also grant freedom to explore a wider range of moon and ring orbits than the circular, coplanar case considered by analytic theory. We examine how pre-encounter inclinations and eccentricities affect the properties of the edge waves. In the case where the moon or ring-edge particle orbits initially have eccentric radial variations that are large compared to the gap width, there is considerable variation in edge wave amplitude depending on the orbital phase of the encounter. Inclined moons also affect the edge wave amplitude, potentially significantly, as well as generate vertical waves on the gap-edges. Recent Cassini images acquired as Saturn approaches equinox and the Sun's elevation on the ringplane is extremely low have revealed long shadows associated with the Keeler gap edge waves created by the embedded moon Daphnis. We interpret these as being cast by ∼1 km high vertical structure in the waves created by Daphnis' out-of-plane perturbations on the ring particles.
Measuring Visual Function in Diabetic Retinopathy: Progress in Basic and Clinical Research

Diabetic retinopathy is a common consequence of diabetes mellitus and the leading cause of vision loss in workingage people in the United States. The pathology of this disease is well characterized by microvascular lesions but also includes deficits in visual function, possibly as a consequence of retinal neurodegeneration. Microvasculature changes are clinically detected by fundus examination and used as the primary method of diagnosis, but functional tests may represent alternative endpoints that may be useful in translational research. Components of visual function can be characterized in a variety of different ways including measures of acuity, contrast sensitivity, dark adaptation and a number of electrophysiological parameters of the retina. This review discusses loss of function as measured both in human and animal models of diabetic retinopathy.
Ensemble classifier with dividing training scheme for Chinese scene character recognition

Scene character recognition problem has attracted a great attention in computer vision field. As one of the most widely used characters, Chinese characters are more complicated than European characters, especially when it comes to those characters appeared in various scene texts. The recognition of Chinese scene characters is still far from a satisfactory level owing to the lack of adequate training data and efficient learning methods. In this paper, we propose a new strategy to divide training data and combine multiple classifiers to get a better performance of Chinese scene character recognition. Besides, facing the problem of data shortage, we propose a scene character synthesis method to gain enough training data. By applying the proposed training strategy, the average recognition accuracies of Random Forest (RF) and Support Vector Machine (SVM) have been improved by nearly 22% and 14%, respectively.
Atrial fibrillation Digoxin-associated mortality : a systematic review and meta-analysis of the literature

There are conflicting data regarding the effect of digoxin use on mortality in patients with atrial fibrillation (AF) or with congestive heart failure (CHF). The aim of this meta-analysis was to provide detailed analysis of the currently available study reports. We performed a MEDLINE and a COCHRANE search (1993–2014) of the English literature dealing with the effects of digoxin on all-cause-mortality in subjects with AF or CHF. Only full-sized articles published in peer-reviewed journals were considered for this meta-analysis. A total of 19 reports were identified. Nine reports dealt with AF patients, seven with patients suffering from CHF, and three with both clinical conditions. Based on the analysis of adjusted mortality results of all 19 studies comprising 326 426 patients, digoxin use was associated with an increased relative risk of all-cause mortality [Hazard ratio (HR) 1.21, 95% confidence interval (CI), 1.07 to 1.38, P , 0.01]. Compared with subjects not receiving glycosides, digoxin was associated with a 29% increased mortality risk (HR 1.29; 95% CI, 1.21 to 1.39) in the subgroup of publications comprising 235 047 AF patients. Among 91.379 heart failure patients, digoxin-associated mortality risk increased by 14% (HR 1.14, 95% CI, 1.06 to 1.22). The present systematic review and meta-analysis of all available data sources suggest that digoxin use is associated with an increased mortality risk, particularly among patients suffering from AF.
Combining background subtraction algorithms with convolutional neural network

Abstract. Accurate and fast extraction of foreground objects is a key prerequisite for a wide range of computer vision applications, such as object tracking and recognition. Thus, many background subtraction (BGS) methods for foreground object detection have been proposed in recent decades. However, this is still regarded as a tough problem due to a variety of challenges, such as illumination variations, camera jitter, dynamic backgrounds, and shadows. Currently, there is no single method that can handle all the challenges in a robust way. We try to solve this problem from a perspective of combining different state-of-the-art BGS algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder–decoder fully convolutional neural network architecture is adapted and trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different BGS algorithms and produce a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single BGS algorithms. We show that our solution is more efficient than other BGS combination strategies.
Non-invasive Neurite Mechanics in Differentiated PC12 Cells

Thermal Fluctuations Spectroscopy (TFS) in combination with novel optical-based instrumentation was used to study mechanical properties of cell-cultured neurites with a spatial resolution limited only by the light diffraction. The analysis of thermal fluctuations together with a physical model of cellular elasticity allow us to determine relevant mechanical properties of neurite as axial tension σ, flexural rigidity B, plasma membrane tension γ, membrane bending rigidity K, and cytoskeleton to membrane-coupling ρbk, whose values are consistent with previously reported values measured using invasive approaches. The value obtained for the membrane-coupling parameter was used to estimate the average number of coupling elements between the plasma membrane and the cytoskeleton that fell in the range of 30 elements per area of the laser spot used to record the fluctuations. Furthermore, to expand the TFS analysis, we investigate the correlation between F-actin linear density and the mechanical features of PC12 neurites. Using a hybrid instrument that combines TFS and a simple fluorescent technique, our results show that the fluctuations are related with the F-actin concentration. These measurements have an advantage of not requiring the application of an external force, allowing as to directly establish a correlation between changes in the mechanical parameters and cytoskeleton-protein concentrations. The sensibility of our method was also tested by the application of TFS technique to PC12 neurite under Paraformaldehyde and Latrunculin-A effect. These results show a dramatic modification in the fluctuations that are consistent with the reported effect of these drugs, confirming the high sensitivity of this technique. Finally, the thermal fluctuation approach was applied to DRG axons to show that its utility is not limited to studies of PC12 neurites, but it is suitable to measure the general characteristic of various neuron-like cells.
Combined effects of iron and copper from atmospheric dry deposition on ocean productivity

Atmospheric deposition can provide nutrients and potential toxicants to marine ecosystem, hence affecting ocean net primary productivity (NPP). Nonetheless, the interactive effects of mixed aerosol components on phytoplankton have rarely been reported. Here we explored the combined effects of iron (Fe) and copper (Cu) on NPP over the East China Sea. In aerosol addition mesocosm experiments, phytoplankton growth was suppressed under high aerosol Cu but was increased when high Cu was accompanied by high Fe in aerosols. A time series of soluble aerosol Fe and Cu deposition was obtained and compared to regional chlorophyll a (Chl a) abundances from Moderate Resolution Imaging Spectroradiometer/Aqua. Strong positive correlations were observed between the dry flux ratios of soluble Fe/Cu and Chl a abundances in the large offshore area, whereas these variables were uncoupled in coastal regions where riverine input and upwelling dominated the biogeochemistry. Current work provides insight into the complex linkage between atmospheric deposition and marine productivity.
Influence of dietary phytase and 1,25-dihydroxycholecalciferol supplementation on the activity of digestive enzymes in chickens

These studies were conducted to find out if a diet supplemented with phytase and 1,25-dihydroxycholecalciferol could change the activity of proteolytic enzymes and concentration of protein in proventriculus mucosa and pancreas in broiler chickens. The experiments were carried out on 45 broiler chickens till the age of 21 d. Animals were divided into one control group and two experimental groups. The animals from the experimental group I received diet with supplementation of phytase and the animals from the experimental group II received diet supplemented with phytase and 1,25 dihydroxycholecalciferol. A significant decrease in proteolytic activity was observed in chickens of both experimental groups as compared to controls. The supplemented diet with phytase limited high level of synthesis and secretion of proteolytic enzymes in broiler chickens fed diet rich in plant proteins.
Relating Latent Class Membership to Continuous Distal Outcomes: Improving the LTB Approach and a Modified Three-Step Implementation

Latent class analysis often aims to relate the classes to continuous external consequences (“distal outcomes”), but estimating such relationships necessitates distributional assumptions. Lanza, Tan, and Bray (2013) suggested circumventing such assumptions with their LTB approach: Linear logistic regression of latent class membership on each distal outcome is first used, after which this estimated relationship is reversed using Bayes’ rule. However, the LTB approach currently has 3 drawbacks, which we address in this article. First, LTB interchanges the assumption of normality for one of homoskedasticity, or, equivalently, of linearity of the logistic regression, leading to bias. Fortunately, we show introducing higher order terms prevents this bias. Second, we improve coverage rates by replacing approximate standard errors with resampling methods. Finally, we introduce a bias-corrected 3-step version of LTB as a practical alternative to standard LTB. The improved LTB methods are validated by a simulation study, and an example application demonstrates their usefulness.
Effect of salinity on arabidopsis thaliana seed germination and acid phosphatase activity

The salt tolerance of four accessions of Arabidopsis thaliana ( COL ( Columbia ) , NOK2, N1438 and N1380 ) was evaluated during germination by the capacity of seeds to germinate in the presence of 50 mM NaCl and to maintain adequate acid phosphatase activity. Our results show that saline conditions reduced the final germination percentage, speed of germination and delayed the germination processes of accessions NOK2, N1438 and N1380 . In contrast, 100% of germination was found in COL under salt-stress conditions. In the presence of NaCl 50 mM, acid phosphatase activity increased in the first 24 h, the activity reaching the control level in germinating seeds of COL , but in the three other accessions NOK2, N1438 and N1380, acid phosphatase activity diminished under salt stress. These findings suggest that changes in the phosphatase enzymes might play an important role in the acclimation of COL seeds to the changing environmental conditions.
Genetic Diversity and Relationships of Piper Species Using Molecular Marker

Piper is an important member of Piperaceae, have a wide distribution in tropical and subtropical regions. The evergreen forest of the Western Ghats in South India is considered as the center of diversity of Piper nigrum and its close relatives. The genus includes important species like Piper nigrum; the black pepper, Piper longum and Piper betel, which are commonly used in the indigenous system of medicine. Among these, black pepper is one among the most widely used spices in the world. It is taxonomically a very difficult genus because of the greater range of variability and minute nature of flowers (Ravindran et al.,1992). However various attempts to classify the Piper species based on morphological, cytological studies by Sharma and Bhattacharya (1959) and chemical constituent data carried out by Sebastian and Sujatha (1996). But differentiation of species through morphological features is inefficient and inaccurate. The use of biochemical and genetic markers for identification of varieties offer a viable alternative method of Williams (1990). According to Lee et al., (1995) DNA based molecular markers are the most useful to detect the levels of genetic diversity in plant genome. RAPD analysis revealed a high degree of genetic diversity among the species examined in the study, which can contribute to the crop involvement. RAPD analysis can also be used for detecting gene flow between species. Furthermore, this technique is less restricting than other molecular technique RFLP (Restricted Fragment Length Polymorphism), as no hybridization and no use of radioisotopes is required, and it is therefore more convenient for use in research centers in developing countries. Thus our results demonstrate that 4C DNA content and RAPD markers provide an effective tool for detection and evaluation of genetic variation existing among 6 promising species of pipers.The result presented in the present study demonstrated the utility of using RAPD markers to characterize genetic diversity among 6 promising species of piper. Differential polymorphism was noted in 6 species of piper showing variation in percentage of polymorphic bands from 24.48% to 06.12% using 12 primers of OPA 1-6 and OPD 1-6. K e y w o r d s
Microbial silicification in Iodine Pool, Waimangu geothermal area, North Island, New Zealand: implications for recognition and identification of ancient silicified microbes

Silicified microbes provide evidence for some of the earliest life forms on Earth. They are extremely important to understanding the early development of life and the conditions that allowed its development. Such discussions commonly rely on comparisons with extant taxa and therefore depend upon the preservation style of the microbes and, in particular, the preservation of the taxonomically important features. Silicified microbes are deceptive: they commonly appear to be well preserved even though their taxonomically critical features have been destroyed by silicification. An understanding of the early taphonomic processes that influence microbial silicification can be obtained by studying extant microbes that are being silicified in modern hot spring pools. Iodine Pool, located in the Waimangu geothermal area on the North Island of New Zealand, is ideal for this purpose. The spring water has a temperature of 69–100 °C, a pH of 8.3–9.0, and 440–457 ppm SiO2. Glass slides left in the shallow marginal waters of this pool for 90 h became covered with thin layers of opaline silica, discrete opal-A spheres, unsilicified microbes, pseudofilaments, partly silicified microbes and silicified microbes. The rapidly silicified microbes appear well preserved with their general morphology, diameter, length, and presence or absence of septa being readily apparent. Most of the silicified microbes, however, lack the key features that would allow accurate comparisons with extant taxa. Only two of the silicified microbes can be tentatively allied with the unsilicified forms, despite being found side by side on the same glass slide. These problems in identifying modern, rapidly silicified microbes suggest that identifications of ancient silicified microbes can be problematic and must be treated with caution.
Fractional order control of the cyber-physical cryogenic isotope separation columns cascade system

The 13C cryogenic isotope separation columns cascade is a multivariable, distributed, nonlinear system, with strong interactions between the subsystems. Being a complex, critical process, a robust, fault tolerant control is needed. The present paper proposes a novel fault tolerant control scheme based on fractional order observer and fractional order PI controllers. In the controller design stage linearized decoupled models are used, while the observer design process is based on the nonlinear model. The real process operation scenarios are simulated to test the proposed control structures.
Selective versus Non-Selective Acquisition of Crowd-Solicited IoT Data and Its Dependability

With the widespread availability of built-in and nondedicated sensors, acquiring crowd-solicited Internet of Things (IoT) data has become possible and convenient via smart mobile devices. Ensuring the dependability of crowd- solicited IoT data is a grand challenge since potential threats originated by adversaries are inevitable. When IoT data is acquired via nondedicated sensors in an opportunistic or participatory manner, the users/sensing data providers are not given the choice to select sensing tasks based on the other participants in the vicinity. In this paper, we perform a thorough feasibility analysis of two possible data acquisition approaches for crowd-solicited IoT data: 1) A mobile edge-based approach, namely the Selective and Reputation-aware Recruitment (SRR) which enables sensing service providers to join a highly dependable community, 2) Cloud- based non-selective reputation-aware recruitment (NSR). The community selectiveness of sensing data providers is basically turning off the corresponding sensors in the mobile devices while having the rest of the sensor array remain on. We present a framework where the selectiveness can be handled at the IoT gateways as data service functions to bypass the communication latency with the cloud-based crowd- sensing platform, and taking advantage of edge- based computing power. Our feasibility study through simulations shows that dependable community selectiveness does not lead to significant energy savings while slight improvement of user and platform utilities indicate the possible viability of cloud-based non-selective data acquisition of crowd- solicited IoT data.
Low-cost diagnostic test for susceptible and drug-resistant tuberculosis in rural Malawi

Background Rural settings where molecular tuberculosis diagnostics are not currently available need easy-to-use tests that do not require additional processing or equipment. While acid-fast bacilli (AFB) smear is the most common and often only tuberculosis diagnosis test performed in rural settings, it is labour intensive, has less-than-ideal sensitivity, and cannot assess tuberculosis drug susceptibility patterns. Objective The objective of this study was to determine the feasibility of a multidrug-resistant (MDR) or extensively drug-resistant (XDR)-tuberculosis coloured agar-based culture test (tuberculosis CX-test), which can detect Mycobacterium tuberculosis growth and evaluate for drug susceptibility to isoniazid, rifampicin and a fluoroquinolone (i.e. ciprofloxacin) in approximately 14 days. Method In this study, 101 participants were enrolled who presented to a rural health clinic in central Malawi. They were suspected of having active pulmonary tuberculosis. Participants provided demographic and clinical data and submitted sputum samples for tuberculosis testing using the AFB smear and tuberculosis CX-test. Results The results showed a high level of concordance between the AFB smear (12 positive) and tuberculosis CX-test (13 positive); only one sample presented discordant results, with the molecular GeneXpert MTB/RIF® test confirming the tuberculosis CX-test results. The average time to a positive tuberculosis CX-test was 10 days. Of the positive samples, the tuberculosis CX-test detected no cases of drug resistance, which was later confirmed by the GeneXpert MTB/RIF®. Conclusion These findings demonstrate that the tuberculosis CX-test could be a reliable low-cost diagnostic method for active pulmonary tuberculosis in high tuberculosis burden rural areas.
Adaptive Non-Linear Digital Self-Interference Cancellation for Full-Duplex Wireless Systems Using Ito-Hermite Polynomials

In this paper, a novel non-linear digital self- interference cancellation algorithm is proposed for full-duplex wireless systems. The proposed algorithm constructs a replica of the nonlinearly-distorted transmitted signal using Itô generalization of the Hermite (Itô-Hermite) polynomials. Because Itô-Hermite polynomials constitute orthogonal basis functions of any non-linear function when the input is a complex Gaussian random process, the proposed least mean squares (LMS) adaptive filtering based on Itô-Hermite polynomials optimally cancels non-linearly distorted self-interference signals in full-duplex systems. From numerical results, it is shown that the proposed algorithm provides noticeable gains in terms of both a fast convergence rate and the low computational complexity for the self-interference cancellation compared to the existing LMS algorithm using Volterra- series as basis functions with basis function pre- orthogonalization.