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A new framework for advanced machine learning-based analysis of hyperspectral datasets HSKL was built using the well-known package scikit-learn. In this paper, we describe HSKL’s structure and basic usage. We also showcase the diversity of models supported by the package by applying 17 classification algorithms and measure their baseline performance in segmenting objects with highly similar spectral properties. 

Advanced stage glioma is the most aggressive form of malignant brain tumors with a short survival time. Real-time pathology assisted, or image guided surgical procedures that eliminate tumors promise to improve the clinical outcome and prolong the lives of patients. Our work is focused on the development of a rapid and sensitive assay for intraoperative diagnostics of glioma and identification of optical markers essential for differentiation between tumors and healthy brain tissues. We utilized fluorescence lifetime imaging (FLIM) of endogenous fluorophores related to metabolism of the glioma from freshly excised brains tissues. Macroscopic time-resolved fluorescence images of three intracranial animal glioma models and surgical samples of patients’ glioblastoma together with the white matter have been collected. Several established and new algorithms were applied to identify the imaging markers of the tumors. We found that fluorescence lifetime parameters characteristic of the glioma provided background for differentiation between the tumors and intact brain tissues. All three rat tumor models demonstrated substantial differences between the malignant and normal tissue. Similarly, tumors from patients demonstrated statistically significant differences from the peritumoral white matter without infiltration. While the data and the analysis presented in this paper are preliminary and further investigation with a larger number of samples is required, the proposed approach based on the macroscopic FLIM has a high potential for diagnostics of glioma and evaluation of the surgical margins of gliomas. 

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features. 

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods. 

Abstract Shortwave infrared radiation (SWIR) is the portion of the electromagnetic spectrum from approximately 900 nm to 2500 nm. Recent advances in imaging systems have expanded the application of SWIR emitters from traditional fields in materials science to biomedical imaging, and the new detectors in SWIR opened an opportunity of deep tissue imaging. Achieving deep photon penetration while maintaining high resolution is one of the main objectives and challenges in bioimaging used for the investigation of diverse processes in living organisms. The application of SWIR emitters in biological settings is, however, hampered by low quantum efficiency. So far, photoluminescent properties in the SWIR region have not been improved by extending concepts that have been developed for the visible (400–650 nm) and near-infrared (NIR, 700–900 nm) wavelengths, which indicates that the governing behavior is fundamentally different in the SWIR. The focus of this minireview is to examine the mechanisms behind the low efficiency of SWIR emitters as well as to highlight the progress in their design for biological applications. Several common mechanisms will be considered in this review: (a) the effect of the energy gap between the excited and ground state on the quantum efficiency, (b) the coupling of the excited electronic states in SWIR emitters to vibrational states in the surrounding matrix, and (c) the role of environment in quenching the excited states. General strategies to improve the quantum yields for a diverse type of SWIR emitters will be also presented. 

Several complexes of “PtL 2 ” composition containing two cyanoxime anions – 2-oximino-2-cyano- N -piperidineacetamide (PiPCO − ) and 2-oximino-2-cyano- N -morpholylacetamide (MCO − ) – have been obtained and characterized both in solution and in the solid state. Complexes exist as two distinct polymorphs: monomeric yellow complexes and dark-green [PtL 2 ] n 1D polymers, while for the MCO − anion a red, solvent containing dimeric [Pt(MCO) 2 ·DMSO] 2 complex has also been isolated. The interconversion of polymorphs was investigated. The monomeric PtL 2 units are arranged into anisotropic extended solid [PtL 2 ] n polymers with the help of Pt⋯Pt metallophilic interactions. Crystal structures of monomeric PtL 2 (L = PiPCO − , MCO − ) and red dimeric [Pt(MCO) 2 ·DMSO] 2 complexes were determined and revealed the cis -arrangement of cyanoxime anions. The Pt–Pt distance in the “head-to-tail” red dimer was found to be 3.133 Å. The structure of the polymeric [Pt(PiPCO) 2 ] n compound was elucidated using the EXAFS method and evidenced the formation of Pt-wires with ∼3.15 Å intermetallic separation. The EPR spectra of both 1D polymers at variable temperatures indicate the absence of Pt( iii ) species. Both pure dark-green [PtL 2 ] n polymers showed a considerable room temperature electrical conductivity of 20–30 S cm −1 , which evidences the formation of a mixed valence Pt( ii )/Pt( iv ) system. We discovered that these 1D polymeric [PtL 2 ] n complexes show an intense NIR fluorescence beyond 1000 nm, while yellow monomeric PtL 2 complexes are not emissive at all. The room temperature excitation spectra of 1D polymeric [PtL 2 ] n complexes demonstrated their strong emission beyond 1000 nm regardless of the used excitation wavelength between 350 and 800 nm, which is typical of systems with delocalized charge carriers. For the first time the formation of mixed valence “metal wires” held together by metallophilic interactions is directly linked both with an intense fluorescence in the NIR region of the spectrum and with the electrical conductivity. The effect of the concentration of [PtL 2 ] n complexes dispersed in the dielectric salt matrix on the photoluminescence wavelength and intensity was investigated. Both polymers show a quantum yield that is remarkably high for this region of the spectrum, reaching ∼2%. Variable temperature emission of polymeric [PtL 2 ] n in the −190–+60 °C range was studied as well.