Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Flare Prediction Based on Distinctions between Flaring and Non-flaring Active Region Spectra

With machine learning entering into the awareness of the heliophysics community, solar flare prediction has become a topic of increased interest. Although machine-learning models have advanced with each successive publication, the input data has remained largely fixed on magnetic features. Despite this increased model complexity, results seem to indicate that photospheric magnetic field data alone may not be a wholly sufficient source of data for flare prediction. For the first time, we have extended the study of flare prediction to spectral data. In this work, we use Deep Neural Networks to monitor the changes of several features derived from the strong resonant Mg II h and k lines observed by the Interface Region Imaging Spectrograph. The features in descending order of predictive capability are: the triplet emission at 2798.77 Å, line core intensity, total continuum emission between the h and k line cores, the k/h ratio, line width, followed by several other line features such as asymmetry and line center. Regions that are about to flare generate spectra that are distinguishable from non-flaring active region spectra. Our algorithm can correctly identify pre-flare spectra approximately 35 minutes before the start of the flare, with an AUC of 86% and an accuracy, precision, and recall of 80%. The accuracy and AUC monotonically increase to 90% and 97%, respectively, as we move closer in time to the start of the flare. Our study indicates that spectral data alone can lead to good predictive models and should be considered an additional source of information alongside photospheric magnetograms.
Effective Sub-clonal Cancer Representation to Predict Tumor Evolution

The majority of cancer treatments end in failure due to Intra-Tumor Heterogeneity (ITH). ITH in cancer is represented by clonal evolution where different sub-clones compete with each other for resources under conditions of Darwinian natural selection. Predicting the growth of these sub-clones within a tumour is among the key challenges of modern cancer research. Predicting tumor behavior enables the creation of risk profiles for patients and the optimisation of their treatment by therapeutically targeting sub-clones more likely to grow. Current research efforts in this space are focused on mathematical modelling of population genetics to quantify the selective advantage of sub-clones, thus enabling predictions of which sub-clones are more likely to grow. These tumor evolution models are based on assumptions which are not valid for real-world tumor micro-environment. Furthermore, these models are often fit on a single instance of a tumor, and hence prediction models cannot be validated. This paper presents an alternative approach for predicting cancer evolution using a data-driven machine learning method. Our proposed method is based on the intuition that if we can capture the true characteristics of sub-clones within a tumor and represent it in the form of features, a sophisticated machine learning algorithm can be trained to predict its behavior. The work presented here provides a novel approach to predicting cancer evolution, utilizing a data-driver approach. We strongly believe that the accumulation of data from microbiologists, oncologists and machine learning researchers could be used to encapsulate the true essence of tumor sub-clones, and can play a vital role in selecting the best cancer treatments for patients.
Tree search algorithms for the Sequential Ordering Problem

We present a study of several generic tree search techniques applied to the Sequential Ordering Problem. This study enables us to propose a simple and competitive tree search algorithm. It consists of an iterative Beam Search algorithm that favors search over inference and integrates dynamic programming inspired cuts. It proves optimality on half of the SOPLIB instances and finds new best known solutions on 6 among 7 open instances of the benchmark in a small amount of time.
A New Corpus for Low-Resourced Sindhi Language with Word Embeddings

Representing words and phrases into dense vectors of real numbers which encode semantic and syntactic properties is a vital constituent in natural language processing (NLP). The success of neural network (NN) models in NLP largely rely on such dense word representations learned on the large unlabeled corpus. Sindhi is one of the rich morphological language, spoken by large population in Pakistan and India lacks corpora which plays an essential role of a test-bed for generating word embeddings and developing language independent NLP systems. In this paper, a large corpus of more than 61 million words is developed for low-resourced Sindhi language for training neural word embeddings. The corpus is acquired from multiple web-resources using web-scrappy. Due to the unavailability of open source preprocessing tools for Sindhi, the prepossessing of such large corpus becomes a challenging problem specially cleaning of noisy data extracted from web resources. Therefore, a preprocessing pipeline is employed for the filtration of noisy text. Afterwards, the cleaned vocabulary is utilized for training Sindhi word embeddings with state-of-the-art GloVe, Skip-Gram (SG), and Continuous Bag of Words (CBoW) word2vec algorithms. The intrinsic evaluation approach of cosine similarity matrix and WordSim-353 are employed for the evaluation of generated Sindhi word embeddings. Moreover, we compare the proposed word embeddings with recently revealed Sindhi fastText (SdfastText) word representations. Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations.
Improved many-body expansions from eigenvector continuation

Quantum many-body theory has witnessed tremendous progress in various fields, ranging from atomic and solid-state physics to quantum chemistry and nuclear structure. Due to the inherent computational burden linked to the ab initio treatment of microscopic fermionic systems, it is desirable to obtain accurate results through low-order perturbation theory. In atomic nuclei however, effects such as strong short-range repulsion between nucleons can spoil the convergence of the expansion and make the reliability of perturbation theory unclear. Mathematicians have devised an extensive machinery to overcome the problem of divergent expansions by making use of so-called resummation methods. In large-scale many-body applications such schemes are often of limited use since no a priori analytical knowledge of the expansion is available. We present here eigenvector continuation as an alternative resummation tool that is both efficient and reliable because it is based on robust and simple mathematical principles.
Les stratagèmes du Plasmodium pour se protéger dans l’organisme qu’il envahit

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Les stratagèmes du Plasmodium pour se protéger dans l’organisme qu’il envahit Dominique Labie
Corrigendum - Factors which affects the growth of grain legumes on a solonized brown soil. I. Genotypic responses to soil physical factors

Lupins (Lupinus angustifolius cvv. Yandee and 75A-258 and L. pilosus cv. P. 20957) and pea (Pisum sativum cv. Dundale) were grown in the field for 43 days on a solonized brown soil. Shoots of L. pilosus and peas grew most rapidly, while L. angustifolius cv. 75A-258 developed a relatively large root system. L. angustifolius cv. Yandee, a commercial lupin cultivar, was poorly adapted; shoot growth was restricted and roots ceased growing 36 days after sowing. The soil factors responsible for these widely differing responses were investigated. Once primary roots of L. angustifolius were 20-30 cm deep, root extension was slow or arrested. Indeed, primary root apices of Yandee were often necrotic in the soil below 20 cm. In contrast, roots proliferated rapidly in the surface 20 cm of the soil, particularly in 7SA-258, suggesting that factors in the deeper soil layers restricted root growth most severely. The vigorous growth of lateral roots of 75A-258 was reflected in a 2.6 fold greater total root length than for Yandee 43 days after sowing. Soil physical properties were not considered a likely explanation for these observations; soil water status and porosity were always favourable for root growth and root sections indicated that no cortical degradation, typical of O2 deficient roots, had occurred. Penetrometer resistance and root tip osmotic pressures suggested that poor root growth could not be ascribed simply to soil mechanical properties. The results suggest, by inference, that soil chemical factors could underlie the phenotypic responses observed.
Seasonal phytoplankton response to increased temperature and phosphorus inputs in a freshwater coastal lagoon, Southern Brazil: a microcosm bioassay

AIM: the present study aimed at assessing the response of phytoplankton biomass (as chlorophyll-a) to simulated conditions of increased water temperature and phosphorus (P) inputs in Peri lagoon, a subtropical coastal lagoon dominated by Cylindrospermopsis raciborskii most of the year; METHODS: microcosm experiments were conducted in thermostatic light chambers during 3 and 7 days in austral summer, autumn and winter 2009. Control temperatures were tested against 3 and 5 °C rises in water temperature in each season. In each temperature treatment, three P concentrations were tested: control (non-enriched, ~1.5 μg.L-1 PO43+) and enrichments of four (+6 μg.L-1 PO43+) and eight (+12 μg.L-1 PO43+) times natural concentrations; RESULTS: the results showed that P enrichments alone did not increase chlorophyll-a concentrations, but temperature increases significantly elevated phytoplankton biomass in autumn and winter microcosms. Water temperature increases were followed by significant elevations in the oxygen saturation levels in all microcosms and seasons. The combined effect of increased temperature and P enrichments resulted in the highest chlorophyll-a levels also in autumn and winter. Summer microcosms seem to have been negatively affected by the experimental conditions (too high water temperatures); CONCLUSIONS: the bioassays showed that global temperature rises can significantly elevate the phytoplankton biomass in Peri coastal lagoon, especially in colder months and if followed by increased P inputs, what can lead to major ecological consequences to the water body and to water supply issues in the region.
Differences in types of artificial cranial deformation are related to differences in frequencies of cranial and oral health markers in pre-Columbian skulls from Peru

Artificial cranial deformation is a cultural practice that modifies the shape of the skull during the early infancy. It is not related to rites of passage, but to different social status in a group. Therefore, the deformed cranium is an expression of individual affirmation and affiliation to a given social group. Osteological material from Pasamayo (AD 1200-1450), a cemetery in central coast of Peru, was analyzed to test whether individuals presenting different types of cranial deformation (interpreted as a sign of different social status) present differences in health status. Three types of cranial deformation were observed and five osteological markers (cribra orbitalia, cranial trauma, antemortem tooth loss, dental caries, and periodontal cavities) related to health status were analyzed in 78 crania. No significant differences were found in terms of these osteological markers among females in relation to the different types of cranial deformation. However, males presenting occipital deformation had significantly less caries and periodontal cavities than the others. Moreover, males presenting fronto-lambdoid deformation had more antemortem tooth loss than the other males. Therefore, although different types of cranial deformation can be potentially associated to distinct social status, differences in health status could only be observed in the male sample.
On the Importance of Opponent Modeling in Auction Markets

The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions. Decision-makers would rather not ignore the impact of other participants on these dynamics and should employ tools and models that take this into account. To this end, we demonstrate the efficacy of applying opponent-modeling in a number of simulated market settings. While our simulations are simplified representations of actual market dynamics, they provide an idealized "playground" in which our techniques can be demonstrated and tested. We present this work with the aim that our techniques could be refined and, with some effort, scaled up to the full complexity of real-world market scenarios. We hope that the results presented encourage practitioners to adopt opponent-modeling methods and apply them online systems, in order to enable not only reactive but also proactive decisions to be made.
Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers.
Land use influence on eutrophication-related water variables: case study of tropical rivers with different degrees of anthropogenic interference

Aim: The importance of land use patterns on the occurrence and magnitude of eutrophication-related variables (total phosphorus, total nitrogen, chlorophyll-a and total suspended solids) was assessed for three tropical rivers located in the same watershed (Ribeira do Iguape watershed, Sao Paulo State, Brazil); Methods: To obtain the percentages of land use by sub-basin, thematic maps were carefully analyzed and a Planimeter was used. Land use patterns were classified as: forest, agriculture, pasture, water, urban area, grassland and others (e.g. clouds and shadows). Samples for determination of Total Phosphorus (TP), Total Nitrogen (TN), Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS) were collected from the three Brazilian rivers; Results: Canha River, more preserved (about 70% of its catchment area is covered by forests), presented some sites characterized as oligotrophic and others as mesotrophic. Pariquera-Acu River in turn showed great oscillation in TSI (Trophic State Index) and Jacupiranguinha River was the most influenced by the eutrophication (TSI results were characteristic of hypereutrophic systems). In this river, the concentrations of total phosphorus reached 124 mg.L–1 in the sample site that reflects the impacts suffered by the aquatic environment due to the discharge of an industrial effluent; Conclusions: The results suggested that the trophic level of the rivers was associated with land use found in their drainage areas and with the specific characteristics of each lotic system, such as presence or absence of riparian vegetation, for instance. It is expected that these results contribute to the advancement of Limnology and serve as stimulus and basis for the beginning of plans and programs to revitalize the studied aquatic ecosystems of Baixo Ribeira do Iguape watershed (SP, Brazil).
Does the Environment Exacerbate Effects of Stress on Black Women’s Food Choices?

Diet and obesity are major risk factors for a large number of non-communicable diseases, for which black women experience disproportionately high morbidity and mortality. Psychosocial stress and the food environment have emerged as two potentially important influences on black women’s food choices. The combination of stress and unsupportive environments may be particularly deleterious to black women’s health. It is possible that black women living in environments with more unhealthy foods and fewer healthy options are more likely to eat energy-dense, nutrient poor foods such as snack foods when exposed to stressors than women who are exposed to stressors but live in environments with more healthy foods. Results of studies testing this hypothesis in the U.S. to date are mixed. To reduce diet- and obesity-related non-communicable diseases, interventions to promote healthy food choices are needed at multiple levels. This includes policies to make the healthy choice the easy food choice and reduce sources of stress, and interventions to help black women cope with stress in their lives. Rwanda and Sub-Saharan Africa have the opportunity to engage in such strategies to promote healthy eating and thus curb the rise in obesity and non-communicable diseases in black women. Keywords: black women, USA, stress, environment, diet, obesity, non-communicable disease
THIXOTROPIC CHANGES IN HUMAN MUSCLE STIFFNESS AND THE EFFECTS OF FATIGUE

Two methods have been used to study the stiffness of the relaxed finger musculature after movement followed by various times at rest. The muscles stiffen considerably as the time at rest increases. The time course of this change has been plotted; it continues at a declining rate for at least 30 min. The increased stiffness after resting can be immediately reduced by active or passive movements but not by isometric efforts. These changes characterize a thixotropic system and suggest a long-term molecular rearrangement in relaxed muscle. Extensive eccentric exercise of the muscles under investigation reduces the overall stiffness and there is a concomitant increase in tremor on movement.
C677T Polymorphism of the Methylenetetrahydrofolate Reductase gene as a possible maternal risk factor for the presentation of Down syndrome in the population of Guayaquil

Aim: to assess the presence of the C677T polymorphism of the MTHFR gene as a possible maternal risk factor for Down syndrome in the population of Guayaquil. Methodology: a case study and control research involving 51 mothers of boys and girls with Down syndrome and 52 women who delivered a healthy offspring of either sex in their last pregnancy. Venous blood samples were collected in the months of December 2010 and May 2011 after that, a DNA extraction and genotyping of the C667T poly morphism of the MTHFR gene were performed. Pearson’s Chi-squared test (X2), and odds Ratio (OR) with confidence interval of 95% (95% CI) were used for the statistical analysis. Values of p<0.05 were considered statistically significant. Results: 51 women mothers of sons / daughters with Down syndrome and 52 control mothers were included. The T-allele frequency was higher in the cases (X²=0.944, p:0.331). In the simultaneous analysis of heterozygous and homozygous genotype variant of MTHFR 667 (CT and TT) in the case and control groups, no risk increase for the syndrome (OR=0.87 [IC 95% 0.34 – 2.20]; p=0.772) was observed. Conclusion: no relationship was found between the presence of MTHFR C677T polymorphism and increased maternal risk for Down syndrome. It is necessary to perform further research on the general Ecuadorian population to determine the association of various polymorphisms in different genes.
Relaxin Treatment Reverses Insulin Resistance in High Fat-Fed Mice Running title: Relaxin Reverses Insulin Resistance

The endogenous hormone relaxin increases vascular reactivity and angiogenesis. We demonstrate that acute relaxin infusion in lean C57BL/6J mice enhances skeletal muscle perfusion and augments muscle glucose uptake during a hyperinsulinemic-euglycemic clamp. However, acute effect was absent in mice fed a high fat (HF) diet for 13 weeks. In contrast, mice fed a HF diet for 13 weeks and continuously treated with relaxin for the final 3 weeks of the diet exhibit decreased fasting glucose. Insulin-stimulated whole-body glucose disappearance and percent suppression of hepatic glucose production are corrected by chronic relaxin. The increase in peripheral glucose utilization is a result of augmented in vivo skeletal muscle glucose uptake. Relaxin intervention improves endothelial-dependent vascular reactivity and induces a 2-fold proliferation in skeletal muscle capillarity. The metabolic effects of the treatment are not attributed to changes in myocellular insulin signaling. Relaxin intervention reverses the accumulation of collagen-III in the liver and collagen-III and -IV in the heart induced by high fat-feeding. These studies show the potential of relaxin in the treatment of diet-induced insulin resistance and vascular dysfunction. Relaxin provides a novel therapeutic approach targeting the extramyocellular barriers to insulin action, which are critical to the pathogenesis of insulin resistance. Page 2 of 35 Diabetes