Elemental analysis: an important purity control but prone to manipulations

Elemental analysis is a powerful analytical tool for purity determination. However, it is prone to manipulations. Herein, we introduce an approach to provide original elemental analysis data, allowing verification of the currently just listed values.

A Critical Review of Capacitance-Resistance Models

A Capacitance Resistance Model (CRM) is an analytical model that only requires production and injection rates to predict reservoir performance. The CRM input is the injection rates and the output is the production rate. The input and output are related by the CRM parameters. The first parameter is the time delay (also called time constant) and is a function of pore volume, total compressibility, and productivity indices. The second parameter is the connectivity (also called gain, or weight), which quantifies the connectivity between producers and injectors (i.e. how much of the input is supporting the output). The CRM was developed for fields with minimum reservoir data, or for small fields not requiring a full reservoir simulation model, which can be time-consuming and expensive. The CRM is a quick, powerful analytical tool that is simple to use and requires readily available data. Most of the time, the injection and production rates are measured accurately and frequently, either weekly or bi-weekly. By solving the continuity equation for a homogenous reservoir (i.e. constant reservoir and fluid properties throughout the reservoir) the solution of the continuity equation can be indicative of the injection and production relation and therefore can be used to optimize injection schemes for higher ultimate hydrocarbon recovery. It is important to recognize that the CRM is not supposed to replace numerical reservoir simulators, which, in essence, are the most accurate means of reservoir performance prediction. Instead, the CRM aims to be a quick and easy way to infer reservoir performance in the absence of full-fledged simulation. The CRM has been used for several purposes as seen in the literature. First, as a tool to optimize waterflooding in oil reservoirs. The CRM can infer inter-well connectivity which will allow the engineer to adjust water injection rates to ensure uniform sweep in the reservoir and reduce the chance of early water breakthrough. The CRM was also used to optimize CO2 sequestration, whereby CO¬2 is captured from the atmosphere and stored in subsurface formations. The main hypothesis in CRM is that the characteristics of the reservoir can be inferred from analyzing production and injection data only. CRM does not require core data, logs, seismic, or any rock or fluids properties. This hypothesis can be challenged easily since most reservoirs have gradients of fluid properties, multi-porosity systems, and heterogeneous formations with different wettability presences. Albeit, several publications have shown that CRM can result in high certainty output. The objective of this report is to explain the concept of the CRM, conduct a critical review of the main CRM publications, compare CRM to other reservoir characterization tools and finally demonstrate some applications of the CRM.

Mesoporous One-Component Gold Microshells as 3D SERS Substrates

Surface-enhanced Raman scattering (SERS) is a powerful analytical tool for label-free analysis that has found a broad spectrum of applications in material, chemical, and biomedical sciences. In recent years, a great interest has been witnessed in the rational design of SERS substrates to amplify Raman signals and optionally allow for the selective detection of analytes, which is especially essential and challenging for biomedical applications. In this study, hard templating of noble metals is proposed as a novel approach for the design of one-component tailor-made SERS platforms. Porous Au microparticles were fabricated via dual ex situ adsorption of Au nanoparticles and in situ reduction of HAuCl4 on mesoporous sacrificial microcrystals of vaterite CaCO3. Elimination of the microcrystals at mild conditions resulted in the formation of stable mesoporous one-component Au microshells. SERS performance of the microshells at very low 0.4 µW laser power was probed using rhodamine B and bovine serum albumin showing enhancement factors of 2 × 108 and 8 × 108, respectively. The proposed strategy opens broad avenues for the design and scalable fabrication of one-component porous metal particles that can serve as superior SERS platforms possessing both excellent plasmonic properties and the possibility of selective inclusion of analyte molecules and/or SERS nanotags for highly specific SERS analysis.

Fully-automated root image analysis (faRIA)

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

Molecular epidemiology of SARS-CoV-2 in Cyprus

Whole genome sequencing of viral specimens following molecular diagnosis is a powerful analytical tool of molecular epidemiology that can critically assist in resolving chains of transmission, identifying of new variants or assessing pathogen evolution and allows a real-time view into the dynamics of a pandemic. In Cyprus, the first two cases of COVID-19 were identified on March 9, 2020 and since then 33,567 confirmed cases and 230 deaths were documented. In this study, viral whole genome sequencing was performed on 133 SARS-CoV-2 positive samples collected between March 2020 and January 2021. Phylogenetic analysis was conducted to evaluate the genomic diversity of circulating SARS-CoV-2 lineages in Cyprus. 15 different lineages were identified that clustered into three groups associated with the spring, summer and autumn/winter wave of SARS-CoV-2 incidence in Cyprus, respectively. The majority of the Cypriot samples belonged to the B.1.258 lineage first detected in September that spread rapidly and largely dominated the autumn/winter wave with a peak prevalence of 86% during the months of November and December. The B.1.1.7 UK variant (VOC-202012/01) was identified for the first time at the end of December and spread rapidly reaching 37% prevalence within one month. Overall, we describe the changing pattern of circulating SARS-CoV-2 lineages in Cyprus since the beginning of the pandemic until the end of January 2021. These findings highlight the role of importation of new variants through travel towards the emergence of successive waves of incidence in Cyprus and demonstrate the importance of genomic surveillance in determining viral genetic diversity and the timely identification of new variants for guiding public health intervention measures.

Bond-selective imaging by optically sensing the mid-infrared photothermal effect

Mid-infrared (IR) spectroscopic imaging using inherent vibrational contrast has been broadly used as a powerful analytical tool for sample identification and characterization. However, the low spatial resolution and large water absorption associated with the long IR wavelengths hinder its applications to study subcellular features in living systems. Recently developed mid-infrared photothermal (MIP) microscopy overcomes these limitations by probing the IR absorption–induced photothermal effect using a visible light. MIP microscopy yields submicrometer spatial resolution with high spectral fidelity and reduced water background. In this review, we categorize different photothermal contrast mechanisms and discuss instrumentations for scanning and widefield MIP microscope configurations. We highlight a broad range of applications from life science to materials. We further provide future perspective and potential venues in MIP microscopy field.

An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications

Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, a multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the representative multi-way models which were used for analyzing NIRS data, as well as the advances, advantages and limitations of different multi-way models. The applications of a multi-way analysis in NIRS for the food industry in terms of food process control, quality evaluation and fraud, identification and classification, prediction and quantification, and image analysis are also reviewed. It is evident from this report that a multi-way analysis is presently an attractive tool for modeling complex NIRS data in the food industry while its full potential is far from reached. The combination of a multi-way analysis with NIRS will be a promising practice for turning food data information into operational knowledge, conducting reliable food analyses and improving our understanding about food systems and food processes. To the best of our knowledge, this is the first paper that systematically reports the advances on models and applications of a multi-way analysis in NIRS for the food industry.

Molecular Epidemiology of SARS-CoV-2 in Cyprus

Whole genome sequencing of viral specimens following molecular diagnosis is a powerful analytical tool of molecular epidemiology that can critically assist in resolving chains of transmission, identifying of new variants or assessing pathogen evolution and allows a real-time view into the dynamics of a pandemic. In Cyprus, the first two cases of COVID-19 were identified on March 9, 2020 and since then 33,567 confirmed cases and 230 deaths were documented. In this study, viral whole genome sequencing was performed on 133 SARS-CoV-2 positive samples collected between March 2020 and January 2021. Phylogenetic analysis was conducted to evaluate the genomic diversity of circulating SARS-CoV-2 lineages in Cyprus. 15 different lineages were identified that clustered into three groups associated with the spring, summer and autumn/winter wave of SARS-CoV2 incidence in Cyprus, respectively. The majority of the Cypriot samples belonged to the B.1.258 lineage first detected in September that spread rapidly and largely dominated the autumn/winter wave with a peak prevalence of 86% during the months of November and December. The B.1.1.7 UK variant (VOC-202012/01) was identified for the first time at the end of December and spread rapidly reaching 37% prevalence within one month. Overall, we describe the changing pattern of circulating SARS-CoV-2 lineages in Cyprus since the beginning of the pandemic until the end of January 2021. These findings highlight the role of importation of new variants through travel towards the emergence of successive waves of incidence in Cyprus and demonstrate the importance of genomic surveillance in determining viral genetic diversity and the timely identification of new variants for guiding public health intervention measures.

Metabolomics and lipidomics in C. elegans using a single sample preparation

Comprehensive metabolomic and lipidomic mass spectrometry methods are in increasing demand, for instance in research related to nutrition and aging. The nematode C. elegans is a key model organism in these fields, due to the large repository of available C. elegans mutants and their convenient natural lifespan. Here, we describe a robust and sensitive analytical method for the semi-quantitative analysis of >100 polar (metabolomics) and >1000 apolar (lipidomics) metabolites in C. elegans, using a single sample preparation. Our method is capable of reliably detecting a wide variety of biologically relevant metabolic aberrations in, for instance, glycolysis and the TCA cycle, pyrimidine metabolism and complex lipid biosynthesis. In conclusion, we provide a powerful analytical tool that maximizes metabolic data yield from a single sample.