Biophysical Techniques in Structural Biology.

Over the past six decades, steadily increasing progress in the application of the principles and techniques of the physical sciences to the study of biological systems has led to remarkable insights into the molecular basis of life. Of particular significance has been the way in which the determination of the structures and dynamical properties of proteins and nucleic acids has so often led directly to a profound understanding of the nature and mechanism of their functional roles. The increasing number and power of experimental and theoretical techniques that can be applied successfully to living systems is now ushering in a new era of structural biology that is leading to fundamentally new information about the maintenance of health, the origins of disease, and the development of effective strategies for therapeutic intervention. This article provides a brief overview of some of the most powerful biophysical methods in use today, along with references that provide more detailed information about recent applications of each of them. In addition, this article acts as an introduction to four authoritative reviews in this volume. The first shows the ways that a multiplicity of biophysical methods can be combined with computational techniques to define the architectures of complex biological systems, such as those involving weak interactions within ensembles of molecular components. The second illustrates one aspect of this general approach by describing how recent advances in mass spectrometry, particularly in combination with other techniques, can generate fundamentally new insights into the properties of membrane proteins and their functional interactions with lipid molecules. The third reviewdemonstrates the increasing power of rapidly evolving diffraction techniques, employing the very short bursts of X-rays of extremely high intensity that are now accessible as a result of the construction of free-electron lasers, in particular to carry out time-resolved studies of biochemical reactions. The fourth describes in detail the application of such approaches to probe the mechanism of the light-induced changes associated with bacteriorhodopsin's ability to convert light energy into chemical energy.

SEDNTERP: a calculation and database utility to aid interpretation of analytical ultracentrifugation and light scattering data.

Proper interpretation of analytical ultracentrifugation (AUC) data for purified proteins requires ancillary information and calculations to account for factors such as buoyancy, buffer viscosity, hydration, and temperature. The utility program SEDNTERP has been widely used by the AUC community for this purpose since its introduction in the mid-1990s. Recent extensions to this program (1) allow it to incorporate data from diffusion as well as AUC experiments; and (2) allow it to calculate the refractive index of buffer solutions (based on the solute composition of the buffer), as well as the specific refractive increment (dn/dc) of proteins based on their composition. These two extensions should be quite useful to the light scattering community as well as helpful for AUC users. The latest version also adds new terms to the partial specific volume calculations which should improve the accuracy, particularly for smaller proteins and peptides, and can calculate the viscosity of buffers containing heavy isotopes of water. It also uses newer, more accurate equations for the density of water and for the hydrodynamic properties of rods and disks. This article will summarize and review all the equations used in the current program version and the scientific background behind them. It will tabulate the values used to calculate the partial specific volume and dn/dc, as well as the polynomial coefficients used in calculating the buffer density and viscosity (most of which have not been previously published), as well as the new ones used in calculating the buffer refractive index.

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy.

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

Semantic Annotation of Experimental Methods in Analytical Chemistry.

A major obstacle for reusing and integrating existing data is finding the data that is most relevant in a given context. The primary metadata resource is the scientific literature describing the experiments that produced the data. To stimulate the development of natural language processing methods for extracting this information from articles, we have manually annotated 100 recent open access publications in Analytical Chemistry as semantic graphs. We focused on articles mentioning mass spectrometry in their experimental sections, as we are particularly interested in the topic, which is also within the domain of several ontologies and controlled vocabularies. The resulting gold standard dataset is publicly available and directly applicable to validating automated methods for retrieving this metadata from the literature. In the process, we also made a number of observations on the structure and description of experiments and open access publication in this journal.

What if using certified reference materials (CRMs) was a requirement to publish in analytical/bioanalytical chemistry journals?

Certified reference materials (CRMs) are routinely used by analytical chemists to validate new analytical methods and to demonstrate the quality of their quantitative measurements. Even though CRMs for trace element and trace organic analysis have been available and widely used for over 50 years, the majority of papers published in analytical chemistry journals do not mention the use of CRMs. What if analytical/bioanalytical chemistry journals required the use of CRMs to publish a paper? This feature article attempts to address this question by providing examples of recent papers that have made exceptional use of CRMs to validate new analytical methods and to describe novel, alternative uses of CRMs that provide new characterization of the CRM. The potential benefits of using a CRM even when it does not have certified values for the analytes of interest are presented.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests.

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Molecular Level Sucrose Quantification: A Critical Review.

Sucrose is a primary metabolite in plants, a source of energy, a source of carbon atoms for growth and development, and a regulator of biochemical processes. Most of the traditional analytical chemistry methods for sucrose quantification in plants require sample treatment (with consequent tissue destruction) and complex facilities, that do not allow real-time sucrose quantification at ultra-low concentrations (nM to pM range) under in vivo conditions, limiting our understanding of sucrose roles in plant physiology across different plant tissues and cellular compartments. Some of the above-mentioned problems may be circumvented with the use of bio-compatible ligands for molecular recognition of sucrose. Nevertheless, problems such as the signal-noise ratio, stability, and selectivity are some of the main challenges limiting the use of molecular recognition methods for the in vivo quantification of sucrose. In this review, we provide a critical analysis of the existing analytical chemistry tools, biosensors, and synthetic ligands, for sucrose quantification and discuss the most promising paths to improve upon its limits of detection. Our goal is to highlight the criteria design need for real-time, in vivo, highly sensitive and selective sucrose sensing capabilities to enable further our understanding of living organisms, the development of new plant breeding strategies for increased crop productivity and sustainability, and ultimately to contribute to the overarching need for food security.

Estimation of the area under a curve via several B-spline-based regression methods and applications.

Estimating the area under a curve (AUC) is an important subject in many fields of medicine and science. The regression model using B-spline functions provides flexibility in curve fitting, making it suitable for AUC estimation with various types of nonlinear trends. Despite the versatility of the B-spline approach, comprehensive discussions regarding relevant AUC estimation techniques using B-spline functions and their comparison with existing methods cannot be found in extant literature. In this paper, we investigate AUC estimation using B-spline regression and B-spline regression with several penalties, as well as discuss corresponding inferences. We carry out an extensive Monte Carlo study to evaluate the performance of the proposed methods in various realistic pharmacokinetics and analytical chemistry data settings. We show that the proposed methods provide robust and reliable AUC estimation regardless of different types of nonlinear models from scientific and medical research areas. Our proposed method is appropriate for general AUC estimation since it does not require nonlinear model specifications and inference techniques corresponding to the specified model.

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques.

BACKGROUND: Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate. OBJECTIVE: This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities. METHODS: We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020. RESULTS: We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China-and a specific city within China through references to the "Wuhan pneumonia"-potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets. CONCLUSIONS: Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

Which are the best parameters to obtain proteins and malondialdehyde from a tear sample?

Predictive, preventive and personalized medicine (PPPM) is a current concept in healthcare based on the analysis of biomarkers through non-invasive methods. Biomarkers for inflammation and oxidative stress are especially used for screening. Quantification of tear total protein content is important to identify potential, specific biomarkers, such as malondialdehyde concerning oxidative stress. The Schirmer strip test is an accessible and simple method for tear analysis. However, it is limited by the low concentration of biomarkers in the human tear. In this preliminary study, different procedures were compared for the extraction of tear proteins and malondialdehyde. Schirmer strips were used to obtain tears from healthy subjects. Ionic strength and surfactant agents were assessed, as well as different centrifugation parameters. Finally, several volumes of n-butanol on the process of malondialdehyde extraction were evaluated. The results showed that ionic strength strongly influences the extraction process, although most studies have suggested that surfactant agents are the most relevant factor; the most efficient results were obtained using a 2 M solution of NaCl in phosphate buffered saline. Regarding centrifugation, leaving the Schirmer strip tip left hanging outside the tube cap and using 1000 rpm was the best option, which is a lower centrifugation speed than the usually reported on literature. Moreover, 250 µL was the optimal n-butanol volume for malondialdehyde extraction. The importance of this study relies on the increasing relevance of the biomarkers in the field of PPPM and the need of a standardized method to extract biomarkers from the tears, to optimise its use.

Enhancing research for undergraduates through a nanotechnology training program that utilizes analytical and bioanalytical tools.

Nanotechnology is a broad field combining traditional scientific disciplines; however, analytical chemistry plays an important role in material design, synthesis, characterization, and application. This article emphasizes the uniqueness of nanotechnology and the importance of providing high-quality undergraduate research experiences to both attract and retain talented individuals to the field of nanotechnology. In response to this need to develop a strong and sustainable nanotechnology work force, strategies to create authentic research experiences are considered within the framework of an interdisciplinary nanotechnology environment at West Virginia University. The program, named NanoSAFE Research Experiences for Undergraduates (REU), embeds students in different departments at West Virginia University and in research laboratories within the National Institute of Occupational Safety and Health. A large number of participants have little or no prior research experience and a strong effort is made to recruit applicants from under-represented populations. Components designed to foster research proficiency include frequent reporting, a strong peer-network, and training for secondary mentors. Evidence, which includes student publications and assessment findings demonstrating self-efficacy, is discussed to substantiate the viability of the strategies used in the 2016-2018 program. Graphical abstract ᅟ.

Development of an Apparatus for Crop-Growth Monitoring and Diagnosis.

To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R²) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R² were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R² of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.

A Slight Smell of Lemon.

We present here an example of urine substituted with a yellow cleaning product that leads us to develop the main risks to consider in urine toxicology analysis, ie, adulteration and analytical interferences, and how to deal with them. This grand round highlights the importance of the dialog between the clinician and a TDM consultant for optimal care of the patient.