User-friendly Setup Research Articles

SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount of data that requires quality assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not be consistent across users and sensors. SentemQC (QA-QC of high temporal resolution sensor data) is a cost-efficient, and open-source Python approach developed to ensure the quality of sensor data by performing data QA and QC on large volumes of high-frequency (HF) sensor data. The SentemQC method is computationally efficient and features a six-step user-friendly setup for anomaly detection. The method marks anomalies in data using five moving windows. These windows connect each data point to neighboring points, including those further away in the moving window. As a result, the method can mark not only individual outliers but also clusters of anomalies. Our analysis shows that the method is robust for detecting anomalies in HF sensor data from multiple water quality sensors measuring nitrate, turbidity, oxygen, and pH. The sensors were installed in three different freshwater ecosystems (two streams and one lake) and experimental lake mesocosms. Sensor data from the stream stations yielded anomaly percentages of 0.1%, 0.1%, and 0.2%, which were lower than the anomaly percentages of 0.5%, 0.6%, and 0.8% for the sensors in Lake and mesocosms, respectively. While the sensors in this study contained relatively few anomalies (<2%), they may represent a best-case scenario in terms of use and maintenance. SentemQC allows the user to include the individual sensor uncertainty/accuracy when performing QA-QC. However, SentemQC cannot function independently. Additional QA-QC steps are crucial, including calibration of the sensor data to correct for zero offsets and implementation of gap-filling methods prior to the use of the sensor data for determination of final real-time concentrations and load calculations.

PDBImages: a command-line tool for automated macromolecular structure visualization.

PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types, and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.

R-package LNIRT for joint modeling of response accuracy and times.

In computer-based testing it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA observations. The information in the RTs can help to improve routine operations in (educational) testing, and provide information about speed of working. In modern applications, the joint models are needed to integrate RT information in a test analysis. The R-package LNIRT supports fitting joint models through a user-friendly setup which only requires specifying RA, RT data, and the total number of Gibbs sampling iterations. More detailed specifications of the analysis are optional. The main results can be reported through the summary functions, but output can also be analysed with Markov chain Monte Carlo (MCMC) output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package is illustrated with two real data applications.

Development of rapid and precise approach for quantification of bacterial taxa correlated with soil health.

The structure and dynamic of soil bacterial community play a crucial role in soil health and plant productivity. However, there is a gap in studying the un-/or reclaimed soil bacteriome and its impact on future plant performance. The 16S metagenomic analysis is expensive and utilize sophisticated pipelines, making it unfavorable for researchers. Here, we aim to perform (1) in silico and in vitro validation of taxon-specific qPCR primer-panel in the detection of the beneficial soil bacterial community, to ensure its specificity and precision, and (2) multidimensional analysis of three soils/locations in Egypt ('Q', 'B', and 'G' soils) in terms of their physicochemical properties, bacteriome composition, and wheat productivity as a model crop. The in silico results disclosed that almost all tested primers showed high specificity and precision toward the target taxa. Among 17 measured soil properties, the electrical conductivity (EC) value (up to 5 dS/m) of 'Q' soil provided an efficient indicator for soil health among the tested soils. The 16S NGS analysis showed that the soil bacteriome significantly drives future plant performance, especially the abundance of Proteobacteria and Actinobacteria as key indicators. The functional prediction analysis results disclosed a high percentage of N-fixing bacterial taxa in 'Q' soil compared to other soils, which reflects their positive impact on wheat productivity. The taxon-specific qPCR primer-panel results revealed a precise quantification of the targeted taxa compared to the 16S NGS analysis. Moreover, 12 agro-morphological parameters were determined for grown wheat plants, and their results showed a high yield in the 'Q' soil compared to other soils; this could be attributed to the increased abundance of Proteobacteria and Actinobacteria, high enrichment in nutrients (N and K), or increased EC/nutrient availability. Ultimately, the potential use of a taxon-specific qPCR primer-panel as an alternative approach to NGS provides a cheaper, user-friendly setup with high accuracy.

Custom-Built Operant Conditioning Setup for Calcium Imaging and Cognitive Testing in Freely Moving Mice.

Operant chambers are widely used in animal research to study cognition, motivation, and learning processes. Paired with the rapidly developing technologies for brain imaging and manipulations of brain activity, operant conditioning chambers are a powerful tool for neuroscience research. The behavioral testing and imaging setups that are commercially available are often quite costly. Here, we present a custom-built operant chamber that can be constructed in a few days by an unexperienced user with relatively inexpensive, widely available materials. The advantages of our operant setup compared with other open-source and closed-source solutions are its relatively low cost, its support of complex behavioral tasks, its user-friendly setup, and its validated functionality with video imaging of behavior and calcium imaging using the UCLA Miniscope. Using this setup, we replicate our previously published findings showing that mice exposed to social defeat stress in adolescence have inhibitory control impairments in the Go/No-Go task when they reach adulthood. We also present calcium imaging data of medial prefrontal cortex (mPFC) neuronal activity acquired during Go/No-Go testing in freely moving mice and show that neuronal population activity increases from day 1 to day 14 of the task. We propose that our operant chamber is a cheaper alternative to its commercially available counterparts and offers a better balance between versatility and user-friendly setup than other open-source alternatives.

Photoemission in microelectronic research: When technological developments allow to tackle new questions in the lab

Photoemission has always played an important role in the development of new microelectronic devices and processes and has quickly focused on the use of monochromatized Al Kd radiation in most commercial instruments. While, for instance, ARXPS using sample tilting or the use of high-energy photoemission at synchrotron facilities were available, their use remained confidential due to technical constraints such as measurement time, repeatability of the measurements and/or access to the facilities. In this paper, we show through some examples that the implementation of these features in a user-friendly setup in the laboratory considerably enhances the information retrieved from photoemission experiments.

A Low-Cost 3-in-1 3D Printer as a Tool for the Fabrication of Flow-Through Channels of Microfluidic Systems.

Recently published studies have shown that microfluidic devices fabricated by in-house three-dimensional (3D) printing, computer numerical control (CNC) milling and laser engraving have a good quality of performance. The 3-in-1 3D printers, desktop machines that integrate the three primary functions in a single user-friendly set-up are now available for computer-controlled adaptable surface processing, for less than USD 1000. Here, we demonstrate that 3-in-1 3D printer-based micromachining is an effective strategy for creating microfluidic devices and an easier and more economical alternative to, for instance, conventional photolithography. Our aim was to produce plastic microfluidic chips with engraved microchannel structures or micro-structured plastic molds for casting polydimethylsiloxane (PDMS) chips with microchannel imprints. The reproducability and accuracy of fabrication of microfluidic chips with straight, crossed line and Y-shaped microchannel designs were assessed and their microfluidic performance checked by liquid stream tests. All three fabrication methods of the 3-in-1 3D printer produced functional microchannel devices with adequate solution flow. Accordingly, 3-in-1 3D printers are recommended as cheap, accessible and user-friendly tools that can be operated with minimal training and little starting knowledge to successfully fabricate basic microfluidic devices that are suitable for educational work or rapid prototyping.

High-throughput and high-sensitivity N-Glycan profiling: A platform for biopharmaceutical development and disease biomarker discovery

Protein glycosylation contributes to critical biological function of glycoproteins. Glycan analysis is essential for the production of biopharmaceuticals as well as for the identification of disease biomarkers. However, glycans are highly heterogeneous, which has considerably hampered the progress of glycomics. Here, we present an improved 96-well plate format platform for streamlined glycan profiling that takes advantage of rapid glycoprotein denaturation, deglycosylation, fluorescent derivatization, and on-matrix glycan clean-up. This approach offers high sensitivity with consistent identification and quantification of diverse N-glycans across multiple samples on a high-throughput scale. We demonstrate its capability for N-glycan profiling of glycoproteins from various sources, including two recombinant monoclonal antibodies produced from Chinese Hamster Ovary cells, EG2-hFc and rituximab, polyclonal antibodies purified from human serum, and total glycoproteins from human serum. Combined with the complementary information obtained by sequential digestion from exoglycosidase arrays, this approach allows the detection and identification of multiple N-glycans in these complex biological samples. The reagents, workflow, and Hydrophilic interaction liquid chromatography with fluorescence detection (HILIC-FLD), are simple enough to be implemented into a straightforward user-friendly setup. This improved technology provides a powerful tool in support of rapid advancement of glycan analysis for biopharmaceutical development and biomarker discovery for clinical disease diagnosis.

The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond

Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional data sets. Nowadays multiple imputation is also applied to impute missing values in hierarchical data sets, address confidentiality concerns, combine data from different sources, or correct measurement errors in surveys. However, software developments did not keep up with these recent extensions. Most imputation software can only deal with item nonresponse in cross-sectional settings and extensions for hierarchical data - if available at all - are typically limited in scope. Furthermore, to our knowledge no software is currently available for dealing with measurement error using multiple imputation approaches. The R package hmi tries to close some of these gaps. It offers multiple imputation routines in hierarchical settings for many variable types (for example, nominal, ordinal, or continuous variables). It also provides imputation routines for interval data and handles a common measurement error problem in survey data: biased inferences due to implicit rounding of the reported values. The user-friendly setup which only requires the data and optionally the specification of the analysis model of interest makes the package especially attractive for users less familiar with the peculiarities of multiple imputation. The compatibility with the popular mice package (Van Buuren and Groothuis-Oudshoorn 2011) ensures that the rich set of analysis and diagnostic tools and post-imputation functions available in mice can be used easily, once the data have been imputed.

A WEB-BASED FAST AND RELIABLE TEXT CLASSIFICATION TOOL

INTRODUCTION
 Opinion analysis in the big data analysis context has been a hot topic in science and the business world recently. Social media has become a key data source for opinions generating a large amount of data every day providing content for further analysis.
 In the Big data age, unstructured data classification is one of the key tools for fast and reliable content analysis. I expect significant growth in the demand for content classification services in the nearest future.
 There are many online text classification tools available providing limited functionality -such as automated text classification in predefined categories and sentiment analysis based on a pre-trained machine learning algorithm. The limited functionality does not provide tools such as data mining support and/or a machine learning algorithm training interface.
 There are a limited number of tools available providing the whole sets of tools required for text classification, i.e. this includes all the steps starting from data mining till building a machine learning algorithm and applying it to a data stream from a social network source. My goal is to create a tool able to generate a classified text stream directly from social media with a user friendly set-up interface.
 METHODS AND MATERIALS
 The text classification tool will have a core based modular structure (each module providing certain functionality) so the system can be scaled in terms of technology and functionality.
 The tool will be built on open source libraries and programming languages running on a Linux OS based server. The tool will be based on three key components: frontend, backend and data storage as described below:
 
 backend: Python and Nodejs programming language with machine learning and text filtering libraries: TensorFlow, and Keras,
 for data storage Mysql 5.7/8 will be used,
 frontend will be based on web technologies built using PHP and Javascript.
 
 EXPECTED RESULTS
 The expected result of my work is a web-based text classification tool for opinion analysis using data streams from social media. The tool will provide a user friendly interface for data collection, algorithm selection, machine learning algorithm setup and training.
 Multiple text classification algorithms will be available as listed below:
 
 Linear SVM
 Random Forest
 Multinomial Naive Bayes
 Bernoulli Naive Bayes
 Ridge Regressio
 Perceptron
 Passive Aggressive Classifier
 Deep machine learning algorithm.
 
 System users will be able to identify the most effective algorithm for their text classification task and compare them based on their accuracy.
 The architecture of the text classification tool will be based on a frontend interface and backend services. The frontend interface will provide all the tools the system user will be interacting with the system. This includes setting up data collection streams from multiple social networks and allocating them to pre-specified channels based on keywords.
 Data from each channel can be classified and assigned to a pre-defined cluster. The tool will provide a training interface for machine learning algorithms.
 This text classification tool is currently in active development for a client with planned testing and implementation in April 2019.

In-depth structural analysis of glycans in the gas phase.

Although there have been substantial improvements in glycan analysis over the past decade, the lack of both high-resolution and high-throughput methods hampers progress in glycomics. This perspective article highlights the current developments of liquid chromatography, mass spectrometry, ion-mobility spectrometry and cryogenic IR spectroscopy for glycan analysis and gives a critical insight to their individual strengths and limitations. Moreover, we discuss a novel concept in which ion mobility-mass spectrometry and cryogenic IR spectroscopy is combined in a single instrument such that datasets consisting of m/z, collision cross sections and IR fingerprints can be obtained. This multidimensional data will then be compared to a comprehensive reference library of intact glycans and their fragments to accurately identify unknown glycans on a high-throughput scale with minimal sample requirements. Due to the complementarity of the obtained information, this novel approach is highly diagnostic and also suitable for the identification of larger glycans; however, the workflow and instrumentation is straightforward enough to be implemented into a user-friendly setup.

In-plane molecular organization of hydrated single lipid bilayers: DPPC:cholesterol.

Understanding the physical properties of cholesterol-phospholipid systems is essential to gain a better knowledge of the function of each membrane constituent. We present a novel, simple and user-friendly setup that allows for the straightforward grazing incidence X-ray diffraction characterization of hydrated individual supported lipid bilayers. This configuration minimizes the scattering from the liquid and allows the detection of the extremely weak diffracted signal of the membrane, enabling the differentiation of the coexisting domains in DPPC:cholesterol single bilayers.

Visible-Light Photocatalysis Employing Dye-Sensitized Semiconductor: Selective Aerobic Oxidation of Benzyl Ethers

The aerobic oxidation is an attractive approach toward environmentally benign synthesis of fine chemicals. In addition, dye-sensitized semiconductors are underdeveloped photocatalysts for selective organic synthesis. With the aid of catalytic eosin Y-sensitized titanium dioxide, we have developed efficient aerobic photooxidation of benzyl ethers to benzoates, featuring low cost, high atom economy, broad substrate scope, and user-friendly setup. Furthermore, preliminary mechanistic studies established that the reaction pathway likely entails a photoinduced, radical-based two-step process via an isolable peroxide intermediate.

MIT-Skywalker: On the use of a markerless system.

This paper describes our efforts to employ the Microsoft Kinect as a low cost vision control system for the MIT-Skywalker, a robotic gait rehabilitation device. The Kinect enables an alternative markerless solution to control the MIT-Skywalker and allows a more user-friendly set-up. A study involving eight healthy subjects and two stroke survivors using the MIT-Skywalker device demonstrates the advantages and challenges of this new proposed approach.

One-step extraction of polar drugs from plasma by parallel artificial liquid membrane extraction.

The new microextraction technique named parallel artificial liquid membrane extraction (PALME) was introduced as an alternative approach to liquid-liquid extraction of charged analytes from aqueous samples. The concept is based on extraction of analytes across a supported liquid membrane sustained in the pores of a thin polymeric membrane, a well-known extraction principle also used in hollow fiber liquid-phase microextraction (HF-LPME). However, the new PALME technique offers a more user-friendly setup in which the supported liquid membrane is incorporated in a 96 well plate system. Thus, high-throughput is achievable, in addition to the green chemistry offered by using PALME. The consumption of organic solvent is minimized to 3-5μL per sample. With a sample volume of 250μL and acceptor solution volume of 50μL, a maximal enrichment factor of five is achievable. Based on these parameters, a new method for extraction of polar basic drugs was developed in the present work. The basic drugs hydralazine, ephedrine, metaraminol, salbutamol, and cimetidine were used as model analytes, and were extracted from alkalized human plasma into an aqueous solution via the supported liquid membrane. The extraction was promoted by a carrier dissolved in the membrane, creating a temporary ion-pair complex between the hydrophilic drug and the carrier. As the model analytes were extracted directly into an aqueous solution, there was no need for evaporation of the extract before injection into LC-MS. Hence, the sample preparation is performed in one step. With optimized conditions, the extraction recoveries were in the range 50-89% from human plasma after 45min extraction. The data from the method evaluation were satisfactory and in line with current guidelines, and revealed an extraction method with substantial potential for high throughput bioanalysis of polar basic drugs.

Using the Free Program MEGA to Build Phylogenetic Trees from Molecular Data

Building evolutionary trees can be an excellent way for students to see how different gene sequences or organisms are related to one another. Molecular Evolutionary Genetics Analysis (MEGA) software is a free package that lets anyone build evolutionary trees in a user-friendly setup. There are several options to choose from when building trees from molecular data in MEGA, but the most commonly used are neighbor joining and maximum likelihood, both of which give good estimates on the relationship between different molecular sequences. In this article, we describe how to collect data from GenBank, insert the data into a text editor, import the data into MEGA, and use the dataset to create phylogenetic trees.

Self-mixing digital closed-loop vibrometer for high accuracy vibration measurements

The novelty of Self-mixing interferometry is represented by the combination of high accuracy and contactless operation with compact, very-low-cost and user-friendly setup. This paper introduces state of the art techniques to monitor vibrations focusing on a novel digital feedback vibrometer. It exploits a control loop to delete interferometric signal distortion and improve measurement accuracy. A digital implementation is proposed to enhance system performances through a real-time elaboration.

Hitlers Heerführer. Die deutschen Oberbefehlshaber im Krieg gegen die Sowjetunion 1941/42 (review)

Reviewed by: Hitlers Heerführer. Die deutschen Oberbefehlshaber im Krieg gegen die Sowjetunion 1941/42 Klaus Schmider Hitlers Heerführer. Die deutschen Oberbefehlshaber im Krieg gegen die Sowjetunion 1941/42. By Johannes Hürter. München: R. Oldenbourg Verlag, 2006. ISBN 3-486-57982-7. Maps. Photographs. Notes. Bibliography. Index. Pp. 719. €=49,80. For the last twenty years or so, scholars on both sides of the Atlantic have endeavoured to come to grips with the issue of the Wehrmacht's complicity in Hitler's war of annihilation against the U.S.S.R. To what extent can the German armed forces in general and the Ostheer in particular be said to have been bystanders, more or less willing accomplices or even key players in the policies which resulted in the death of millions of Soviet civilians and POWs? The views put forward on the issue range from those willing to at least occasionally give the accused the benefit of the doubt (Jürgen Förster, Christian Hartmann, Klaus Jochen Arnold) to those fervently convinced of German guilt (Christian Streit, Omer Bartov, Christian Gerlach). In view of the ink already spilt on the subject (Hürter's bibliography takes up twenty-six pages of very fine print) and the apparent impossibility of reconciling some of the opposing views, the challenge of bringing a genuinely fresh perspective to the debate would seem to be daunting to say the least. Johannes Hürter has managed to do so by adressing the issue of personal responsibility. Rather than cast damnation on "the Wehrmacht" as a collective entity, he puts those individuals in the dock without whose acquiesence not one political commissar or Jewish civilian could have been shot in 1941: the twenty-five generals who during the first twelve months of the war in the East held command of an army, army group, or both. Even though arguably the victorious allies had already attempted to do something similar in 1948 in the twelfth Nürnberg trial, Hürter is able to do so from a much broader documentary base (including many private papers) and [End Page 562] makes a point of incorporating the formative experience each of these officers had gone through, first in the Kaiser's and then the Weimar Republic's army (pp. 21– 122). He follows suit by looking at each individual's reaction—insofar as there is a record of it—to the marriage of convenience between Hitler and the old elites (pp. 123–201). At this point, the chronological structure of the book changes over to a thematic one: while this presupposes a certain foreknowledge of the Russian campaign, it has the major advantage of giving the remainder of the book a very user-friendly setup. After a chapter dealing with the preparations for war and the issuing of the orders which flew in the face of international law (pp. 203–65), the author devotes considerable space to the operational problems involved in actually waging the campaign (pp. 266–358), followed by the Ostheer's treatment of enemy combatants (pp. 359–441) and civilians (pp. 442–508). The final chapter is devoted to examining the role played by his protagonists in the first months of the Holocaust on Soviet soil (pp. 509–99). Apart from the breathtaking thoroughness of Hürter's research, this reviewer was struck by two features which set his work apart from that of most of his predecessors. Firstly, he has a serious grounding in military history and as such, is able to put many of the actions of his "defendants" in a historical context (the small chapter on the daily workings of an Armeeoberkommando is an excellent example of this). Just as importantly, he has by and large managed to withstand the overpowering urge which so many of his predecessors (German ones in particular) have been unable to resist: to relentlessly castigate those responsible for so much human suffering if not in person, then at least in print. As a result, the overall picture which Hürter paints of the actions of this small elite group, while still mostly black, has shades of grey in it and is all the more credible for...

KnoWind: a database-oriented approach to determine the pedestrian level wind environment around buildings

High buildings generally have an adverse effect on the wind conditions in their immediate vicinity, sometimes giving rise to dangerous wind conditions. So far, the whole issue is hardly considered in the design stage since there are no easy tools available for use by designers. To fill this gap, a user-friendly database-oriented computer programme has been developed, called KnoWind, with which the pedestrian level wind environment around buildings can be determined easily, provided that the windclimate of the country considered is available. Complex building configurations must be schematised to their main dimensions. The roughness of the environment of the building under consideration is taken into account, but not adjacent buildings. With remarkable accuracy, the programme can be used to predict the potential wind nuisance and wind danger around buildings in simple and even complex situations, provided that the building under consideration is higher than the surrounding buildings. The user-friendly set-up, the simple input and the fast calculations make the programme suitable as a tool for laymen in the field of wind engineering and for quick scans of potential wind problems.

New development in ion implantation and RTP equipment for high productivity

The productivity of semiconductor processing equipment can be expressed as the product of three factors: yield, throughput and availability. In this article new systems and equipment features for high productivity, developed at Varian for ion implantation and rapid thermal processing, will be reviewed. Yield-related features will be highlighted by real-time monitoring and control capabilities for critical process issues, such as uniformity, wafer charging, wafer temperature control, particulates, etc. Throughput enhancement features, such as those provided by increased beam current, new wafer cooling technology, high-speed wafer handling with integral wafer orientation and centering functions, and automation, will be presented. Availability improvements will focus on such system design aspects as control systems (including diagnostics, predictive maintenance and user-friendly setup capability), intelligent wafer-handling technology, vacuum system design and serviceability features. In the selection of examples particular emphasis will be placed on features of our new high-current ion implanter.