Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Kinase C-ε Regulates the Apoptosis and Survival of Glioma Cells

In this study, we examined the role of protein kinase C (PKC)-e in the apoptosis and survival of glioma cells using tumor necrosis factor–related apoptosis inducing ligand (TRAIL)-stimulated cells and silencing of PKCe expression. Treatment of glioma cells with TRAIL induced activation, caspase-dependent cleavage, and down-regulation of PKCe within 3 to 5 hours of treatment. Overexpression of PKCe inhibited the apoptosis induced by TRAIL, acting downstream of caspase 8 and upstream of Bid cleavage and cytochrome c release from the mitochondria. A caspase-resistant PKCe mutant (D383A) was more protective than PKCe, suggesting that both the cleavage of PKCe and its down-regulation contributed to the apoptotic effect of TRAIL. To further study the role of PKCe in glioma cell apoptosis, we employed short interfering RNAs directed against the mRNA of PKCe and found that silencing of PKCe expression induced apoptosis of various glioma cell lines and primary glioma cultures. To delineate the molecular mechanisms involved in the apoptosis induced by silencing of PKCe, we examined the expression and phosphorylation of various apoptosis-related proteins. We found that knockdown of PKCe did not affect the expression of Bcl2 and Bax or the phosphorylation and expression of Erk1/2, c- Jun -NH2-kinase, p38, or STAT, whereas it selectively reduced the expression of AKT. Similarly, TRAIL reduced the expression of AKT in glioma cells and this decrease was abolished in cells overexpressing PKCe. Our results suggest that the cleavage of PKCe and its down-regulation play important roles in the apoptotic effect of TRAIL. Moreover, PKCe regulates AKT expression and is essential for the survival of glioma cells.
Aentive Group Recommendation

Due to the prevalence of group activities in people’s daily life, recommending content to a group of users becomes an important task inmany information systems. A fundamental problem in group recommendation is how to aggregate the preferences of group members to infer the decision of a group. Most existing group recommender systems applied a predened strategy for preference aggregation. We argue that such static strategies are insucient to model the complicated process of group decision making and result in the suboptimal performance for group recommendation. In particular, group members should have non-uniform weights in forming a group, and more importantly, the weights should be varied when the group interacts with dierent items. In this work, we address the fundamental problem of preference aggregation in group recommendation by learning the aggregation strategy from data. We contribute a novel solution, namely AGREE (short for “Aentive Group REcommEndation”), based on the recent developments of aention network and neural collaborative ltering (NCF). Specically, we adopt an aention mechanism to adapt the representation of a group, and learn the interaction between groups and items from data under the NCF framework. Moreover, since many group recommender systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. rough this way, we can reinforce the two tasks of recommending items for both groups and users. By experimenting on two real-world datasets, we demonstrate that our AGREE model not only improves the group recommendation performance but also enhances the recommendation for users, especially for cold-start users that have no historical interactions individually.
Using stable oxygen and hydrogen isotopes to assess plant water relations in grasslands exposed to drought

Understanding the soil-plant-atmosphere continuum as a part of the water cycle becomes an essential task under global climate change. Particularly as the climate variability and the frequency of extreme climate events, such as drought spells, will increase very likely by the end of this century. Recent climate models project a significant decrease of precipitation for Central Europe. For the humid northern part of Switzerland, precipitation is projected to decrease by about 20% during spring/summer until the end of this century. This change in water availability during the vegetation period will have a significant impact on agriculture and ecosystems, as water is a key variable in plant life. On the other hand, plants have developed different strategies to cope with water shortage and it is suggested that plants, such as grasses, do adapt their water uptake by shifting to deeper soil layers. Grasslands are a very abundant agroecosystem across the globe and represent the basis for ruminant husbandry. The productive and agriculturally relevant grasslands of Central Europe depend on sufficient water and typically are dominant in regions with high precipitation, such as northern Switzerland. In respect to climate change, it is therefore important to assess how reduced water availability under summer drought will affect the water uptake of grasslands. Stable water isotopes are a powerful tool to investigate plant water relations, as they allow to trace water without interfering with natural processes. Precipitation and soil water represent the water sources of plants, which are driving the isotopic signal of plant water. Therefore, it is important to understand potential factors influencing the isotopic composition of precipitation and soil water for the interpretation of ecohydrological isotope data. This thesis comprises several experiments to assess water uptake of drought-affected grasslands by means of δ18O and δ2H in plant and soil water. Central part of this thesis was a three-year precipitation manipulation experiment at three different sites in Switzerland. Summer drought was simulated by means of transparent shelters, which were installed between six to twelve weeks per year to reduce the rain input by about 30%.
Using Online and Hands-on Activities to Improve Self-efficacy in Mechanics

Our study is an evaluation of an introductory solid mechanics course incorporating a pedagogical reform using concepts of active-learning. The focus is on exploring if online activities promote better understanding of conceptual fundamental knowledge of introductory solid mechanics, as well as enhance mechanics-concepts self-efficacy for students. Introductory mechanics courses have long been focused on mathematical and theoretical concepts, and traditionally these courses have been shown to be biased toward instructor engagement versus student engagement. Many studies have reported that this bias creates a gap between instructors’ teaching styles and students’ learning styles. Despite this discrepancy, many introductory mechanics courses are still taught passively focusing on note-taking, which is believed to play a role in the contribution of students’ self-efficacy to persist in their engineering studies as well as in their engineering career choices. Self-efficacy, which is a person’s belief in his or her own ability, has been shown to be highly correlated with an individual’s performance level. With high self-efficacy, a student will use more cognitive resources and more effective metacognitive strategies and will more likely select challenging tasks than students with lower self-efficacy. Upon encountering a problem, an individual with higher self-efficacy is often more persistent in problem solving than a student with lower self-efficacy. This paper presents an innovative pedagogical approach to teaching an introductory mechanics course, which included weekly lectures, in-class activities, online activities, and hands-on lab exercises. The goal of this paper is to assess the impact of online activities on students’ self-efficacy of mechanics concepts, as well as to track students’ efficacy levels at various points during the course offering. Several factors, such as students’ gender, major, and the education of their parents associated with sense of selfefficacy in the concepts learned are explored.
Three-dimensional simulation of the Ring effect in observations of scattered sun light using Monte Carlo radiative transfer models

Abstract. We present a new technique for the quantitative simulation of the "Ring effect" for scattered light observations from various platforms and under different atmospheric situations. The method is based on radiative transfer calculations at only one wavelength λ0 in the wavelength range under consideration, and is thus computationally fast. The strength of the Ring effect is calculated from statistical properties of the photon paths for a given situation, which makes Monte Carlo radiative transfer models in particular appropriate. We quantify the Ring effect by the so called rotational Raman scattering probability, the probability that an observed photon has undergone a rotational Raman scattering event. The Raman scattering probability is independent from the spectral resolution of the instrument and can easily be converted into various definitions used to characterise the strength of the Ring effect. We compare the results of our method to the results of previous studies and in general good quantitative agreement is found. In addition to the simulation of the Ring effect, we developed a detailed retrieval strategy for the analysis of the Ring effect based on DOAS retrievals, which allows the precise determination of the strength of the Ring effect for a specific wavelength while using the spectral information within a larger spectral interval around the selected wavelength. Using our technique, we simulated synthetic satellite observation of an atmospheric scenario with a finite cloud illuminated from different sun positions. The strength of the Ring effect depends systematically on the measurement geometry, and is strongest if the satellite points to the side of the cloud which lies in the shadow of the sun.
COMPARISON OF THE CARIES STATUS USING DMFT, ICDAS II AND CAST INDEX SYSTEM.

Dr Ipshita Potlia 1 , Dr P G Naveen Kumar (MDS) 2 , Dr Prashant G M (MDS) 3 , Dr Sushanth V H (MDS) 4 , Dr Mohamed Imranulla (MDS) 4 , Dr Swati Mallick 1 and Dr Rubel M 1 . 1. Public Health Dentistry, College of Dental Sciences, Davangere. 2. HOD and Professor, Public Health Dentistry, College of Dental Sciences, Davangere. 3. Professor, Public Health Dentistry, College of Dental Sciences, Davangere. 4. Reader, Public Health Dentistry, College of Dental Sciences Davangere . Manuscript Info Abstract . Manuscript History
Ethnofiction : genre hybridity in theory and practice-based research

The thesis and the two films form a practice PhD in drama exploring ethnofiction ? anexperimental ethnographic filmmaking approach pioneered by visual anthropologistJean Rouch. In the mid-1950? s Rouch started to experiment with fiction and? projective improvisation? in ethnographic films such as Jaguar (1957-1967), Moi, unnoir (1957) and La pyramide humaine (1959). Film critics would call these films? ethnofictions? After agreeing a story outline, the camera simply follows the subjects? improvisations of their own, and others? , lived experiences. The aim is to showaspects of ethnographic research otherwise hard to represent. A key question of thedoctoral research has been whether a nuanced understanding of foreign cultures canbe created and mediated by combining ethnographic research methods with theprocesses of dramatic work.Even though Rouch made ethnofictions as part of his ethnographic research, heinfused the genre with elements of surrealism and poetry, and often opposed anyonetrying to establish theories about his films. Defying Rouch? s view on this matter, thisthesis explores ethnofiction as an ethnographic filmmaking method by drawing on theexperiences from fieldwork and filmmaking among transgendered Brazilians living inS? o Paulo.The fieldwork resulted in a feature-length ethnofiction and an ethnographicdocumentary short:Transfiction focuses on identity and discrimination in the daily lives of Braziliantravestis and transsexuals. Informed by transgendered artists, prostitutes, healthworkers and political activists, Fabia Mirassos projected her life through the role ofMeg, a transsexual hairdresser confronting intolerance and re-living memories ofabuse. Savana ? Bibi? Meirelles plays Zilda who makes her living as one of the manytransgendered sex workers in S? o Paulo, as she struggles to find her way out ofprostitution.Drama Queens is an ethnographic documentary short and contains four scenes fromthe over 200 hours of rushes that were recorded during the fieldwork. The scenes arefrom S? o Paulo? s annual Pride Parade and present Bibi, Fabia and Phedra who werethe main informants of the research conducted at the theatre Os Satyros in central S? oPaulo.
Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews

These days with TV-shows and starred chefs, new kinds of cuisines appear in the market. The main cuisines like French, Italian, Japanese, Chinese and Indian are always appreciated but they are no longer the most popular. The new trend is the fusion cuisine, which is obtained by combining different main cuisines. The opening of a new restaurant proposing new kinds of cuisine produces a lot of excitement in people. They feel the need to try it and be part of this new culture. Yelp is a platform which publishes crowd sourced reviews about different businesses, in particular, restaurants. For some restaurants in Yelp if the kind of cuisine is available, usually, there is a tag only for the main cuisines, but there is no information for the fusion cuisine. There is a need to develop a system which is able to identify restaurants proposing fusion cuisine (novel or unknown cuisines). This proposal is to address the novelty detection task using Yelp reviews. The idea is that the semi-supervised Machine Learning models trained only on the reviews of restaurants proposing the main cuisine will be able to discriminate between restaurants providing the main cuisine and restaurants providing the novel ones. We propose effective novelty detection approaches for the unknown cuisine type identification problem using Long Short Term Memory (LSTM), autoencoder and Term-Frequency and Inverse Document Frequency(). Our main idea is to obtain features from LSTM, autoencoder and TF-IDF and use these features with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest and One-class Support Vector Machines (SVM) to identify the unknown vii cuisines. We conducted extensive experiments that prove the effectiveness of our approaches. The score that we obtained has a very high discrimination power because the best value of AUROC for the novelty detection problem is 0.85 from LSTM. LSTM outperforms our baseline model of TF-IDF and the main motivation is due to its ability to retain only the useful parts of a sentence.
A QAOA solution to the traveling salesman problem using pyQuil

The traveling salesman problem (TSP) is a famous NP-complete optimization task. In the TSP we are presented a graph of cities with weighted edges that represent the distance between the cities. We are then asked to find the shortest path in the graph that visits all of the cities exactly once. TSP is a classic combinatorial-optimization problem which, in the worst case, must enumerate every possible path and check its value. However, there are many classical heuristics which can do much better than this O(n! ) runtime. Classical algorithms which rely on solving relaxed problems and then polishing for feasible solutions, such as the branch-and-bound algorithm, often achieve far superior performance from the expected runtime of the naive approach [1]. However, in the worst case, these are still O(n! ). Given how closely TSP relates to other combininatorialoptimization problems and its NP-completeness, any method which improves upon the factorial run time would revolutionize the world. Recently, quantum algorithms have offered novel solutions to this canonical problem. Examples include solutions via phase estimation [2] and a Quantum Approximate Optimization Algorithm (QAOA) [3]. Expanding on our Project 2 problem, we implement a QAOA [4] solution in pyQuil [5]. To do so, we defined a Hamiltonian that enforces the constraints of the problem; namely a Hamiltonian that penalizes paths that visit cities more than once, paths that go between cities without edges, and paths that are long. Therefore, by finding the ground state of our Hamiltonian, we can efficiently find a solution to this NP-hard problem. We build on this solution by implementing socalled mixer operators, as described in [3]. TSP is an optimization problem where we are interested in determining a solution within a specified subspace. Within this subspace there are hard constraints that must always hold, such as the cities must have a path connecting them. In addition, there are soft constraints, which refer to constraints we want to optimize over, such as the total path length. Therefore, as Hadfield et al. describe, it is possible to refocus on the unitary operators rather than Hamiltonians to more efficiently implement mixers.
A Coupled A–H Formulation for Magneto-Thermal Transients in High-Temperature Superconducting Magnets

The application of high-temperature superconductors to accelerator magnets for future particle colliders is under study. Numerical methods are crucial for an accurate evaluation of the complex dynamical behavior of the magnets, especially concerning the magnetic field quality and thermal behavior. We present a coupled <inline-formula><tex-math notation="LaTeX">${{\mathbf {A}}}\! -\! {{\mathbf {H}}}$</tex-math></inline-formula> field formulation for the analysis of magneto-thermal transients in accelerator magnets. The magnetic field strength <inline-formula><tex-math notation="LaTeX">${{\mathbf {H}}}$</tex-math></inline-formula> accounts for the eddy current problem in the source regions containing the superconducting domains, while the magnetic vector potential <inline-formula><tex-math notation="LaTeX">${{\mathbf {A}}}$</tex-math></inline-formula> represents the magnetoquasistatic problem in the normal and nonconducting domains. Furthermore, we include a thin-shell approximation for the source regions, making the formulation suitable for large-scale models composed of thousands of tapes. In this article, the relevant equations are derived and discussed, with emphasis on the coupling conditions. The weak formulation is derived, and numerical results are provided in order to both verify the formulation and scale it to the size of an accelerator magnet.
Fault Management in Software-Defined Networking: A Survey

Software-defined networking (SDN) has emerged as a new network paradigm that promises control/data plane separation and centralized network control. While these features simplify network management and enable innovative networking, they give rise to persistent concerns about reliability. The new paradigm suffers from the disadvantage that various network faults may consistently undermine the reliability of such a network, and such faults are often new and difficult to resolve with existing solutions. To ensure SDN reliability, fault management, which is concerned with detecting, localizing, correcting and preventing faults, has become a key component in SDN networks. Although many SDN fault management solutions have been proposed, we find that they often resolve SDN faults from an incomplete perspective which may result in side effects. More critically, as the SDN paradigm evolves, additional fault types are being exposed. Therefore, comprehensive reviews and constant improvements are required to remain on the leading edge of SDN fault management. In this paper, we present the first comprehensive and systematic survey of SDN faults and related management solutions identified through advancements in both the research community and industry. We apply a systematic classification of SDN faults, compare and analyze existing SDN fault management solutions in the literature, and conduct a gap analysis between solutions developed in an academic research context and practical deployments. The current challenges and emerging trends are also noted as potential future research directions. This paper aims to provide academic researchers and industrial engineers with a comprehensive survey with the hope of advancing SDN and inspiring new solutions.
An off-the-shelf artificial cardiac patch improves cardiac repair after myocardial infarction in rats and pigs

Acellular patches embedded with encapsulated cell-secreted factors improve cardiac repair after acute myocardial infarction in rodents and pigs. Acellular advantage Cell therapy for cardiac remodeling after myocardial infarction is therapeutic, in part, because of paracrine effects. Capitalizing on this, Huang et al. created acellular cardiac patches from decellularized myocardium and encapsulated factors secreted from human cardiac stromal cells. Patches retained potency after cryopreservation and improved cardiac function, reduced infarct size, and increased angiogenesis when applied to rat and pig hearts after myocardial infarction. This cell-free approach can help augment cardiac remodeling. Cell therapy has been a promising strategy for cardiac repair after injury or infarction; however, low retention and engraftment of transplanted cells limit potential therapeutic efficacy. Seeding scaffold material with cells to create cardiac patches that are transplanted onto the surface of the heart can overcome these limitations. However, because patches need to be freshly prepared to maintain cell viability, long-term storage is not feasible and limits clinical applicability. Here, we developed an off-the-shelf therapeutic cardiac patch composed of a decellularized porcine myocardial extracellular matrix scaffold and synthetic cardiac stromal cells (synCSCs) generated by encapsulating secreted factors from isolated human cardiac stromal cells. This fully acellular artificial cardiac patch (artCP) maintained its potency after long-term cryopreservation. In a rat model of acute myocardial infarction, transplantation of the artCP supported cardiac recovery by reducing scarring, promoting angiomyogenesis, and boosting cardiac function. The safety and efficacy of the artCP were further confirmed in a porcine model of myocardial infarction. The artCP is a clinically feasible, easy-to-store, and cell-free alternative to myocardial repair using cell-based cardiac patches.
The Statistical Analysis of Chaparral and Other Plant Communities by Means of Transect Samples

In many field investigations some method of sampling vegetation must be employed to procure data from which can be made computations of the abundance and importance of species. The method should be both accurate and easy to use. The methods generally used in the past have been lacking in one or the other of these characteristics. The easiest and least accurate method of ascertaining the relative importance of the several species in a plant community is by ocular estimation, which is merely making the best guess possible. The quantitative relations among the species are sometimes expressed by assigning them to abundance classes described by such terms as: very rare, rare, infrequent, locally frequent, scarce, sparse, occasional, scattered, common, copious, locally abundant, moderately abundant, abundant, moderately dense, dense and dominant. When these terms are not standardized, the assigning of species to such classes is highly subjective and the method often lacks the exactness necessary for scientific analysis. There are various methods of determining the quantitative relations among the species of a community. Most of these are based on the examination of samples, located either at random or along definite lines through the area studied. The measurements of the vegetation in these samples are tabulated and abundance or other