Raman Open Database: first interconnected Raman-X-ray diffraction open-access resource for material identification

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

PLBD: protein-ligand binding database of thermodynamic and kinetic intrinsic parameters

We introduce a protein-ligand binding database (PLBD) that presents thermodynamic and kinetic data of reversible protein interactions with small molecule compounds. The manually curated binding data are linked to protein-ligand crystal structures, enabling structure-thermodynamics correlations to be determined. The database contains over 5500 binding datasets of 556 sulfonamide compound interactions with the 12 catalytically active human carbonic anhydrase isozymes defined by fluorescent thermal shift assay, isothermal titration calorimetry, inhibition of enzymatic activity and surface plasmon resonance. In the PLBD, the intrinsic thermodynamic parameters of interactions are provided, which account for the binding-linked protonation reactions. In addition to the protein-ligand binding affinities, the database provides calorimetrically measured binding enthalpies, providing additional mechanistic understanding. The PLBD can be applied to investigations of protein-ligand recognition and could be integrated into small molecule drug design. Database URL https://plbd.org/.

High-throughput computation of Raman spectra from first principles

Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Interpretation of the spectra requires comparison to known references and to this end, experimental databases of spectra have been collected. Reference Raman spectra could also be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. In this work, we developed an optimized workflow to calculate the Raman spectra efficiently and taking full advantage of the phonon properties found in existing material databases. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database. Finally, the contents of database are analyzed and the calculated spectra are shown to agree well with the experimental ones.

Disease-Associated Multimolecular Signature in the Urine of Patients with Lyme Disease Detected Using Raman Spectroscopy and Chemometrics

A urine-based screening technique for Lyme disease (LD) was developed in this research. The screen is based on Raman spectroscopy, iterative smoothing-splines with root error adjustment (ISREA) spectral baselining, and chemometric analysis using Rametrix software. Raman spectra of urine from 30 patients with positive serologic tests (including the US Centers for Disease Control [CDC] two-tier standard) for LD were compared against subsets of our database of urine spectra from 235 healthy human volunteers, 362 end-stage kidney disease (ESKD) patients, and 17 patients with active or remissive bladder cancer (BCA). We found statistical differences (p < 0.001) between urine scans of healthy volunteers and LD-positive patients. We also found a unique LD molecular signature in urine involving 112 Raman shifts (31 major Raman shifts) with significant differences from urine of healthy individuals. We were able to distinguish the LD molecular signature as statistically different (p < 0.001) from the molecular signatures of ESKD and BCA. When comparing LD-positive patients against healthy volunteers, the Rametrix-based urine screen performed with 86.7% for overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. When considering patients with ESKD and BCA in the LD-negative group, these values were 88.7% (accuracy), 83.3% (sensitivity), 91.0% (specificity), 80.7% (PPV), and 92.4% (NPV). Additional advantages to the Raman-based urine screen include that it is rapid (minutes per analysis), is minimally invasive, requires no chemical labeling, uses a low-profile, off-the-shelf spectrometer, and is inexpensive relative to other available LD tests.

Occurrence of SiC and Diamond Polytypes, Chromite and Uranophane in Breccia from Nickel Laterites (New Caledonia): Combined Analyses

Different techniques have been combined to identify the structure and the chemical composition of siliceous breccia from a drill core of nickel laterites in New Caledonia (Tiebaghi mine). XRD analyses show quartz as a major phase. Micro-Raman spectroscopy confirmed the presence of reddish microcrystalline quartz as a major phase with inclusion of microparticles of iron oxides and oxyhydroxide. Lithoclasts present in breccia are composed of lizardite, chrysotile, forsterite, hedenbergite and saponite. The veins cutting through the breccia are filled with Ni-bearing talc. Furthermore, for the first time, we discovered the presence of diamond microcrystals accompanied by moissanite polytypes (SiC), chromite (FeCr2O4) and uranophane crystals (Ca(UO2)2(SiO3OH)2.5(H2O)) and lonsdaleite (2H-[C-C]) in the porosities of the breccia. The origin of SiC and diamond polytypes are attributed to ultrahigh-pressure crystallization in the lower mantle. The SiC and diamond polytypes are inherited from serpentinized peridotites having experienced interaction with a boninitic melt. Serpentinization, then weathering of the peridotites into saprolite, did not affect the resistant SiC polytypes, diamond and lonsdaleite. During karstification and brecciation, silica rich aqueous solutions partly digested the saprolite. Again, the SiC polymorph represent stable relicts from this dissolution process being deposited in breccia pores. Uranophane is a neoformed phase having crystallized from the silica rich aqueous solutions. Our study highlights the need of combining chemical and mineralogical analytical technologies to acquire the most comprehensive information on samples, as well as the value of Raman spectroscopy in characterizing structural properties of porous materials.

SALDI-MS and SERS Multimodal Imaging: One Nanostructured Substrate to Rule Them Both

Imaging techniques based on mass spectrometry or spectroscopy methods inform in situ about the chemical composition of biological tissues or organisms, but they are sometimes limited by their specificity, sensitivity, or spatial resolution. Multimodal imaging addresses these limitations by combining several imaging modalities; however, measuring the same sample with the same preparation using multiple imaging techniques is still uncommon due to the incompatibility between substrates, sample preparation protocols, and data formats. We present a multimodal imaging approach that employs a gold-coated nanostructured silicon substrate to couple surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) and surface-enhanced Raman spectroscopy (SERS). Our approach integrates both imaging modalities by using the same substrate, sample preparation, and data analysis software on the same sample, allowing the coregistration of both images. We transferred molecules from clean fingertips and fingertips covered with plasticine modeling clay onto our nanostructure and analyzed their chemical composition and distribution by SALDI-MS and SERS. Multimodal analysis located the traces of plasticine on fingermarks and provided chemical information on the composition of the clay. Our multimodal approach effectively combines the advantages of mass spectrometry and vibrational spectroscopy with the signal enhancing abilities of our nanostructured substrate.

Investigation of Raman Spectroscopic Signatures with Multivariate Statistics: An Approach for Cataloguing Microbial Biosignatures

Spectroscopic instruments are increasingly being implemented in the search for extraterrestrial life. However, microstructural spectral analyses of alien environments could prove difficult without knowledge on the molecular identification of individual spectral signatures. To bridge this gap, we introduce unsupervised K-means clustering as a statistical approach to discern spectral patterns of biosignatures without prior knowledge of spectral regions of biomolecules. Spectral profiles of bacterial isolates from analogous polar ice sheets were measured with Raman spectroscopy. Raman analysis identified carotenoid and violacein pigments, and key cellular features including saturated and unsaturated fats, triacylglycerols, and proteins. Principal component analysis and targeted spectra integration biplot analysis revealed that the clustering of bacterial isolates was attributed to spectral biosignatures influenced by carotenoid pigments and ratio of unsaturated/saturated fat peaks. Unsupervised K-means clustering highlighted the prevalence of the corresponding spectral peaks, while subsequent supervised permutational multivariate analysis of variance provided statistical validation for spectral differences associated with the identified cellular features. Establishing a validated catalog of spectral signatures of analogous biotic and abiotic materials, in combination with targeted supervised tools, could prove effective at identifying extant biosignatures.

Mud-Based Construction Material: Promising Properties of French Gravel Wash Mud Mixed with Byproducts, Seashells and Fly Ash as a Binder

The French gravel industry produces approximatively 6.5 million tons of gravel wash mud each year. This material offers very promising properties which require an in-depth characterization study before its use as a construction material, otherwise it is removed from value cycles by disposal in landfills. We examined the suitability of gravel wash mud and seashells, with fly ash as a binder, as an unfired earth construction material. Thermal and mechanical characterizations of the smart mixture composed of gravel wash mud, Crepidula fornicata shells and fly ash are performed. The new specimens exhibit high compressive strengths compared to usual earth construction materials, which appears as a good opportunity for a reduction in the thickness of walls. The use of fly ash and Crepidula shells in addition to gravel wash mud provides high silica and calcium contents, which both react with clay, leading to the formation of tobermorite and Al-tobermorite as a result of a pozzolanic reaction. Considering the reduction in porosity and improvements in strength, these new materials are good candidates to contribute significantly to the Sustainable Development Goals (SDGs) and reduce carbon emissions.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Microplastic Spectral Classification Needs an Open Source Community: Open Specy to the Rescue!

Microplastic pollution research has suffered from inadequate data and tools for spectral (Raman and infrared) classification. Spectral matching tools often are not accurate for microplastics identification and are cost-prohibitive. Lack of accuracy stems from the diversity of microplastic pollutants, which are not represented in spectral libraries. Here, we propose a viable software solution: Open Specy. Open Specy is on the web (www.openspecy.org) and in an R package. Open Specy is free and allows users to view, process, identify, and share their spectra to a community library. Users can upload and process their spectra using smoothing (Savitzky-Golay filter) and polynomial baseline correction techniques (IModPolyFit). The processed spectrum can be downloaded to be used in other applications or identified using an onboard reference library and correlation-based matching criteria. Open Specy's data sharing and session log features ensure reproducible results. Open Specy houses a growing library of reference spectra, which increasingly represents the diversity of microplastics as a contaminant suite. We compared the functionality and accuracy of Open Specy for microplastic identification to commonly used spectral analysis software. We found that Open Specy was the only open source software and the only software with a community library, and Open Specy had comparable accuracy to popular software (OMNIC Picta and KnowItAll). Future developments will enhance spectral identification accuracy as the reference library and functionality grows through community-contributed spectra and community-developed code. Open Specy can also be used for applications beyond microplastic analysis. Open Specy's source code is open source (CC-BY-4.0, attribution only) (https://github.com/wincowgerDEV/OpenSpecy).

Validation of the Crystallography Open Database using the Crystallographic Information Framework

Data curation practices of the Crystallography Open Database are described with greater focus being placed on the cif_validate program, capable of validating crystallographic information files against both DDL1 and DDLm dictionaries.

Data curation practices of the Crystallography Open Database (COD) are described with additional focus being placed on the formal validation using the Crystallographic Information Framework (CIF). The cif_validate program, capable of validating CIF files against both the DDL1 and the DDLm dictionaries, is presented and used to process the entirety of the COD. Validation results collected from over 450 000 CIF files are demonstrated to be a useful resource in the data maintenance process as well as the development of the underlying ontologies. A set of programs intended to aid in the dictionary migration from DDL1 to DDLm is also presented.

Influence of Lime and Volcanic Ash on the Properties of Dune Sand as Sustainable Construction Materials

This study focused on evaluating dune sand stabilized with lime and volcanic ash as base course materials in engineering construction. Dune sands are found in Saudi Arabia in huge quantities. Due to the high demand for construction materials, this makes them highly suitable for construction. A testing program was designed to investigate the effect of adding different percentages by weight of lime (L: 0, 2, 4, and 6%) and volcanic ash (VA: 0, 1, 3, and 5%) on the engineering properties of the stabilized mixture. Unconfined compressive strength (UCS) and California bearing ratio (CBR) tests were conducted. In addition, Raman spectroscopy and laser-scanning microscopy (LSM) tests were performed to explore the chemical characteristic, packing, and structure of the mixture. The results showed that the UCS, CBR, and the Young's modulus (E_s) of the treated dune sand increased with the increase in percentage of both stabilizers. Furthermore, LSM images of mortar blended with intermediate L-to-VA blend ratio ≈0.55 (L: 6% and VA: 5%) exhibit compact packing of sand grains, indicating strong adhesion and higher cementing value. The results of the study are promising and encourage using the treated dune sand in engineering construction even with a low percentage use of lime (2%) and volcanic ash (1-3%) as stabilizers.

The PANGAEA mineralogical database

Future human missions to the surface of the Moon and Mars will involve scientific exploration requiring new support tools to enable rapid and high quality science decision-making. Here, we describe the PANGAEA (Planetary ANalogue Geological and Astrobiological Exercise for Astronauts) Mineralogical Database developed by ESA (European Space Agency): a catalog of petrographic and spectroscopic information on all currently known minerals identified on the Moon, Mars, and associated with meteorites. The catalog also includes minerals found in the analog field sites used for ESA's geology and astrobiology training course PANGAEA, to broaden the database coverage. The Mineralogical Database is composed of the Summary Catalog of Planetary Analog Minerals and of the Spectral Archive and is freely available in the public repository of ESA PANGAEA. The Summary Catalog provides essential descriptive information for each mineral, including name (based on the International Mineralogical Association recommendation), chemical formula, mineral group, surface abundance on planetary bodies, geological significance in the context of planetary exploration, number of collected VNIR and Raman spectra, likelihood of detection using different spectral methods, and bibliographic references evidencing their detection in extraterrestrial or terrestrial analog environments. The Spectral Archive provides a standard library for planetary in-situ human and robotic exploration covering Visual-Near-Infrared reflective (VNIR) and Raman spectroscopy (Raman). To populate this library, we collected VNIR and Raman spectra for mineral entries in the Summary Catalog from open-access archives and analyzed them to select the ones with the best spectral features. We also supplemented this collection with our own bespoke measurements. Additionally, we compiled the chemical compositions for all the minerals based on their empirical formula, to allow identification using the measured abundances provided by LIBS and XRF analytical instruments. When integrated into an operational support system like ESA's Electronic Fieldbook (EFB) system, the Mineralogical Database can be used as a real-time and autonomous decision support tool for sampling operations on the Moon, Mars and during astronaut geological field training. It provides both robust spectral libraries to support mineral identification from instrument outputs, and relevant contextualized information on detected minerals.