A Low-Measurement-Cost-Based Multi-Strategy Hyperspectral Image Classification Scheme.

The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model's dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU).

Synergistic effects of dietary selenium nanoparticles and vitamin C improve growth performance, immune response, and antioxidant status of juvenile common carp (Cyprinus carpio).

INTRODUCTION: This study aimed to investigate the synergistic effects of dietary selenium nanoparticles (Se-NPs) and vitamin C (VC) on growth, body composition, antioxidant defense, immunity, and serum biochemical indexes of common carp (Cyprinus carp) juveniles. METHODOLOGY: The test diets were supplemented with three levels of Se-NPs (0, 0.5, and 1 mg/Kg) and three levels of VC (0, 500, and 1000 mg/Kg): the basal diet without supplemental Se-NPs and VC (VC0SeNPs0; control), 0.5 mg Se-NPs /Kg (VC0SeNPs0.5), 1 mg Se-NPs /Kg (VC0SeNPs1), 500 mg VC/Kg (VC500SeNPs0), 1000 mg VC/Kg (VC1000SeNPs0), 500 mg VC/Kg and 0.5 mg Se-NPs (VC500SeNPs0.5), 1000 mg VC/Kg and 0.5 mg Se-NPs (VC1000SeNPs0.5), 500 mg VC/Kg and 1 mg Se-NPs (VC500SeNPs1), 1000 mg VC/Kg and 1 mg Se-NPs (VC1000SeNPs1). The fish were randomly divided into nine experimental groups in triplicate tanks per treatment and fed on their respective diets for 60 days. RESULTS: The findings displayed that fish fed with VC500SeNPs1 and VC500SeNPs0.5 diets had significantly (P < 0.05) higher specific growth rates when compared to other groups. The lowest feed conversion ratio was detected in the VC1000SeNPs1 group and the highest in the control group (P < 0.05). VC, Se-NPs, and their interaction had no significant effect on serum malondialdehyde, ACH50, and IgM (P > 0.05). However, the best parameters associated with antioxidant capacity (higher serum levels of superoxide dismutase and total reduced glutathione) and physiological status (higher concentration of serum globulin and lower amounts of aspartate aminotransferase and lactate dehydrogenase) belonged to the VC1000SeNPs1 and VC500SeNPs1 groups. The results suggest that the Se-NPs and VC combination more efficiently influence the common carp's growth performance, antioxidant status, immunity, and physiological parameters. CONCLUSION: Overall, the diet enriched with 500 mg VC and 1 mg Se-NPs /Kg (VC500SeNPs1) is suitable for boosting the growth and immunity of common carp.

Innovations in forensic science: Comprehensive review of hyperspectral imaging for bodily fluid analysis.

Hyperspectral imaging (HSI) has become a crucial innovation in forensic science, particularly for analysing bodily fluids. This advanced technology captures both spectral and spatial data across a wide spectrum of wavelengths, offering comprehensive insights into the composition and distribution of bodily fluids found at crime scenes. In this review, we delve into the forensic applications of HSI, emphasizing its role in detecting, identifying, and distinguishing various bodily fluids such as blood, saliva, urine, vaginal fluid, semen, and menstrual blood. We examine the benefits of HSI compared to traditional methods, noting its non-destructive approach, high sensitivity, and capability to differentiate fluids even in complex mixtures. Additionally, we discuss recent advancements in HSI technology and their potential to enhance forensic investigations. This review highlights the importance of HSI as a valuable tool in forensic science, opening new pathways for improving the accuracy and efficiency of crime scene analyses.

Advanced Global Prototypical Segmentation Framework for Few-Shot Hyperspectral Image Classification.

With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.

Does hyperspectral always matter? A critical assessment of near infrared versus hyperspectral near infrared in the study of heterogeneous samples.

Near Infrared spectroscopy (NIR), in combination with Chemometrics, has been used for many years in diverse scenarios, mostly focused on the classification and quantitation of properties in food, pharmaceutical preparations, artwork material, etc. This success has been possible due to their desirable properties: fast, reliable (under certain conditions), non-destructive, easy to implement from a hardware perspective, and able to create robust and transferable multivariate models. For some years now, another modality has been gaining the attention of NIR users, especially in the Food sector. That is the plausibility of using NIR in the hyperspectral (HSI) domain. This adds to the previously mentioned abilities, the benefit of scanning the whole surface of samples, acquiring much richer spatial information and, therefore, assuring the quality of the final product more accurately by including parameters that depend on the surface distribution of certain components. This is especially relevant in heterogeneous samples. While this statement is generally true, there are certain situations where this oversampling feature is not strictly needed, and the problem can be easily solved with a classical NIR spectrophotometer. Besides, NIR-hyperspectral imaging (NIR-HSI), despite the abovementioned advantages, has several drawbacks that must be highlighted as well, like their measuring speed, instability, or price. This manuscript will demonstrate that for certain situations, tuning the focal distance of a NIR spectrophotometer is a more feasible, reliable, and inexpensive strategy to collect all the needed information of samples with a certain degree of heterogeneity.

Evaluation of fish habitat suitability based on stream hydrodynamics and water quality using SWAT and HEC-RAS linked simulation.

The objective of this study was to evaluate fish habitat suitability by simulating hydrodynamic and water quality factors using SWAT and HEC-RAS linked simulation considering time-series analysis. A 2.9 km reach of the Bokha stream was selected for the habitat evaluation of Zacco platypus, with hydrodynamic and water quality simulations performed using the SWAT and HEC-RAS linked approach. Based on simulated 10-year data, the aquatic habitat was assessed using the weighted usable area (WUA), and minimum ecological streamflow was proposed from continuous above threshold (CAT) analysis. High water temperature was identified as the most influential habitat indicator, with its impact being particularly pronounced in shallow streamflow areas during hot summer seasons. The time-series analysis identified a 28% threshold of WUA/WUAmax, equivalent to a streamflow of 0.48 m3/s, as the minimum ecological streamflow necessary to mitigate the impact of rising water temperatures. The proposed habitat modeling method, linking watershed-stream models, could serve as a useful tool for ecological stream management.

A Joint Network of Edge-Aware and Spectral-Spatial Feature Learning for Hyperspectral Image Classification.

Hyperspectral image (HSI) classification is a vital part of the HSI application field. Since HSIs contain rich spectral information, it is a major challenge to effectively extract deep representation features. In existing methods, although edge data augmentation is used to strengthen the edge representation, a large amount of high-frequency noise is also introduced at the edges. In addition, the importance of different spectra for classification decisions has not been emphasized. Responding to the above challenges, we propose an edge-aware and spectral-spatial feature learning network (ESSN). ESSN contains an edge feature augment block and a spectral-spatial feature extraction block. Firstly, in the edge feature augment block, the edges of the image are sensed, and the edge features of different spectral bands are adaptively strengthened. Then, in the spectral-spatial feature extraction block, the weights of different spectra are adaptively adjusted, and more comprehensive depth representation features are extracted on this basis. Extensive experiments on three publicly available hyperspectral datasets have been conducted, and the experimental results indicate that the proposed method has higher accuracy and immunity to interference compared to state-of-the-art (SOTA) method.

Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection.

Non-Alcoholic Fatty Liver Disease (NAFLD) is characterized by the accumulation of excess fat in the liver. If left undiagnosed and untreated during the early stages, NAFLD can progress to more severe conditions such as inflammation, liver fibrosis, cirrhosis, and even liver failure. In this study, machine learning techniques were employed to predict NAFLD using affordable and accessible laboratory test data, while the conventional technique hepatic steatosis index (HSI)was calculated for comparison. Six algorithms (random forest, K-nearest Neighbors, Logistic Regression, Support Vector Machine, extreme gradient boosting, decision tree), along with an ensemble model, were utilized for dataset analysis. The objective was to develop a cost-effective tool for enabling early diagnosis, leading to better management of the condition. The issue of imbalanced data was addressed using the Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN). Various evaluation metrics including the F1 score, precision, accuracy, recall, confusion matrix, the mean absolute error (MAE), receiver operating characteristics (ROC), and area under the curve (AUC) were employed to assess the suitability of each technique for disease prediction. Experimental results using the National Health and Nutrition Examination Survey (NHANES) dataset demonstrated that the ensemble model achieved the highest accuracy (0.99) and AUC (1.00) compared to the machine learning techniques that we used and HSI. These findings indicate that the ensemble model holds potential as a beneficial tool for healthcare professionals to predict NAFLD, leveraging accessible and cost-effective laboratory test data.

Differentiation of Listeria monocytogenes serotypes using near infrared hyperspectral imaging.

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.

Greedy Ensemble Hyperspectral Anomaly Detection.

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples.

In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial-spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples.

If You Want to Prevent Hamstring Injuries in Soccer, Run Fast: A Narrative Review about Practical Considerations of Sprint Training.

Hamstring strain injuries (HSIs) are one of the most common injuries in sprint-based sports. In soccer, the ability to sprint is key, not only because of its relation to performance but also due to its possible protective effect against HSIs. Although many authors have focused on the "how", "when", and "what" training load should be implemented, there is a lack of practical proposals for sprint training in a high-level professional environment. The objective of this narrative review is, after a deep review of the scientific literature, to present a practical approach for sprint training, trying to answer some of the questions that most strength and conditioning coaches ask themselves when including it in soccer. Once the literature published on this topic was reviewed and combined with the practical experience of the authors, it was concluded that sprint training in soccer, although it presents an obvious need, is not something about which there is methodological unanimity. However, following the practical recommendations from this narrative review, strength and conditioning coaches can have a reference model that serves as a starting point for optimal management of the internal and external training load when they wish to introduce sprint training in the competitive microcycle in professional soccer players, with the aim of reducing HSIs.

Unlocking genetic insights: Evaluating wheat RILs for physiobiochemical traits under terminal heat stress conditions.

BACKGROUND: The increasing impacts of heat stress on wheat production due to climate change has entailed the development of heat-resilient crop varieties. To address this, two hundred recombinant inbred lines (RILs) derived from a cross between WH711/WH1021 were evaluated in a randomized block design (RBD) with two replications at CCSHAU, Hisar, during 2018-19 under heat stress and non-stress conditions. Heat stress was induced by altering the date of sowing so that the grain filling stage coincide with heat stress. RESULTS: Heat stress adversely affects RILs performance, as illustrated by alterations in phenotypic traits. Highest coefficients of variations were recorded for TAA, CTD 1, WUE, CTD 2, Cc and A under non-stress and heat stress conditions whereas gs, WUEi and GY under non-stress and SPAD 1, SPAD 2, GY and NDVI 2 under heat-stress conditions recorded moderate estimates of coefficient of variations. CTD 2, TAA, E, WUE and A displayed a significant occurrence of both high heritability and substantial genetic advance under non-stress. Similarly, CTD 2, NDVI 2, A, WUEi, SPAD 2, gs, E, Ci, MDA and WUE exhibited high heritability with high genetic advance under heat-stress conditions. CONCLUSIONS: Complementary and duplicate types of interactions with number of controlling genes were observed for different parameters depending on the traits and environments. RILs 41, 42, 59, 74, 75, 180 and 194 were categorized as heat tolerant RILs. Selection preferably for NDVI 1, RWC, TAA, A, E and WUEi to accumulate heat tolerance favorable alleles in the selected RILs is suggested for development of heat resilient genotypes for sustainable crop improvement. The results showed that traits such as such as NDVI, RWC, TAA, A, E, and WUEi, can be effective for developing heat-resilient wheat genotypes and ensuring sustainable crop improvement.

Haematological, biochemical, enzymological changes and mitochondrial dysfunction of liver in freshwater climbing perch Anabas testudineus during their acute and chronic exposure to sodium fluoride.

Anthropogenic activities are increasing fluoride concentration in watercourses. The present study focuses on the sublethal toxicity of sodium fluoride during sub-chronic and chronic time periods in the freshwater fish Anabas testudineus. The 96-hour LC50 value for fluoride was found to be 616.50 mg/L. Excessive mucous production and hyper excitability, followed by loss of balance, were seen in fish under acute fluoride exposure. Significant reduction in yield and specific growth rate of fish were assessed at 15, 30 and 45-days exposure intervals. Different bio-indicators like Hepatosomatic-index, Gonadosomatic-index and fecundity were reduced significantly in fish exposed to 10% (61.6 mg/L) and 20% (123.2 mg/L) of 96 h of LC50 values of fluoride in comparison to control. Toxicant concentrations directly correlated with parameter lowering. Fluoride exposure increased plasma glucose, creatinine, AST, and ALT and reduced total RBC, haemoglobin content, Hct (%), plasma protein, and cholesterol. Moreover, fluoride exposure significantly reduces the mitochondrial membrane potential in liver. This may result in metabolic depression, haematological, biochemical, and enzymological stress. The in-silico structural analysis predicts that fluoride may impede cytochrome c oxidase of the electron transport system, hence inhibiting mitochondrial functionality. These findings collectively highlight the urgent need for stringent regulation and monitoring of fluoride levels in freshwater ecosystems, as the subchronic and chronic effects observed in A. testudineus may have broader implications for aquatic ecosystems.

Study on the Diffusion and Optimization of Sucrose in Gaido Seak Based on Finite Element Analysis and Hyperspectral Imaging Technology.

As a traditional Chinese dish cutting technology process, Gaidao artificially create cuts embedded in the food surface by cutting through it with knife, a process that currently plays an important role in the beef marinating process. And different Gaidao processes directly affect the beef marination flavour and marination efficiency. This study is the first to propose the use of Hyperspectral imaging technology (HSI) combined with finite element analysis to investigate the effect of Gaidao process on the quality of marinated beef. The study was carried out by collecting spectral information of beef marinated with different sucrose concentrations and combining various pre-processing methods and algorithms such as PLS, BiPLS, iPLS, and SiPLS to establish a quantitative model of sucrose concentration in beef, and finally optimizing parameters such as the length, position and number of Gaidao by Finite Element Analysis (FEA), which showed that when marinated with 1.0 mol/m³ sucrose solution, the concentration of sucrose in all tissues in the Gaidao steak reached 0.8 mol/m³ and above, which greatly improved the diffusion effect of the marinade. This work provides new ideas and methods to optimize the beef marinade Gaidao process, which has important practical value and research significance.

Hispanic/Latinx STEM Majors Applying to Graduate School: The Role of Family, Peers, and Undergraduate Research Programs in Facilitating Community Cultural Wealth.

This paper examines the critical transition from undergraduate to graduate biomedical education and focuses on Hispanic/Latinx students who participated in a biomedical undergraduate research program at a Hispanic-Serving Institution located on the US-Mexico border. We use the community cultural wealth (CCW) framework (Yosso, 2005) to analyze 13 qualitative interviews about students' experiences applying to graduate school in biomedical fields and how different program activities allowed students to navigate the graduate school application process. Our findings suggest that different programmatic activities (research experiences, research mentorship, workshops, family involvement, and advising) facilitated students' graduate school application process by enhancing different types of cultural capital: aspirational, familial, social, navigational, and resistant.

Commentary: Cigarette dependence in menthol and non-menthol young adult cigarette smokers.

FDA's Proposed Final Rule to ban menthol cigarettes asserts that "menthol cigarettes contribute to greater nicotine dependence in youth and young adults than non-menthol cigarettes." However, none of the publications referenced included young adults. To provide empirical evidence on the subject, we examine smoking frequency and Heaviness of Smoking Index (HSI) dependence among 2,194 young adult (ages 18-25 years) menthol and non-menthol smokers from 31 online survey samples. Unpaired t-tests examined if daily smoking or the proportion of daily smokers who are low or high dependence on the HSI vary by menthol cigarette smoking status. Young adult menthol smokers were less likely to be daily smokers than young adult non-menthol smokers. There were no differences in the percentages of daily menthol and non-menthol smokers categorized as low or high dependence on the HSI. Smoking menthol cigarettes, therefore, does not appear to be associated with greater cigarette dependence among young adults than smoking non-menthol cigarettes.

Peer leader perspectives from a PLTL implementation in a Hispanic-serving institution.

Peer-Led Team Learning (PLTL) is a pedagogical approach that has been shown to benefit all students, especially underrepresented minority students and peer leaders in Science, Technology, Engineering, and Mathematics (STEM) disciplines. In this work, we present results from our study of the impact of PLTL on our peer leaders from a controlled implementation in general biology, general chemistry, and statistics courses at a Hispanic-serving, minority-serving institution. More specifically, we have measured our PLTL program's impact on our peer leaders' skill development, engagement with the subject material, and sense of belonging as peer leaders. Weekly peer leader reflections analyzed using the Dreyfus model exhibited a consistent set of skills, while those analyzed using the Pazos model revealed a consistent type of student-peer leader interactions, allowing for peer leaders to be assigned to specific levels in the hierarchy of each of the models. Analysis of eight skill-based Likert-scale questions on the SALG survey showed an overall positive shift at the highest level. Independent of the skill or interaction level of the peer leader, we observed several instances of peer leaders acknowledging development in their communication skills, sincere attempts at creating an engaging classroom, and a deep investment in their student's success. Peer leaders also reported improvements in understanding of the subjects they were teaching, wanting to persevere and solve problems independently, and feeling passionate about helping other students.

Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study.

Introduction: Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery. Methods: A lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350-1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study. Results: Focusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery. Discussion: This work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.

Bibliometric analysis of the current status and trends on medical hyperspectral imaging.

Hyperspectral imaging (HSI) is a promising technology that can provide valuable support for the advancement of the medical field. Bibliometrics can analyze a vast number of publications on both macroscopic and microscopic levels, providing scholars with essential foundations to shape future directions. The purpose of this study is to comprehensively review the existing literature on medical hyperspectral imaging (MHSI). Based on the Web of Science (WOS) database, this study systematically combs through literature using bibliometric methods and visualization software such as VOSviewer and CiteSpace to draw scientific conclusions. The analysis yielded 2,274 articles from 73 countries/regions, involving 7,401 authors, 2,037 institutions, 1,038 journals/conferences, and a total of 7,522 keywords. The field of MHSI is currently in a positive stage of development and has conducted extensive research worldwide. This research encompasses not only HSI technology but also its application to diverse medical research subjects, such as skin, cancer, tumors, etc., covering a wide range of hardware constructions and software algorithms. In addition to advancements in hardware, the future should focus on the development of algorithm standards for specific medical research targets and cultivate medical professionals of managing vast amounts of technical information.