Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association of Resistin Polymorphism (3'UTR+62GA) with Insulin Resistance and Hypertension at High Fat Diet Induced Type2 Diabetes in Rats: Experimental Study

Resistin, an adipocyte- and monocyte- derived cytokine that thought to be the missing link between obesity and diabetes. It also probably plays an important role in the development of insulin resistance. This factor considered to play an important role in the pathogenesis of type2 diabetes and insulin resistance-related hypertension. Recently, gene polymorphism is considered one of the most important factors that determine occurrence of diseases. Resistin gene polymorphism (UTR+62G>ASNP) may be a possible factor that link the hypertension with type 2DM. Aim: We aimed to investigate the correlation between resistin gene polymorphism (UTR+62G>A SNP) with serum resistin levels and if it is associated with insulin resistance-related hypertension in high fat diet- induced diabetes in rats (type 2 DM). Method: The current study was carried on 100 adult albino rats that have been divided into two groups. Group I: included 20 rats that were served as control group. Group II: that included 80 rats and they were given high fat diet for 16 weeks. At the end of the study, the blood pressure has been measured for both groups. Then the blood samples were collected to examine the serum glucose level, lipid profile level, serum resistin levels, the serum insulin levels and the allele and genotype frequencies of the single-nucleotide polymorphisms (UTR+62G>A SNP) in both groups. Insulin resistance was determined using HOMA-IR. The correlation of the gene variant to the hypertension, plasma resistin and insulin resistance have been investigated. Results: In the different diabetic groups, there was a significant association of the resistin A and G alleles of group IIb and group IIc when compared to controls (X 2 =12.21, X 2 =46.88, P=0.000) respectively. However, in group IIa the A and G allele were not significantly changed when compared to controls (X 2 =1.457, P=0.2). Serum resistin levels were significantly increased in group IIa, group IIb and group IIc in comparison with the control group (t=4.72, t= 9.82, and t=17.64/ P=0.000) respectively with a significant increase in group IIc than group IIb (t=8.23, P=0.000). There was a significant positive correlation between serum resistin and insulin levels(r=0.939, P< 0.0001). In all groups, rats with GG genotypes carriers were found to have a significant increase of serum resistin, total cholesterol (TC), fasting blood glucose (FBG), serum insulin levels and HOMA index levels compared to AA genotype (P<0.0001). In conclusion: This study suggested that, the 62G/A polymorphism in resistin gene; genotype GG and G allele were significantly associated with increased serum resistin levels, hypercholesterolemia, increased insulin levels, and also associated with increased risk of insulin resistance and hypertension in obese diabetic rats.
Effectiveness of neuro-feedback treatment with alpha/theta method on PTSD symptoms and their executing function

Neuro-feedback trains the brain to produce calm status. The specific areas of the brain affected by PTSD can also be targeted and trained to produce healthier patterns. Present study aimed to determine level of neuro-feedback treatment effectiveness with alpha/theta method on PTSD symptoms and also on their executing function. The present study was an experimental clinical trial with pre-test, post-tests and follow-up. Statistical population of present study includes all patients with PTSD in Kermanshah City, in which sampling was performed by convenience sampling. It shall be pointed out that all the samples were male. Individuals were randomly assigned in two experimental (n=15) and control (n=15) groups. Experiment group was put to attend 25 sessions, each lasting for 30 to 40 minutes of four times week for neuro-feedback individually. Follow Up was performed in final session after 45 days from both experiment and reference groups. Alpha/theta protocol was used in present research. Impact of Event Scale-Revised, Beck Depression Inventory-II, Wisconsin Card Sorting Test and Tower of London were used to collect data. Shapiro Wilk, Levene test, Means, Standard Deviations and Repeated measures were performed to analyse data. Results showed significant improvement for depression and PTSD symptoms; although improvements in depression were lower than PTSD symptoms. Treatment in initial meetings with these people because of chronic illness and sometimes unsuccessful efforts need plenty of patience so establishment relationship and understanding in this area and demonstrate scientific and medical understanding, cooperation and trust of patients are very effective.
Disguised Faces in the Wild

Existing research in the field of face recognition with variations due to disguises focuses primarily on images captured in controlled settings. Limited research has been performed on images captured in unconstrained environments, primarily due to the lack of corresponding disguised face datasets. In order to overcome this limitation, this work presents a novel Disguised Faces in the Wild (DFW) dataset, consisting of over 11,000 images for understanding and pushing the current state-of-the-art for disguised face recognition. To the best of our knowledge, DFW is a first-of-a-kind dataset containing images pertaining to both obfuscation and impersonation for understanding the effect of disguise variations. A major portion of the dataset has been collected from the Internet, thereby encompassing a wide variety of disguise accessories and variations across other covariates. As part of CVPR2018, a competition and workshop are organized to facilitate research in this direction. This paper presents a description of the dataset, the baseline protocols and performance, along with the phase-I results of the competition.
Growth strategy determines network performance

The interplay between structure and function is crucial in determining some emerging properties of many natural systems. Here we use an adaptive neural network model inspired in observations of synaptic pruning that couples activity and topological dynamics and reproduces experimental temporal profiles of synaptic density, including an initial transient period of relatively high synaptic connectivity. Using a simplified framework, we prove that the existence of this transient is critical in providing ordered stationary states that have the property of being able to store stable memories. In fact, there is a discontinuous phase transition between the ordered memory phase and a disordered one as a function of the initial transient synaptic density. We also show that intermediate synaptic density values are optimal in order to obtain these stable memory states with a minimum energy consumption, and that ultimately it is the transient heterogeneity in the network what determines the stationary state. Our results here could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and, eventually, anomalies such as autism and schizophrenia associated, respectively, with a deficit or an excess of pruning.
Fracture surfaces of heterogeneous materials: A 2D solvable model

Using an elastostatic description of crack growth based on the Griffith criterion and the principle of local symmetry, we present a stochastic model describing the propagation of a crack tip in a 2D heterogeneous brittle material. The model ensures the stability of straight cracks and allows for the study of the roughening of fracture surfaces. When neglecting the effect of the nonsingular stress, the problem becomes exactly solvable and yields analytic predictions for the power spectrum of the paths. This result suggests an alternative to the conventional power law analysis often used in the analysis of experimental data.
Investigating SAR algorithm for spaceborne interferometric oil spill detection

The environmental damages and recovery of terrestrial ecosystems from oil spills can last decades. Oil spills have been responsible for loss of aquamarine lives, organisms, trees, vegetation, birds and wildlife. Although there are several methods through which oil spills can be detected, it can be argued that remote sensing via the use of spaceborne platforms provides enormous benefits. This paper will provide more efficient means and methods that can assist in improving oil spill responses. The objective of this research is to develop a signal processing algorithm that can be used for detecting oil spills using spaceborne SAR interferometry (InSAR) data. To this end, a pendulum formation of multistatic smallSAR carrying platforms in a near equatorial orbit is described. The characteristic parameters such as the effects of incidence angles on radar backscatter, which support the detection of oil spills, will be the main drivers for determining the relative positions of the small satellites in formation. The orbit design and baseline distances between each spaceborne SAR platform will also be discussed. Furthermore, results from previous analysis on coverage assessment and revisit time shall be highlighted. Finally, an evaluation of automatic algorithm techniques for oil spill detection in SAR images will be conducted and results presented. The framework for the automatic algorithm considered consists of three major steps. The segmentation stage, where techniques that suggest the use of thresholding for dark spot segmentation within the captured InSAR image scene is conducted. The feature extraction stage involves the geometry and shape of the segmented region where elongation of the oil slick is considered an important feature and a function of the width and the length of the oil slick. For the classification stage, where the major objective is to distinguish oil spills from look-alikes, a Mahalanobis classifier will be used to estimate the probability of the extracted features being oil spills. The validation process of the algorithm will be conducted by using NASA’s UAVSAR data obtained over the Gulf of coast oil spill and RADARSAT-1 data.
ISOLATION OF NEWCASTLE DISEASE VIRUS ( NDV ) IN EMBRYONATED CHICKEN EGGS

Newcastle disease virus (NDV) strains have been divided into three groups: virulent (velogenic), moderately virulent (mesogenic), and non-virulent strains (lentogenic). The nonvirulent virus strain (LaSota strain) has been used as a live vaccine, which gives good immunity against the virulent strains. The aim of the study was to grow and propagate Newcastle disease virus in the lab, determination ofcytopathic effects in chicken embryos, and confirmation of virus growth by hemagglutination test. Non-virulent strain (LaSota strain)represented by live vaccine was used for this purpose. Embryonated eggs were inoculated with the virus and incubated for 48 hours; and theallantoic fluids were then collected for further processing. Petichial hemorrhages were clearly observed in the embryos following infection while in the un-inoculated eggs; the embryos appeared normal and did not show any lesions. For further virus growth confirmation, the presence of virus in the allantoic fluid was determined by hemagglutiation test. This finding is considered as a starting point for Newastle disease virus antigen preparation, which is essential for the applications of several laboratory techniques.
Majorana bound state engineering via efficient real-space parameter optimization (Conference Presentation)

Recent progress toward the fabrication of Majorana-based qubits has sparked the need for systematic approaches to optimize experimentally relevant parameters for the realization of robust Majorana bound states. Here, we introduce an efficient numerical method for the real-space optimization of tunable parameters, such as electrostatic potential profiles and magnetic field textures, in Majorana wires. Combining ideas from quantum control and quantum transport, our algorithm, applicable to any noninteracting tight-binding model, operates on a largely unexplored parameter space and opens new routes for Majorana bound states with enhanced robustness. Contrary to common belief, we find that spatial inhomogeneities of parameters can be a resource for the engineering of Majorana bound states.
Wireless Sensor Network for Forest Conservation using Energy Efficient Protocol

One of the biggest threats to humanity and nature in recent times is the change in climate such as the Global warming that is caused by the excess emissions of carbon to the atmosphere. Apart from these there are also other notable climate changes that affect nature. The major cause for Global warming is deforestation even though factors such as industrial effluents, automobile exhaust also contribute a little to this catastrophe. Many serious measures have been taken by the Government of various countries to keep in control of the rustlers involved in Deforestation. Some of these methods include fixing of a large number of cameras in certain areas of the forest spread over a wide range, awareness programs including public awareness, automatic alarm testing, use of satellites to provide images of forests, etc. Some of the issues to be considered when implementing such schemes are hugs cost and power consumption. Also the lifetime of the materials and hardware used should be considered. In this paper, an efficient wireless sensor networking algorithm is proposed that can be used to prolong the life time of the network by using the Heterogeneous Hybrid Energy Efficient Distributed Protocol (H-HEED).
Probing Rényi entanglement entropy via randomized measurements

An entropic look into entanglement Quantum systems are predicted to be better at information processing than their classical counterparts, and quantum entanglement is key to this superior performance. But how does one gauge the degree of entanglement in a system? Brydges et al. monitored the build-up of the so-called Rényi entropy in a chain of up to 10 trapped calcium ions, each of which encoded a qubit. As the system evolved, interactions caused entanglement between the chain and the rest of the system to grow, which was reflected in the growth of the Rényi entropy. Science, this issue p. 260 The buildup of entropy in an ion chain reflects a growing entanglement between the chain and its complement. Entanglement is a key feature of many-body quantum systems. Measuring the entropy of different partitions of a quantum system provides a way to probe its entanglement structure. Here, we present and experimentally demonstrate a protocol for measuring the second-order Rényi entropy based on statistical correlations between randomized measurements. Our experiments, carried out with a trapped-ion quantum simulator with partition sizes of up to 10 qubits, prove the overall coherent character of the system dynamics and reveal the growth of entanglement between its parts, in both the absence and presence of disorder. Our protocol represents a universal tool for probing and characterizing engineered quantum systems in the laboratory, which is applicable to arbitrary quantum states of up to several tens of qubits.
Urdu Word Segmentation using Machine Learning Approaches

Word Segmentation is considered a basic NLP task and in diverse NLP areas, it plays a significant role. The main areas which can be benefited from Word segmentation are IR, POS, NER, sentiment analysis, etc. Urdu Word Segmentation is a challenging task. There can be a number of reasons but Space Insertion Problem and Space Omission Problems are the major ones. Compared to Urdu, the tools and resources developed for word segmentation of English and English like other western languages have record-setting performance. Some languages provide a clear indication for words just like English which having space or capitalization of the first character in a word. But there are many languages which do not have proper delimitation in between words e.g. Thai, Lao, Urdu, etc. The objective of this research work is to present a machine learning based approach for Urdu word segmentation. We adopted the use of conditional random fields (CRF) to achieve the subject task. Some other challenges faced in Urdu text are compound words and reduplicated words. In this paper, we tried to overcome such challenges in Urdu text by machine learning methodology.
A Cross-Talk between the Renin-Angiotensin and Adrenergic Systems in Cardiovascular Health and Disease

It is well accepted that a number of cardiovascular (CV) and renal diseases are characterized by long-term activation of both renin-angiotensin system (RAS) and sympathetic nervous system (SNS) which also contribute to the pathophysiology of structural and functional CV abnormalities as well as to final clinical outcome. Moreover, there is a growing body of conclusive evidence that these systems do not operate independently, but interact at different levels throughout the CV system. Mediation of renin release from juxtaglomerular epithelioid (JGE) cells in kidney by SNS is well established and accepted. On the other hand, in recent years it became evident that RAS, by its main effector angiotensin II (Ang II), induces SNS activity in various organs and tissues. Thus, there is a growing effort to clarify pathophysiological mechanisms of interaction and more evident mutual potentiation of these two systems in different pathological states. Since it become evident that a high salt (HS) intake, which is a major risk factor for hypertension development, has a deleterious impact on vascular and endothelial function (even in the absence of blood pressure changes) it became necessary to investigate and clarify the effect of HS loading on major regulating systems, RAS and SNS precisely in healthy individuals. The present review aimed to summarize the interactions between the RAS and SNS in health and diseases (e.g. cardiovascular, renal), with special focus on these two systems interaction during HS intake in healthy normotensive population.
1064 nm rotational Raman lidar for particle extinction and lidar-ratio profiling: cirrus case study

Abstract. For the first time, vertical profiles of the 1064 nm particle extinction coefficient obtained from Raman lidar observations at 1058 nm (nitrogen and oxygen rotational Raman backscatter) are presented. We applied the new technique in the framework of test measurements and performed several cirrus observations of particle backscatter and extinction coefficients, and corresponding extinction-to-backscatter ratios at the wavelengths of 355, 532, and 1064 nm. The cirrus backscatter coefficients were found to be equal for all three wavelengths keeping the retrieval uncertainties in mind. The multiple-scattering-corrected cirrus extinction coefficients at 355 nm were on average about 20–30 % lower than the ones for 532 and 1064 nm. The cirrus-mean extinction-to-backscatter ratio (lidar ratio) was 31 ± 5 sr (355 nm), 36 ± 5 sr (532 nm), and 38 ± 5 sr (1064 nm) in this single study. We further discussed the requirements needed to obtain aerosol extinction profiles in the lower troposphere at 1064 nm with good accuracy (20 % relative uncertainty) and appropriate temporal and vertical resolution.
Forestillinger om børn og unge

The words ”children” and ”young people” are so often combined that they almost have the character ofidiomatic expressions. But which differences in the use of the words can be found? This article seeksto study the prevalent, dominant discourses about children and young people. In particular, it endeavours to answer the following question: What is thematised, and how are the thematisations created andmaintained? The study is theoretically inspired by Foucault’s discourse analysis. The methodologicalapproach is that of a multiple case study. Thus, it provides examples of how children are typically referred to in positive terms such as innocent, imaginative, cheerful, spontaneous, creative and competent(a surplus discourse), while young people are typically referred to in negative terms such as irresponsible, rootless, violent, dysfunctional, hedonistic and unaccountable (a deficit discourse).
Dual-polarization radar rainfall estimation in Korea according to raindropshapes obtained by using a 2-D video disdrometer

Abstract. Polarimetric measurements are sensitive to the sizes, concentrations, orientations, and shapes of raindrops. Thus, rainfall rates calculated from polarimetric radar are influenced by the raindrop shapes and canting. The mean raindrop shape can be obtained from long-term raindrop size distribution (DSD) observations, and the shapes of raindrops can play an important role in polarimetric rainfall algorithms based on differential reflectivity (ZDR) and specific differential phase (KDP). However, the mean raindrop shape is associated with the variation of the DSD, which can change depending on precipitation types and climatic regimes. Furthermore, these relationships have not been studied extensively on the Korean Peninsula. In this study, we present a method to find optimal polarimetric rainfall algorithms for the Korean Peninsula by using data provided by both a two-dimensional video disdrometer (2DVD) and the Bislsan S-band dual-polarization radar. First, a new axis-ratio relation was developed to improve radar rainfall estimations. Second, polarimetric rainfall algorithms were derived by using different axis-ratio relations. The rain gauge data were used to represent the ground truth situation, and the estimated radar-point hourly mean rain rates obtained from the different polarimetric rainfall algorithms were compared with the hourly rain rates measured by a rain gauge. The daily calibration biases of horizontal reflectivity (ZH) and differential reflectivity (ZDR) were calculated by comparing ZH and ZDR radar measurements with the same parameters simulated by the 2DVD. Overall, the derived new axis ratio was similar to the existing axis ratio except for both small particles (≤ 2 mm) and large particles (≥ 5.5 mm). The shapes of raindrops obtained by the new axis-ratio relation carried out with the 2DVD were more oblate than the shapes obtained by the existing relations. The combined polarimetric rainfall relations using ZDR and KDP were more efficient than the single-parameter rainfall relation for estimated 2DVD rainfall; however, the R(ZH, ZDR) algorithm showed the best performance for radar rainfall estimations, because the rainfall events used in the analysis consisted mainly of weak precipitation and KDP is relatively noisy at lower rain rates (≤ 10 mm h−1). Some of the polarimetric rainfall algorithms can be further improved by new axis-ratio relations.
Identification and Quantification of Phenolic Compounds from Red Currant (Ribes rubrum L.) and Raspberries (Rubus idaeus L.)

The extracts obtained from two types of berries: red currant and raspberries, were evaluated for their phenolic content. They were identified and quantified by using an optimized HPLC method. During the analyze several phenolic compounds were found, like: gallic acid, (+)catechin, syringic acid, cinnamic acid, chlorogenic acid, ferulic acid, rutin and quercetin. The total amount of phenolic compounds analyzed found in red currant was greater than the one found in raspberries, due to the low variety of phenolic compounds extracted. The greatest amount of gallic acid, (+)catechin, syringic acid, cinnamic acid, chlorogenic acid, ferulic acid and rutin was determined from the extraction of red currant berries and the raspberries extract contained the greatest source of quercetin. This study shows that red currant can provide the highest and most varied content of phenolic compounds from the analyzed berries.