Miracle-dz/UniFAR-FTUs huggingface dataset:
J an 2 01 9 AMENABILITY , DEFINABLE GROUPS , AND AUTOMORPHISM GROUPS

We prove several theorems relating amenability of groups in various categories (discrete, definable, topological, automorphism group) to modeltheoretic invariants (quotients by connected components, Lascar Galois group, G-compactness, .). Among the main tools, which is possibly of independent interest, is the adaptation and generalization of Theorem 12 of [28] to other settings including amenable topological groups whose topology is generated by a family of subgroups. On the side of definable groups, we prove that if G is definable in a model M and G is definably amenable, then the connected components G M and G M coincide, answering positively a question from [23]. We also prove some natural counterparts for topological groups, using our generalizations of [28]. By finding a dictionary relating quotients by connected components and Galois groups of ω-categorical theories, we conclude that if M is countable and ω-categorical, and Aut(M) is amenable as a topological group, then T := Th(M) is G-compact, i.e. the Lascar Galois group GalL(T ) is compact, Hausdorff (equivalently, the natural epimorphism from GalL(T ) to GalKP (T ) is an isomorphism). We also take the opportunity to further develop the model-theoretic approach to topological dynamics, obtaining for example some new invariants for topological groups, as well as allowing a uniform approach to the theorems above and the various categories.
Liraglutide Attenuates Nonalcoholic Fatty Liver Disease by Modulating Gut Microbiota in Rats Administered a High-Fat Diet

This study aimed to determine whether modulation of the gut microbiota structure by liraglutide helps improve nonalcoholic fatty liver disease (NAFLD) in rats on a high-fat diet (HFD). Rats were administered an HFD for 12 weeks to induce NAFLD and then administered liraglutide for 4 additional weeks. Next-generation sequencing and multivariate analysis were performed to assess structural changes in the gut microbiota. Liraglutide attenuated excessive hepatic ectopic fat deposition, maintained intestinal barrier integrity, and alleviated metabolic endotoxemia in HFD rats. Liraglutide significantly altered the overall structure of the HFD-disrupted gut microbiota and gut microbial composition in HFD rats in comparison to those on a normal diet. An abundance of 100 operational taxonomic units (OTUs) were altered upon liraglutide administration, with 78 OTUs associated with weight gain or inflammation. Twenty-three OTUs positively correlated with hepatic steatosis-related parameters were decreased upon liraglutide intervention, while 5 OTUs negatively correlated with hepatic steatosis-related parameters were increased. These results suggest that liraglutide-mediated attenuation of NAFLD partly results from structural changes in gut microbiota associated with hepatic steatosis.
Article Heavy rainfall events and mass movements in the Funchal area (Madeira

The article presents new information on the spatial distribution of intense rainfall and a new map of susceptibility to the formation of mass movements in the mountainous streams of the municipality of Funchal, the capital of the Autonomous Region of Madeira, an archipelago of Portugal. The methodology that was adopted is based on the spatial analysis of weighted overlap of variables, with influence in the occurrence of hydro-geomorphological processes that are at the origin of catastrophic events, marked by the mobilization of solid material towards and along the fluvial channels. Intense precipitations are effectively the main triggering factor of mass movements, which is why their statistical characteristics and local contrasts are analyzed, to integrate this layer of information into the new susceptibility assessment model of mass movements produced in this article. This type of spatialized information is of strategic importance to support the planning of urban expansion, which requires a land use management practice in accordance with the existing risk in the Madeira Island.
Theoretical Aspects, Modern Treatment Options and Practical Case Presentations in Hip and Knee Tumoral and Revision Bone Defect Reconstruction Surgery

Bone defects are a challenge to any and in fact every orthopedic surgeon, be they as a consequence of trauma, peri-implant bone loss as is the case in revision surgery or, more often than not, in tumors of the bone. These defects are in most cases difficult to reconstruct, but even more so in the case when they are located around the major joints of the lower limb, i.e. the hip and the knee. We focus in this article on acetabular bone defects as well as on defects around the knee (distal femur and proximal tibia). We present implant possibilities and modern means of reconstructing the bone defect using augments. We also present three representative cases from our Clinic, to further exemplify the discussed concepts. We present our opinions on reconstructing bone defects after tumor and revision surgery in the hip and knee and we draw conclusions.
Assessing the Performance of SDM-based Robot Navigation with Diﬀerent Image Processing Techniques

— Vision is one of the preferred sources of perceptions to implement intelligent robot navigation: it is biologically inspired, requires inexpensive hardware and a single image does contain huge amounts of information. However, problems such as illumination changes, scenario changes and electric noise in the image sensors pose challenges which are diﬃcult to overcome. In previous work the authors developed algorithms to navigate a robot based on sequences of visual memories stored into a Sparse Distributed Memory—a kind of associative memory suitable to work with high-dimensional binary vectors, which also exhibits behaviours in many aspects similar to those of the human brain. This paper analyses the impact of using diﬀerent image processing techniques to minimise the impact of noise in the images used: histogram equalisation, contrast normalisation and smoothing using a Gaussian ﬁlter. The results show that equalisation and smoothing have a positive eﬀect on the performance of the system.
Nutrition-related aspects of ascites in poultry

Pulmonary hypertension syndrome or ascites is a complex problem in poultry production. This disease has multiple causes, including genetic factors (genotype, sex) and non-genetic factors (altitude, improper egg incubation, ambient conditions in the poultry house, diet, etc.). Results from numerous studies have shown that improper diet and its interaction with other factors are among major reasons for the incidence of ascites symptoms. The objective of this study was to highlight major nutritional aspects related to pulmonary hypertension syndrome in broiler chickens. Therefore, particular attention should be paid to the dietary levels of antioxidant compounds, some minerals, energy and protein, feed forms, type of diet, and feed supplementation with ingredients which reduce the incidence of ascites.
Helical computed tomography - anatomy of the cat abdomen.

Computed tomography (CT) is a most valuable technique for the diagnosis of abdominal disorders. In veterinary medicine, x-ray and ultrasound are the imaging modalitys of choice for the abdomen, but CT can provide information that cannot be obtained by other means. One mature, female, domestic cat, 4 kg weight was used for this study. The cat was clinically healthy, with no abnormal findings at haematological and biochemical analysis. It was premedicated with 20 Âµg/kg medetomidine and 0.3mg/kg butorphanol IM. For induction of anaesthesia, 3 to 5 mg/kg propofol was administered IV. Anaesthesia was maintained with a mixture of isoflurane and oxygen. The patient was positioned in sternal recumbency. CT scans were performed with a helical CT scanner Hi Speed CT/e Dual; General Electric. Technical factors were 120 kVp and 50 mA settings, with 1 rotation/s. All sections were imaged with soft-tissue window. CT scans were performed after IV injection of iodinated contrast medium, iopamidol, 2 mL/kg. The images are presented cranialy to caudal progression from the 8th thoracic vertebra to the level of the pelvic inlet. Identifiable anatomical structures are labelled on the CT images. The stomach is left-sided with the gastric folds less numerous and thinner than those in the dogs. The normal small intestine contains less gas in cats. The feline cecum is very short. The liver showed a low attenuation with the mediastinum-vascular window settings, allowing better visualisation of the hepatic lobes and interlobular fissures. The gallbladder and bile ducts were also better visualised. CT imaging studies can provide additional information in patients with radiographic and ultrasonographic patterns of abdominal disorders, as a specialised imaging method, a useful preoperative technique and a precise staging tool. CT improves the image quality further and allows better timing of vascular phases. Helical CT angiography enables visualization of contrast-enhanced abdominal vessels using only a peripheral venous injection of iodinated contrast medium.
Transient Analysis of CTMCs: Uniformization or Matrix Exponential?

—Many numerical solution methods exist for the transient analysis of continuous-time Markov chains, such as differential equations, matrix exponential and uniformization methods. Uniformization is preferable for reasons of its numerical stability, pre-determined error bounds and acceptable time complexity. However, latest results reveal that computation of matrix exponentials can be carried out much more efﬁciently than before. In this paper, we compare uniformization and matrix exponential—through Pad´e approximation—coupled with the scaling and squaring method tailored to the IEEE ﬂoating-point speciﬁcation. We show that, in certain circumstances, the use of Pad´e approximation is advisable, even though in most cases uniformization performs better.
Learning transfer using deep convolutional features for remote sensing image retrieval

— Convolutional neural networks (CNNs) have recently witnessed a notable interest due to their superior performance demonstrated in computer vision applications; including image retrieval. This paper introduces an optimized bilinear-CNN architecture applied in the context of remote sensing image retrieval, which investigates the capability of deep neural networks in learning transfer from general data to domain-specific application, i.e. remote sensing image retrieval. The proposed deep learning model involves two parallel feature extractors to formulate image representations from local patches at deep convolutional layers. The extracted features are approximated into low-dimensional features by a polynomial kernel projection. Each single geographic image is represented by a discriminating compact descriptor using a modified compact pooling scheme followed by feature normalization. An end-to-end deep learning is performed to generate the final fine-tuned network model. The model performance is evaluated on the standard UCMerced land-use/land-cover (LULC) dataset with high-resolution aerial imagery. The conducted experiments on the proposed model show high performance in extracting and learning complex image features, which affirms the superiority of deep bilinear features in the context of remote sensing image retrieval.
ePubWU Institutional Repository

This dissertation is cumulative and encompasses three self-contained research articles. These essays share one common theme: the probability that a given stochastic process crosses a certain boundary function, namely the boundary crossing probability, and the related financial and statistical applications. In the first paper, we propose a new Monte Carlo method to price a type of barrier option called the Parisian option by simulating the first and last hitting time of the barrier. This research work aims at filling the gap in the literature on pricing of Parisian options with general curved boundaries while providing accurate results compared to the other Monte Carlo techniques available in the literature. Some numerical examples are presented for illustration. The second paper proposes a Monte Carlo method for analyzing the sensitivity of boundary crossing probabilities of the Brownian motion to small changes of the boundary. Only for few boundaries the sensitivities can be computed in closed form. We propose an efficient Monte Carlo procedure for general boundaries and provide upper bounds for the bias and the simulation error. The third paper focuses on the inverse first-passage-times. The inverse firstpassage-time problem deals with finding the boundary given the distribution of hitting times. Instead of a known distribution, we are given a sample of first hitting times and we propose and analyze estimators of the boundary. Firstly, we consider the empirical estimator and prove that it is strongly consistent and derive (an upper bound of) its asymptotic convergence rate. Secondly, we provide a Bayes estimator based on an approximate likelihood function. Monte Carlo
Interactive comment on “Heterogeneous photochemistry of imidazole-2-carboxaldehyde: HO2 radical formation and aerosol growth” by L. González Palacios et al

22 The multiphase chemistry of glyoxal is a source of secondary organic aerosol (SOA), including its 23 light-absorbing product imidazole-2-carboxaldehyde (IC). IC is a photosensitizer that can 24 contribute to additional aerosol ageing and growth when its excited triplet state oxidizes 25 hydrocarbons (reactive uptake) via H-transfer chemistry. We have conducted a series of 26
Local Computation Algorithms Motivation Behind The Model

Of course, having a small output did not necessarily mean that we could right away obtain a sublinear time algorithm for these problems. However, one thing should be clear: if the output itself is “large”, i.e., proportional to the input size, then there should be no hope of getting a sublinear time algorithm for the problem as simply writing down the output would be too time consuming. But what if we indeed aim to solve a problem with a large output, say, output a proper coloring of a given graph?
Title: Association of candidate Single Nucleotide Polymorphisms Related to Candidate Genes in an Iranian sample of Patients with Schizophrenia Authors:

Authors: Seyedeh Sara Karimian, Mohammad Taghi Akbari * , Seyed Saeed Sadr, Gholamreza Javadi 1 Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran 2 Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modarres University, Tehran, Iran 3 Tehran Medical Genetics Laboratory, Tehran, Iran 4 Department of Psychiatry, Imam Hussein Hospital, Shahid Beheshti University, Tehran, Iran * Correspondence to: Mohammad Taghi Akbari, Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modarres University, And P.O. Box: 14115-331, Tehran, Iran. email: mtakbari@modares.ac. ir
Knowledge Discovery from Scientometrics Database

—This paper proposed a unified ranking system for classification of scientific content. A list of parameters for unified ranking of scientific content (journals) is prepared first. A few quality indicators for evaluating the quality of journals are taken from existing parameters which are in use. These indicators include eigen factor, audience factor, impact factor, article influence, and citations. We also proposed one new metric, prestige of journal (PoJ) for the evaluation of journals. The values of different journals for the proposed indicators including the newer one is stored in an integrated database. A popular data mining technique for unsupervised classification named K Means clustering is applied to group the journals in different clusters. Clustering is an unsupervised classification technique in which the un-clustered classes do not bear any label. The clusters are labeled to find the exact rank of a science journal by using a state of the art technique of labeling clusters developed by the author of this paper. The experimental design of the paper is done and the parameters to evaluate the experiment are finalized. The results obtained from different experiments are in process yet and will be published in the extended version of this paper.
Accuracy on Bovine Bone Fracture Detection of Two Dimensional B – Mode Ultrasound Images using Polynomial – Intensity Gradient

The detection of bone fracture uses X-rays or CTscans device typically. These instruments have a negative effect of radiation and need high security for both patients and medical technicians. In this paper, we proposed a framework using the high–order polynomial approach and intensity gradient of two – dimensional B – mode ultrasound images for bone fracture detection. According to the ultrasound probe position, bone scanning process produce curved and flat contour surface. The local phase symmetry and morphology operation is used to extract the bone surface feature from the speckles and other noise. Then, a high order polynomial equation is used to obtain the center mass in the bone area. Two methods, Polynomial Tangent Perpendicular Line (PTPL) and Axis Perpendicular Line method are applied to determine the intensity gradient between adjacent columns based on the center of the mass bone area. These methods are tested to the bovine bone with no – fracture bone, and bone with transverse, oblique and comminuted fractures. Both PTPL and APL methods had 100% accuracy in the detection of fracture occurrence. For estimation width of fracture, the PTPL was more accurate than APL method. In the curved contour bone surface, the PTPL method has 1.14% error with the mean absolute error (MAE) of 0.016 mm. While the APL method has 2.63% error with the MAE of 0.04 mm. Meanwhile, in the flat contour bone surface, the PTPL method has 2.41% error with the MAE of 0.03 mm while the APL method has 3.21% error with the MAE of 0.04 mm.
The deep ocean in a changing climate

The largest inhabited space on the planet is located more than 200 m below the ocean surface, where darkness is almost total. These ocean depths play a major role in mitigating climate change through heat and anthropogenic CO2 sequestration. In addition to undergoing gradual warming and acidification, deep waters are less well ventilated, which reduces oxygen availability. Changes in surface phytoplankton production also affect the quantity and quality of nutrient resources available in the deep ocean. What will be the consequences of these disturbances on this vast and largely unexplored environment? Models set the framework and predict 50 to 80-year trends, but struggle to provide answers about the near future. Since observations reveal faster changes than model predictions do, there is a pressing need to adapt human activities to address potential risks. Many ecosystem services are linked to exchanges between the seabed and ocean surface ecosystems. These ecosystems play a role in long-term CO2 and CH4 sequestration by trapping carbon in the form of carbonates or organic matter (living organisms, debris, particles, or compounds dissolved in water). Increasing temperature, and decreasing oxygen and pH affect species distribution and, more generally, the entire nutrient cycle on which sustainable economic activities, such as artisanal fisheries, are based. Without a better understanding of these phenomena in space and over time, anticipating the consequences of climate change on biodiversity and deep ecosystems remains very difficult, as does assessing the impacts of new industrial activities combining with climate change consequences. Implementing key climate change adaptation measures must be based on an unprecedented effort to acquire the new knowledge needed to establish a legislative framework and effective management tools. The deep ocean in a changing climate Nadine Le Bris
EFFECT OF ARTEMISIA SANTONICA ON SCHISTOSOMA MANSONI INFECTED MICE: PARASITOLOGIOCAL, HISTOPATHOLOGICAL AND SCANNING ELECTRON MICROSCOPICAL STUDIES

Schistosomiasis is one of the most wide spread tropical diseases. Millions of people suffer severe illness related to schistosomiasis. This study evaluated the anti-parasitic effects of crude Artemisia santonica and their aqueous and hexane fractions against Schistosoma mansoni infected mice by some parasitologiocal and histopathological studies as worm load, liver egg load, intestine egg load, oogram pattern and also by surface ultrastructure of recovered worms using SEM. The result reflected that mice treated with crude extracts of A. santonica and their aqueous fractions gave a promise anti-inflammatory and anti-schistosomal agent.
A Hybrid Data Mining Approach for Diabetes Prediction and Classification

— Diabetes has emerged as the most chronic disease that may cause mortality to the diabetic patients. However, early prediction of diabetes can be helpful in reducing the severe effects of diabetes. Several approaches have been developed for diabetes prediction, recently, the data mining based machine learning approaches have gained huge attraction from research community. However, conventional approaches suffer from the performance related issues. Hence, in this work we introduce a novel data mining approach for diabetes prediction and classification. The proposed approach includes missing value imputation, data clustering, dimension reduction and Bayesian regularized neural network classification. The proposed approach is carried out using open source available Pima Indian Diabetes dataset and implemented using MATLAB simulation tool. The obtained performance is compared with the existing techniques; this comparative study shows a significant improvement in the prediction performance using proposed approach.
Optimization of Cellulose Production by Curvularia pallescens Isolated from Textile Effluent

Introduction: Celluloses are important industrial enzymes and find application in several industrial processes. Effects of pH, temperature, incubation time, source of carbon and nitrogen were tested in submerged fermentation process in the production of cellulose by Curvularia pallescens isolated from textile effluent. Aims: The present study was attempted in a fungus; Curvularia pallescens isolated from textile effluent for maximizing its production under optimal conditions in submerged fermentation by using inexpensive substrate wheat bran. Study Design: The production medium was prepared in distilled water, supplemented with 4.5% wheat bran, 0.05% KCl, 0.2% KH2PO4, (carbon source), yeast extract (nitrogen source), maintained with pH of 5.5 and incubated at 28oC for 120 h was found optimal for the production of cellulose. Results: The test fungus achieved maximum FPA activity followed by cellobiohydrolase, endoglucanase and β-glucosidase activity at 46.76, 42.06, 26.94 and 3.56 U/ml respectively at pH 5.5 (Fig. 4). The temperature of 280C produced maximum cellulase activity. Highest activity recorded was of FPA (38.94 U/ml), followed by cellobiohydrolase (30.29 U/ml), endoglucanase (22.41 U/ml), and β-glucosidase (3.98 U/ml). The effect of process parameters such as the effect of temperature, pH and inoculum size was investigated. Maximum cellulase and xylanase having an enzyme activity of 694.45 and 931.25 IU, respectively, were produced at 30oC incubation temperature. Short Communication Mehla and Datta; JAMB, 20(1): 11-17, 2020; Article no.JAMB. 49091 12 Conclusion: The effect of process parameters such as effect of temperature, pH and inoculum size was also investigated. The production of primary metabolites by microorganisms is highly influenced by their growth, which is determined by the availability of the nutrients in the substrates.