nils-herrmann/clean_scirepeval huggingface dataset:
Correlation for the maximum oxygen transfer capacity in shake flasks for a wide range of operating conditions and for different culture media

Characterization of shake flasks is essential to ensure a sufficient oxygen supply for aerobic microorganism. To determine the volumetric mass transfer coefficient k L a or the maximum oxygen transfer capacity OTR max the sulfite system is commonly applied. It has been used for non baffled shake flasks based on the assumption of a proportionality between the k L a of the sulfite system and of a biological culture. This work proves this to be valid only for shaking frequencies above 300 rpm. Below the proportionality factor depends on the shaking frequency. More than 350 experiments with different concentrations of the sulfite system, 16 different media compositions and 10 different microorganisms were evaluated varying the shaking diameter (1.25 10 cm) flask diameter (51 131 mm) filling volume (2 160 mL) and shaking frequency (100 450 rpm) For the first time the properties of the culture media are coherently represented by a term based on the easily accessible osmolality. An universal consistent equation to calculate the OTR max for non baffled shake flasks for an exhaustive range of shaking conditions was developed and validated by experimental data. The deviations are within a remarkable narrow range of 5 mmol L 1 h 1
Easy to use and reliable technique for online dissolved oxygen tension measurement in shake flasks using infrared fluorescent oxygen sensitive nanoparticles
Venue: Microbial Cell Factories

BackgroundDespite the progressive miniaturization of bioreactors for screening purposes, shake flasks are still widespread in biotechnological laboratories and industry as cultivation vessels. Shake flasks are often applied as the first or second step in applications such as strain screening or media optimization. Thus, there are ongoing efforts to develop online measurement techniques for shake flasks, to gain as much information as possible about the cultured microbial system. Since dissolved oxygen tension (DOT) is a key experimental parameter, its accurate determination during the course of experiment is critical. Some of the available DOT measurement techniques can lead to erroneous measurements or are very difficult to handle. A reliable and easy to use DOT measurement system, based on suspended oxygen sensitive nanoparticles, is presented in this work.ResultsIn a cultivation of Kluyveromyces lactis, a new DOT measurement technique via suspended oxygen sensitive nanoparticles was compared with the conventional DOT measurement via fixed sensor spots. These experiments revealed the main disadvantage of applying sensor spots. With further cultivations of Escherichia coli and Hansenula polymorpha, the new measurement technique was successfully validated. In combination with a RAMOS device, kLa values were determined during the presented cultivations. The determined kLa values are in good agreement with a correlation recently found in the literature.ConclusionsThe presented DOT measurement technique via suspended oxygen sensitive nanoparticles in shake flasks turned out to be easy to use, robust and reliable under all applied combinations of shaking frequencies and filling volumes. Its applicability as an online monitoring system for cultivations was shown by means of four examples. Additionally, in combination with a RAMOS device, the possibility of experimental kLa determination was successfully demonstrated.
Molecular weight and viscosifying power of alginates produced in Azotobacter vinelandii cultures in shake flasks under low power input

The aim of this study was to evaluate the viscosifying power and mean molecular weight (MMW) of alginate synthesized by Azotobacter vinelandii in a region of very low power input; as well as to scale up the process of alginate production in a 3 L fermenter using the power input profile (determined in shake flasks) as criterion.  In cultures developed at very low power input (P/V) (0.02 to 0.68 kW m 3) and maximal oxygen transfer rate (OTRmax) from to 0.85 to 2.8 mmol L 1 h 1, both the specific growth rate and alginate production were negatively affected. In contrast, the viscosifying power of the alginate increased significantly, with respect to that obtained at the highest power input (1.6 kW m 3) This behavior was related to the synthesis of alginates with a high MMW (2240 kDa) When profiles of power input determined in shake flasks at very low P/V were reproduced in a stirred fermentor, it was possible to reproduce the same trends in both the alginate production and the polymer viscosifying power.  This study provides useful information in order to implement new scale up strategies for alginate production, exhibiting chemical characteristics and viscosifying properties superior to commercial alginates.
Nitrogen and phosphorus as the factors affecting ginsenoside production in hairy root cultures of Panax quinquefolium cultivated in shake flasks and nutrient sprinkle bioreactor
Venue: Acta Physiologiae Plantarum

The study assesses the influence of different concentrations of nitrogen and phosphorus sources on ginsenoside biosynthesis in Panax quinquefolium hairy roots cultivated in shake flasks and a nutrient sprinkle bioreactor. The saponin content was determined using HPLC. The maximum yield (12.45 mg g 1 dw) of the sum of six examined ginsenosides (Rb1, Rb2, Rc, Rd, Re and Rg1) in hairy roots cultivated in shake flasks was achieved in modified Gamborg B 5 medium containing 0.83 mM l 1 phosphate, 12.4 mM l 1 nitrate and 0.5 mM l 1 ammonium. The yield itself was 1.93 times higher than that achieved in standard Gamborg medium. The modified medium also favourably influenced the biosynthesis of studied saponins in bioreactor cultures. The saponin content (35.11 mg g 1 d.w. was 2.75 times higher than that achieved in control medium.
Parallelised online biomass monitoring in shake flasks enables efficient strain and carbon source dependent growth characterisation of Saccharomycescerevisiae
Venue: Microbial Cell Factories

BackgroundBaker's yeast, Saccharomyces cerevisiae, as one of the most often used workhorses in biotechnology has been developed into a huge family of application optimised strains in the last decades. Increasing numbers of strains render their characterisation highly challenging, even with the simple methods of growth based analytics. Here we present a new sensor system for the automated, non invasive and parallelisable monitoring of biomass in continuously shaken shake flask cultures, called CGQ "cell growth quantifier" The CGQ implements a dynamic approach of backscattered light measurement, allowing for efficient and accurate growth based strain characterisation, as exemplarily demonstrated for the four most commonly used laboratory and industrial yeast strains, BY4741, W303 1A, CEN.PK2 1C and Ethanol Red.ResultsGrowth experiments revealed distinct carbon source utilisation differences between the investigated S. cerevisiae strains. Phenomena such as diauxic shifts, morphological changes and oxygen limitations were clearly observable in the growth curves. A strictly monotonic non linear correlation of OD600 and the CGQ's backscattered light intensities was found, with strain to strain as well as growth phase related differences. The CGQ measurements showed high resolution, sensitivity and smoothness even below an OD600 of 0.2 and were furthermore characterised by low background noise and signal drift in combination with high reproducibility.ConclusionsWith the CGQ, shake flask fermentations can be automatically monitored regarding biomass and growth rates with high resolution and parallelisation. This makes the CGQ a valuable tool for growth based strain characterisation and development. The exceptionally high resolution allows for the identification of distinct metabolic differences and shifts as well as for morphologic changes. Applications that will benefit from that kind of automatized biomass monitoring include, amongst many others, the characterization of deregulated native or integrated heterologous pathways, the fast detection of co fermentation as well as the realisation of rational and growth data driven evolutionary engineering approaches.
Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma
Venue: Cell

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II III IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low to high grade disease. Whole genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH wild type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.
A Survey of event extraction methods from text for decision support systems
Venue: Decis. Support Syst.

Event extraction, a specialized stream of information extraction rooted back into the 1980s, has greatly gained in popularity due to the advent of big data and the developments in the related fields of text mining and natural language processing. However, up to this date, an overview of this particular field remains elusive. Therefore, we give a summarization of event extraction techniques for textual data, distinguishing between data driven, knowledge driven, and hybrid methods, and present a qualitative evaluation of these. Moreover, we discuss common decision support applications of event extraction from text corpora. Last, we elaborate on the evaluation of event extraction systems and identify current research issues. We identify data driven, knowledge driven, and hybrid event extraction approaches.A wide variety of decision support applications can benefit from event extraction.Pressing research issues to be addressed are scalability and domain dependencies.Evaluation with annotated data from standard benchmarks or crowdsourcing is advised.
Overview of Clinical Decision Support Systems

Clinical decision support systems (CDSS) are computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made.With the increased focus on the prevention of medical errors that has occurred since the publication of the landmark Institute of Medicine report, To Err Is Human, computer based physician order entry (CPOE) systems, coupled with CDSS, have been proposed as a key element of systems' approaches to improving patient safety.1, 2, 3, 4 If used properly, CDSS have the potential to change the way medicine has been taught and practiced. This chapter will provide an overview of clinical decision support systems, summarize current data on the use and impact of clinical decision support systems in practice, and will provide guidelines for users to consider as these systems begin to be incorporated in commercial systems, and implemented outside the research and development settings. The other chapters in this book will explore these issues in more depth.
Playing Musical Chairs in Big Data to Reveal Variables' Associations

Testing for associations in big data faces the problem of multiple comparisons, with true signals buried inside the noise of all associations queried. This is particularly true in genetic association studies where a substantial proportion of the variation of human phenotypes is driven by numerous genetic variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. While successful, this approach does not leverage the environmental and genetic factors shared between the multiple phenotypes collected in contemporary cohorts. Here we develop a method that improves the power of detecting associations when a large number of correlated variables have been measured on the same samples. Our analyses over real and simulated data provide direct support that large sets of correlated variables can be leveraged to achieve dramatic increases in statistical power equivalent to a two or even three folds increase in sample size.
Close reading big data: The Echo Nest and the production of (rotten) music metadata
Venue: First Monday

Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web based data. This paper traces the historical development of music metadata management and its ties to the growing of the field of 'big data' knowledge production. In particular, it explores the data catching mechanisms enabled by the Spotify owned company The Echo Nest, and provides a close reading of parts of the company's collection and analysis of data regarding musicians. Doing so, it reveals evidence of the ways in which trivial, random, and unintentional data enters into the data steams that power today's digital music distribution. The presence of such curious data needs to be understood as a central part of contemporary algorithmic knowledge production, and calls for a need to re conceptualize both (digital) musical artifacts and (digital) musical expertize.
Oving K Means Clustering using discretization technique in Network Intrusion Detection System
Venue: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS)

Network Intrusion Detection Systems (NIDSs) have always been designed to enhance and improve the network security issue by detecting, identifying, assessing and reporting any unauthorized and illegal network connections and activities. The purpose of this research is to improve on the existing Anomaly Based Intrusion Detection (ABID) method using K Means clustering technique as to maximize the detection rate and accuracy while minimizing the false alarm. The problem with outliers may disturb the K Means clustering process as it might be avoided in the clustering process from mixing with the normal data that make the NIDSs become less accurate. Thus this research aims to improve the performance of the ABID systems that balance the loss of information or ignored data in clustering. An integrated machine learning algorithm using K Means Clustering with discretization technique and Naive Bayes Classifier (KMC D+NBC) is proposed against ISCX 2012 Intrusion Detection Evaluation Dataset. The outcome depicts that the proposed method generates better detection rate and accuracy up to 99.3% and 99.5% respectively and reduces the false alarm to 1.2% with better efficiency of 0.03 seconds time taken to build model.
Metal Removal and Antimicrobial Properties of Watermelon rind modified with clove

The current rapid development of industrial activity indirectly discharged pollutant into the local water stream. One of the harmful industrial wastes that enter public drainage is heavy metal owing to its toxic, non biodegradable and persistent in nature. Improper treatment of domestic waste also will discharged high amount of microbial. Various types of technology were developed for removing pollutant in wastewater, but most of the technology employed to address on removing organic content in wastewater. Other pollutant namely, heavy metals and microbial indirectly treated at low concentration of pollutant loading. Among various available technologies for water treatment adsorption process is considered a promising technology as compared to other methods because of convenience, easy operational and low cost of treatment. The aim of this study is to investigate potential used of local fruit rind modified with herb as adsorbent material. The rind present strong potential due to its high content of cellulose, pectin, hemicellulose and lignin as active binding sites for metal sorption. Modified rind improves absorption by having anti microbial properties to kill pathogenic organisms. The concentration of heavy metal ions was analysed using ICP MS. E. coli and total colifrm were plated out using chromocult agar. The results showed significant reductions of heavy metals and microbial concentration after wastewater was treated with clove modified rind.
Cardiac stroke volume variability measured non invasively by three methods for detection of central hypovolemia in healthy humans
Venue: European Journal of Applied Physiology

PurposeHypovolemia decreases preload and cardiac stroke volume. Cardiac stroke volume (SV) and its variability (cardiac stroke volume variability, SVV) have been proposed as clinical tools for detection of acute hemorrhage. We compared three non invasive SV measurements and investigated if respiration induced fluctuations in SV may detect mild and moderate hypovolemia in spontaneously breathing humans.MethodsTen healthy subjects underwent experimental central hypovolemia induced by lower body negative pressure to 60 mmHg or onset of presyncopal symptoms. SV beat to beat was estimated simultaneously by ultrasound Doppler, finger arterial blood pressure curve and impedance cardiography. SVV was calculated by spectral analysis between 0.15 and 0.40 Hz.ResultsRelative changes in SV did not show significant differences between the methods. The SVV measured by ultrasound Doppler and arterial blood pressure curve decreased at 30 mmHg to 32 (ultrasound Doppler: 95 CI 18 47, arterial blood pressure curve: 95 CI 21 43) and at maximal simulated hypovolemia to 23 (ultrasound Doppler: 95 CI 14 81) and 21 (arterial blood pressure curve: 95 CI 9 33) of baseline variability. The variability in cardiac stroke volume from the impedance cardiography did not change significantly during the simulated hypovolemia, to 88 and 76 of baseline variability.ConclusionCardiac stroke volume estimated by ultrasound Doppler and by arterial blood pressure curve showed parallel variations beat to beat during simulated hemorrhage, whereas impedance cardiography did not appear to track beat to beat changes in cardiac stroke volume. The variability in cardiac stroke volume was decreased during mild and moderate hypovolemia and could be used for early detection of hypovolemia.
Charge trap memory based on few layer black phosphorus.
Venue: Nanoscale

Atomically thin layered two dimensional materials, including transition metal dichalcogenide (TMDC) and black phosphorus (BP) have been receiving much attention, because of their promising physical properties and potential applications in flexible and transparent electronic devices. Here, for the first time we show nonvolatile charge trap memory devices, based on field effect transistors with large hysteresis, consisting of a few layer black phosphorus channel and a three dimensional (3D) Al2O3/HfO2/Al2O3 charge trap gate stack. An unprecedented memory window exceeding 12 V is observed, due to the extraordinary trapping ability of the high k HfO2. The device shows a high endurance of over 120 cycles and a stable retention of ~30% charge loss after 10 years, even lower than the reported MoS2 flash memory. The high program/erase current ratio, large memory window, stable retention and high on/off current ratio, provide a promising route towards flexible and transparent memory devices utilising atomically thin two dimensional materials. The combination of 2D materials with traditional high k charge trap gate stacks opens up an exciting field of nonvolatile memory devices.
Corporate Governance and Capital Structure Interactions in New Technology Based Firms The Effects of Ownership Structure and Board of Directors on Firm s Leverage

The paper aims to explore the effect of some corporate governance features on the capital structure of NTBFs in term of firm's leverage. It was hypothesized that different ownership and board of directors structures can negatively affect the debt financing choices of this type of firm. Based on a sample of 303 Italian firms' observations over the period 2004 2013, the results suggest that NTBFs with a concentrated ownership denote a reduction of firm's leverage. In addition, also manager shareholders prefer to reduce the level of firms' debt. Nevertheless, the board size seems to have no effects on the capital structure of NTBF's, as well as the presence of independent directors in the board does. The paper offers some contributions on literature and extends the understanding of corporate governance effects on capital structure in NTBFs.
Fast robust monocular depth estimation for Obstacle Detection with fully convolutional networks
Venue: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer enough to allow for safe avoidance and path planning. Current solutions often make assumption on the motion of the vehicle that limit their applicability, or work at very limited ranges due to intrinsic constraints. We propose a novel appearance based Object Detection system that is able to detect obstacles at very long range and at a very high speed 300Hz) without making assumptions on the type of motion. We achieve these results using a Deep Neural Network approach trained on real and synthetic images and trading some depth accuracy for fast, robust and consistent operation. We show how photo realistic synthetic images are able to solve the problem of training set dimension and variety typical of machine learning approaches, and how our system is robust to massive blurring of test images.