Chemical profiling and probiotic viability assessment in Gueuze-style beer: Fermentation dynamics, metabolite and sensory characterization, and in vitro digestion resistance.

Probiotic viability, metabolite concentrations, physicochemical parameters, and volatile compounds were characterized in Gueuze beers formulated with probiotic lactic acid bacteria (LAB) and yeast. Additionally, the sensory profile of the beers and the resistance of the probiotics to digestion were determined. The use of 2 International Bitterness Units resulted in high concentrations of probiotic LAB but a decline in probiotic yeast as pH decreased. Secondary fermentation led to the consumption of maltose, citric acid, and malic acid, and the production of lactic and propionic acids. Carbonation and storage at 4 °C had minimal impact on probiotic viability. The addition of probiotic LAB resulted in a distinct aroma profile with improved sensory characteristics. Our results demonstrate that sour beers produced with probiotic LAB and a probiotic yeast, and fermented using a two-step fermentation process, exhibited optimal physicochemical parameters, discriminant volatile compound profiles, promising sensory characteristics, and high probiotic concentrations after digestion.

Exploring the role of Yuxuebi tablet in neuropathic pain with the method of similarity research of drug pharmacological effects based on unsupervised machine learning.

Introduction: Having multiple pharmacological effects is a characteristic of Traditional Chinese Medicine (TCM). Currently, there is a lack of suitable methods to explore and discover modern diseases suitable for TCM treatment using this characteristic. Unsupervised machine learning technology is an efficient strategy to predict the pharmacological activity of drugs. This study takes Yuxuebi Tablet (YXB) as the research object. Using the unsupervised machine learning technology of drug cell functional fingerprint similarity research, the potential pharmacological effects of YXB were discovered and verified. Methods: LC-MS combined with the in vitro intestinal absorption method was used to identify components of YXB that could be absorbed by the intestinal tract of rats. Unsupervised learning hierarchical clustering was used to calculate the degree of similarity of cellular functional fingerprints between these components and 121 marketed Western drugs whose indications are diseases and symptoms that YXB is commonly used to treat. Then, based on the Library of Integrated Network-based Cellular Signatures database, pathway analysis was performed for selected Western drugs with high similarity in cellular functional fingerprints with the components of YXB to discover the potential pharmacological effects of YXB, which were validated by animal experiments. Results: We identified 40 intestinally absorbed components of YXB. Through predictive studies, we found that they have pharmacological effects very similar to non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids. In addition, we found that they have very similar pharmacological effects to anti-neuropathic pain medications (such as gabapentin, duloxetine, and pethidine) and may inhibit the NF-κB signaling pathway and biological processes related to pain perception. Therefore, YXB may have an antinociceptive effect on neuropathic pain. Finally, we demonstrated that YXB significantly reduced neuropathic pain in a rat model of sciatic nerve chronic constriction injury (CCI). Transcriptome analysis further revealed that YXB regulates the expression of multiple genes involved in nerve injury repair, signal transduction, ion channels, and inflammatory response, with key regulatory targets including Sgk1, Sst, Isl1, and Shh. Conclusion: This study successfully identified and confirmed the previously unknown pharmacological activity of YXB against neuropathic pain through unsupervised learning prediction and experimental verification.

An intelligent sensing platform for detecting and identifying biochemical substances based on terahertz spectra.

This paper presents the development of an intelligent sensing platform dedicated to accurately identifying terahertz (THz) spectra obtained from various biochemical substances. The platform currently has two distinct identification modes, which focus on identifying five amino acids, namely phenylalanine, methionine, lysine, leucine, and threonine, and five carbohydrates, namely aspartame, fructose, glucose, lactose monohydrate, and sucrose based on their THz spectra. The first mode, called One-dimensional THz Spectrum Identification (OTSI), combines THz time-domain spectroscopy (THz-TDS) with the proposed mini convolutional neural network (MCNN) model. THz-TDS detects biochemical substances, while the MCNN model identifies the THz spectra. The MCNN model has a simple structure and only needs to deal with the THz absorption coefficients of biochemical substances, which are less computationally intensive and easily converged. The model can achieve 99.07 % accuracy in identifying one-dimensional THz spectra of the ten biochemical substances. The second mode, THz Spectrum Image-based Identification (TSII), applies the YOLO-v5 target detection model to THz spectral image recognition. The YOLO-v5 model uses THz absorption peaks as identification features and can identify biochemical substances based on only one or several THz absorption peaks. The overall identifying accuracy of the YOLO-v5 model for ten biochemical substances is 96.20 %. We also compared the MCNN and YOLO-v5 models with other deep learning and machine learning models, which demonstrate that they have better performance. This feature broadens the platform's utility in biomolecular analysis and paves the way for further research and development in detecting and analyzing diverse biological compounds.

Characterization and In vitro Release & In vivo Behavior Study of Self-Assembled Nano-Emulsion in XiaoYao Pill for Enhanced Drug Delivery.

BACKGROUND: Traditional Chinese medicine formulations often contain hydrophobic components with limited solubility and stability, leading to low oral bioavailability. Self-assembled nanoparticles (SANs) have shown promise in enhancing oral bioavailability of these components. However, whether un-decocted Chinese herbal pellets can generate SANs and the impact of SANs formed by multiple components on pharmacokinetic parameters remains unexplored. METHODS: In this study, single-factor approach was employed to determine the optimal separation method of nano-emulsion phase of XiaoYao pill (N-XY). Morphological and particle size analyses confirmed the nanoscale nature of N-XY. High-performance liquid chromatography (HPLC) fingerprint analysis was conducted to compare the distribution of active ingredients among three different phases of XiaoYao pill (XY pill). In vitro release studies were performed to evaluate the release mechanism of four ingredients from N-XY. Additionally, in vivo pharmacokinetics and tissue distribution behaviors were investigated in rats. RESULTS: N-XY exhibited uniform and stable characteristics as a water-in-oil (O/W) nano-emulsion. Fingerprint analysis identified 25 characteristic peaks and 8 key ingredients in N-XY, with the highest peak areas. In vitro release studies showed a sustained release behavior of N-XY. The pharmacokinetics study showed that the ferulic acid of N-XY had a 1.37-fold higher AUC, 1.44-fold lower Vd/F, 1.39-fold lower CL/F, and a prolonged t1/2 than A-XY, indicating enhanced bioavailability due to reduced elimination. Furthermore, the tissue distribution revealed that the levels of paeoniflorin and ferulic acid from N-XY significantly increased in liver, spleen, lungs, uterus and ovaries, exhibiting targeting characteristics. CONCLUSION: This study comprehensively explored the formation, characterization, and pharmacokinetics of nano-emulsion in XY pill, introducing novel perspectives and initiating preliminary research on potential SANs in un-decocted traditional Chinese medicine formulations. It also emphasized the importance of enhancing pharmacokinetics of hydrophobic components in Chinese herbal formulations and laid the foundation for future nano-formulation research for XY pill.

Identification of Decedents by Restoring Mummified Fingerprints: Forensic Dermatology in the Investigation of Mummy Dermatoglyphics.

Fingerprints are created by elevations and depressions on the fingertip pads. Each person has their own unique fingerprints which can be used in the identification of that individual when alive, during the immediate postmortem period, or even after the digits have become mummified. Mummification can occur naturally; it can be partial (such as localized to only the hands and feet, extensive, or complete. Obtaining fingerprints after the skin has become mummified can be attempted while the digits remaining intact with the hand; however, the digits may need to be removed from the hand and the finger pads may also need to be separated from the underlying bone to secure an adequate fingerprint. Frequently, the mummified tissue needs to be rehydrated; numerous solutions have been used that increase the turgor of the digits, provide softening and pliability of the tissue, and enhance the details of the finger pad ridges. An aqueous solution of sodium carbonate (either combined with acetic acid or combined with 95 percent ethanol and distilled water) was found to be most effective for rehydration. Thereafter, various techniques can be attempted to obtain the fingerprint. These include the traditional method of inking and rolling of the finger or photographing the finger. Powders (such as aluminum powder, black fingerprint powder, white cornstarch-based powder, or fluorescent powder) can be used to enhance the ridge features; adhesive tape can be pressed against the powdered digit and the print pattern preserved by applying the adhesive tape to a clear transparency sheet. In addition, molds (using modeling clay or silicone rubber) and casts (using plaster of Paris, dental casting materials, or putty) can be made of the digits; either the molds or the casts or both can be photographed with or without prior application of fingerprint powder. Transillumination, using a fiber optic light source to illuminate the epidermis and underlying remaining dermis of a scraped and defleshed finger pad can be used to demonstrate the finger ridge pattern when the photographing the tip of the digit. In summary, forensic dermatology can have an integral role in obtaining fingerprints from mummified digits, which can be successfully used for the identification of the decedent.

Insight into flavor difference of cherry (Prunus avium L.) grown in facility environment and outdoors through metabolomics and correlation analysis.

The flavor profiles of cherries cultivated in greenhouse and those grown in open fields show significant variations, however, the underlying flavor-contributing factors remain unidentified. Hence, a joint investigation with widely targeted metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis for the Russia 8 and Tieton cherry cultivars was conducted using UPLC-MS/MS and GC × GC-TOFMS to clarify the flavor differences of open-air and greenhouse-grown cherries. The study found that open-air cultivation could lead to the accumulation of non-volatile flavor substances and prompted appearance of higher acidity, astringency, plum-like flavor, and fresh herb notes; most of differential metabolites were significantly positively correlated with astringency, plum-like flavor and bitterness. Through correlation analysis and path analysis, potential flavor components and key important pathways contributing to flavor disparities were provided, and light intensity, soil moisture content, temperature and humidity were inferred as the main factors affecting the flavor profiles of open-air and greenhouse-grown cherries.

[Quality identification of Aurantii Fructus Immaturus from different origins and their adulterants].

Aurantii Fructus Immaturus(AFI) is a traditional Chinese herbal medicine with multiple origins from Citrus aurantium and its legally cultivated variants. With advancements in agricultural biotechnology, many new cultivated varieties have sprung up,leading to an abundance of AFI adulterants and chaos in the herbal medicine markets. This study developed a specific identification method for AFI and its closely related adulterants by examining the appearance trait, content of extract, and multiple ingredients,involving indicators such as the ratio of pulp capsule to cross section diameter(Pc/Cs ratio), the content of extract, and the profile of 11 ingredients. The research finds that:(1) Pc/Cs ratio can conveniently identify adulterants such as Poncirus trifoliata, Ju, and Babagan from the genuine AFI.(2) The extract content can be used to identify adulterants originated from C. wilsonii with C. aurantium.(3) The contents of synephrine in all the samples were in accordance with the Chinese Pharmacopoeia except for the adulterants from P. trifoliata, C. wilsonii, C. aurantium 'Changshanhuyou' and orah mandarins. The synephrine content was high as 1. 40% in some C. sinensis varieties. The mass fraction of hesperidin was over 10. 00% in C. sinensis, while it was below 2. 50% in C. aurantium. C. aurantium contained high levels of naringin(3. 96%-15. 21%) and neo-hesperidin(9. 38%-21. 93%).(4) The compositions of adulterants from P. trifoliata and C. wilsonii were more similar to that of C. aurantium 'Daidai', but with significantly lower neo-hesperidin content(0. 03%-0. 14%) than that in C. aurantium, and they lacked hesperetin and tangeretin. C. maxima(originating from C. maxima) showed closer composition to Choucheng and hybrid originated from Citrus aurantium × Poncirus trifoliata, but had higher hesperidin content(3. 13%) than that in C. aurantium. Ju was closely related to C. sinensis and neither contained naringin nor neo-hesperidin. Hesperidins in Babagan and orah mandarins were similar to that in C. sinensis, with none containing rhoifolin. These quality indicators in combination can accurately distinguish between C. sinensis, C. aurantium, and their closely related adulterants(P. trifoliata, C. wilsonii, C. maxima, orah mandarins and C. reticulata), which are expected to provide a systematic method for quality control of AFI.

Multi-level fingerprinting and immune activity evaluation for polysaccharides from Dioscorea opposita Thunb.

To establish the quality control method of Dioscorea opposita Thunb., the multi-level fingerprinting of polysaccharides was established and the relationship between fingerprint and immune activity was analyzed. The two molecular weight segments Mw1 (1.38 × 105-1.63 × 106 Da) and Mw2 (3.27 × 103-4.37 × 103 Da), thirteen infrared absorption peaks (3399.26 cm-1, 2929.32 cm-1, 1631.78 cm-1, 1400.39 cm-1, 1351.80 cm-1, 1123.58 cm-1, 1024.76 cm-1, 931.53 cm-1, 854.76 cm-1, 760.43 cm-1, 708.14 cm-1, 616.47 cm-1, and 526.78 cm-1), and four monosaccharides (Man, Rha, GalA, and Glc) were used to evaluate the quality of Dioscorea opposita Thunb. The molecular weight fragments of Mw1, FT-IR absorption peaks of 1631.78 cm-1, and two monosaccharides (Man and Glc) would be used to identify Dioscorea opposita Thunb. polysaccharide (DOP) from different origins. The relationship of spectrum-effect showed that polysaccharides with features such as higher Mw1, a lower peak height of 1631.78 cm-1, higher content of Man, and lower content of Glc exerted stronger immune activity. In conclusion, this study established a polysaccharide-based quality evaluation method for Dioscorea opposita Thunb. and explored the relationship between polysaccharide fingerprints and in vitro immune activity, which provided a basis for further research on Dioscorea opposita Thunb.

Pongo's ecological diversity from dental macrowear analysis.

OBJECTIVES: Orangutans are found in tropical rainforests of Borneo (Pongo pygmaeus) and Sumatra (Pongo abelii and Pongo tapanuliensis), and they are primarily considered frugivorous species. However, ecological studies reported differences in feeding behavior between these species. P. pygmaeus spend more time feeding on low-quality foods, such as bark and tough vegetation than do P. abelii. The aim of this study is to investigate if there is any geographic variation in molar macrowear pattern between the two species. METHODS: We analyzed the macrowear pattern of second mandibular molars of 58 orangutan specimens combining dental topographic measurements (occlusal relief index, percentage of dentine exposure, and percentage of enamel wear) with the occlusal fingerprint analysis method. RESULTS: The molar macrowear patterns of P. abelii and P. pygmaeus show no significant differences in most of the variables examined in this study. While P. pygmaeus molars are generally characterized by flatter occlusal morphology with slightly larger crushing and grinding areas than those of P. abelii, these differences are not statistically significant. However, we do observe a more variable wear in P. pygmaeus. CONCLUSIONS: Similarities in molar macrowear patterns between the two species could be linked to overlapping long-term diets, primarily composed of ripe and pulpy fruits. Dental macrowear is a cumulative process that occurs during the individual's lifetime, and it may be difficult to detect seasonal changes through its examination. Future studies could integrate tooth micro- and macrowear data for a better understanding of primate dental ecology.

Effective use of skin microbiome signatures for fingerprint identification.

BACKGROUND: Recent advances have increased the importance of the human microbiome, including the skin microbiome. Despite the hand microbiome research, the factors affecting the composition of the hand microbiome and their personal characteristics are incompletely known. OBJECTIVES: Despite changing environmental factors and personal variation, we aimed to indicate the interpersonal distinction between skin microbiota using simple and rapid molecular methods. METHODS: Over a non-consecutive 10-day period, samples were taken from 10 adult individuals, and ribotyping analysis of the 16S and 23S genes of S. epidermidis was performed on each skin sample. Additionally, EcoRI and HindIII enzyme reactions and variable number tandem repeat (VNTR) reactions of S. epidermidis obtained from DNA samples were performed. The skin microbiomes of individuals were evaluated along with the microbiome profiles left on the surfaces they touched. RESULTS: In the environmental samples taken, it has been observed that people preserve their core skin microbiota characters and carry them to their environment. It was determined that the highest similarity rate was 77.14%, and the lowest similarity rate was 31.74%. CONCLUSION: Our study showed that the core skin microbiota retains its characteristics and leaves traces in environments. The fact that the personal microbiome remains unchanged despite environmental differences and has characteristic features has shown that it can be used in forensic sciences to distinguish individuals from each other. These results with simple and rapid methods further increased the importance and significance of the study. The findings indicate that personal skin microbiota can provide a significant contribution to criminal investigations by increasing accuracy and reliability, especially in forensic analyses.

A multi-view feature representation for predicting drugs combination synergy based on ensemble and multi-task attention models.

This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The 'MVME' model is assessed using the O'Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC).

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management. However, deep ensemble learning has not been investigated properly in the case of 6G. It is anticipated that it will incorporate drone-based BTS and cellular networks that, in one way or another, may be subjected to jamming, intentional interferences, or other dangers from unauthorized UAVs. Thus, this study is conducted based on Radio Frequency Fingerprinting (RFF) of drones identified to detect unauthorized ones so that proper actions can be taken to protect the network's security and integrity. This paper proposes a novel method-a Composite Ensemble Learning (CEL)-based neural network-for drone signal classification. The proposed method integrates wavelet-based denoising and combines automatic and manual feature extraction techniques to foster feature diversity, robustness, and performance enhancement. Through extensive experiments conducted on open-source benchmark datasets of drones, our approach demonstrates superior classification accuracies compared to recent benchmark deep learning techniques across various Signal-to-Noise Ratios (SNRs). This novel approach holds promise for enhancing communication efficiency, security, and safety in 6G networks amidst the proliferation of drone-based applications.

Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network.

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network.

Full composition-wide association study identifies the chemical markers to distinguish different processed camellia oils: Integrating multi-targets with chemometrics.

To identify chemical-markers from hot-pressed, cold-pressed, organic-solvent, aqueous-enzymatic and water extracted camellia oils (HPO, CPO, OSO, AEO, WEO). We report a full composition-wide association study based on GC-MS, LC-MS and 1HNMR. Squalene, ß-amyrin and lupeol were potential-markers in distinguishing different oils through GC-MS. Naringenin, FA 18:1 + 10, undecanedioic acid and tridecanedioic acid exhibited were up-regulated in HPO. 16-Hydroxyhexadecanoic acid, octadecanoic acid and 9-hydroxyoctadecadienoic acid were potential-metabolites in CPO. Characteristic-markers in WEO were hydroquinidine and undecanedioic acid. Gallic acid, hydroquinidine, lichesterylic acid and 7,4'-dihydroxyflavone were biomarkers in AEO. Oleic acid, linoleic acid and triacylglycerols may be potential key markers to distinguish AEO from others via 1HNMR. Finally, Naringenin, gallic acid, kaempferol, 7,4'-dihydroxyflavone, (Z)-5,8,11-trihydroxyoctadec-9-enoic acid and ß-amyrin were screened and validate through integration of nonglyceride minor components and trace metabolites. Results provided understanding of chemical diversity for different processed-camellia oils, and proposed a complementary strategy to distinguish different camellia oils for multidimensional perspective.

Single cell sequencing delineates T-cell clonality and pathogenesis of the parapsoriasis disease group.

BACKGROUND: Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma, is often underdiagnosed in early stages due to similarities with benign dermatoses such as atopic dermatitis (AD). Furthermore, the delineation from so-called "parapsoriasis en plaque," a disease that can appear either in a small- or large-plaque form, is still controversial. OBJECTIVE: To characterize the parapsoriasis disease spectrum. METHODS: We performed single-cell RNA sequencing of skin biopsies from patients within the parapsoriasis-to-early-stage MF spectrum, stratified for small and large plaques, and compared them to AD, psoriasis and healthy control skin. RESULTS: 6 out of 8 large-plaque lesions harbored either an expanded alpha/beta or gamma/delta T-cell clone with downregulation of CD7 expression, consistent with a diagnosis of early-stage MF. By contrast, 6 out of 7 small-plaque lesions were polyclonal in nature thereby lacking a lymphomatous phenotype, and also revealed a less inflammatory microenvironment than early-stage MF or AD. Of note, polyclonal small- and large-plaque lesions characteristically harbored a population of NPY+ innate lymphoid cells and displayed a stromal signature of complement upregulation and antimicrobial hyperresponsiveness in fibroblasts and sweat gland cells, respectively. These conditions were clearly distinct from AD or psoriasis, which uniquely harbored CD3+CRTH2+ IL13-expressing "Th2A" cells or strong type 17 inflammation, respectively. CONCLUSION: These data position polyclonal small- and large-plaque dermatitis lesions as a separate disease entity, that characteristically harbors a so far undescribed ILC population. We thus propose the new term "polyclonal parapsoriasis en plaque" to this kind of lesions, as they can be clearly differentiated from early and advanced-stage MF, psoriasis and AD on several cellular and molecular levels.

Multi-Elemental Analysis and Geographical Discrimination of Greek "Gigantes Elefantes" Beans Utilizing Inductively Coupled Plasma Mass Spectrometry and Machine Learning Models.

Greek giant beans, also known as "Gigantes Elefantes" (elephant beans, Phaseolus vulgaris L.,) are a traditional and highly cherished culinary delight in Greek cuisine, contributing significantly to the economic prosperity of local producers. However, the issue of food fraud associated with these products poses substantial risks to both consumer safety and economic stability. In the present study, multi-elemental analysis combined with decision tree learning algorithms were investigated for their potential to determine the multi-elemental profile and discriminate the origin of beans collected from the two geographical areas. Ensuring the authenticity of agricultural products is increasingly crucial in the global food industry, particularly in the fight against food fraud, which poses significant risks to consumer safety and economic stability. To ascertain this, an extensive multi-elemental analysis (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, Se, Sr, Ta, Ti, Tl, U, V, W, Zn, and Zr) was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Bean samples originating from Kastoria and Prespes (products with Protected Geographical Indication (PGI) status) were studied, focusing on the determination of elemental profiles or fingerprints, which are directly related to the geographical origin of the growing area. In this study, we employed a decision tree algorithm to classify Greek "Gigantes Elefantes" beans based on their multi-elemental composition, achieving high performance metrics, including an accuracy of 92.86%, sensitivity of 87.50%, and specificity of 96.88%. These results demonstrate the model's effectiveness in accurately distinguishing beans from different geographical regions based on their elemental profiles. The trained model accomplished the discrimination of Greek "Gigantes Elefantes" beans from Kastoria and Prespes, with remarkable accuracy, based on their multi-elemental composition.

All-Dielectric Metasurface-Based Terahertz Molecular Fingerprint Sensor for Trace Cinnamoylglycine Detection.

Terahertz (THZ) spectroscopy has emerged as a superior label-free sensing technology in the detection, identification, and quantification of biomolecules in various biological samples. However, the limitations in identification and discrimination sensitivity of current methods impede the wider adoption of this technology. In this article, a meticulously designed metasurface is proposed for molecular fingerprint enhancement, consisting of a periodic array of lithium tantalate triangular prism tetramers arranged in a square quartz lattice. The physical mechanism is explained by the finite-difference time-domain (FDTD) method. The metasurface achieves a high quality factor (Q-factor) of 231 and demonstrates excellent THz sensing capabilities with a figure of merit (FoM) of 609. By varying the incident angle of the THz wave, the molecular fingerprint signal is strengthened, enabling the highly sensitive detection of trace amounts of analyte. Consequently, cinnamoylglycine can be detected with a sensitivity limit as low as 1.23 µg·cm-2. This study offers critical insights into the advanced application of THz waves in biomedicine, particularly for the detection of urinary biomarkers in various diseases, including gestational diabetes mellitus (GDM).

Non-Targeted Nuclear Magnetic Resonance Analysis for Food Authenticity: A Comparative Study on Tomato Samples.

Non-targeted NMR is widely accepted as a powerful and robust analytical tool for food control. Nevertheless, standardized procedures based on validated methods are still needed when a non-targeted approach is adopted. Interlaboratory comparisons carried out in recent years have demonstrated the statistical equivalence of spectra generated by different instruments when the sample was prepared by the same operator. The present study focused on assessing the reproducibility of NMR spectra of the same matrix when different operators performed individually both the sample preparation and the measurements using their spectrometer. For this purpose, two independent laboratories prepared 63 tomato samples according to a previously optimized procedure and recorded the corresponding 1D 1H NMR spectra. A classification model was built using the spectroscopic fingerprint data delivered by the two laboratories to assess the geographical origin of the tomato samples. The performance of the optimized statistical model was satisfactory, with a 97.62% correct sample classification rate. The results of this work support the suitability of NMR techniques in food control routines even when samples are prepared by different operators by using their equipment in independent laboratories.

Standardizing fingerprint minutiae: A comprehensive inventory and statistical analysis based on Brazilian data.

This study introduces a comprehensive inventory of 54 fingerprint minutiae and their variations aimed at standardizing characteristic point identification within forensic science. By analyzing a strategically sampled collection of fingerprints from the Brazilian Federal Police database, stratified by sex and geographic location, our research uncovers the complex interplay between various levels of fingerprint details (L1D, L2D, and L3D) and demographic factors such as sex and finger type. The sample encompassed the entire proposed list of minutiae, affirming the diversity and representativeness of the Brazilian populace, which had, for the first time, its minutiae frequency distributions studied. This investigation proposes a systematic approach for enhancing fingerprint identification accuracy by minimizing data categorization losses and lays the groundwork for more uniform comparative studies in the field. Our findings, derived from a review of contemporary studies and traditional identification manuals, suggest a step towards establishing a universally accepted standard for fingerprint minutiae classification.

Forensic science capacity development: A case study of Timor-Leste.

Forensic science is socially constructed within particular contexts, with notable challenges for countries of the Global South. This study explored forensic capacity development taking place under a bilateral agreement between the governments of Timor-Leste and Australia through the Timor-Leste Police Development Program. Data were collected through in-country site visits, observations, and interviews with key personnel from both countries. The findings indicate tangible developments, including the establishment of a forensic fingerprint laboratory, training in fingerprint expertise from crime scene to court, and engagement in innovative practices. These developments contribute to optimism amongst leaders and practitioners about the potential for forensic capacity. However, concerns exist about the precariousness of achievements, the need for continued training and development within and beyond the forensic team, untapped potential for inter-agency collaboration, and other human resource considerations. The findings suggest a need for organisational commitment and ongoing high-quality partnerships to maintain momentum and facilitate long-term sustainability.