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In this work, we present a comprehensive experimental and theoretical study of the vibrational spectra of PAH molecules recently detected in the interstellar medium: 1-cyanonaphthalene and 2-cyanonaphthalene. The room temperature IR spectra of 1- and 2-cyanonaphthalene in the region 100-3100 cm-1 and their vibrational Raman spectra in the region 35-3100 cm-1 are reported here for the first time. A detailed spectral analysis is carried out using quantum chemical calculations employing the DFT methodology. Anharmonic corrections using the VPT2 method yield excellent agreement with the experimental spectra. A re-investigation of the vibrational spectrum of the parent molecule: naphthalene validates the experimental and theoretical methods used. A consistent set of assignments is reported for the fundamental bands of 1- and 2-cyanonapththalene. The experimental and theoretical data presented here would be useful inputs for modelling the role of cyanonaphthalene in astrophysical processes.

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The fraudulent adulteration of goat milk with cheaper and more available milk of other species such as cow milk is occurrence. The aims of the present study were to investigate the effect of goat milk adulteration with cow milk on the mid-infrared (MIR) spectrum and further evaluate the potential of MIR spectroscopy to identify and quantify the goat milk adulterated. Goat milk was adulterated with cow milk at 5 different levels including 10%, 20%, 30%, 40%, and 50%. Statistical analysis showed that the adulteration had significant effect on the majority of the spectral wavenumbers. Then, the spectrum was preprocessed with standard normal variate (SNV), multiplicative scattering correction (MSC), Savitzky-Golay smoothing (SG), SG plus SNV, and SG plus MSC, and partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR) were used to establish classification and regression models, respectively. PLS-DA models obtained good results with all the sensitivity and specificity over 0.96 in the cross-validation set. Regression models using raw spectrum obtained the best result, with coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) of cross-validation set were 0.98, 2.01, and 8.49, respectively. The results preliminarily indicate that the MIR spectroscopy is an effective technique to detect the goat milk adulteration with cow milk. In future, milk samples from different origins and different breeds of goats and cows should be collected, and more sophisticated adulteration at low levels should be further studied to explore the potential and effectiveness of milk mid-infrared spectroscopy and chemometrics.

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BACKGROUND: Several oxylipins including hydroxy- and epoxy-polyunsaturated fatty acids act as lipid mediators. In biological samples they can be present as non-esterified form, but the major part occurs esterified in phospholipids (PL) or other lipids. Esterified oxylipins are quantified indirectly after alkaline hydrolysis as non-esterified oxylipins. However, in this indirect analysis the information in which lipid class oxylipins are bound is lost. In this work, an untargeted liquid chromatography high-resolution mass spectrometry (LC-HRMS) method for the direct analysis of PL bearing oxylipins was developed. RESULTS: Optimized reversed-phase LC separation achieved a sufficient separation of isobaric and isomeric PL from different lipid classes bearing oxylipin positional isomers. Individual PL species bearing oxylipins were identified based on retention time, precursor ion and characteristic product ions. The bound oxylipin could be characterized based on product ions resulting from the α-cleavage occurring at the hydroxy/epoxy group. PL sn-1/sn-2 isomers were identified based on the neutral loss of the fatty acyl in the sn-2 position. A total of 422 individual oxPL species from 7 different lipid classes i.e., PI, PS, PC, PE, PC-P, PC-O, and PE-P were detected in human serum and cells. This method enabled to determine in which PL class supplemented oxylipins are incorporated in HEK293 cells: 20:4;15OH, 20:4;14Ep, and 20:5;14Ep were mostly bound to PI. 20:4;8Ep and 20:5;8Ep were esterified to PC and PE while other oxylipins were mainly found in PC. SIGNIFICANCE: The developed LC-HRMS method enables the comprehensive detection as well as the semi-quantification of isobaric and isomeric PL species bearing oxylipins. With this method, we show that the position of the oxidation has a great impact and directs the incorporation of oxylipins into the different PL classes in human cells.

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This study employs a high-dimensional consensus mass spectral (HDCMS) similarity scoring technique to discriminate isomers collected using an electron ionization mass spectrometer. The HDCMS method was previously introduced and applied to the discrimination of mass spectra of constitutional isomers, methamphetamine and phentermine, collected with direct analysis real-time mass spectrometry (DART-MS). The method formulates the problem of discriminating mass spectra in a mathematical Hilbert space and is hence called "high dimensional." It requires replicate mass spectra to build a Gaussian model and evaluate the inner products between these functions. The resulting measurement variability is used as a signature by which to discriminate spectra. In this work, HDCMS is tested on electron impact ionization (EI) mass spectra of 7 terpene and terpene-related (C10H16 and C10H14) isomers with experimental retention indices that differ by less than 30 and with traditional cosine similarity scores greater than 0.9, on a scale of 0 to 1, when compared with at least one other compound in the test set. Using identical instrument parameters, 15 replicate gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) spectra of each isomer were collected and separated into distinct library and query sets. The HDCMS algorithm discriminated each isomer, indicating the method's potential. Because the method requires replicate measurements, observations from a simple heuristic study of the number of replicates required to discriminate these isomers is presented. The paper concludes with a discussion of compound discrimination using HDCMS and the benefits and drawbacks of applying the method to EI-MS data.

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Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph-neural-network architecture featuring universal embedding with automatic optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials and applications.

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A new convenient method for identifying colorant compounds (CCs) in food matrices was developed using high-performance liquid chromatography with a diode array detector and quadrupole-time-of-flight mass spectrometer (HPLC-DAD-Q/TOF-MS) combined with theoretical calculations. A model sample containing three typical CCs was completely separated via HPLC-DAD. The obtained 3D ultraviolet-visible (UV-vis) spectra revealed the maximum absorption wavelengths (MAWs) of all CCs (yellow, 430 nm; red, 520 nm; blue, 620 nm) in the range of 400-800 nm, and their colors were determined based on their MAWs. Temporary structures of the CCs were obtained using Q/TOF-MS analysis. Theoretical calculations were then performed to obtain the theoretical MAWs and colors of the CCs according to their calculated UV-vis spectra based on temporary structures. The structures of the CCs were confirmed without the need for authoritative standards by comparing the consistency between their experimental and theoretical MAWs and colors. This method is particularly suitable for identifying CCs or compounds with UV-Vis absorption, including new compounds, compounds for which standards are difficult to obtain, and known compounds without reporting relevant molecular information.

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Over the last three decades, the technology that makes it possible to follow chemical processes in the solid state in real time has grown enormously. These studies have important implications for the design of new functional materials for applications in optoelectronics and sensors. Light-matter interactions are of particular importance, and photocrystallography has proved to be an important tool for studying these interactions. In this technique, the three-dimensional structures of light-activated molecules, in their excited states, are determined using single-crystal X-ray crystallography. With advances in the design of high-power lasers, pulsed LEDs and time-gated X-ray detectors, the increased availability of synchrotron facilities, and most recently, the development of XFELs, it is now possible to determine the structures of molecules with lifetimes ranging from minutes down to picoseconds, within a single crystal, using the photocrystallographic technique. This review discusses the procedures for conducting successful photocrystallographic studies and outlines the different methodologies that have been developed to study structures with specific lifetime ranges. The complexity of the methods required increases considerably as the lifetime of the excited state shortens. The discussion is supported by examples of successful photocrystallographic studies across a range of timescales and emphasises the importance of the use of complementary analytical techniques in order to understand the solid-state processes fully.

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A new three-dimensional (3D) coordination polymer, namely, poly[diaqua[µ5-2,2'-(1,3,5,7-tetraoxo-1,2,3,5,6,7-hexahydropyrrolo[3,4-f]isoindole-2,6-diyl)diacetato]barium(II)], [Ba(C14H6N2O8)(H2O)2]n, (I), has been synthesized by the microwave-irradiated reaction of Ba(NO3)2 with N,N'-bis(glycinyl)pyromellitic diimide {BGPD, namely, 2,2'-(1,3,5,7-tetraoxo-1,2,3,5,6,7-hexahydropyrrolo[3,4-f]isoindole-2,6-diyl)diacetatic acid, H2L}. The title compound was structurally characterized by single-crystal X-ray diffraction analysis and powder X-ray diffraction analysis, as well as IR spectroscopy. In the crystal structure of (I), the BaII ion is nine-coordinated by six carboxylate O atoms from five symmetry-related L2- dianions and one imide O atom, as well as two water O atoms. The coordination geometry of the central BaII ion can be described as a spherical capped square antiprism. One carboxylate group of the ligand serves as a µ3-bridge linking the BaII cations into a one-dimensional polynuclear secondary building unit (SBU). Another carboxylate group of the ligand acts as a µ2-bridge connecting the 1D SBUs, thereby forming a two-dimensional (2D) SBU. The resulting 2D SBUs are extended into a 3D framework via the pyromellitic diimide moiety of the ligand as a spacer. The 3D Ba framework can be simplified as a 5-connected hexagonal boron nitride net (bnn) topology. The intermolecular interactions in the 3D framework were further investigated by Hirshfeld surface analysis and the results show that the prominent interactions are H...O (45.1%), Ba...O (11.1%) and C...H (11.1%), as well as H...H (11.1%) contacts. The thermal stability, photoluminescence properties and UV-Vis absorption spectra of (I) were also investigated. The coordination polymer exhibits a fluorescence emission with a quantum yield of 0.071 and high thermal stability.

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Two new two-dimensional (2D) coordination polymers (CPs), namely, poly[diaqua[µ4-2,2'-(1,3,5,7-tetraoxo-1,2,3,5,6,7-hexahydropyrrolo[3,4-f]isoindole-2,6-diyl)diacetato-κ4O:O':O'':O''']cadmium(II)], [Cd(C14H6N2O8)(H2O)2]n (1), and poly[[tetraaqua[µ4-2,2'-(1,3,5,7-tetraoxo-1,2,3,5,6,7-hexahydropyrrolo[3,4-f]isoindole-2,6-diyl)diacetato-κ4O:O':O'':O'''][µ2-2,2'-(1,3,5,7-tetraoxo-1,2,3,5,6,7-hexahydropyrrolo[3,4-f]isoindole-2,6-diyl)diacetato-κ2O:O']dizinc(II)] dihydrate], {[Zn(C14H6N2O8(H2O)2]·H2O}n (2), have been synthesized by the microwave-irradiated reaction of Cd(CH3COO)2·2H2O and Zn(CH3COO)2·2H2O, respectively, with N,N'-bis(glycinyl)pyromellitic diimide {BGPD, namely, 2,2'-(1,3,5,7-tetraoxo-1,2,3,5,6,7-hexahydropyrrolo[3,4-f]isoindole-2,6-diyl)diacetic acid, H2L}. In the crystal structure of 1, the CdII ion is six-coordinated by four carboxylate O atoms from four symmetry-related L2- dianions and two coordinated water molecules, furnishing an octahedral coordination geometry. The bridging L2- dianion links four symmetry-related CdII cations into a 2D layer-like structure with a 3,4-connected bex topology. In the crystal structure of 2, the ZnII ion is five-coordinated by three carboxylate O atoms from three different L2- dianions and two coordination water molecules, furnishing a trigonal bipyramidal coordination geometry. Two crystallographically independent ligands serve as µ4- and µ2-bridges, respectively, to connect the ZnII ions, thereby forming a 2D layer with a 3,3-connected hcb topology. Crystal structure analysis reveals the presence of n→π* interactions between two carbonyl groups of the pyromellitic diimide moieties in 1 and 2. CP 1 exhibits an enhanced fluorescence emission compared with free H2L. The framework of 2 decomposes from 720 K, indicating its high thermal stability. A comparative analysis of a series of structures based on the BGPD ligand indicates that the metal-ion size has a great influence on the connection modes of the metal ions due to different steric effects, which, in turn, affects the structures of the SBUs (secondary building units) and frameworks.

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This study investigates olfaction, a complex and not well-understood sensory modality. The chemical mechanism behind smell can be described by so far proposed two theories: vibrational and docking theories. The vibrational theory has been gaining acceptance lately but needs more extensive validation. To fill this gap for the first time, we, with the help of data-driven classification, clustering, and Explainable AI techniques, systematically analyze a large dataset of vibrational spectra (VS) of 3018 molecules obtained from the atomistic simulation. The study utlizes image representations of VS using Gramian Angular Fields and Markov Transition Fields, allowing computer vision techniques to be applied for better feature extraction and improved odor classification. Furthermore, we fuse the PCA-reduced fingerprint features with image features, which show additional improvement in classification results. We use two clustering methods, agglomerative hierarchical (AHC) and k-means, on dimensionality reduced (UMAP, MDS, t-SNE, and PCA) VS and image features, which shed further insight into the connections between molecular structure, VS, and odor. Additionally, we contrast our method with an earlier work that employed traditional machine learning on fingerprint features for the same dataset, and demonstrate that even with a representative subset of 3018 molecules, our deep learning model outperforms previous results. This comprehensive and systematic analysis highlights the potential of deep learning in furthering the field of olfactory research while confirming the vibrational theory of olfaction.

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The biophysical characterization and engineering of optogenetic tools and photobiological systems has been hampered by the lack of efficient methods for spectral illumination of microplates for high-throughput analysis of action spectra. Current methods to determine action spectra only allow the sequential spectral illumination of individual wells. Here we present the open-source RainbowCap-system, which combines LEDs and optical filters in a standard 96-well microplate format for simultaneous and spectrally defined illumination. The RainbowCap provides equal photon flux for each wavelength, with the output of the LEDs narrowed by optical bandpass filters. We validated the RainbowCap for photoactivatable G protein-coupled receptors (opto-GPCRs) and enzymes for the control of intracellular downstream signaling. The simultaneous, spectrally defined illumination provides minimal interruption during time-series measurements, while resolving 10 nm differences in the action spectra of optogenetic proteins under identical experimental conditions. The RainbowCap is also suitable for studying the spectral dependence of light-regulated gene expression in bacteria, which requires illumination over several hours. In summary, the RainbowCap provides high-throughput spectral illumination of microplates, while its modular, customizable design allows easy adaptation to a wide range of optogenetic and photobiological applications.

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The James Webb Space Telescope (JWST) opened a new era for the identification of molecular systems in the interstellar medium (ISM) by vibrational features. One group of molecules of increasing interest is cyano-derivatives of aromatic organic molecules, which have already been identified in the ISM on the basis of the analysis of rotational signatures, and so, are plausible candidates for the detection by the JWST. Benzonitrile considered in this work represents a suitable example for the validation of a computational strategy, which can be further applied for different, larger, and not-yet observed molecules. For this purpose, anharmonic simulations of infrared (IR) spectra have been compared with recent FTIR experimental studies. The anharmonic computations using the generalized second-order vibrational perturbation theory (GVPT2) in conjunction with a hybrid force field combining the harmonic part of revDSD-PBEP86-D3/jun-cc-pVTZ with anharmonic corrections from B3LYP-D3/SNSD show very good agreement with those in the experiment, with a mean error of 11 c m - 1 for all fundamental transitions overall and only 2 c m - 1 for the C ≡ N stretching fundamental at 4.49 µ m . The inclusion of overtones up to three-quanta transitions also allowed the prediction of spectra in the near-infrared region, which shows distinct features due to C ≡ N overtones at the 2.26 µ m and 1.52 µ m . The remarkable accuracy of the GVPT2 results opens a pathway for the reliable prediction of spectra for a broader range of cyano-astroCOMs.

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A novel mixed ligand Mn(II) complex of 6-Bromopyridine-2-carboxylic acid (6Brpca) and 4,4'-dimethyl-2,2'-dipyridyl has been prepared and structurally characterized using single-crystal X-ray diffraction. The spectroscopic properties were also analyzed by using FT-IR and UV-Vis spectral techniques. The coordination complexes having transition metal ions are known to have promising optical nonlinearity behavior. Therefore, B3LYP level density functional theory was used to investigate first- and second-order hyperpolarizabilities (ß and γ) and provide a deep understanding of the relation between the structure and NLO properties. The calculations of frequency-dependent α, ß, and γ at frequencies of ω = 0.0856252 and 0.0428126 au. for 6Brpca and Dmdpy ligands as well as Mn(II) complex have been also carried out using B3LYP/LanL2DZ level. Especially second harmonic generation (SHG) first and second hyperpolarizabilities (ß(-2ω;ω,ω) and γ (-2ω;ω,ω,0)) parameters for Mn(II) complex have been calculated as 11448 × 10-30 and 680035 × 10-36 esu, respectively. It has been determined that there is a tremendous increase in ß and γ parameters when 6Brpca and Dmdpy ligands coordinate to the high spin multiplicity Mn(II) ion. Theoretical calculations revealed that the large first- and second-order hyperpolarizabilities are caused by strong intramolecular charge transfer between the transition metal and the coordinated ligands. These results indicate that the the organometallic complex under investigation is valuable candidate for optoelectronic and photonic applications.

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Three-dimensional fluorescence spectra are often affected by scattering effects, traditional scattering elimination methods rely excessively on parameter settings and cannot automatically eliminate scattering in batches, thereby limiting the application of fluorescence spectroscopy technology in rapid online monitoring and analysis of samples. In this study, we have developed a model based on a deep learning CycleGAN to rapidly eliminate scattering from three-dimensional fluorescence spectra. The proposed model efficiently eliminates scattering by simply inputting single or batches of contaminated fluorescent spectra. By training the CycleGAN using a large dataset of simulated three-dimensional fluorescence spectra and employing data augmentation, to the model can transform fluorescence spectra with scattering into ones without scattering. To validate the effectiveness of the proposed methed, we confirmed its generalization and reliability by eliminating scattering from two sets of previously unseen real experimental three-dimensional fluorescence spectra. We evaluated the effectiveness of scattering elimination across various noise levels and scattering widths, using metrics such as the mean absolute error, peak signal-to-noise ratio, structural similarity and cosine similarity. Furthermore, we conducted a component analysis using PARAFAC on the spectra post-scattering elimination, yielding correlation coefficients of >0.97 when compared to that in case of actual components. Finally, we compared the proposed model with traditional mathematical methods, such as blank subtraction and Delaunay triangulation. Results showed that the proposed model can automatically and efficiently eliminate scattering from fluorescence spectra in batches, substantially improving the efficiency of scattering elimination.

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Routine milk samples are commonly subjected to spectroscopic analysis within the mid-infrared (MIR) region of the electromagnetic spectrum to estimate macro-constituents of milk like fat, protein, lactose, and urea content. These spectra, however, can also be used to predict other traits, such as daily body condition score (BCS) change. The objective of the present study was to assess the transferability across countries of equations to predict daily body condition score change (ΔBCS) developed using milk MIR data collected in Ireland and in Canada. Body condition was scored on a scale from 1 (emaciated) to 5 (obese) in both countries. A total of 347,254 BCS records from 80,400 Canadian cows were available along with 73,193 BCS records from 6,572 Irish cows. Partial least squares regression (PLSR) and neural networks (NN) were separately used to predict daily ΔBCS. Two scenarios were studied 1) using Canadian and Irish data combined as the calibration data set to predict daily ΔBCS in Canada and in Ireland separately, and 2) Canadian and Irish data used separately to predict daily ΔBCS in each country separately. These prediction methods were applied to data with and without pretreatment (i.e., first derivative of the spectrum) as well as with and without standardizing daily ΔBCS across countries. For all the scenarios investigated, the correlation between actual and predicted daily ΔBCS when calibrated and validated (using cross-validation) in the same country ranged from 0.92 to 0.94, and from 0.85 to 0.87 for the Canadian and Irish data sets, respectively. When the data from Canada and Ireland were combined in the calibration process to predict daily ΔBCS, the correlations between actual and predicted ΔBCS were ≥ 0.90 and ≥ 0.80 for Canadian and Irish daily ΔBCS, respectively indicating no improvement in predictive ability. Predictive performance when calibrated using just Canadian data and validated using just Irish data was poor, and vice versa. Nonetheless, when developing equations for a country for which a limited database (i.e., 100 records) of gold standard and MIR data were available, predictive performance improved when the limited database was supplemented with the large data set from the other country. In general, for some of the investigated scenarios, standardizing the daily ΔBCS data within country before undertaking the calibration improved prediction accuracy. In conclusion, the benefit of merging data from different countries, at least based on the trait (i.e., daily ΔBCS) and countries (i.e., Ireland and Canada) considered in the present study were limited and, in cases, counter-productive.

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In French dairy sheep, Fourier transform infrared (FTIR) milk spectral data routinely predict the major milk components used in national genetic evaluations. The direct influence of genetic and environmental factors on milk FTIR spectra has been widely studied in dairy cattle, and relatively little in dairy ewes. In this study, 36,873 milk test-day records were available for 4,712 French Lacaune ewes farmed on 8 commercial farms. Our main goals were to provide the first description of spectral data and estimate the genetic parameters of French Lacaune dairy sheep during lactation. Principal component analysis (PCA) results demonstrated the impact of the lactation period on specific wavenumbers, allowing the identification of FTIR spectra collected at early (mo 2-4) and late (mo 5-7) lactation stages. The average estimated heritability (±mean SE) of the FTIR milk spectra from 2,971 to 926 cm-1 (446 wavenumbers) was 0.29 ± 0.02, ranging from 0.13 ± 0.01 to 0.42 ± 0.02. Furthermore, the heritabilities of spectra collected at the beginning or end of lactation changed at each point of the spectrum. However, at each wavenumber, the genomic correlation of transmittance values between these 2 lactation periods was high (>0.77), indicating the absence of a genotype-environment interaction. The genomic correlations between spectral regions and milk production traits (i.e., daily milk yield, fat and protein content, somatic cell score) varied from moderate to high. The results suggested that the most heritable areas of the spectrum were also genetically associated with dairy traits. Finally, the genomic correlations observed between the ewes' feed efficiency traits and the FTIR spectrum were moderate to high, while the genomic correlations between the change in body condition score and spectral data were rather low to moderate. This study confirmed that spectral data from Lacaune ewe milk were heritable, evolved phenotypically and genetically during lactation and were genetically correlated with traits included in breeding goals or traits of interest to the dairy industry.

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Human-computer interaction (HCI) with screens through gestures is a pivotal method amidst the digitalization trend. In this work, a gesture recognition method is proposed that combines multi-band spectral features with spatial characteristics of screen-reflected light. Based on the method, a red-green-blue (RGB) three-channel spectral gesture recognition system has been developed, composed of a display screen integrated with narrowband spectral receivers as the hardware setup. During system operation, emitted light from the screen is reflected by gestures and received by the narrowband spectral receivers. These receivers at various locations are tasked with capturing multiple narrowband spectra and converting them into light-intensity series. The availability of multi-narrowband spectral data integrates multidimensional features from frequency and spatial domains, enhancing classification capabilities. Based on the RGB three-channel spectral features, this work formulates an RGB multi-channel convolutional neural network long short-term memory (CNN-LSTM) gesture recognition model. It achieves accuracies of 99.93% in darkness and 99.89% in illuminated conditions. This indicates the system's capability for stable operation across different lighting conditions and accurate interaction. The intelligent gesture recognition method can be widely applied for interactive purposes on various screens such as computers and mobile phones, facilitating more convenient and precise HCI.

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Lycopene, a biologically active phytochemical with health benefits, is a key quality indicator for cherry tomatoes. While ultraviolet/visible/near-infrared (UV/Vis/NIR) spectroscopy holds promise for large-scale online lycopene detection, capturing its characteristic signals is challenging due to the low lycopene concentration in cherry tomatoes. This study improved the prediction accuracy of lycopene by supplementing spectral data with image information through spectral feature enhancement and spectra-image fusion. The feasibility of using UV/Vis/NIR spectra and image features to predict lycopene content was validated. By enhancing spectral bands corresponding to colors correlated with lycopene, the performance of the spectral model was improved. Additionally, direct spectra-image fusion further enhanced the prediction accuracy, achieving RP2, RMSEP, and RPD as 0.95, 8.96 mg/kg, and 4.25, respectively. Overall, this research offers valuable insights into supplementing spectral data with image information to improve the accuracy of non-destructive lycopene detection, providing practical implications for online fruit quality prediction.

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Phycocyanin (PC) is a naturally occurring green pigment in Spirulina. It was extracted by ultrasonic extraction using green technology, and its structure was studied using IR- and NMR-spectroscopy. Spectral data confirmed the PC structure. This study also involves an in silico assessment of the diverse applications of green pigment PC. Utilizing QSAR, PreADME/T, SwissADME, and Pro-Tox, this study explores the safety profile, pharmacokinetics, and potential targets of PC. QSAR analysis reveals a favorable safety profile, with the parent structure and most metabolites showing no binding to DNA or proteins. PreADME/T indicates low skin permeability, excellent intestinal absorption, and medium permeability, supporting oral administration. Distribution analysis suggests moderate plasma protein binding and cautious blood-brain barrier permeability, guiding formulation strategies. Metabolism assessments highlight interactions with key cytochrome P450 enzymes, influencing drug interactions. Target prediction analysis unveils potential targets, suggesting diverse therapeutic effects, including cardiovascular benefits, anti-inflammatory activities, neuroprotection, and immune modulation. Based on the in silico analysis, PC holds promise for various applications due to its safety, bioavailability, and potential therapeutic benefits. Experimental validation is crucial to elucidate precise molecular mechanisms, ensuring safe and effective utilization in therapeutic and dietary contexts. DFT calculations, including geometry optimization, MEP analysis, HOMO-LUMO energy surface, and quantum reactivity parameters of the PC compound, were obtained using the B3LYP/6-311G(d,p) level. This integrated approach contributes to a comprehensive understanding of PC's pharmacological profile and informs future research directions.

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BACKGROUND: Studying the composition rules and evolution mechanisms of genome sequences are core issues in the post-genomic era, and k-mer spectrum analysis of genome sequences is an effective means to solve this problem. RESULT: We divided total 8-mers of genome sequences into 16 kinds of XY-type due to XY dinucleotides number in 8-mers. Previous works explored that the independent unimodal distributions observed only in three CG-type 8-mer spectra, while non-CG type 8-mer spectra have not the universal phenomenon from prokaryotes to eukaryotes. On this basis, we analyzed the distribution variation of non-CG type 8-mer spectra across 889 animal genome sequences. Following the evolutionary order of animals from primitive to more complex, we found that the spectrum distributions gradually transition from unimodal to tri-modal. The relative distance from the average frequency of each non-CG type 8-mers to the center frequency is different within a species and among different species. For the 8-mers contain CG dinucleotides, we further divided these into 16 subsets, where each 8-mer contains both CG and XY dinucleotides, called XY1_CG1 subsets. We found that the separability values of XY1_CG1 spectra are closely related to the evolution and specificity of animals. Considering the constraint of Chargaff's second parity rule, we finally obtained 10 separability values as the feature set to characterize the evolution state of genome sequences. In order to verify the rationality of the feature set, we used 14 common classification algorithms to perform binary classification tests. The results showed that the accuracy (Acc) ranged between 98.70% and 83.88% among birds, other vertebrates and mammals. CONCLUSION: We proposed a credible feature set to characterizes the evolution state of genomes and obtained satisfied results by the feature set on large scale classification of animals.

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