Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Succinate on Manganese Toxicity in Pea Plants

ABSTRACT Pea (Pisum sativum cv. Citrine) plants were grown in nutrient solution containing various manganese (Mn) concentrations in the presence or absence of succinate to evaluate the potential role of succinate in the plant tolerance to Mn excess. Supplying pea plants with excess Mn led to a reduction in the relative growth rate (RGR), chlorophyll a and b content, photosynthetic O2 evolution activity, and photosystem II (PSII) activity, as measured in the light-adapted state (φPSII) in comparison to the control. The primary photochemical efficiency of PSII, estimated by the Fv/Fm ratio, was less affected by increasing Mn concentration. Chloroplasts from Mn-treated leaves exhibited significant changes in their ultrastructure, depending on the strength of Mn toxicity. The concentration of Mn in roots, stem, and leaves increased with the increase of Mn in the nutrient solution. Addition of succinate before and after Mn treatment did not reduce the inhibitory effect of Mn on the plant growth, chlorophyll fluorescence parameters, photosynthetic O2 evolution activity, and chloroplast structure of the pea plants. It was found that supply of exogenous succinate at a high Mn concentration (over 1500 μM) in the nutrient solution led to an increase of Mn uptake in the roots accompanied by a decrease in a Mn translocation to the leaves and stems compared to Mn-treated pea-plants. However, differences in the toxicity effect of Mn in both Mn and Mn/Succinate-treated pea plants were not detected. Thus, such changes in Mn distribution within the Mn/succinate-treated plant did not confer tolerance of Mn excess to pea plants. These results suggest that succinate probably has an affinity for Mn and may function as a “terminal acceptor” of large amounts of Mn, decreasing Mn transport to the stem and leaves, but does not contribute to Mn tolerance.
STUDY ON THE PERCOLATION THRESHOLD of CONDUCTIVE HYBRID YARN

The aim of the project is to produce conductive hybrid yarns, in which conductive fibers are used as the sheath along with polyester fibers and Glass/PEEK filament yarn as the core. To produce the conductive hybrid yarn, at first blended sliver is produced in a conventional draw frame by mixing steel and polyester sliver. Then the hybrid yarns are produced by DREF 2000 friction spinning technique using the blended sliver and Glass/PEEK filament yarn. It is observed that the electrical properties of hybrid yarns are changed remarkably if the amount of sheath fibers is decreased or increased. The resistance of these yarns increases with the increase of length and decrease with the increase of the amount of yarn sheath. The relationship is nearly linear. It is also observed that the resistance of these yarns also depends on the core filament of the yarn. The characteristics of electrical resistance property of hybrid yarns are also explained from the view point of percolation threshold.
Assessing the Variation of Visual Complexity in Multi-Scale Maps with Clutter Measures

Mapping applications, where a multi-scale navigation is available, display multi-scale maps, i.e. a set of maps at different scales. Across scales, the map levels (Fig. 1) can present large differences in terms of representation, due to cartographic generalisation. In our research project [2], we assume that adding intermediate representations between existing map levels could be one way to reduce these differences and to enable smooth transitions while zooming.
Evaluation of the role hydroxyl radicals and iron play in hydrogen peroxide killing of Xanthomonas campestris pv. phaseoli

Hydroxyl radical, a product of H2O2 metabolism in the Fenton reaction, is a major reactive oxygen species involved in H2O2 killing of Xanthomonas campestris pv. phaseoli (Xp). Compounds which absorbed hydroxyl radicals protected Xanthomonas from H2O2 killing, but not from killing by a superoxide generator or an organic peroxide. Addition of iron potentiated H2O2 killing. On the other hand, pre-treatment of Xp with an iron chelator showed no protective effects and H2O2 killing was actually enhanced. Unexpectedly, resuspension of Xp in either water or phosphate buffer induced high-level resistance to H2O2 killing. The unknown protection mechanism was independent of new protein synthesis.
Learned Index Structures: An Evalution of their Performance in Key-Value Storage Solutions

Traditional index structures like B-Trees and hash tables have reached the peaks of their performance. Their existing implementations are highly tuned to use the hardware as efficiently as possible. However, they are static solutions and do not exploit existing inner structures in the indexed data which would offer another source for further performance increases. Additionally, some of their underlying techniques are fundamentally flawed. For instance, the hash function in a hash table that hashes keys to uniformly distributed random numbers will always inevitably cause a collision rate of approximately 36% due to statistic phenomena. Thereby, creating a demand for better solutions. One of those solutions could be learned index structures. The traditional index structures are in the wide sense just models taking a key and predicting an index position. If the resulting array is sorted, the index is effectively modeling a cumulative distribution function. If this distribution can be learned by a machine learning model like a neural network, the potential to reduce collisions further is given. In this thesis, an in-memory key-value store named HTableDB was implemented. It is able to use learned index models as well as regular hash functions for indexing and thus offers an easy way to compare the two. Using HTableDB this comparison was made for keys consisting of various file paths taken from a scientific computing environment. The learned index model used was a two-stage feed-forward network. For datasets with keys that belong to a cluster of similar ones, like the ImageNet dataset, the learned index models showed a collision rate of 43% while being approximately 45 times slower than regular hash functions. However, more sophisticated network architectures could lower the collision rate of learned index models in the future.
LDec: One Pass Time Synchronous Decoder

The search for the most probable word sequence is in automatic speech recognition called decoding and is usually carried out by the Viterbi algorithm, an efﬁcient search strategy based on dynamic programming. The paper discusses implementation issues and methods to further reduce computational costs when performing recognition with large vocabularies and stochastic language models. Such methods can be divided into two categories: those that eliminate unnecessary computations (such as tree structured lexicons) and those that exclude computations according to the probability that those computations will lead to the desired result (pruning).
Physical localization of yeast CYS3, a gene whose product resembles the rat γ‐cystathionase and Escherichia coli cystathionine γ‐synthase enzymes

We have cloned, sequenced and physically mapped the CYS3 gene of Saccharomyces cerevisiae. This gene can complement the cys3–1 allele, and disruptions at this locus lead to cysteine auxotrophy. The predicted CYS3 product is closely related (46% identical) to the rat cystathionine γ‐lyase (Erickson et al., 1990), but differs in lacking cysteine residues. These results provide further evidence that the S288C strain of yeast resembles mammals in synthesizing cysteine solely via a trans‐sulfuration pathway. The CYS3 product was found to have strong homology to three other enzymes involved in cysteine metabolism: the Escherichia coli metB and metC products and the S. cerevisiae MET25 gene product. The trans‐sulfuration enzymes appears to form a diverged family and carry out related functions from bacteria to mammals.
Numerical Simulation of Dynamic Pressure and Kinetic Energy Fields of Turbulent Oil Flow in Staggered Baffled Pipes

1 Unit of Research on Materials and Renewable Energies, Department of Physics, Faculty of Sciences, Abou Bekr Belkaid University, P.O. Box 119-13000-Tlemcen, Algeria 2 Mechanical Engineering Department, Prince Sultan Endowment for Energy and Environment, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia 3 Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, Parma 43124, Italy 4 Department of Technology, University Centre of Naama (Ctr Univ Naama), PO. Box 66, 45000 Naama, Algeria 5 Department of Physics, University of M’sila, M’sila 28000, Algeria
Effects of Temperature on Photosynthesis by Maize and Wheat

Maize and wheat plants were grown in controlled environments with day temperatures of 13, 18, 23, or 28 °C. Leaves from maize grown at 23 °C photosynthesized faster than leaves from maize grown at 13 or 18 °C and, except when measured at 28 °C, faster than leaves from maize grownat28 °C; leaves of maize grown at 13 °C were yellow and photosynthesized at insignificant rates. Leaves from wheat grown at 18 ° or 13 °C had faster rates of photosynthesis than leaves from wheat grown at 23 or 28 °C. The best rates for maize were faster than the best rates for wheat when the measurements were made at 23 or 28 °C but at 13 or 18 °C the best rates for maize were not significantly better than the best rates for wheat. Leaves of maize that developed in the environment with 23 °C as the day temperature did not rapidly lose their green colour when transferred to the environment with the day temperature of 13 °C and the rate of photo synthesis of these leaves did not decline rapidly. However, new leaves expanding in the cooler conditions were yellow and not effective in photosynthesis. At 13 or 18 °C maize, a C4 plant, which photorespires slowly, did not photosynthesize more effectively than wheat, which photo respires rapidly. The maize did not produce its most effective leaves at 13 or 18 °C and its optimum temperature for photosynthesis was 23°C or higher. It may therefore be considered ill-adapted to the temperate climate.
MOLECULAR BASIS OF NEONATAL DIABETES MELLITUS

Neonatal diabetes mellitus (NDM) is one of the rarest forms of the diabetes, which can be characterized by the hyperglycaemic condition. This is usually witnessed at the early stages of life. This condition can be categorized into two types namely transient neonatal diabetes mellitus (TNDM) or permanent neonatal diabetes mellitus (PNDM). transient neonatal diabetes mellitus (TNDM) is medical condition which can be treated within few months of the onset of the disease where as permanent neonatal diabetes mellitus (PNDM) will persist life long and intense care should be given to treat and manage the condition. The root cause of the Neonatal diabetes mellitus (NDM) was unclear until the recent development in the field of molecular and genetic diagnosis techniques which illuminated the molecular basis of the disease. The primary objective of this study is to get an overview of the molecular basis of the Neonatal diabetes mellitus (NDM) and to understand the diagnosis, treatment & management of this medical condition and to spread awareness. keywords: NDM, TNDM, PNDM
Effect of CO2 Concentration on Growth, Carbon Distribution and Loss of Carbon from the Roots of Maize

The effects of three ranges of C02 concentration on growth, carbon distribution and loss of carbon from the roots of maize grown for 14 d and 28 d with shoots in constant specific activity 14C02 are described. Increasing concentrations of C02 led to enhancement of plant growth with the relative growth rate (RGR) of the roots affected more than the RGR of the shoots. Between 16% and 21% of total net fixed carbon (defined as 14C retained in the plant plus 14C lost from the root) was lost from the roots at all C02 concentrations at all times but the amounts of carbon lost per unit weight of plant decreased with time. Possible mechanisms to account for these observations are discussed.
Generalizing the altimetric information of the Topographic Database of Catalonia at 1:5,000: classification and selection of break lines

The Topographic Database at 1:5000 scale (BT-5M) is the most detailed database that covers Catalonia. During the steroplotting process all the vector objects required to generate a digital terrain model (DTM) and a digital surface model (DSM) in triangle format, 1.5 meter accuracy, are also compiled. Mainly the DTM is used for deriving contour lines and shaded relief and the DSM for rectifying orthophotoimages and generating city models. The use of the altimetric information of the BT-5M to derive elevation models, originally designed for scales close to 1:5000, has been extended to smaller scales. The high level of detail and the huge volume of data, especially in the case of the break lines, can produce problems, as costly processes during orthophoto rectification, which should be solved applying terrain generalization processes. These generalization processes must be focussed more in the geomorphologic classification than in the pure geometric simplification. The usefulness of the geomorphometrical studies to the cartographic generalization methods applied to the terrain, has been previously demonstrate in applications focused on the spot height selection (Palomar and Pardo, 2008; Baella et al. ̧ 2007) and also in several test developed for digital elevation models (DEM) generalization (Palomar et al., 2005).
Ferric Reducing and Radical Scavenging Activities of Selected Important Polyphenols Present In Foods

In the present study, the antioxidant and free radical scavenging activities of five major polyphenolic compounds (resveratrol, quercetin, myricetin, catechin, gallic acid) were compared by performing FRAP and DPPH radical scavenging assays followed by their reaction kinetics. Results obtained for the conditions used in this study demonstrated that the quercetin showed the highest ferric reducing ability, while gallic acid was the strongest radical scavenger. Resveratrol exhibited the lowest antioxidant and radical scavenger among the polyphenols assayed. To the best of our knowledge, this is the first comparative report on these polyphenols.
First Thoughts for the Orchestration of Generalisation Methods on Heterogeneous Landscapes

Cartographic generalisation is a process that seeks to summarise and characterise geographical information from a geo-database in order to produce a less detailed and readable map. First, research on generalisation dealt with algorithms and measures that are small bricks necessary to build an automatic generalisation process. Then, research in generalisation focused on generalisation models that aim at knowing when, where and how [McMaster & Shea 1988] to automatically trigger the generalisation algorithms. Such models are able to produce correct automatic results on given relevance domains i.e. on particular data, target scales or landscapes. Nevertheless, the existing models all have limitations that prevent them from being the solution to provide complete automatic cartographic generalisation. No model has been able yet to automatically generalise all themes of a map jointly, particularly with heterogeneous landscapes. It seems unlikely that a new generalisation model may be developed in order to solve this problem. However, the existing generalisation models and processes seem sufficient to locally solve the different issues raised by generalisation. Hence, we believe that it is interesting to find a way to automatically orchestrate some of the existing models in order to apply them on the relevant situations. The core issue of our work is the design of a generic model that would orchestrate generalisation models on relevant geographic spaces. It should be able to integrate any model in its orchestration process.
Identifying Bottlenecks in a Visualization Platform for Tracing Adaptation Decisions

Measuring the performance of diﬀerent operations as part of a software system can help to identify performance bottlenecks. This information can be used to improve the performance of the monitored system. In previous work, we proposed a visualization platform for observing and understanding adaptation decisions in self-adaptive systems. In this paper, we apply Kieker as a monitoring solution to measure the runtime of diﬀerent operations based on the granularity of methods and tasks. As a result, we identiﬁed possibilities for improvements in our implementation.
Photodegradation and Adsorption of Methyl Orange and Methylene Blue Dyes on TiO2

Dyes are extensively used in the textile industry and a considerable amount of water is consumed during dyeing and finishing operations. These wastewaters are discharged in rivers or public sewerage treatment plants and are highly coloured and contaminated. This creates a major environmental problem primarily due to substantial amounts of unfixed dyes released in wastewater. These dyes are characterized by low biodegrability which makes them a source of pollution. In this research work, using two model dyes (Methyl Orange and Methylene Blue) the possibility of degrading the dye wastewaters is investigated with the objective of their effective removal from wastewaters. Photocatalytic degradation was used for dye removal. TiO2 was used as a photocatalyst. As a source of UV light, UV tubes were used and a UV/Vis spectrophotometer was used to monitor the changes in dye concentrations. From the changes in concentration during irradiation, the rate of degradation was determined. After irradiation, the amount of Methyl Orange adsorbed on the surface of TiO2 catalyst was determined for the various concentrations. The results showed that TiO2 photocatalyst shows a high activity for (Methyl Orange /Methylene Blue) degradation under UV irradiation. Amount of adsorbed Methyl Orange increases with dye concentration and for concentrations higher than 1 × 10 M it increases only slightly. It means that there is a direct relation between amount adsorbed and degradation rate and thus Photodegradation of dyes using TiO2 can be used for treatment of colored industrial effluents containing dyes. In place where we have plenty of sunlight solar radiation can be used for degradation.
Integration and Performances of Spark on a PBS-based HPC Environment

In this technical report we analyse a methodology to integrate the Apache Spark Framework into a TORQUE HPC batch environment. We will show the pro and cons to integrate SPARK within TORQUE, underlining the compatibility issues and showing how to overcome the most common problems. We will also give the main technical details about the use of the aforementioned architecture. In the experimental assessment we also take into account the BLAS library advantages and we describe how to install/configure them properly to correctly run within the architecture. Finally, we will show the empirical results using a very challenge use case, namely the realization of a document search engine using the whole PubMed literature, aiming at the evaluation of the performances of this kind of architecture.
0-1 Semideﬁnite Programming for Spectral Clustering: Modeling and Approximation

In the past few years, spectral clustering has caught much attention in the machine learning community and numerous algorithms have been proposed and well-studied. In this paper, we present a uniﬁed framework for these methods based on a new optimization model: 0-1 semideﬁnite programming (0-1 SDP). We also show how the so-called balanced clustering can be embedded into the new 0-1 SDP model. Secondly, we consider the issue of how to solve the underlying 0-1 SDP problem. We consider two approximation methods based on principal component analysis (PCA) and projected PCA, respectively and prove that both algorithms can provide a 2-approximation to the original clustering problem. The complexity of these approximation algorithms are also discussed.
Rule-Based Normalization of German Twitter Messages

In this article, we conduct quantitative and qualitative analyses of unknown words in German Twitter messages, and propose a normalization method which prepares German tweets for standard text processing tools. In the ﬁrst part, the prevalence of diﬀerent types of out-of-vocabulary (OOV) tokens and non-standard language in German Twitter data is determined. In a second step, we present a set of ad-hoc techniques which can tackle some of the most prominent eﬀects found during the analysis. We show how this set of techniques helps us lower the average rate of out-of-vocabulary tokens in Twitter messages and how this lower OOV-rate in turn helps improve the quality of automatic part-of-speech tagging.
Comparative Analysis of Key Inference Models for Musical Metacreation

Creative musical systems must be equipped with certain intelligent abilities to understand fundamental aspects of music, particularly in order to autonomously interact with other creative agents. Key inference, a relatively simple task for trained human experts, is one such intelligent ability required to normalize and analyze melodic compositions. We assess the accuracy of several traditional and machine learning approaches to the key and key signature inference problems on a dataset of 480 melodies in MIDI format. We compare these accuracies with those of trained human musicians on the same tasks. We evaluate the impact of including note duration and note repetition as learning features. We ﬁnd machine learning approaches outperform traditional key inference methods. The highest accuracies (0.729 for key inference and 0.896 for key signature inference) were achieved using a 4-gram language model. Including note duration improved the results of traditional approaches when inferring key, but had the opposite effect for key signature inference. Our ﬁndings suggest that the key of a melodic passage depends more heavily on the sequence of the notes rather than their frequency or distribution.
Parameter Estimation in Potts Models as an Instance of Probabilistic Programming via Imperatively Defined Factor Graphs

To gain insights about the physical structure of protein domains, a Potts model is trained to represent pairwise statistical dependencies between amino-acids. For the first time, this Direct Coupling Analysis is performed within the framework of Imperatively Defined Factor Graphs. This enables the usage of the probabilistic programming framework Factorie. After laying out the problems of a maximum-likelihood based approach, the training is performed using Contrastive Divergence. The Contrastive Divergence gradients are used with the AdaGrad Stochastic Gradient Descent algorithm. To induce sparsity on the pairwise parameter estimate l1-regularization performed via Regularized Dual Averaging. The results are compared to a näıve mutual information based analysis and evaluated against radio-crystallography data. The code can be found under: https://github.com/ahane/proteinshake Zusammenfassung In dieser Arbeit wird ein Pottsmodell verwendet, um Vorhersagen über die dreidimensionale Struktur eines Proteins zu treffen. Hierfür wird das Model an mehrere verschiedene Proteinsequenzen (Multiple Sequence Alingments) aus verschiedenen Organismen angepasst, um die Norm der so gefundenen Parameter als Schätzer für die physische Distanz der einzelnen Aminosäuren im Protein zu nehmen. Dieses Verfahren ist unter dem Namen Direkte Kupplungs Analyse (Direct Coupling Analysis) bekannt. Entscheidend für die Eignung der Pottsmodellparameter zur Modellierung von Aminosäuren-Interaktionen, ist das Einführen eines Strafterms auf die `1-Norm der Parameter (`1-Regularization) während des Anpassungsprozesses. Traditionelle Methoden der Modellanpassung versagen bei dem verwendeten Pottsmodell, weswegen der kontrastive Divergenz (Contrastiv Divergence) Algorithmus verwendet wird. Die Ergebnisse werden mit einem naiven Transinformationsschätzer verglichen und durch Radiokristallographiemessdaten auf ihr Korrektheit überprüft.