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The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations with genetic profiles for DRP remains unclear. This study conducts a comprehensive evaluation of the efficacy of various drug molecular representations employing cutting-edge machine learning models under various experimental settings. Our findings reveal that the inclusion of molecular representations from either PubChem fingerprints or SMILES can significantly enhance the performance of DRPs when used in conjunction with deep learning models. However, the optimal choice of drug molecular representation can vary depending on the predictive model and the specific DRP task. The insights derived from our study offer useful guidance on selecting the most suitable drug molecular representations for constructing efficient predictive models for DRPs, aiding for drug repurposing, personalized medicine, and new drug discovery. [ABSTRACT FROM AUTHOR]

In the fingermark detection field, background illustrations may negatively impact the visibility of the marks in presence. They can indeed locally reduce the contrast or mask ridge details. When conducting a research project, the choice for plain or illustrated substrates is mostly driven by the research objectives as well as the development level of the investigated technique (e.g., new reagent, comparison between two techniques, semi‐operational test). Also, it is not clear how the human assessors in charge of grading the quality of the detected fingermarks actually cope with fingermarks standing astride background prints. In recent publications, latent quality metrics (LQM) and improved latent fingerprint quality metrics (ILFQM) algorithms have been identified as potential alternatives to traditional human assessment. The authors of the present study were therefore interested in assessing the performance of these algorithms with regards to illustrated backgrounds. To reach that goal, 480 fingermarks were deposited on porous (240) and non‐porous (240) items presenting various kinds of illustrations (e.g., colored areas, writings, geometric shapes, lines). Several detection techniques were then applied in sequence: 1,2‐indanedione/zinc + ninhydrin + physical developer for porous items, and superglue fuming followed by dye‐staining or black magnetic powder for non‐porous items. A total of 884 pictures were obtained and their quality assessed by LQM and ILFQM. The results were then analyzed to determine if background prints may influence the metrics provided both algorithms, and if yes, to which extent. [ABSTRACT FROM AUTHOR]

Fingerprints hold paramount importance in various fields due to their unique characteristics, making them invaluable for identification and verification purposes. This paper presents a state-of-the-art methodology for fingerprint recognition leveraging the Hybrid Deep Learning Model - SpatioTemporalNet (STNet), enhanced by Hybrid GAPSO Optimization for Feature Matching. By fusing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced optimization techniques, this approach revolutionizes the landscape of fingerprint analysis. STNet integrates both static and dynamic feature analysis, enabling adaptability to diverse conditions encountered in real-world scenarios. The integration of STNet and Hybrid GAPSO Optimization represents a significant advancement in fingerprint recognition technology, offering unparalleled levels of security and reliability. By harnessing the power of deep learning and optimization, the proposed methodology excels in handling various challenges such as partial prints, distortions, and variations in fingerprint impressions. Implemented using Python, the proposed methodology undergoes rigorous experimentation, demonstrating exceptional performance with an accuracy rate of 98.7%. This high level of accuracy underscores the effectiveness and reliability of the approach in accurately identifying individuals based on their unique fingerprint patterns. [ABSTRACT FROM AUTHOR]

The electrostatic behavior of a hydrogen sulfide (H 2 S)-based double-gate (DG) single-electron transistor (SET) for the charge detection of toxic H 2 S gas has been investigated and its potential for switching applications with different orientations of H 2 S quantum dot explored. The electronic properties of the SET operating in the coulomb blockage region have been analyzed using advanced modeling techniques like density functional theory (DFT) and non-equilibrium Green's function formalism, implemented in the QuantumWise-ATK. Through simulations, the charging energies of H 2 S molecules within the SET environment have been calculated, and the plot of total energy with gate voltage developed, which serves as a basis to generate the charge stability diagram. This diagram illustrates the nature of electron conduction in different charge states, which act as unique electronic fingerprints for the identification of H 2 S gas in different orientations. Moreover, it is observed that operating this SET model under negative gate bias is more energetically efficient than under positive bias. [ABSTRACT FROM AUTHOR]

Ischemic stroke is currently a major public health hazard.Yangyin Tongnao Granules (YYTN), a traditional Chinese medicinal prescription, exerts potential therapeutic effects on subsequent cerebral ischemia-reperfusion injury (CIRI) after ischemic stroke. However, further studies are required to comprehend the underlying mechanism of YYTN for treating CIRI and the associated spectrum-effect mechanisms. To investigate the coordinated correlation between the fingerprint and the pharmacodynamic indexes of the effective components (total flavonoids, total saponins, total alkaloids, and total phenolic acids) in YYTN for treating CIRI in rats. The fingerprints of five specific components (ligustrazine, puerarin, ferulic acid, calycosin, and formononetin) of YYTN in rats with middle cerebral artery occlusion (MCAO) were established using high-performance liquid chromatography (HPLC), and their peak areas were quantified in plasma samples. The pharmacodynamic indexes of tumor necrosis factor-alpha (TNF-α), cytochrome c (Cyt-C), and total superoxide dismutase (T-SOD) were integrated using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to create a comprehensive evaluation index. Spectrum-effect correlation was analyzed by performing gray relation analysis (GRA), correlation analysis (CA), and partial least squares regression (PLSR). The Borda method was then applied to integrate the obtained results. In MCAO rats, the effective components of YYTN reduced TNF-α and Cyt-C and increased T-SOD, which indicates their anti-inflammatory, antiapoptotic, and antioxidant effects. Spectrum-effect CA revealed certain associations between the chromatographic peaks of the five main components and the comprehensive pharmacodynamic evaluation index. Of these components, formononetin displayed the highest correlation, whereas ferulic acid exhibited the lowest correlation. All components showed a positive correlation. Using the Borda method, the components were ranked as follows based on correlation: formononetin > calycosin > ligustrazine > puerarin > ferulic acid. The effective components of YYTN exhibited synergistic effects in the treatment of MCAO rats, which could potentially be attributed to their multitarget and multipathway mechanisms. The Borda method-based spectrum-effect correlation analysis provides a coordinated approach to investigate the relationship between fingerprint and pharmacodynamics of traditional Chinese medicine (TCM). [Display omitted] [ABSTRACT FROM AUTHOR]

Aggregation-induced emission enhancement is one of the main methods for designing copper clusters (CuNCs) with high fluorescence intensity. In this study, H 2 O can rapidly constructs stable hydrogen bonds with 2-thiobarbituric acid ligands of CuNCs, leading to denser aggregation between CuNCs. This process can occur at a level of intensity visible to the naked eye and can be completed within 30 s. As a result, the fluorescence turns to yellow with emission wavelength at 573 nm from red with emission wavelength at 638 nm, and the quantum yield has been increased to 50.03 %. The noticeable "turn on" phenomenon can be utilized for detecting alcohol content in low-alcohol liquor quality control. Additionally, bright luminous make Y-CuNCs can be employed for accurate identification of human latent fingerprints LFPs, which can accurately identify the third level structure of the LFPs. [Display omitted] • Copper cluster. • H 2 O builds hydrogen bonding networks. • Aggregation-induced emission (AIE). • QY = 50.03 %. • Alcohol content detection and latent fingerprints recognition. Aggregation-induced emission enhancement is one of the main methods for designing copper clusters (CuNCs) with high fluorescence intensity. In this study, H 2 O can rapidly construct stable hydrogen bonds with 2-thiobarbituric acid ligands of CuNCs, leading to denser aggregation between CuNCs. This process can occur at a level of intensity visible to the naked eye and can be completed within 30 s. As a result, the fluorescence turns to yellow with emission wavelength at 573 nm from red with emission wavelength at 638 nm, and the quantum yield has been increased to 50.03 %. The noticeable "turn on" phenomenon can be utilized for detecting alcohol content in low-alcohol liquor quality control. Additionally, bright luminous make Y-CuNCs can be employed for accurate identification of human latent fingerprints (LFPs), which can accurately identify the third level structure of the LFPs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Iridium developer with high-brightness AIE is created. • High-contrast LFP luminous imaging to level 3 details is achieved. • Exceptional aging resistance and repeatability are demonstrated by LFPs. • Imaging mechanism of LFPs driven by vdWs force is confirmed. Luminous imaging of latent fingerprints (LFPs) necessitates the possession of high-brightness aggregation-state luminescence by developers to ensure sufficient imaging contrast and resolution. A novel strategy involving incremental rotor modification is presented for AIE activation of the iridium developer. The rotor proliferation prominently improves the rotational activity of groups and facilitates high-efficiency RIM, thereby prompting the AIE activation of iridium developer with high luminous efficiency. Subsequently, a prompt, high-contrast, and robust LFP imaging protocol is developed utilizing the high-brightness AIE-active iridium developer. This innovative protocol realizes the luminous imaging and quantification of microscopic features in fingerprint ridges and furrows, including ridge widths, edge morphology of ridges, included angles, pores, and pore pitches with exceptional imaging contrast and refined detail resolution. Moreover, it allows for accurate identification of individual traits across diverse substrates without any pre-/post-processing to LFPs. The high-brightness AIE-active iridium developer provides outstanding aging resistance to developed fingerprints, thereby strongly supporting the acquisition, transfer, and preservation of fingerprint evidence. The luminous imaging protocol of LFPs based on high-brightness AIE exhibits robust adaptability to actual scenes and offers a premium scheme for facilitating forensic investigation. [ABSTRACT FROM AUTHOR]

The quality control of Traditional Chinese Medicine (TCM) is crucial for ensuring its efficacy and safety. Traditional methods, however, have limitations in fully capturing the natural essence of TCM, thus hindering the comprehensive exhibition of its authenticity. Addressing this challenge requires the establishment of a scientific and reasonable quality control system, which poses significant complexities due to the TCM unique attributes. Our research team conducted in-depth exploration of the elemental fingerprints of TCM, amassing a robust theoretical and practical foundation. In this review, we presented a comprehensive review of the core value, advanced technologies, and classic cases of elemental fingerprints. We introduced a novel perspective that integrated inorganic and organic components, overcoming traditional paradigms. The review analysis highlighted unique role of elemental fingerprints in revealing the scientific connotation of TCM. Furthermore, we proposed an innovative strategy for identifying key components, which effectively addressed the limitations of traditional methods and elevated the overall evaluation standards for TCM. This strategy was supported by emerging technologies such as artificial intelligence, metallomics, and hyperspectral imaging. Looking ahead, the application prospects of the combined strategy of elemental fingerprints and key components were promising. It not only provided a solid foundation for the formulation of TCM quality control strategies but also introduced new methodologies and tools to the field of regulatory science and scientific oversight of TCM. [Display omitted] • Elemental fingerprinting and key component integration innovative strategies for QC of TCM. • Development and application of integrated technologies are insighted. • New technologies, methods and tools are provided for the scientific regulation of TCM. [ABSTRACT FROM AUTHOR]

In this study, Ho³⁺-doped BaGd₂ZnO₅ (1–11 mol%) nanophosphors (BGZO:Ho3+ NPs) were synthesized through combustion synthesis, utilizing Spirulina leaf extract as a natural green fuel. Luminescence studies revealed that the BGZO:Ho³⁺ phosphors exhibit a strong green emission primarily resulting from the 5S 2 →5I 8 transition of Ho³⁺ ions. The optimal doping concentration was found to be 7 mol % Ho³⁺, which yielded the highest luminescence efficiency. Furthermore, the CIE chromaticity coordinates for BGZO:7Ho³⁺ were accurately determined to be (0.2595, 0.7290), with a color purity (CP) of 99.98 %. These results indicate the potential of this material for producing high-quality green emissions suitable for solid-state lighting and display applications. Optimized BGZO:Ho3+ NPs were utilized for latent fingerprints (LFPs) development via the powder dusting method. Under 365 nm UV light, the NPs effectively revealed Level I-III fingerprints (FPs) details, including ridge patterns, minutiae, and sweat pore features, on various substrates such as glass, plastic, and metal. The high luminescence of BGZO:Ho3+ NPs under UV illumination offers a sensitive and non-destructive method for enhancing FPs visibility, making it a promising tool for forensic applications. In addition, this study focuses on FPs poroscopy, examining the detailed pore structure of LFPs for forensic identification. Using advanced imaging techniques, we analyzed sweat pore distribution, size, and shape across various substrates and conditions. A detailed poroscopic analysis of LFPs, focusing on key parameters such as pore interspacing (165–332 μm), pore size (4140–9956 μm2), shapes (elliptical, rhomboid, triangular, square and rectangular) and pore angle (167°–179°). Further, a mathematical model was developed using Python -based software to enable precise and accurate analysis of FPs, enhancing clarity and identification accuracy, supporting the uniqueness of FPs beyond ridge patterns. The results demonstrate the potential of poroscopy for enhancing FPs analysis by offering an additional layer of precise biometric data. [Display omitted] • Ho3+ doped BaGd₂ZnO₅ nanophosphors were synthesized using Spirulina leaf extract as a natural fuel via combustion synthesis. • Optimized nanophosphors were used to create latent fingerprints on surfaces through a simple powder dusting method. • Poroscopy involves studying sweat pores on skin ridges, crucial for identification. • Various pore parameters like size, shape, number, position, type, and inter-distance were thoroughly researched to enhance identification accuracy. • A Python-based mathematical model was developed to analyze fingerprints for better FPs characteristic insights. [ABSTRACT FROM AUTHOR]

Herein, a simple colorimetric sensor array sensitive to organic acids was designed by gold (Au) and silver (Ag) nanoparticles modified with four different compounds for the identification of 16 famous Baijius containing different organic acids. In detail, the sensing mechanism of the colorimetric sensor array was based on the charge transfer between the nanoparticle surface protectant and H+, which reduced the electrostatic force between the nanoparticles, leading to the aggregation and color change of the nanoparticles. The unique color change of the colorimetric array before and after the reaction was used as a unique fingerprint profile for each specific analyte, which can be identified by the naked eye. The generated digital database was analyzed using pattern recognition methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). All Baijius can be easily identified by fingerprint mapping or PCA scoring maps. 32 blind samples were tested by LDA and 30 samples were correctly classified. The HCA results showed that all samples were correctly classified and even very similar 1 % dilutions of the Baijiu can be easily distinguished, demonstrating the potential of the constructed colorimetric sensor array technology for quality control applications of Baijius and other beverages. [Display omitted] • The H+-mediated interaction organic acid colorimetric sensor array was designed. • Unique fingerprint profiles were applied to identify each specific analyte. • Even very similar 1 % dilutions of the Baijiu can be easily distinguished. [ABSTRACT FROM AUTHOR]