Sporotrichosis caused by Sporothrix brasiliensis is a highly virulent zoonotic mycosis rapidly expanding in Brazil, with increasing records in Pará since 2018. The reference diagnosis relies on culture and the time-consuming induction of fungal dimorphism, which delays taxonomic confirmation for weeks. The objective of the study was to develop and validate an in-house spectral library by MALDI-TOF MS for the rapid identification of S. brasiliensis in the Amazon region of Pará, prioritizing the feasibility of direct identification from the filamentous phase (primary growth). Thirty-seven Main Spectral Profiles (MSPs) from regional isolates were created, with 24 derived from the yeast phase and 13 from the filamentous phase. For validation, 46 molecularly characterized isolates were used in both filamentous and yeast-like morphologies, followed by the application of 39 routine clinical isolates obtained from primary culture. The library achieved 100% accuracy (score ≥ 2.0) in species identification for both fungal phases. Notably, all 39 routine clinical isolates were correctly identified directly from the filamentous growth, reducing the diagnostic time to an interval of 5 to 7 days. Although the yeast phase presented superior scores and greater proteomic stability, the mycelial signature proved robust for clinical use. The customized library overcomes the gaps in commercial databases and establishes a high-throughput workflow for the surveillance of S. brasiliensis. This technical optimization allows for more agile One Health surveillance, which is essential for epidemiological mapping and the control of zoonotic expansion in the region.

Species-dependent colony size variation of environmental Enterococcus on sodium azide-containing selective agar.

Traffic-related air pollution is increasingly recognized as a modifier of pollen physicochemical properties and allergenic potential, yet field-based mechanistic evidence remains limited. This study investigated the effects of traffic-related pollution on the morphology, chemical composition, and Bet v 1 allergen content of Betula pendula pollen using a multi-method approach. Pollen samples were collected from 16 B. pendula trees across Ankara, Bingöl, and Tunceli during April 2025 along a traffic-exposure gradient, with air-quality data spanning 1 June 2024-31 May 2025, and sampling sites linked to Türkiye's national air-quality monitoring network (PM₁₀, PM₂.₅, NO₂, NOₓ, SO₂, CO). Pollen was analysed using light microscopy, SEM, FT-IR ATR spectroscopy, and ELISA. Polluted sites (n = 13) showed morphological and biochemical differences compared with clean reference sites (n = 3): pollen exhibited significantly increased exine wall thickness and equatorial dimensions, alongside greater particle deposition near apertures; polar axis showed weaker group-level differences. FT-IR analysis revealed altered protein-related spectral profiles, including an increased Amide I/Amide II area ratio and reduced O-H band areas, consistent with modifications in protein secondary structure. Bet v 1 concentrations did not differ significantly between polluted (95.7 ± 23.8µg/g) and clean (85.5 ± 24.6µg/g) samples; given the small reference group (n = 3), this comparison is interpreted as non-directional. These findings provide field-based evidence that traffic-related pollution is associated with modifications in the structural and biochemical characteristics of B. pendula pollen in ways consistent with enhanced allergenic potential, underscoring the need to incorporate air-quality effects into aeroallergen risk assessment.

Comparing aperiodic brain activity between eyes open rest and dynamic visual input using magnetoencephalography.

Understanding how deeply near-infrared (NIR) radiation penetrates into matter is essential for interpreting (hyper)spectral imaging (SI) data, yet comprehensive assessments of penetration depth remain limited. Although NIR-SI is traditionally considered a surface analytical technique, subsurface contributions may affect spectral profiles, challenging this assumption. This study presents a systematic and quantitative investigation of NIR penetration in a controlled SI setup using multilayer polymeric samples manufactured by 3D printing. Layered cubes and cylinders composed of polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) were analyzed using two independent short-wave NIR-SI systems. Penetration behavior was evaluated through an integrated chemometric workflow combining exploratory analysis (principal component analysis, PCA), unmixing (classical least squares, CLS), supervised linear (partial least squares, PLS) and nonlinear (convolutional neural network, CNN) regression methods. The results demonstrated that NIR-SI can retrieve chemical information from subsurface layers to depths of ∼1 cm. Penetration depth was strongly influenced by material composition, internal structure, and illumination conditions. PCA and CLS revealed nonlinear attenuation and scattering phenomena, while regression models successfully retrieved subsurface information across the full sample height. CNN consistently outperformed PLS, highlighting the importance of nonlinear modeling approaches. The robustness of the proposed strategy arises from the use of designed ad hoc samples, two independent instruments, and multiple complementary chemometric methods converging toward consistent results. Overall, this work challenges the conventional surface-limited view of NIR-SI and provides a robust framework for investigating NIR penetration, supporting the development of depth-resolved and potentially tomographic NIR-SI approaches.

Retrieving optical information from photons traversing scattering media is essential in fields relating to detection and imaging. Raman spectroscopy offers high chemical specificity for molecular analysis, and the emerging deep Raman techniques, such as transmission Raman spectroscopy (TRS), enable subsurface probing. However, as biological tissues are highly scattering media, in vivo Raman applications are severely limited by tissue-induced signal attenuation and spectral distortion, which undermine quantitative accuracy. Here, we developed a deep learning-based framework to restore Raman spectra acquired after propagating through biological tissue. We built a comprehensive data set of 4410 paired pre- and post-transmission Raman or surface-enhanced Raman scattering (SERS) spectra from 18 Raman-active samples. Using this data set, we systematically characterized tissue-induced spectral distortions and trained a 1D U-Net model to learn the inverse transformation. This model effectively restored attenuated intensities, suppressed noise, and reconstructed spectral profiles. On the independent testing set, the restored spectra exhibited remarkably improved similarity to ground-truth profiles, achieving >95% average cosine similarity and substantially reduced distortions in both absolute and relative intensities. Furthermore, restoration enhanced molecular quantification, yielding clearer concentration-response relationships for mixed SERS nanoparticles. These results demonstrate the effectiveness of our model and show that data-driven full-spectrum restoration can effectively counteract tissue-induced degradation, improving the accuracy of Raman-based quantification through scattering of biological media.

Exposure to ionizing radiation has been associated with the development of neoplasms in humans and other animals. Detection of both neoplastic growth and effects of radiation with standard laboratory testing (hematology and serum chemistry analyses) is problematic. There are no specific biomarkers present in biological fluids (blood, urine) for early detection of neoplastic growth or long-term effects of radiation exposure, but these would be extremely useful for monitoring both treatment efficacy and detecting undesirable side effects in patients undergoing radiotherapy. We conducted a retrospective study of 76 urine samples from rhesus macaques using a novel Raman spectroscopy-based analytical technology (Raman Molecular Urinalysis) to establish a urine fingerprint for past radiation treatment. Forty-nine animals were irradiated (of which 12 developed sarcomas following experimental exposure to total body ionizing radiation) and 27 animals were not irradiated (2 of which developed non-radiation-associated sarcomas). We applied an unsupervised principal component analysis and supervised discriminant analysis of principal components and averaged the spectra. The results showed the groups had a highly similar average spectra, but more variability and outliers existed in the irradiated group of animals. The unique characteristics of these outliers were unclear, and further investigation is needed. Animals with sarcomas had a distinct spectral profile with high specificity and high negative predictive value. A definitive radiation molecular spectral fingerprint was not identified in the urine of irradiated macaques in comparison to unirradiated control animals, but this study provides initial evidence and insights for future research.

Attention deficit in children with developmental coordination disorder: evidence from resting state EEG.

Ultraviolet-Visible Spectrophotometric Analysis of Intracranial Cyst Fluid: A Technical Note.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge. Human serum albumin (HSA), the most abundant blood protein synthesized exclusively by the liver, undergoes measurable structural modifications as liver disease advances, making it a potential molecular marker of disease severity. Using high-resolution liquid chromatography-mass spectrometry (LC-HR-MS), we quantified native HSA and nine modified isoforms in plasma from 172 CLD patients spanning all fibrosis stages and 82 healthy controls. Native HSA declined markedly with disease severity, reaching 4.1-4.2g/L in decompensated cirrhosis versus 12.2g/L in controls. Modified isoforms showed stage-specific patterns, and their ratios to native HSA amplified the diagnostic signal for advanced disease. A machine learning classifier trained on the full albumin spectral profile achieved substantial agreement with standard staging, and demonstrated higher accuracy than FIB-4 index (81.5% vs. 59.3% accuracy). Within the study cohort, these results were reproduced on two independent instruments from different manufacturers (McNemar p = 0.149), confirming the reproducibility across different platforms of the albumin signature. These findings establish HSA spectral profiling as a promising non-invasive staging tool for CLD, with cross-platform reproducibility supporting its potential for translation to multicenter clinical practice.

ABSTRACT Timely and accurate crop mapping is important for agricultural monitoring and resource management. In this study, multi-temporal Sentinel-2 imagery acquired during 2024 and 2025 was analysed using an unsupervised classification approach to generate 10 m spatial resolution distribution maps of makhana cultivation in selected districts of North Bihar, India. Temporal spectral reflectance patterns of makhana were examined across its growth period using Sentinel-2 multispectral observations. Vegetation indices showed progressive temporal variation, with NDVI, EVI, and NDRE increasing from 0.11 to 0.53, 0.10 to 0.77, and 0.03 to 0.32, respectively, between April and July, consistent with increasing vegetation cover. In contrast, NDWI, MNDWI, and NDBI decreased from −0.11 to −0.41, −0.10 to −0.25, and −0.01 to −0.19, respectively, indicating reduced open-water spectral contribution during canopy development. District-level mapping estimated makhana cultivation areas of 31,325 ha in 2024 and 37,878.7 ha in 2025 across the study region. The classification achieved an overall accuracy of 78.4%, demonstrating the potential of multi-temporal Sentinel-2 data for regional-scale identification of makhana cultivation. The derived spectral reflectance profiles and index trajectories provide observational insights into seasonal crop behaviour and contribute to the development of remote sensing approaches for monitoring floating aquatic cropping systems.

The neurotransmitter acetylcholine (ACh) is essential in both the central and peripheral nervous systems. Recent studies highlight the significance of interactions between ACh and various neuromodulators in regulating complex behaviors. The ability to simultaneously image ACh and other neuromodulators can provide valuable information regarding the mechanisms underlying these behaviors. Here we developed a series of red fluorescent G-protein-coupled receptor activation-based ACh sensors, with a wide detection range and expanded spectral profile. The high-affinity sensor rACh1h reliably detects ACh release in various brain regions, including the nucleus accumbens, amygdala, hippocampus and cortex. Moreover, rACh1h can be coexpressed with green fluorescent sensors to record ACh release together with other neurochemicals in various behavioral contexts using fiber photometry, mesoscopic imaging and two-photon imaging with high spatiotemporal resolution.

This topical review explores the fundamentals of hot-carrier cooling using time-resolved photoluminescence (TRPL) spectroscopy and diverse analysis methods, including full lineshape modeling, to reliably extract hot-carrier temperature dynamics, while highlighting strategies to tune cooling rates for advanced optoelectronics. TRPL spectroscopy stands out as a uniquely powerful tool for probing hot-carrier dynamics, directly accessing radiative recombination from thermalized carrier populations to uncover intrinsic carrier temperature and its temporal evolution. Unlike absorption-based techniques, which often target nonemissive states or demand indirect modeling of state filling and bleaching, TRPL delivers a physically intuitive perspective on carrier cooling via the emitted photon energy distribution. Yet, accurate extraction of hot-carrier parameters requires rigorous spectral modeling; common approaches like peak shifts or high-energy tail fitting, despite their popularity, suffer from ambiguities tied to spectral broadening, fitting-range selection, and overlapping relaxation processes. Full lineshape modeling represents a pivotal advance, utilizing the entire spectral profile for self-consistent retrieval of carrier temperatures and cooling rates, rooted in solid physical foundations. This methodology proves crucial for setting reliable benchmarks as interest surges in hot-carrier materials and devices -especially hot-carrier solar cells (HCSCs) -facilitating robust comparisons across materials and experiments to inform next-generation optoelectronic innovations. Here, TRPL paired with advanced modeling offers a timely framework for deciphering hot-carrier behavior. Challenges persist for viable HCSCs: hotphonon bottlenecks (HPB) hinder cooling through delayed LO-phonon decay, but achieved lifetimes still fall short for practical device extraction. Colloidal core/shell quantum dots (II-VI and heterostructures) exhibit slower dynamics owing to phonon confinement, diminished escape pathways, and interfacial scattering, though they often miss elite optoelectronic qualities. Metal-halide perovskites shine with superior absorption, extended diffusion lengths, defect resilience, and tunable bandgaps, yet cool more rapidly. Hybrid designs, such as perovskite heterostructures or core/shell mimics, hold promise by fusing these assetsamplifying HPB effects alongside efficient charge transport -to propel HCSCs toward practical deployment.

Introduction. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is widely used for rapid micro-organism identification and has recently been explored for antifungal susceptibility testing (AFST).Hypothesis. Although MALDI-TOF MS has emerged as a promising tool for AFST, simplified and clinically applicable strategies for rapid fluconazole (FLZ) susceptibility detection in Candida spp. remain insufficiently validated. We hypothesized that a streamlined AFST-MS approach would demonstrate good categorical agreement (CA) with the European Committee on Antimicrobial Susceptibility Testing (EUCAST) reference method while significantly reducing turnaround time.Aim. To establish a simplified MALDI-TOF MS-based AFST approach for detecting FLZ resistance in Candida species.Methodology. Fifty-one clinical isolates and reference strains were incubated for 3 h in the presence of FLZ at two concentrations (32 and 4 µg ml−1) and in drug-free controls. Spectral profiles were compared with the EUCAST reference method.Results. Overall CA between AFST-MS and EUCAST was 85.2% (κ=0.7306). Species-specific accuracy was 100% for Candida auris, Pichia kudriavzevii (formerly Candida krusei), Candida tropicalis and Candida parapsilosis; 92.9% for Candida albicans and 40% for Nakaseomyces glabrata (formerly Candida glabrata); however, these estimates should be interpreted cautiously given the limited number of isolates per species. All discrepancies were minor errors, with no major or very major errors observed. The method reduced analysis time from 24 to 3 h and enabled presumptive FLZ susceptibility detection with good overall agreement with the reference methodConclusion. These findings support the potential of MALDI-TOF MS as a rapid adjunct tool for antifungal susceptibility assessment and may contribute to earlier therapeutic decision-making.

High dose rate brachytherapy (HDR-BT) is an attractive option for patients with favourable-risk intermediate-grade prostate cancer. However the relationship between radiation dose and the biochemical response of tumours remains poorly understood. The aim of this study was to investigate the tissue composition of prostate core-needle biopsies, before and after a single dose (13.5 Gy) of HDR-BT using Raman Spectroscopy (RS) and Group and Basis Restricted Non-Negative Matrix Factorisation (GBR-NMF) modelling. RS was used to measure the spectral profiles of benign and malignant regions of prostate tissue. GBR-NMF was employed to derive biochemical profiles from Raman spectra of core-needle biopsies, encompassing both benign and malignant areas, from patients with intermediate risk prostate cancer (PCa). Thirty-four biochemicals were included in the RS evaluation of 40 biopsy samples from 20 patients. Eight biochemicals were significantly differentially expressed between pre- and post-HDR-BT benign tissue (13.5 Gy single fraction): citric acid, collagen, cysteine, DNA, glycerol, palmitic acid, tryptophan, and lycopene (by Mann-Whitney U test). The model identified phenylalanine, stearic acid and retinol to be differentially expressed in malignant tissue after a single dose of 13.5 Gy high dose rate brachytherapy. β-Carotene was found to differ in expression between benign and malignant prostate tissue, irrespective of radiation exposure. Carotenoids and phospholipids may influence the tumour microenvironment (TME) through several mechanisms that affect cancer progression and response to treatments like radiation therapy. These biochemical changes could identify potential therapeutic targets for a personalised radiation treatment approach.

Ovarian cancer (OC) is typically treated with cytoreductive surgery (CRS). Hyperspectral imaging (HSI) is an emerging non-invasive, label-free technique that enables whole-area scanning, making it a promising tool for real-time tumour recognition. However, developing tumour recognition algorithms requires a foundational understanding of spectral variability in normal tissues. This study focusses on the in vivo spectral profiles of key abdominal and pelvic organs encountered during CRS, including the uterus, ovaries, intestines, mesentery, omentum, peritoneum, and fallopian tubes, and evaluates the potential for organ recognition using HSI data. Intraoperative HSI data were from healthy patients. Two machine learning models, a support vector machine (SVM) and a 3D convolutional neural network (3DCNN), were trained to classify the organs based on their spectral signatures. In total, 15 patients were included in the dataset. The 3DCNN slightly outperformed the SVM in terms of the average accuracy (0.889 vs. 0.878), sensitivity (0.648 vs. 0.604), specificity (0.936 vs. 0.930), and Dice Similarity Coefficient (0.595 vs. 0.569). This study demonstrates the feasibility of using HSI for organ differentiation in the clinical setting, although in some cases separability remains a challenge, especially when organs have similar spectra. This is a critical step towards a generalizable in vivo abdominal tumour recognition algorithm, by carefully investigating spectral fingerprints of abdominal tissues.

We present a real-time method based on the propagation of the time-dependent Schrödinger equation in the space of electronic states to compute near edge x-ray absorption fine structure (NEXAFS) spectra of molecules from the ground or a valence excited state. Transition dipole moments between a core and a valence state are computed from linear-response time-dependent density functional theory implemented in the Amsterdam Modeling Suite package by using Slater-Condon rules following two distinct core and valence excited-state calculations. The implementation is compatible with any singly excited ansatz and generalizable to correlated wavefunction methods. The method has been applied to the ultrafast internal conversion observed in the gas-phase thymine, when excited to bright ππ* (S2) state. We have computed the NEXAFS O K-edge from the electronic ground state, S2, and the dark nπ* (S1) state. We have reproduced the experimental spectrum [Wolf etal., Nat. Commun. 8, 29 (2017)] after the pump, showing the peak at 526.5eV associated with S1. The stochastic Schrödinger equation has been used to get a time-resolved NEXAFS signal, introducing the experimental S2 → S1 decay time of 60fs. An implicit pump initializes the thymine in the S2 state, and an x-ray pulse probes the system at various delay times (and distinct thymine structures), leading to a time-resolved spectral profile that captures the S2 → S1 population transfer. Slower relaxation from S1 to the ground state has been also considered in a multiple-channel modeling of the dynamics. Ground- and excited-state NEXAFS spectra of cis and trans isomers of azobenzene have been also computed.

The study presents score-based quantitative principal component analysis (SQPCA) combined with two-trace two-dimensional (2T2D) correlation spectroscopy as an approach for semi-quantitative analysis of plant extract using attenuated total reflection infrared (ATR FT-IR) spectral data. The SQPCA algorithm was applied to determine the content of four flavonoids, baicalin, baicalein, wogonin, and wogonoside, in a series of extracts from Scutellaria baicalensis Georgi roots. Due to the complex nature of the spectral profiles, characterized by congested, overlapping bands, appropriate data preprocessing was necessary to enhance the quantitative performance of the SQPCA procedure. A slice of the 2T2D disrelation spectrum was used to construct a multiplicative filter, i.e., disrelation filter (DF), to derive signals corresponding mainly to the analytes of interest. The concentrations of analytes calculated by SQPCA and SQPCA DF were compared with those determined using the high-performance thin-layer chromatography (HPTLC) method. For the analyzed flavonoids, a reasonable spectral resolution was achieved, making the combined approach especially useful for the identification and quantification of analytes in the presence of interferents.

Kasha's rule, which states that the emitting electronic level of a given multiplicity is the lowest excited level of that multiplicity, is central to the understanding of photochemistry, and the exception due to a large (S1-S2) energy gap, exemplified by azulene, is well understood. Over the last few years, examples of large flexible molecules with modest (S1-S2) energy gaps have been reported to show anti-Kasha (AK) emission, and have been rationalised on the basis of highly spatially separated wavefunctions of the two states. Recently, a fluorophore, having such characteristics, was constructed from dicyanomethylene-4H-pyran (DCM) and integrated fluorescein-chromene (IFC), where a spirolactane open/closed switch was shown to regulate AK behaviour. The open form of the molecule illustrated dual emission which was interpreted as emission from both S1 and S2 states. Here, we investigate theoretically both the FC region and the interpolated pathways towards S2/S1-intersection region using implicit solvation. We calculate excited state energies and oscillator strengths employing both time-dependent density functional theory (with a range of functionals) and high-level wavefunction theories, characterising that the S1 state is the brighter of the valence excited states, dominated by locally excited character, whereas the S2 state is of charge-transfer character and is darker in comparison. Our ab initio calculations indicate that the spectral absorption profiles for the two states overlap, and that there are barrierless internal conversion pathways from the FC region on the S2 surface towards the S2/S1-intersection region, which is consistent with efficient and rapid S2 population decay in this molecule making AK emission less likely. Moreover, energy difference between S1 and S2 states at each of the S1 and S2 excited state minima is unlikely to favour thermal equilibration of population at timescales relevant to emission. Taken together, the present calculations suggest that the experimentally observed anomalous emission requires an alternative interpretation.

Sweet whey (SW) and nejayote (NE), two agro-industrial wastewaters generated in Mexico, were evaluated as growth media for the cultivation of an alkaliphilic microalgae–cyanobacteria consortium (AMC) which has been reported to contain Nannochloropsis sp. and Pseudanabaena sp. at different initial pH (8, 9, and 10). Phototrophic-mixotrophic cultivation was conducted for 14 d using nejayote with biomass (NEB), sweet whey with biomass (SWB), and a mixture of nejayote and sweet whey with biomass (NESWB) to assess organic matter removal, biomass formation, and metabolite dynamics. The highest chemical oxygen demand (COD) removal was observed in NEB at pH 8, reaching 91.66% and a final COD of 1.04 g L−1. Initial pH values of 9 and 10 maintained alkaline conditions through phototrophic–mixotrophic cultivation, indicating stable biological pH regulation associated with photosynthetic activity. NESWB promoted higher biomass production, particularly at pH 9, suggesting enhanced conversion of organic matter into suspended solids. Moreover, the highest intracellular protein content (30.50 ± 0.90% dry weight) was obtained in NESWB at pH 10, supported by FTIR and SDS-PAGE analyses that indicated changes in protein-related spectral features and band profiles. Biomass reached 4.77 ± 0.80 g L−1 and COD decreased from 14.60 ± 0.70 to 4.18 ± 0.31 g L−1. These results demonstrate that the integration of sweet whey and nejayote under alkaline conditions enables simultaneous wastewater treatment and production of protein-rich biomass, highlighting a sustainable strategy for agro-industrial residue valorization.