Multi-task aquatic toxicity prediction model based on multi-level features fusion.

INTRODUCTION: With the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability. OBJECTIVES: This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. METHODS: The model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi. RESULTS: The experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi. CONCLUSION: In summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.

Component analysis using UPLC-Q-TOF/MS and quality evaluation using fingerprinting and chemometrics for hops.

Hops, extensively cultivated in China for their food and medicinal applications, currently lack well-defined chemical markers to evaluate variations in their quality. The study aimed to explore variations in the quality of Chinese hops by the chemical characteristics of hops, employing UPLC-Q-TOF/MS, integrated with chemical fingerprinting and chemometrics. The results indicated that Chinese hops are abundant in polyphenols and bitter acids. The integration of UPLC-Q-TOF/MS, Chemical fingerprinting, and chemometrics revealed to be an accurate and effective approach for assessing the quality of Chinese hops. In this study, ten important chemical markers were found to be useful in differentiating various hop varieties. Moreover, the support vector machine showed a prediction accuracy of 92.3077% in identifying Chinese hop varieties. The strategy of the study lays the groundwork for classifying Chinese hop varieties and serves as a prerequisite for future quality control studies, particularly focusing on chemical compositions.

DNA typing of cyanoacrylate fumed latent fingerprints using GlobalFiler™ and ForenSeq™ Signature Prep kits.

DNA typing of latent fingerprints is highly desirable to increase chances of individualization. We recovered DNA from Cyanoacrylate (CA) fumed fingerprints and used both GlobalFiler™ and ForenSeq™ DNA Signature Prep kits for DNA typing. For GlobalFiler™, samples were processed using a protocol modified for Low Template (LT)-DNA samples (half-volume reactions, 30 cycles) while for ForenSeq™ DNA Signature Prep, samples were processed using a standard protocol and fluorometer-based library quantitation. We evaluated genotyping success and quality of profiles in terms of completeness, Peak Height Ratio/Allele Coverage Ratio, presence of PCR artifacts and drop-in alleles. With GlobalFiler™, average autosomal STR (aSTR) profile completeness was 44.4% with 2-20 pg, 54.3% with 22-60 pg, and 95% with 64-250 pg DNA input. CODIS uploadable profiles were obtained in 2/10, 3/11, and 11/12 samples in these ranges. With ForenSeq™ DNA Signature Prep, average aSTR profile completeness was 19.7% with 1-20 pg and 45.2% with 22-47 pg but increased to 78.3% with 68-122 pg and 86.7% with 618-1000 pg DNA input. Uploadable profiles were obtained in 0/12, 4/11, 4/7, and 3/3 samples for these ranges. Results show very high sensitivity using both kits. Half-volume reactions and 30 cycles had minimal negative effect on Globalfiler™ profile quality, providing support for wider use after validation experiments to routinely improve results from LT samples. A standard protocol for the ForenSeq™ DNA Signature Prep kit was also highly successful with LT DNA obtained from CA-fumed fingerprints with additional information from isometric STR alleles and other markers.

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting.

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Assessment of particulate Zn and Pb sources in the Orne watershed (Northeast France) using geochemical tools.

The Orne River, a tributary of the Moselle River, was highly impacted by industrial activities for more than one century. Land use along the Orne River is highly contrasted, with local specificity from its source to its junction with the Moselle River. The intense industrial activity left behind tons of steelmaking wastes (SMW) on the land surface and within the Orne riverbed. To assess the sources of particulate Zn and Pb transported as suspended sediment in the Orne River, different sets of samples from likely Zn- and Pb-bearing particle sources within the Orne watershed were collected. Three sets of samples were taken from potential sources representing detrital, urban, and inherited industrial particles. Mineralogy, element contents, and Zn and Pb isotope compositions were obtained to characterize and reveal the fingerprint of each set of samples. Soil samples were collected on distinct geomorphological areas characterized by different soil types and land uses. They all display detrital minerals assigned to the geological background. Urban dusts and steelmaking residues display specific mineral phases (sulfates and iron oxides, respectively). Element compositions present strong discrepancies between the distinct sets of samples. SMWs are particularly enriched in Fe, Zn, and Pb. Concerning isotopic composition, SMWs exhibit δ66Zn values ranging from - 0.67 to 1.66‰. Urban samples display δ66Zn values between - 0.11 and 0.13‰, and soils present δ66Zn values between - 0.24 and 0.47‰. The 206Pb/204Pb ratio was estimated to range from 17.550 to 18.807 for soils, from 17.973 to 18.219 for urban samples, and from 18.313 to 18.826 for SMWs. For each of the three sets of samples (soils, urban, industrial), variations of geochemical fingerprint were observed. For soils, the relatively large variations of Zn and Pb isotopic compositions were attributed to distinct land use and the contribution of atmospheric deposition. For industrial samples, the variations were more intense and may be attributed either to distinct industrial processes in the production of pig iron or to distinct furnace-flume treatment modes. The three sets of samples (urban, industrial, and detrital) could be distinguished based on Zn and Pb contents and isotopes. Finally, this study not only highlighted the sources that released particulate Zn and Pb into the Orne River system, it also demonstrated that urban particles are well defined in terms of Zn and Pb isotopic signatures, and those isotopic signatures could be extrapolated to other case studies.

Metabolomics on Apple (Malus domestica) Cuticle-Search for Authenticity Markers.

The profile of secondary metabolites present in the apple cuticular layer is not only characteristic of a particular apple cultivar; it also dynamically reflects various external factors in the growing environment. In this study, the possibility of authenticating apple samples by analyzing their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. A total of 274 authentic apple samples from four cultivars harvested in the Czech Republic and Poland between 2020 and 2022 were analyzed. The complex data generated, processed using univariate and multivariate statistical methods, enabled the building of classification models to distinguish apple cultivars as well as their geographical origin. The models showed very good performance in discriminating Czech and Polish samples for three out of four cultivars: "Gala", "Golden Delicious" and "Idared". Moreover, the validity of the models was tested over several harvest seasons. In addition to metabolites of the triterpene biosynthetic pathway, the diagnostic markers were mainly wax esters. "Jonagold", which is known to be susceptible to mutations, was the only cultivar for which an unambiguous classification of geographical origin was not possible.

Hydrazo Pyrazole-Pyridone Fluorescent tag for NLO, Live cell imaging, LFPs visualization, Photophysical probing, and Electrochemical sensor for Dopamine detection.

The present communication reports on the synthesis of a novel methyl-pyridone azo fluorescent tag (MPAFT) were proven through 1H (NMR), FT-IR, UV-vis, and high-resolution mass spectrometry. The quantum chemical parameters of MPAFT were evaluated using density functional theory (DFT) analysis. It was further investigated for its latent fingerprint (LFPs) in various surfaces and anticounterfeiting applications. By exposing Level I-Level III, ridge features to UV light with a wavelength of 365 nm, a bioimaging investigation has also demonstrated the potential of MPAFT's emission behaviour. The cyclic voltammetry (CV) and linear sweep voltammetry (LSV) at MPAFT/MGCE (modified glassy carbon electrode) were used to explore the electrochemical sensitivity and reliable detection of dopamine (DA) in neutral PBS (pH 7) electrolyte solution, and the results show good sensitivity and detection. The lower detection limit for LSV was 0.81 µM under optimum conditions.

Quality markers of Polygala fallax Hemsl decoction based on qualitative and quantitative analysis combined with network pharmacology and chemometric analysis.

INTRODUCTION: Polygala fallax Hemsl (PFH) is a widely used herbal medicine in Guangxi, China. At present, research on PFH mainly focuses on extraction technology and cultivation, lacking quality control standards for systematic evaluation. OBJECTIVES: The study aimed to assess the quality of PFH from different sources and to predict markers that would help assess quality. METHODS: Fingerprinting of 15 batches of PFH samples was performed by ultra-high performance liquid chromatography (UPLC) and similarity was assessed using hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination (OPLS-DA). Differential components were screened by mathematical analysis, and a "component-target-pathway" network map was constructed in combination with network pharmacology, quality markers (Q-markers) of PFH were predicted, and quantitative analysis was performed. RESULTS: Fifteen batches were fingerprinted for PFH, with 11 common peaks, and peak 5 was identified as 4-hydroxybenzoic acid, which was generally consistent with the results of HCA, PCA, and OPLS-DA. Network pharmacology screened 18 potential compounds, 45 core targets, and 20 key pathways, integrating fingerprinting, pattern recognition, and network pharmacology methods. One of the potential Q-markers that can identify the principle of testability, efficacy, and specificity is 4-hydroxybenzoic acid, whose content ranges from 0.0188 to 1.4517 mg/g. CONCLUSION: The potential Q-markers of PFH were predicted by integrating fingerprinting, pattern recognition, and network pharmacological analysis, which provided a scientific basis for the overall control and evaluation of the quality of PFH and a theoretical reference for the study of the quality standard of multi-base traditional Chinese medicine.

One chiral fingerprint to find them all.

Molecular fingerprints are indispensable tools in cheminformatics. However, stereochemistry is generally not considered, which is problematic for large molecules which are almost all chiral. Herein we report MAP4C, a chiral version of our previously reported fingerprint MAP4, which lists MinHashes computed from character strings containing the SMILES of all pairs of circular substructures up to a diameter of four bonds and the shortest topological distance between their central atoms. MAP4C includes the Cahn-Ingold-Prelog (CIP) annotation (R, S, r or s) whenever the chiral atom is the center of a circular substructure, a question mark for undefined stereocenters, and double bond cis-trans information if specified. MAP4C performs slightly better than the achiral MAP4, ECFP and AP fingerprints in non-stereoselective virtual screening benchmarks. Furthermore, MAP4C distinguishes between stereoisomers in chiral molecules from small molecule drugs to large natural products and peptides comprising thousands of diastereomers, with a degree of distinction smaller than between structural isomers and proportional to the number of chirality changes. Due to its excellent performance across diverse molecular classes and its ability to handle stereochemistry, MAP4C is recommended as a generally applicable chiral molecular fingerprint. SCIENTIFIC CONTRIBUTION: The ability of our chiral fingerprint MAP4C to handle stereoisomers from small molecules to large natural products and peptides is unprecedented and opens the way for cheminformatics to include stereochemistry as an important molecular parameter across all fields of molecular design.

ENFSI 2022 multidisciplinary collaborative exercise: organisation and outcomes.

The use of collaborative exercises (CE) and proficiency tests (PT) as part of the governance programme for any forensic science laboratory has become commonplace and recommended by several international organisations. Traditionally these have been discipline-specific exercises testing a laboratory's ability in a single area of forensic science. However, the "real" world is normally more complex and, in many instances, forensic material must be examined for a number of different evidence types. This article summarises the concepts, planning, design, preparation, implementation, co-ordination and evaluation of the 2022 Multidisciplinary Collaborative Exercise (2022-MdCE) covering a range of forensic disciplines, specifically DNA, fingerprint, documents and handwriting. The exercise consisted of a questioned letter with typescript text and a signature. In addition, the letter contained a visible bloody fingermark in the area of the signature, a visible staining in the lower left-hand corner, a latent fingermark and an indented impression. The analysis of the results showed that, in the investigation of the bloody fingermark, the priority was given to the DNA examination. Some critical issues emerged in relation to the biological (DNA)/ink sampling strategies when applied before fingermark visualisation. Another outcome of the exercise has been to demonstrate the importance of indented impressions, which have been underestimated by a significant number of participants. As setters, more in-depth studies are needed to produce consistent samples. This concerns all the disciplined involved but especially DNA and fingermarks. Based on this exercise, it is believed that this approach to testing of forensic disciplines allows the analysis of good practice within the various scientific areas, as well as scrutinising the process and sequence of events for examining the material within a forensic laboratory in the best conservative way for all kind of evidences.

A direct analysis method using sheath flow probe electrospray ionisation-mass spectrometry (sfPESI-MS) to detect drug residues from fingerprint forensic gel lifts.

Latent fingerprints at crime scenes are frequently recovered using forensic gel-lifters, which can help to preserve the crime scene and to enhance visualisation of traces such as blood or paint. In addition to providing fingerprint ridge detail, additional chemical information can also be recovered from gel lifts that may prove pertinent to an investigation. However, while DNA and metal ions have been shown to be able to be detected in gel-lifted fingerprints, the determination of other types of chemical information such as the presence of drugs in gel-lifted prints has not been previously shown. This study demonstrates the application of an ambient ionisation method, sheath flow probe electrospray ionisation-mass spectrometry (sfPESI-MS), to the direct analysis of gel-lifted fingerprints. A model drug compound (zolpidem) is successfully detected from gel-lifted prints from three different surface types: glass, metal, and paper. The surface activity-based separation associated with probe electrospray approaches is shown to resolve zolpidem ions from background phthalate species, significantly enhancing the response obtained from the gel-lifter. A depletion series experiment shows that the drug residue can be detected with up to 100% efficiency after eight consecutive contacts; however, detection efficiency drops to 20% after 30 contacts. The developed approach has potential application to analysis of historical gel-lifters to obtain additional chemical information.

Analysis of GC × GC fingerprints from medicinal materials using a novel contour detection algorithm: A case of Curcuma wenyujin.

This study introduces an innovative contour detection algorithm, PeakCET, designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram (GC × GC). This method innovatively combines contour edge tracking with affinity propagation (AP) clustering for peak detection in GC × GC fingerprints, the first in this field. Contour edge tracking significantly reduces false positives caused by "burr" signals, while AP clustering enhances detection accuracy in the face of false negatives. The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin. PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples. Furthermore, this algorithm compares the GC × GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins. The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues. Each sample exhibits unique characteristic components alongside common ones, and variations in content may influence their therapeutic effectiveness. This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional (2D) fingerprint analysis of GC × GC data.

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties.

Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabolism of xenobiotics. Inhibition of any of these five CYP isozymes causes drug-drug interactions with high pharmacological and toxicological effects. So, the inhibition or non-inhibition prediction of these isozymes is of great importance. Many techniques based on machine learning and deep learning algorithms are currently being used to predict whether these isozymes will be inhibited or not. In this study, three different molecular or substructural properties that include Morgan, MACCS and Morgan (combined) and RDKit of the various molecules are used to train a distinct SVM model against each isozyme (1A2, 2C9, 2C19, 2D6, and 3A4). On the independent dataset, Morgan fingerprints provided the best results, while MACCS and Morgan (combined) achieved comparable results in terms of balanced accuracy (BA), sensitivity (Sn), and Mathews correlation coefficient (MCC). For the Morgan fingerprints, balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4 on an independent dataset ranged between 0.81 and 0.85, 0.61 and 0.70, 0.72 and 0.83, respectively. Similarly, on the independent dataset, MACCS and Morgan (combined) fingerprints achieved competitive results in terms of balanced accuracies (BA), Mathews correlation coefficients (MCC), and sensitivities (Sn) against each CYPs isozyme, 1A2, 2C9, 2C19, 2D6, and 3A4, which ranged between 0.79 and 0.85, 0.59 and 0.69, 0.69 and 0.82, respectively.

pyPGCF: A Python Software for Phylogenomic Analysis, Species Demarcation, Identification of Core, and Fingerprint Proteins of Bacterial Genomes That Are Important for Plants.

This computational protocol describes how to use pyPGCF, a python software package that runs in the linux environment, in order to analyze bacterial genomes and perform: (i) phylogenomic analysis, (ii) species demarcation, (iii) identification of the core proteins of a bacterial genus and its individual species, (iv) identification of species-specific fingerprint proteins that are found in all strains of a species and, at the same time, are absent from all other species of the genus, (v) functional annotation of the core and fingerprint proteins with eggNOG, and (vi) identification of secondary metabolite biosynthetic gene clusters (smBGCs) with antiSMASH. This software has already been implemented to analyze bacterial genera and species that are important for plants (e.g., Pseudomonas, Bacillus, Streptomyces). In addition, we provide a test dataset and example commands showing how to analyze 165 genomes from 55 species of the genus Bacillus. The main advantages of pyPGCF are that: (i) it uses adjustable orthology cut-offs, (ii) it identifies species-specific fingerprints, and (iii) its computational cost scales linearly with the number of genomes being analyzed. Therefore, pyPGCF is able to deal with a very large number of bacterial genomes, in reasonable timescales, using widely available levels of computing power.

Ultrasound-driven facile fabrication of Pd doped SnO2 hierarchical superstructures: Structural, growth mechanism, dermatoglyphics, and anti-cancer activity.

This research introduces a novel method that leverages Spirulina extract (S.E) as a bio-surfactant in the ultrasound-assisted synthesis (UAS) of Pd3+ (0.25-10 mol%) doped tin oxide (SnO2) self-assembled superstructures. Nanotechnology has witnessed significant advancements in recent years, driven by the exploration of novel synthesis methods and the development of advanced nanomaterials tailored for specific applications. Metal oxide nanoparticles, particularly SnO2, have garnered considerable attention due to their versatile properties and potential applications in various fields, including gas sensing, catalysis, and biomedical engineering. The study explores how varying influential parameters like S.E concentration, sonication time, pH, and sonication power can influence the resulting superstructures' morphology, size, and shape. A theoretical model for forming different hierarchical superstructures (HS) is proposed. X-ray diffraction (XRD) analysis confirms the crystalline tetragonal rutile phase of the SnO2:Pd HS. Raman spectroscopy reveals a red shift in the A1g mode, indicating phonon confinement due to various defects in the SnO2 structure. Further characterization using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) provides insights into particle size, surface morphology, elemental composition, and binding energy. The study also demonstrates the application of optimized SnO2:3Pd HS in developing latent fingerprints (LFPs) on different surfaces using a simple powder dusting (PD) method, with the fingerprints (FPs) visualized under normal light. A mathematical model developed in Python-based software is used to analyze various features of the developed FPs, including pore properties such as number, position, inter-spacing, area, and shape. Additionally, an in vitro MTT assay shows concentration-dependent anticancer activity of SnO2:3Pd nanoparticles (NPs) on MCF7 cell lines, highlighting their potential as a promising cancer treatment option. Overall, the study suggests that the optimized HS can serve as multifunctional platforms for biomedical and dermatoglyphics applications, demonstrating the versatility and potential of the synthesized materials.

Migratory history of the threespine stickleback Gasterosteus aculeatus in western Ireland.

Microchemical analysis of trace elements in otoliths and bio-mineralised earstones of teleost fishes is an emerging approach to analysing the environmental migratoryand life histories of fish species. The migration history of the three-spine stickleback (Gasterosteus aculeatus) collected in western Ireland was examined using calcium (Ca) and strontium (Sr) concentrations in otoliths. The otolith Sr:Ca values fluctuated with the habitat. The habitat use and migration history of G. aculeatus can be categorised into two types, as determined by the mean value and life history transect of the otolith Sr:Ca; that is, freshwater and estuarine residents, whereas there were no anadromous sticklebacks which is believed to be a typical migration pattern in the species. The otolith Sr:Ca profiles of the freshwater resident fishes exhibited constantly low Sr:Ca values, averaging 0.41-0.58 × 10-3 from the core towards the edge. However, the otolith Sr:Ca profiles of the estuarine resident fishes exhibited constantly high Sr:Ca values from the core towards the edge without a clear transition point from low to high Sr:Ca values, as found in the anadromous fish, averaging 1.82-4.26 × 10-3. The present study is the first published confirmation that 100 % of sticklebacks living in coastal habitats in Ireland > have an estuarine resident migratory pattern, constantly residing in marine environments or brackish water throughout their lifespan and not in freshwater environments in Ireland.

Red Emitting Solid-State CDs/PVP with Hydrophobicity for Latent Fingerprint Detection.

Fluorescent carbon dots (CDs) are a new type of photoluminescent nanomaterial. Solid-state CDs usually undergo fluorescence quenching due to direct π-π* interactions and superabundant energy resonance transfer. Therefore, the preparation of solid-state fluorescent CDs is a challenge, especially the preparation of long wavelength solid-state CDs. In this research, long wavelength emission CDs were successfully synthesized by solvothermal methods, and the prepared CDs showed good hydrophobicity. The composite solid-state CDs/PVP (Polyvinyl pyrrolidone) can emit strong red fluorescence, and the quantum yield (QY) of the CDs/PVP powder reaches 18.9%. The prepared CDs/PVP solid-state powder was successfully applied to latent fingerprint detection. The results indicate that the latent fingerprints developed by CDs/PVP powder have a fine definition and high contrast visualization effect, which proves that the prepared CDs/PVP has great application potential in latent fingerprint detection. This study may provide inspiration and ideas for the design of new hydrophobic CDs.

Study of the fingerprints of a Spanish sample for the determination of the hand and finger.

The individuality and permanence of fingerprints make of them a very useful feature in the identification of individuals. There are now automated computer programmes that allow a quick comparison between a fingermark and a database. However, in order to assess the strength of evidence in fingerprint identification, complementary information on the frequencies of the different morphological features of the dermopapillary ridges is required. This idea is used in this work as a starting point to evaluate the frequencies of the parameters used in the determination of the hand and finger in a large sample of 2600 fingerprints taken from 134 male and 127 female Spanish population. Based on these fingerprints, the frequencies of different categories of the following parameters were obtained: type of pattern, slope of the apex ridge, subtype of two-delta pattern, ridge tracing, major angle, major ridge count, bisector, rotation of the central ridge, assimilation to loops and slant. Moreover, the results have shown that these characters are useful for the determination of the hand in whorls (two-delta pattern) and loops (one-delta pattern), but not for the determination of the finger. The most useful and classificatory parameter when determining the hand of origin of a two-delta fingerprint is the slope of the apex ridge, and for the one-delta pattern, knowing the location of the delta allows the correct estimation of the hand of a fingerprint in more than 93% of the cases. The data presented in this paper are novel and can be used by latent print examiners to improve the statistical basis of their decisions in reaching conclusions.

Changes in the Main Physicochemical Properties and Electrochemical Fingerprints in the Production of Sea Buckthorn Juice by Pectinase Treatment.

Enzymatic hydrolysis using pectinase is critical for producing high-yield and quality sea buckthorn juice. This study determined the optimal temperature, time, and enzyme dosage combinations to guide manufacturers. A temperature of 60 °C, hydrolysis time of 3 h, and 0.3% enzyme dosage gave 64.1% juice yield-25% higher than without enzymes. Furthermore, monitoring physicochemical properties reveals enzyme impacts on composition. Higher dosages increase soluble solids up to 15% and soluble fiber content by 35% through cell wall breakdown. However, excessive amounts over 0.3% decrease yields. Pectin concentration also declines dose-dependently, falling by 91% at 0.4%, improving juice stability but needing modulation to retain viscosity. Electrochemical fingerprinting successfully differentiates process conditions, offering a rapid quality control tool. Its potential for commercial inline use during enzymatic treatment requires exploration. Overall, connecting optimized parameters to measured effects provides actionable insights for manufacturers to boost yields, determine enzyme impacts on nutrition/functionality, and introduce novel process analytical technology. Further investigations of health properties using these conditions could expand sea buckthorn juice functionality.

A Novel LIBS Sensor for Sample Examinations on a Crime Scene.

In this work, we present a compact LIBS sensor developed for characterization of samples on a crime scene following requirements of law enforcement agencies involved in the project. The sensor operates both in a tabletop mode, for aside measurements of swabbed materials or taken fragments, and in handheld mode where the sensor head is pointed directly on targets at the scene. The sensor head is connected via an umbilical to an instrument box that could be battery-powered and contains also a color camera for sample visualization, illumination LEDs, and pointing system for placing the target in focus. Here we describe the sensor's architecture and functionalities, the optimization of the acquisition parameters, and the results of some LIBS measurements. On nano-plotted traces at silica wafer and in optimized conditions, for most of the elements the detection limits, in term of the absolute element masses, were found to be below 10 picograms. We also show results obtained on some representative materials, like fingerprints, swabbed soil and gunshot residue, varnishes on metal, and coated plastics. The last, solid samples were used to evaluate the depth profiling capabilities of the instrument, where the recognition of all four car paint layers was achieved.