nils-herrmann/clean_scirepeval huggingface dataset:
Desenvolvimento sustentavel no Brasil: uma analise textual dos foruns de discussao
Year: 2021

The imbalance of the relationship between the human being and the environment has generated worldwide discussions about the future of the planet, giving rise to several global discussion forums since the 1970s. Under the coordination of the United Nations (UN) discussions on the subject have progressed and, based on the Millennium Development Goals (MDG) have given rise to the Sustainable Development Goals (SDG) In a conjuncture with a view to the 2030s, this research aims to investigate whether the Brazilian context, especially concerning the country's legislative activity, is in line with the contemporary discourses of Sustainable Development (SD) starting with the mapping of the concept of SD in the world and Brazil, using, also, the Text Mining (TM) method of analysis. This work has an exploratory character and seeks to answer if Brazil is aligned with sustainable development, as the DS is defended in the forums of worldwide discussion, through the analysis of the Brazilian legal framework. The results presented evidences that, legally, Brazil incorporated the advances obtained in the discussion on the theme throughout the different historical moments. This can be seen from the analysis of the Brazilian legal framework, including integrating the Magna Carta as a decisive landmark, characterizing the country's legislation on the subject as one of the most complete in the world.
Hepatitis B cure: From discovery to regulatory approval.
Venue: Journal of hepatology
Year: 2017

The majority of persons currently treated for chronic hepatitis B require long term or lifelong therapy. New inhibitors of hepatitis B virus entry, replication, assembly, or secretion and immune modulatory therapies are in development. The introduction of these novel compounds for chronic hepatitis B necessitates a standardised appraisal of the efficacy and safety of these treatments and definitions of new or additional endpoints to inform clinical trials. To move the field forward and to expedite the pathway from discovery to regulatory approval, a workshop with key stakeholders was held in September 2016 to develop a consensus on treatment endpoints to guide the design of clinical trials aimed at hepatitis B cure. The consensus reached was that a complete sterilising cure, i.e. viral eradication from the host, is unlikely to be feasible. Instead, a functional cure characterised by sustained loss of hepatitis B surface antigen with or without hepatitis B surface antibody seroconversion, which is associated with improved clinical outcomes, in a higher proportion of patients than is currently achieved with existing treatments is a feasible goal. Development of standardised assays for novel biomarkers toward better defining hepatitis B virus cure should occur in parallel with development of novel antiviral and immune modulatory therapies such that approval of new treatments can be linked to the approval of new diagnostic assays used to measure efficacy or to predict response. Combination of antiviral and immune modulatory therapies will likely be needed to achieve functional hepatitis B virus cure. Limited proof of concept monotherapy studies to evaluate safety and antiviral activity should be conducted prior to proceeding to combination therapies. The safety of any new curative therapies will be paramount given the excellent safety of currently approved nucleos(t)ide analogues.
Text analysis for Bengali Text Summarization using Deep Learning
Venue: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
Year: 2019

Text summarization is an approach by which the size of one or more document is shortened and the shorten passage presents the core information of the document. In this modern era of information technology, we are over flooded with online data which raised the necessity of summary of the original text. Many methods have already implemented for English text and the effort for Bengali text are gaining alongside. In this paper, we propose an extractive text summarization technique based on a deep learning model of Recurrent Neural Network (RNN) for single document summary. Our method is to classify the sentences as significant or not for the summary. We have used Long Short Term Memory (LSTM) Gated Recurrent Units (GRU) based RNN. Between them, we found LSTM more promising and we achieved average F1 scores 0.63, 0.59, 0.56 for Rouge 1, Rouge 2 and Rouge 3 in some respects.
Novel disease resistance gene paralogs created by CRISPR/Cas9 in soy
Venue: Plant cell reports
Year: 2021

Key message Novel disease resistance gene paralogues are generated by targeted chromosome cleavage of tandem duplicated NBS LRR gene complexes and subsequent DNA repair in soybean. This study demonstrates accelerated diversification of innate immunity of plants using CRISPR.  Nucleotide binding site leucine rich repeat (NBS LRR) gene families are key components of effector triggered immunity. They are often arranged in tandem duplicated arrays in the genome, a configuration that is conducive to recombinations that will lead to new, chimeric genes. These rearrangements have been recognized as major sources of novel disease resistance phenotypes. Targeted chromosome cleavage by CRISPR/Cas9 can conceivably induce rearrangements and thus emergence of new resistance gene paralogues. Two NBS LRR families of soy have been selected to demonstrate this concept: a four copy family in the Rpp1 region (Rpp1L) and a large, complex locus, Rps1 with 22 copies. Copy number variations suggesting large scale, CRISPR/Cas9 mediated chromosome rearrangements in the Rpp1L and Rps1 complexes were detected in up to 58.8% of progenies of primary transformants using droplet digital PCR. Sequencing confirmed development of novel, chimeric paralogs with intact open reading frames. These novel paralogs may confer new disease resistance specificities. This method to diversify innate immunity of plants by genome editing is readily applicable to other disease resistance genes or other repetitive loci.
A Fortran Keras Deep Learning Bridge for Scientific Computing
Venue: Sci. Program.
Year: 2020

Implementing artificial neural networks is commonly achieved via high level programming languages such as Python and easy to use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran Keras Bridge (FKB) This two way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model's emergent behavior to be assessed, i.e. when fit imperfections are coupled to explicit planetary scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.
Design of manufacturable 3D extremal elastic microstructure
Year: 2014

We present a method to design manufacturable extremal elastic materials. Extremal materials can possess interesting properties such as a negative Poisson's ratio. The effective properties of the obtained microstructures are shown to be close to the theoretical limit given by mathematical bounds, and the deviations are due to the imposed manufacturing constraints. The designs are generated using topology optimization. Due to high resolution and the imposed robustness requirement they are manufacturable without any need for post processing. This has been validated by the manufacturing of an isotropic material with a Poisson's ratio of n 0.5 and a bulk modulus of 0.2% times the solid base material's bulk modulus.
Minimum Near Convex Shape Decomposition
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2013

Shape decomposition is a fundamental problem for part based shape representation. We propose the minimum near convex decomposition (MNCD) to decompose arbitrary shapes into minimum number of "near convex" parts. The near convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of nonintersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via binary integer linear programming. Both theoretical analysis and experiment results show that our approach outperforms the state of the art results without introducing redundant parts and thus leads to robust shape representation.
Beyond Subculture and Post subculture? The Case of Virtual Psytrance
Year: 2006

Recently, there has been an engaging debate in youth studies between the efficacy of subcultural theory versus post subcultural perspectives. While positions have ranged from reinterpretation and alternatives to youth subcultural theory, to a return to some form of subcultural analysis and emphasis on leisure inequalities, equally there have been both critiques and defences of post subcultural and postmodernist perspective emphasising the fragmented and transitory aspects of youth styles. However, much of the debate here has taken place on the terrain of theory and critique/counter critique, rather than in terms of empirical studies. This article presents an exploration of the global subculture of 'virtual psytrancers' (people who visit psytrance dance style discussion forums on the Internet) while also investigating the validity of both subcutural and post subcultural theories. A total of 569 virtual psytrancers from over 40 countries were involved in an Internet based, online questionnaire based study, which assessed the shared attitudes, global spaces and international connections of the group. The findings presented here reject a simple theoretical dichotomy, and challenge the notion that subcultures must be primarily local (rather than global/virtual) and singularly class based, as well as simultaneously refuting postmodern conceptions that global youth styles are, by definition, depthless, transitory and internally fragmented.
The role of religiousness in anxiety, depression, and happiness in a Jewish community sample: A preliminary investigation
Year: 2009

Although social scientists have convincingly demonstrated relationships between religious beliefs/practices and mental health, almost none of the empirical findings or related theory apply specifically to Jews. To address this limitation, we investigated the role of Jewish religiousness in anxiety, depression, and happiness, in a large Jewish community sample (n 565) Several facets of global Jewish religiousness were examined, as well as a theoretically based Jewish religious variable, trust in God. A self report measure of trust in God was created, and factor analyses yielded two reliable and valid subscales: trust in God and mistrust in God. Contrary to our hypotheses, global Jewish religiousness was on the whole unrelated to mental health functioning. As expected, higher levels of trust in God were associated with less anxiety and depression, and greater personal happiness, whereas inverse associations emerged for the unanticipated but robust mistrust subscale.
Intrauterine Growth Retardation Modulates Sirt1 Deacetylase, Foxo3 Transcription Factor, and Superoxide Dismutase 2 Expression in the Postnatal Cerebral Cortex. 441
Year: 2007

Uteroplacental insufficiency and subsequent intrauterine growth retardation (IUGR) alter cerebral postnatal gene expression and increase the risk for neurodevelopmental morbidities. IUGR causes histone hyperacetylatilation in d0 and d21 hippocampus of IUGR male rats. SIRT1, a NAD dependent histone deacetylase, has an important role in chromatin silencing, genome stability, neuronal protection, and longevity. SIRT1deacetylates the FOXO3 transcription factor and as a result prevents FOXO3 mediated apoptosis and stimulates FOXO3 mediated resistance to oxidative stress. Deacetylated FOXO3 up regulates SOD2 expression.  We hypothesize that IUGR decreases postnatal SIRT1 mRNA and protein levels and as a result decreases SOD2 gene expression. In this study, we analyzed SIRT1, FOXO3, and SOD2 mRNA expression and protein levels in cerebral cortex of IUGR rats.  To test our hypothesis, we used term pups from an IUGR model obtained by bilateral uterine artery ligation. Pups from sham surgeries were used as controls. Protein and mRNA levels were measured on d0 and d21 by performing real time RT PCR and Western blot analysis, respectively.  SIRT1 protein levels decreased to 73.33% of controls in IUGR d0 males (p .023) followed by SIRT1 decreased mRNA levels (84% of controls) on d21. This is associated with a significant decrease in SOD2 mRNA levels (p .013, 62.66% of controls) in IUGR d21 brains with a predominant effect in males (p .01, 61.2% of controls) On day 21, there is also a decrease in IUGR FOXO3 mRNA levels (p .012, 86.8% of controls) without a significant difference between genders.  We conclude that IUGR decreases SIRT1 gene expression in male rats, which subsequently decreases SOD2 mRNA levels. This finding may explain the neurologic morbidities and the hippocampal hyperacetylation in d0 and d21 IUGR males, which does not persist in the females. We speculate that alterations in SIRT1 gene expression early in life may contribute to changes in histone acetylation, oxidative stress resistance, and apoptosis that contribute to the long term neurologic morbidities of the IUGR fetus.
Comparative anatomy of the nasal cavity in buffaloes, camels and donkeys.
Year: 2019

The aim of this study was to reveal the comparative anatomy of the nasal cavity in buffaloes, donkeys and camels. It was carried out on 30 heads of apparently healthy adult animals, 10of each species. Heads were fixed and used for gross and cross sectional anatomy. The study provided information about the peculiarities of nasal anatomy in each species. The conchal arrangement was greatly different in camels, while that of buffaloes and donkeys were similar to other ruminants and equine. In camels, the nasal conchae were condensed in the caudal two thirds, the ventral nasal concha was shorter and twisted and the alar and basal folds were raised from a common extension. Camels have a vestibular pouch known as lateral nasal diverticulum. It was a cylindrical tube anatomically different from the nasal diverticulum of donkeys and other equine. The cartilaginous skeleton of narial aperture was reduced in donkeys and camels. Moreover, the rostral portion of nasal septum in camels was formed of muscles instead of cartilage. In the three species, vomeronasal organ and dorsal and ventral swelling bodies were present on both sides of the nasal septum. However, in camels the vomeronasal organ was notably longer and wider. In conclusion, the anatomy of camel nasal cavity in contrast to other domestic animals was presented interesting anatomical features similar to those of proboscis bearing mammals.
A Novel Solution to the Century Old Light Speed Paradox; Divorce of the Light Postulate from Special Relativity; Relativity of Electromagnetic Fields.
Year: 2013

From elementary algebra and common sense, we know that given that V is different from zero. Yet we have lived with one of the daunting paradoxes in the history of science " for more than one hundred years, where C is the speed of light and V is the velocity of an observer. All known experiments, including those performed to disprove it, confirmed it. Over a period of one hundred years, the scientific community has exhausted on three theories to define and resolve this paradox: the ether theory, the emission theory and special relativity. The former two have long been rejected decisively. The majority of the scientific community assumes that this paradox has already been resolved by special relativity. Yet scientists outside the mainstream thought have always realized that relativity is not a true theory of nature, and thus looking back to the long rejected ether and emission theories. Special relativity has remained counterintuitive since its inception and has resulted in many unsolved paradoxes, creating many more paradoxes than it solved. The scientific community has been stuck in relativity for over a century because of three factors: 1. The lack of any alternative theory that could explain the long standing problems of reference frames and solve the light speed paradox and the apparent success of special relativity in resolving these paradoxes 2. The subtly unquestioned (yet false) bond between special relativity and the light postulate, which made relativity undefeatable 3. And the firm experimental foundation of the light (the second) postulate. Because of the perceived (and stated) link between the light postulate and special relativity, most attempts to disprove relativity focused on disproving the light postulate, and hence failed. No one ever thought of the possibility that the light postulate could be correct and relativity wrong. Therefore, a scientist who disliked Einstein's relativity theory automatically rejected the light postulate. This paper introduces a new way to resolve the light speed paradox and hence divorcing Einstein's light postulate from his theory of special relativity. The light wave contracts towards (or expands away from) the source depending on the relative velocity V of the source and the observer so that the speed of light is always equal to C relative to the observer. The apparent velocity (C' of light relative to the source changes so that the speed of light relative to the observer is always equal to C i.e. C C' V (vector sum/difference) This theory is self evident as it is an immediate consequence of the well established facts: constancy of the speed of light and Doppler effect of light, in which wavelength changes and speed of light remains constant.
Conversion of plastic waste to fuel
Year: 2020

Plastic waste generation has progressively increased over the past 30 years. The recycling techniques of waste plastics presently used include and filling, incineration, chemical or mechanical recycling, and conversion to fuel. There are a number of conversion techniques presently used; they include thermal pyrolysis, catalytic pyrolysis, copyrolysis and gasification. The techniques used depend on types of plastic waste. The fuel oils from waste plastics have properties (calorific valve, density, and viscosity) which are better or equal to that of commercial diesel fuel. The conversion of plastic waste into fuel helps to resolve two important problems: one is avoiding the negative impacts of plastic pollution on the environment and the second is to produce an alternative fuel for diesel engines, thus reducing the demand on fossil fuels.
Enhance Social Recommendation with Adversarial Graph Convolutional Networks
Venue: ArXiv
Year: 2020

Recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attributed to: (1) a majority of users only have a very limited number of neighbors in social networks and can hardly benefit from relations; (2) social relations are noisy but they are often indiscriminately used; (3) social relations are assumed to be universally applicable to multiple scenarios while they are actually multi faceted and show heterogeneous strengths in different scenarios. Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks. In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems. Concretely, for the relation sparsity and noises problems, a GCN based autoencoder is developed to augment the relation data by encoding high order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the original social profile to guarantee the validity of new identified neighborhood. After obtaining enough purified social relations for each user, a GCN based attentive social recommendation module is designed to capture the heterogeneous strengths of social relations. These designs deal with the three problems faced by social recommender systems respectively. Finally, we adopt adversarial training to unify and intensify all components by playing a minimax game and ensure a coordinated effort to enhance social recommendation. Experimental results on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component.
A motor unit based model of muscle fatigue
Venue: PLoS Comput. Biol.
Year: 2017

Muscle fatigue is a temporary decline in the force and power capacity of skeletal muscle resulting from muscle activity. Because control of muscle is realized at the level of the motor unit (MU) it seems important to consider the physiological properties of motor units when attempting to understand and predict muscle fatigue. Therefore, we developed a phenomenological model of motor unit fatigue as a tractable means to predict muscle fatigue for a variety of tasks and to illustrate the individual contractile responses of MUs whose collective action determines the trajectory of changes in muscle force capacity during prolonged activity. An existing MU population model was used to simulate MU firing rates and isometric muscle forces and, to that model, we added fatigue related changes in MU force, contraction time, and firing rate associated with sustained voluntary contractions. The model accurately estimated endurance times for sustained isometric contractions across a wide range of target levels. In addition, simulations were run for situations that have little experimental precedent to demonstrate the potential utility of the model to predict motor unit fatigue for more complicated, real world applications. Moreover, the model provided insight into the complex orchestration of MU force contributions during fatigue, that would be unattainable with current experimental approaches.