Combinatorial Strategies for Synthesis and Characterization of Alloy Microstructures over Large Compositional Ranges. - Li, Yanglin; Jensen, Katharine E; Liu, Yanhui; Liu, Jingbei; Gong, Pan; Scanley, B Ellen; Broadbridge, Christine C; Schroers, Jan (ACS combinatorial science, 2016)
The exploration of new alloys with desirable properties has been a long-standing challenge in materials science because of the complex relationship between composition and microstructure. In this Research Article, we demonstrate a combinatorial strategy for the exploration of composition dependence of microstructure. This strategy is comprised of alloy library synthesis followed by high-throughput microstructure characterization. As an example, we synthesized a ternary Au-Cu-Si composition library containing over 1000 individual alloys using combinatorial sputtering. We subsequently melted and resolidified the entire library at controlled cooling rates. We used scanning optical microscopy and X-ray diffraction mapping to explore trends in phase formation and microstructural length scale with composition across the library. The integration of combinatorial synthesis with parallelizable analysis methods provides a efficient method for examining vast compositional ranges. The availability of microstructures from this vast composition space not only facilitates design of new alloys by controlling effects of composition on phase selection, phase sequence, length scale, and overall morphology, but also will be instrumental in understanding the complex process of microstructure formation in alloys.
Keywords: *Alloys/ch [Chemistry]; *Combinatorial Chemistry Techniques/mt [Methods]; *Copper/ch [Chemistry]; *Gold/ch [Chemistry]; *Silicon/ch [Chemistry]; Alloys; Combinatorial Chemistry Techniques; Copper; Gold; High-Throughput Screening Assays; Materials Testing; Physical Phenomena; Silicon; alloy; alloy microstructure; chemistry; combinatorial chemistry; combinatorial materials science; copper; dept.phy; gold; high throughput screening; high-throughput; materials testing; phase mapping; physical phenomena; procedures; silicon

Computational nanocharacterization for combinatorially developed bulk metallic glass - Gossett, Eric; Scanley, Ellen; Liu, Yanhui; Li, Yanglin; Liu, Ze; Sohn, Sungwoo; Schroers, Jan; Broadbridge, Christine; Schwendemann, Todd C. (International Journal of High Speed Electronics and Systems, 2015)
Bulk metallic glasses synthesized at specialized facilities at Yale using magnetron cosputtering are sent to Southern Connecticut State University for elemental characterization. Characterization is done using a Zeiss Sigma VP SEM coupled with an Oxford EDS. Characterization is automated using control software provided by Oxford. Collected data is processed and visualized using computational methods developed internally. Processed data is then organized into a database suitable for web retrieval. This technique allows for the rapid characterization of a combinatorial wafer to be carried out in ~11 hours for a single wafer containing ~600 unique compounds. © 2015 World Scientific Publishing Company.
Keywords: Bulk metallic glass; Combinatorial synthesis; Computational methods; Control software; Glass; Magnetron co-sputtering; Magnetron sputtering; Metallic glass; Nano-characterization; Single wafer; Spectrography; Web retrieval; combinatorial synthesis; computational methods; dept.phy; energy dispersive X-ray spectrography; magnetron sputtering; nano-characterization; peer-reviewed

Combinatorial exploration of color in gold-based alloys - Liu, Jingbei; Liu, Yanhui; Gong, Pan; Li, Yanglin; Moore, Kimberly M.; Scanley, Ellen; Walker, Fred; Broadbridge, Christine; Schroers, Jan (Gold Bulletin, 2015)
Combinatorial approaches comprised of combinatorial magnetron co-sputtering deposition and fast screening methods are introduced to study color as a function of composition in Au-based alloys. The microstructures of the thin films and bulk alloys are identified by X-ray diffraction, and their colors of the alloys are characterized by optical reflectivity. The results reveal that when comparing microstructures and reflectivity, thin films are similar to bulk alloys. In Au-Ag-Cu solid solutions, the color of the ternary alloy follows the rule of mixture. For colors resulting from AuAl2 intermetallic, the color of an alloy scales with the percentage of the intermetallic phase and the deviation from its ideal binary composition. In the Au-Al-Cu library, we found a ∼90 % AuAl2 area fraction compositional window where copper addition can be tuned to improve mechanical properties while keeping purple color, even though Al and CuAl2 phases exist. Moreover, when comparing the color in Au-Cu-Si-Ag amorphous and crystalline state solid solution for the same composition, the colors are essentially identical. © 2015 The Author(s).
Keywords: Aluminum; Amorphous alloys; Amorphous silicon; Amorphousalloys; Binary composition; Color; Combinatorial approach; Crystalline materials; Crystalline state; Decorative thin film coating; Decorative thin films; Gold; Gold alloys; Gold based alloys; Gold coatings; Intermetallic phase; Intermetallics; Magnetron co-sputtering; Magnetrons; Microstructure; Optical reflectivity; Reflection; Silver; Silver alloys; Solid solution; Solid solutions; Thin films; X ray diffraction; dept.phy

Noble metal high entropy alloys - Sohn, Sungwoo; Liu, Yanhui; Liu, Jingbei; Gong, Pan; Prades-Rodel, Silke; Blatter, Andreas; Scanley, B. Ellen; Broadbridge, Christine C.; Schroers, Jan (Scripta Materialia, 2017)
A series of noble metal high entropy alloys with up to six constituent elements has been produced by casting. PtPdRhIrCuNi forms single-phase face-centered cubic solid solution, and its stability is confirmed by annealing experiments. This alloy deforms homogeneously to ~30% to a high ultimate compression strength of 1839MPa. We discuss rules for the formation of single-phase solid solution.
Keywords: Annealing experiments; Compressive properties; Compressive property; Constituent elements; Entropy; FCC structure; Face-centered cubic; Fcc structures; High entropy alloys; Phase stability; Precious metals; Single phase; Solid solution; Solid solutions; Ultimate compression; dept.phy

Combinatorial screening of Pd-based quaternary electrocatalysts for oxygen reduction reaction in alkaline media - Li, Jinyang; Stein, Helge S.; Sliozberg, Kirill; Liu, Jingbei; Liu, Yanhui; Sertic, Genevieve; Scanley, Ellen; Ludwig, Alfred; Schroers, Jan; Schuhmann, Wolfgang; Taylor, André D. (Journal of Materials Chemistry, 2017)
The implementation of electrochemical systems such as fuel cells has been hindered by the slow development of low cost high activity catalysts. Here we examine the oxygen reduction reaction performance of a combinatorial Pd-Au-Ag-Ti thin film library using high-throughput screening and correlate the electrochemical behavior to the crystallographic properties. We find compositions of ca. 40-60 at% Pd and 30-35 at% Au exhibit both a low overpotential of close to the value of pure Pt as well as high current density. We also observe a volcano-like relationship between the overpotential and the solid formation strain. This study provides compositional guidance towards the future synthesis of nanostructured quaternary Pd-Au-Ag-Ti alloys and suggests the potential for broader application of high-throughput electrochemical characterization by means of an automatic scanning droplet cell. © The Royal Society of Chemistry.
Keywords: Catalyst activity; Combinatorial screenings; Crystallographic properties; Electrocatalysts; Electrochemical behaviors; Electrochemical characterizations; Electrochemical systems; Electrolytic reduction; Fuel cells; Gold; High current densities; High throughput screening; Oxygen reduction reaction; Reduction; Silver; Silver alloys; Throughput; Titanium alloys; dept.phy

Combinatorial development of antibacterial Zr-Cu-Al-Ag thin film metallic glasses. - Liu, Yanhui; Padmanabhan, Jagannath; Cheung, Bettina; Liu, Jingbei; Chen, Zheng; Scanley, B Ellen; Wesolowski, Donna; Pressley, Mariyah; Broadbridge, Christine C; Altman, Sidney; Schwarz, Udo D; Kyriakides, Themis R; et al. (Scientific Reports, 2016)
Metallic alloys are normally composed of multiple constituent elements in order to achieve integration of a plurality of properties required in technological applications. However, conventional alloy development paradigm, by sequential trial-and-error approach, requires completely unrelated strategies to optimize compositions out of a vast phase space, making alloy development time consuming and labor intensive. Here, we challenge the conventional paradigm by proposing a combinatorial strategy that enables parallel screening of a multitude of alloys. Utilizing a typical metallic glass forming alloy system Zr-Cu-Al-Ag as an example, we demonstrate how glass formation and antibacterial activity, two unrelated properties, can be simultaneously characterized and the optimal composition can be efficiently identified. We found that in the Zr-Cu-Al-Ag alloy system fully glassy phase can be obtained in a wide compositional range by co-sputtering, and antibacterial activity is strongly dependent on alloy compositions. Our results indicate that antibacterial activity is sensitive to Cu and Ag while essentially remains unchanged within a wide range of Zr and Al. The proposed strategy not only facilitates development of high-performing alloys, but also provides a tool to unveil the composition dependence of properties in a highly parallel fashion, which helps the development of new materials by design.
Keywords: *Alloys/ch [Chemistry]; *Aluminum/ch [Chemistry]; *Anti-Bacterial Agents/ch [Chemistry]; *Combinatorial Chemistry Techniques; *Silver/ch [Chemistry]; Alloys; Alloys/pd [Pharmacology]; Aluminum; Anti-Bacterial Agents; Anti-Bacterial Agents/pd [Pharmacology]; Combinatorial Chemistry Techniques; Copper; Copper/ch [Chemistry]; Escherichia coli; Escherichia coli/de [Drug Effects]; Escherichia coli/gd [Growth & Development]; Escherichia coli/ul [Ultrastructure]; Glass; Glass/ch [Chemistry]; Microscopy, Atomic Force; Silver; Structure-Activity Relationship; Zirconium; Zirconium/ch [Chemistry]; alloy; aluminum; antiinfective agent; atomic force microscopy; chemistry; combinatorial chemistry; copper; dept.phy; drug effect; glass; growth, development and aging; peer-reviewed; silver; structure activity relation; ultrastructure; zirconium

Visible resonance raman spectroscopy in human brain tissues - Zhou, Yan; Liu, Cheng-hui; Zhu, Ke; Wu, Binlin; Yu, Xinguang; Alfano, Robert R. (Neurophotonics and Biomedical Spectroscopy, 2019)
This chapter first reviews the research and applications of nonresonance and resonance Raman spectroscopy for analysis of human brain normal and abnormal tissues. Next, special emphasis is made on our recent achievements of visible resonance Raman (VRR) technique in primary human brain tumor disease investigation and diagnosis. Visible resonance Raman (VRR) spectroscopy technique uses excitation of visible light (532 nm) to evaluate the resonant and nonresonant molecular vibrational modes in biological tissues. The VRR signal intensities are enhanced by two to three orders of magnitude for faster use in medical applications in quasi real time. VRR opens up a new stainless “molecular optics based histopathology” diagnosis approach. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Brain cancer margin assessment; Glioblastoma multiforme (GBM); Glioblastoma multiforme (gbm); Gliomas; Meninges; Optical biopsy; Visible resonance Raman (VRR) spectroscopy; Visible resonance raman (vrr) spectroscopy; Wavelength 532 nm; Wavelength 532nm; dept.phy

Quantitative characterization for identification of brain cancer surgical margin using resonance Raman spectroscopy - Zhou, Yan; Liu, Chenghui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhang, Chunyuan; Alfano, R R (OSA - The Optical Society, 2017)
A new criterion was developed to characterize brain tissue using resonance Raman spectroscopy, by which, negative margins of cancer can be differentiated from normal tissues. This method may help a surgeon better decide surgical margins. © OSA 2017.
Keywords: Brain cancer; Brain tissue; Diseases; Normal tissue; Quantitative characterization; Raman spectroscopy; Resonance Raman spectroscopy; Surgery; Tissue; dept.phy

Optical Molecular Signatures of IDH Genotype for Classification of Human Glioma Using Visible Resonance Raman Spectroscopy - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, C.; Alfano, Robert; Yu, Xinguang (Journal of Biophotonics, 2026)
Keywords: Biomarker; Gene; Genotype; Glioma; IDH mutation; IDH1; Isocitrate dehydrogenase; Mutation; Raman spectroscopy; Resonance Raman spectroscopy; dept.phy; glioma; optical molecular biopsy; visible resonance Raman spectroscopy

Human glioma tumor diagnosis using visible resonance Raman spectroscopy and deep learning - Llanos, N.E.; Zhou, Y.; Yu, X.; Zhang, S.; Zhu, K.; Liu, C.-H.; Wu, B.; Alfano, R.R. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2024)
Convolutional neural network (CNN) based deep learning is used to analyze spectral data collected by visible resonance Raman (VRR) spectroscopy to distinguish human glioma tumors from healthy brain tissues using binary classification and identify the cancer grades of the glioma tumors using multi-class classification. Classification was performed using both raw spectral data and baseline-subtracted data for comparison. The classification using both datasets yielded high accuracy, with the results obtained from baseline subtracted spectra slightly better than that obtained from raw spectra. The study showed VRR combined with deep learning provides a robust molecular diagnostic tool for accurately distinguishing glioma tumors from normal tissues and glioma tumor tissues at different cancer grades. Deep learning aided VRR technique may be used for in-situ intraoperative diagnosis of brain cancer. It may help a surgeon to identify cancer margins and even cancer grades during surgery. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Keywords: cancer grading; convolutional neural network; deep learning; dept.phy; glioma tumor; human brain tissues; machine learning; optical molecular pathology; peer-reviewed; visible resonance Raman (VRR)

A handheld visible resonance raman analyzer used in intraoperative detection of human glioma - Zhang, Liang; Zhou, Yan; Wu, Binlin; Zhang, Shengjia; Zhu, Ke; Liu, Cheng-Hui; Yu, Xinguang; Alfano, Robert R. (Cancers, 2023)
There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.
Keywords: dept.phy; glioma; human brain; intraoperative; optical biopsy; optical molecular pathology; peer-reviewed; portable optical-fiber-probe Raman analyzer; visible resonance Raman (VRR)

Diagnosis of glioma brain cancer using visible resonance Raman spectroscopy - Zhou, Yan; Wu, Binlin; Liu, Chenghui; Yu, Xinguang; Cheng, Gangge; Wang, Kai; Zhang, Chunyuan; Shi, Lingyan; Alfano, R R (OSA, 2018)
Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).
Keywords: Brain cancer; Brain tissue; Different grades; Glioma tissues; High-accuracy; Principal component analysis; Raman spectroscopy; Resonance Raman spectroscopy; Spectral data; Tumors; dept.phy

Glioma tumors classified using visible resonance raman spectroscopy and machine learning - Wu, Binlin; Zhou, Yan; Zhang, Liang; Zhang, Shengjia; Yu, Xinguang; Wang, Eric; Zhu, Ke; Liu, Cheng-hui; Alfano, Robert R. (The Optical Society, 2020)
Machine learning algorithms were used to classify and analyze spectral data collected by visible resonance Raman spectroscopy to distinguish normal human brain tissue and glioma tumor tissues at different grades and show promising results. © OSA 2020 © 2020 The Author(s)
Keywords: dept.phy; ⛔ No DOI found

Erratum to: Determination of critical cooling rates in metallic glass forming alloy libraries through laser spike annealing (Scientific Reports, (2017), 7, 1, (7155), 10.1038/s41598-017-07719-2) - Bordeenithikasem, P.; Liu, J.; Kube, S.A.; Li, Y.; Ma, T.; Scanley, B.E.; Broadbridge, C.C.; Vlassak, J.J.; Singer, J.P.; Schroers, J. (Scientific Reports, 2018)
A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper. © 2018, The Author(s).
Keywords: dept.phy; erratum; error

Resonance raman of BCC and normal skin - Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; M.d, Hugh Beckman; Alfano, Robert R. (Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2017)
The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.
Keywords: Basal cell carcinoma; Basel cell carcinoma (BCC); Biopsy; Cell carcinoma; Diagnostic algorithms; Histology; Human skin; Human skin tissues; Laser excitation; Principal component analysis; Principal component analysis (PCA); Resonance; Resonance Raman spectra; Resonance Raman spectroscopy; Skin cancer; Skin cancers; Spectral differences; Statistical methods; Support vector machine (SVM); Support vector machines; Tissue; Tissue engineering; dept.phy

Optical spectroscopic analysis of cerebrospinal fluid of human brain tumor using visible resonance Raman spectroscopy - Zhou, Y.; Wu, B.; Zhang, L.; Zhu, K.; Zhang, S.; Wang, X.; Zhang, C.; Liu, C.-H.; Yu, X.; Alfano, R.R. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2024)
Currently, liquid biopsy method is mainly used for tumor detection based on genomic molecular alterations in vitro. Liquid biopsy is superior to traditional tissue biopsy techniques and its diagnosis time of disease and repeated diagnosis of liquid biopsy are new breakthroughs in clinical application. Liquid biopsy method can be used to detect most human disease based on genetic biomarkers from body fluids, among which, special biomarkers in blood and cerebrospinal fluid (CSF) samples are the main research objects, and have made good achievements in preliminary clinical applications. The application of optical spectroscopy in the field of liquid biopsy has aroused great interest among researchers and demonstrated the potential of its clinical application for oncology. The aim of this study is to reveal the optical spectroscopic characteristics of the main biochemical components of CSF of brain tumor using visible resonance Raman (VRR) spectroscopy ex vivo. Tumor-associated proteins, glucose, lactate and other metabolites released to CSF can be used as markers for liquid biopsy. We studied the VRR spectra of CSF samples from 7 types of brain tumor patients. The characteristic VRR modes that were found and may be used as a combination of multiple analyte biomarkers include amyloid-β and tau protein, excess neurotransmitters such as glutamic acid derived from the exchange with interstitial fluid (ISF), DNA, glucose, lactate, etc. for optical liquid biopsy analyses. Another interesting finding was that CSF of different types of tumors showed different images similar to the crystallization of water under the optical microscope. Considering our previous study, the current study on CSF provides another proof that the VRR system can provide a complete scan region of 200 - 4000cm-1 as a clinical tool for non-invasive diagnosis of brain disease. © 2024 SPIE.
Keywords: Optical liquid biopsy; amyloid-β (Aβ); cerebrospinal fluid (CSF) ex vivo; dept.phy; glioblastoma (GBM); human brain tumor; tau proteins; visible resonance Raman (VRR) spectroscopy

Determination of critical cooling rates in metallic glass forming alloy libraries through laser spike annealing. - Bordeenithikasem, Punnathat; Liu, Jingbei; Kube, Sebastian A; Li, Yanglin; Ma, Tianxing; Scanley, B Ellen; Broadbridge, Christine C; Vlassak, Joost J; Singer, Jonathan P; Schroers, Jan (Scientific reports, 2017)
The glass forming ability (GFA) of metallic glasses (MGs) is quantified by the critical cooling rate (R C). Despite its key role in MG research, experimental challenges have limited measured R C to a minute fraction of known glass formers. We present a combinatorial approach to directly measure R C for large compositional ranges. This is realized through the use of compositionally-graded alloy libraries, which were photo-thermally heated by scanning laser spike annealing of an absorbing layer, then melted and cooled at various rates. Coupled with X-ray diffraction mapping, GFA is determined from direct R C measurements. We exemplify this technique for the Au-Cu-Si system, where we identify Au56Cu27Si17 as the alloy with the highest GFA. In general, this method enables measurements of R C over large compositional areas, which is powerful for materials discovery and, when correlating with chemistry and other properties, for a deeper understanding of MG formation.
Keywords: dept.phy; peer-reviewed

Assessment of basal cell carcinoma from normal human skin tissues using Resonance Raman spectroscopy - Liu, Chenghui; Wu, Binlin; Sordillo, Laura A; Boydstonwhite, Susie; Sriramoju, Vidyasagar; Zhang, Chunyuan; Beckman, Hugh; Zhang, Lin; Pei, Zhe; Shi, Lingyan; Alfano, R R (OSA - The Optical Society, 2018)
VRR spectroscopy was used for BCC and normal skin tissues with 532nm excitation. The spectra showed significant changes in collagen, carotenoids and lipids. These enhanced fingerprints demonstrate a potential use as label-free pathology method. © 2018 The Author(s).
Keywords: Basal cell carcinoma; Histology; Human skin; Label free; Normal skin; Resonance Raman spectroscopy; Tissue; dept.phy

Resonance raman imaging for detecting and monitoring molecular pathological changes in human brain tumors related to Warburg effect - Zhou, Yan; Liu, Cheng-hui; Zhu, Ke; Zhang, Chunyuan; Yang, Yang; Yu, Xinguang; Hu, Hailong; Cheng, Gangge; Wu, Binlin; Shi, Lingyan; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
The goal of the research is to determine the prognostic molecular pathological changes in components and composition, for human brain glioma gradings in comparison with normal tissues in three-dimensional Raman imaging profiles by visible Resonance Raman (VRR) imaging. VRR images from twenty-five specimens including three healthy tissues, one normal control, and twenty-one glioma tissues of grades II, II-III and III-IV with histology examination were measured and investigated using WITec300R confocal micro Raman imaging system with laser excitation of 532nm. Two-dimensional RR spectral mappings performed in 20μm x 20μm generated 400 images which integrated the intensity of the specific biochemical bonds as the third dimension. The three-dimension (3D) map demonstrated the spatial distributions of three selected sets of RR spectra of molecular biomarkers, and revealed significant differences in the spectra between normal and glioma tissues of different grades due to the composition changes in key molimageecules. These RR molecular spectral fingerprints have displayed: a clear enhancement of RR vibrational modes at 1129-1131cm-1 and 2934cm-1 which are supposed to be arising from lipoproteins; evident decreased RR vibrational modes at 1442cm-1 and 2854cm-1 which are from saturated fatty acids bonds in all-grades of glioma brain tissues compared with normal tissues; and the enhanced RR spectral modes of 1129 cm-1 and 2938cm-1 which suggest contribution from lactate. These findings may provide a novel proof for anaerobic glycolysis metabolic process in brain glioma cancer tissues that has been explained by Warburg effects.
Keywords: 2-d imaging; 2D imaging; Biopsy; Brain; Gliomas; Histology; Human Brain Tissues; Human brain; Laser excitation; Molecular imaging; Resonance; Resonance Raman; Saturated fatty acids; Tumors; Visible resonance Raman (VRR); Warburg effect; Warburg effects; dept.phy

Characterization and discrimination of basal cell carcinoma and normal human skin tissues using resonance Raman spectroscopy - Liu, C.-H.; Wu, B.; Boydston-White, S.; Beckman, H.; Sriramoju, V.; Sordillo, L.; Zhang, C.; Smith, J.; Zhang, L.; Shi, L.; Alfano, R.R. (OSA - The Optical Society, 2017)
Resonance Raman spectroscopy is used for rapid detection of skin BCC cancer. The cross-validated classification accuracy is achieved to be as high as 98% using nonnegative matrix factorization along with support vector machine statistical method. © OSA 2017.
Keywords: Basal cell carcinoma; Classification accuracy; Factorization; Human skin; Matrix algebra; Nonnegative matrix factorization; Raman spectroscopy; Rapid detection; Resonance Raman spectroscopy; dept.phy

Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers - Zhou, Yan; Liu, Cheng-Hui; Pu, Yang; Wu, Binlin; Nguyen, Thien An; Cheng, Gangge; Zhou, Lixin; Zhu, Ke; Chen, Jun; Li, Qingbo; Alfano, Robert R. (Journal of Innovative Optical Health Sciences, 2019)
The purpose of this study is to examine optical spatial frequency spectroscopy analysis (SFSA) combined with visible resonance Raman (VRR) spectroscopic method, for the first time, to discriminate human brain metastases of lung cancers adenocarcinoma (ADC) and squamous cell carcinoma (SCC) from normal tissues. A total of 31 label-free micrographic images of three types of brain tissues were obtained using a confocal micro-Raman spectroscopic system. VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532[Formula: see text]nm from the same sites of the tissues. Using SFSA method, the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian function fitting. The standard deviations, [Formula: see text] calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine (SVM) classifier. The key VRR biomolecular fingerprints of carotenoids, tryptophan, amide II, lipids and proteins (methylene/methyl groups) were also analyzed using SVM classifier. All three types of brain tissues were identified with high accuracy in the two approaches with high correlation. The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues, which are on-site, real-time and label-free and may improve the accuracy of brain biopsy.
Keywords: Amides; Amino acids; Article; Biopsy; Brain; Diseases; Histology; Human brain; Image analysis; Metastatic lung cancer; Pathology; Raman spectrometry; Resonance; Resonance Raman; Spatial frequency; Spatial frequency spectroscopy analysis (SFSA); Spatial variables measurement; Spectroscopic analysis; Spectrum analysis; Support vector machines; Tissue; accuracy; amide; brain metastasis; brain tissue; cancer classification; carbene; carotenoid; controlled study; dept.phy; diagnostic test accuracy study; excitation; fatty acid; high density lipoprotein; histopathology; human; human brain; human tissue; lipid; low density lipoprotein; lung adenocarcinoma; metastatic lung cancer; methyl group; molecular imaging; peer-reviewed; photomicrograph image; sensitivity and specificity; spatial frequency spectroscopy analysis; spectroscopy; squamous cell lung carcinoma; support vector machine; support vector machine (SVM); tryptophan; visible resonance Raman (VRR); visible resonance Raman spectroscopy

A portable visible resonance Raman analyzer with a handheld optical fiber probe for in vivo diagnosis of brain glioblastoma multiforme in an animal model - Zhou, Y.; Zhang, S.; Wu, B.; Yu, X.; Cheng, G.; Zhu, K.; Zhao, M.; Zheng, J.; Zhang, M.; Liang, Q.; Liu, C.-H.; Alfano, R.R. (Optical Society of America (OSA), 2019)
A VRR-LRR analyzer with handheld fiber‐optic probe is reported for the first time for diagnosis of brain GBM in vivo. The sensitivity for identification is 80% compared with histopathology examination. © OSA 2019. The Author(s).
Keywords: Animal model; Fiberoptic probes; Glioblastoma multiforme; Handhelds; In-vivo; Optical fiber probe; Optical fibers; Probes; Resonance Raman; dept.phy

Statistical analysis and machine learning algorithms for optical biopsy - Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.
Keywords: Artificial neural network; Biological basis; Biopsy; Cancer diagnosis; Cancerous tissues; Factorization; Learning algorithms; Learning systems; Least squares approximations; Machine learning; Matrix algebra; Neural networks; Nonnegative matrix factorization; Optical biopsies; Optical biopsy; Partial least square (PLS); Partial least squares; Principal component analysis; Resonance Raman spectroscopy; Spatial signature; Support vector machine; Support vector machines; Tissue; Tissue engineering; dept.phy

Invited Article: Molecular biomarkers characterization for human brain glioma grading using visible resonance Raman spectroscopy - Zhou, Yan; Liu, Cheng-hui; Wu, Binlin; Zhang, Chunyuan; Yu, Xinguang; Cheng, Gangge; Chen, Hong; Li, Shenglin; Liang, Qijun; Zhang, Mingqian; Zhu, Ke; Shi, Lingyan; et al. (APL Photonics, 2018)
The accurate identification of the human brain tumor boundary and the complete resection of the tumor are two essential factors for the removal of the glioma tumor in brain surgery. We present a visible resonance Raman (VRR) spectroscopy technique for differentiating the brain tumor margin and glioma grading. Eighty-seven VRR spectra from twenty-one human brain specimens of four types of brain tissues, including the control, glioma grade II, III, and IV tissues, were observed. This study focuses on observing the characteristics of new biomarkers and their changes in the four types of brain tissue. We found that two new RR peaks at 1129 cm-1 and 1338 cm-1 associated with molecular vibrational bonds in four types of brain tissues are significantly different in peak intensities of VRR spectra. These two resonance enhanced peaks may arise from lactic acid/phosphatidic acid and adenosine triphosphate (ATP)/nicotinamide adenine dinucleotide, respectively. We found that lactic acid and ATP concentrations vary with glioma gratings. The higher the grade of malignancy, the more the increase in lactic acid and ATP concentrations. These two RR peaks may be considered as new molecular biomarkers and used to evaluate glioma grades and identify the margin of gliomas from the control tissues. The metabolic process of lactic acid and ATP in glioma cells based on the VRR spectral changes may reveal the Warburg hypothesis. © 2018 Author(s).
Keywords: Adenosinetriphosphate; Biomarkers; Brain; Control tissues; Grading; Histology; Human brain tumors; Lactic acid; Metabolic process; Molecular biomarker; Resonance; Resonance Raman; Resonance Raman spectroscopy; Resonance enhanced; Spectral change; Tumors; dept.phy; peer-reviewed

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy. - Zhou, Yan; Liu, Cheng-Hui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Wang, Kai; Zhang, Chunyuan; Zhao, Mingyue; Zong, Rui; Zhang, Lin; Shi, Lingyan; et al. (Journal of biomedical optics, 2019)
Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.
Keywords: *Biomarkers, Tumor/me [Metabolism]; *Brain Neoplasms/me [Metabolism]; *Brain Neoplasms/pa [Pathology]; *Glioma/me [Metabolism]; *Glioma/pa [Pathology]; *Spectrum Analysis, Raman/mt [Methods]; Amides; Amino acids; Biomarkers; Biomarkers, Tumor; Biomarkers, Tumor/ch [Chemistry]; Biopsy; Brain; Brain Neoplasms; Brain Neoplasms/su [Surgery]; Brain/me [Metabolism]; Brain/pa [Pathology]; Brain/su [Surgery]; Cancer grades; Cancer margins; Carotenoids; Carotenoids/me [Metabolism]; Diseases; Glioblastoma; Glioblastomas; Glioma; Glioma/su [Surgery]; Gliomas; Grading; Histology; Histopathology; Humans; Label free; Label-free; Lipid Metabolism; Margins of Excision; Neoplasm Grading; Nerve Tissue Proteins; Nerve Tissue Proteins/ch [Chemistry]; Nerve Tissue Proteins/me [Metabolism]; Optical Phenomena; Optical biopsies; Optical biopsy; Pigments; Principal Component Analysis; Principal component analysis; Protein Structure, Secondary; Raman spectrometry; Resonance; Resonance Raman; Spectrum Analysis, Raman; Support Vector Machine; Support vector machines; Surgery; Tryptophan; Tryptophan/me [Metabolism]; Tumors; brain; brain tumor; cancer grading; carotenoid; chemistry; dept.phy; glioma; human; light related phenomena; lipid metabolism; metabolism; nerve protein; pathology; peer-reviewed; principal component analysis; procedures; protein secondary structure; support vector machine; surgical margin; tryptophan; tumor marker

Visible resonance Raman spectroscopy detect key molecular biomarker vibrations to characterize for human brain gliomas - Zhou, Yan; Liu, Cheng-hui; Wu, Binlin; Yu, Xinguang; Gangge Cheng; Zhang, Chunyuan; Chen, Hong; Li, Shenglin; Zong, Rui; Liang, Qijun; Zhang, Ce; Zhang, Mingqian; et al. (Biomedical Imaging and Sensing Conference, 2018)
Visible resonant Raman (VRR) spectroscopy provides an effective way to enhance Raman signal from particular bonds associated with key molecules due to changes on molecular level. This paper reports on the VRR use for detection of human brain the control and gliomas of three grades. From the RR spectra additional two molecular vibrational biomarkers at 1129cm-1 and 1338cm-1, for the four types of brain tissues are significantly different in intensity. The new RR spectral peaks can be used as molecular biomarkers to evaluate glioma grades and identify the margin of gliomas from the controls. The metabolic process of glioma cells based on the RR spectral changes may reveal the Warburg hypothesis.
Keywords: 532nm; Biomarkers; Brain; Human brain tumors; Medical imaging; Raman probe; Raman spectroscopy; Resonance Raman; Tumors; Visible resonance Raman (VRR); confical Raman probe; dept.phy; gliomas; human brain tumor

Dynamical masses of young M dwarfs: masses and orbital parameters of gj 3305 ab, the wide binary companion to the imaged exoplanet host 51 eri - Montet, Benjamin T; Bowler, Brendan P; Shkolnik, Evgenya L; Deck, Katherine M; Wang, Ji; Horch, Elliott P; Liu, Michael C; Hillenbrand, Lynne A; Kraus, Adam L; Charbonneau, David; Hillenbrand, Lynne A; Kraus, Adam L; et al. (The Astrophysical Journal, 2015)
We combine new high resolution imaging and spectroscopy from Keck/NIRC2, Discovery Channel Telescope/DSSI, and Keck/HIRES with published astrometry and radial velocities to measure individual masses and orbital elements of the GJ 3305 AB system, a young (∼20 Myr) M+M binary (unresolved spectral type M0) member of the β Pictoris moving group comoving with the imaged exoplanet host 51 Eri. We measure a total system mass of 1.11 ± 0.04 , a period of 29.03 ± 0.50 year, a semimajor axis of 9.78 ± 0.14 AU, and an eccentricity of 0.19 ± 0.02. The primary component has a dynamical mass of 0.67 ± 0.05 and the secondary has a mass of 0.44 ± 0.05 . The recently updated BHAC15 models are consistent with the masses of both stars to within Given the observed masses the models predict an age of the GJ 3305 AB system of 37 ± 9 Myr. Based on the observed system architecture and our dynamical mass measurement, it is unlikely that the orbit of 51 Eri b has been significantly altered by the Kozai-Lidov mechanism. © 2015. The American Astronomical Society. All rights reserved..
Keywords: astrometry; binaries: close; dept.phy; stars: fundamental parameters; stars: individual (GJ 3305 AB)

Human glioma tumors detection by a portable visible resonance Raman analyzer with a hand-held optical fiber probe - Zhou, Yan; Zhang, Shengjia; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Zhao, Mingyue; Zheng, Jichun; Zhang, Liang; Zhang, Mingqian; Liang, Qijun; Go, Yun Ii; et al. (Biomedical Vibrational Spectroscopy 2020: Advances in Research and Industry, 2020)
Based on Visible Resonance Raman (VRR) method, we have developed a novel label-free portable VRR LRR2000 Raman analyzer with a portable fiber-optic probe and used it for the classification of human gliomas ex vivo and for the analysis of changes in tumor chemical compositions in molecular level. The purpose of this study was to examine the performance of the LRR2000 Raman analyzer as an optical biopsy tool for detecting human brain tumors compared to the commercial laboratory HR800 and WITec300 micro confocal Raman spectroscopy instruments. As of 2018, a total 1,938 VRR spectra were collected using LRR2000, HR800 and WITec300 Raman system, ex vivo. Identification of the four grades of glioma tumors and control tissues was performed based on the characteristic native molecular fingerprints. LRR2000 demonstrated consistent diagnostic results with HR800 and WITec300 Raman systems. LRR2000 showed the advantages of high speed, convenience and low cost compared to the two confocal micro Raman systems. Using artificial intelligence (AI)-based analysis of part of the data, the cross-validated accuracy for identifying glioma tumors is ~90% compared with gold standard histopathology examination.
Keywords: Biopsy; Brain; Chemical analysis; Handhelds; Histology; Human brain; Industrial research; Molecular biology; Optical biopsies; Optical fibers; Optical molecular Pathology; Palmprint recognition; Principal component analysis; Principle component analysis; Probes; Resonance; Resonance Raman; Spectroscopy; Spectrum analysis; Support vector machines; Tissue engineering; Tumors; Vibrational spectroscopy; dept.phy; glioma tumor; handheld LRR2000 analyzer; human brain tissues; optical biopsy; principle component analysis(PCA); support vector machine (SVM); visible resonance Raman (VRR)

Human brain glioma grading using label free laser-induced fluorescence spectroscopy - Zhou, Y.; Liu, C.-H.; Zhu, K.; Wu, B.; Yu, X.; Cheng, G.; Zhao, M.; Zong, R.; Zhang, C.; Chen, H.; Li, S.; Liang, Q.; et al. (SPIE, 2019)
Laser-induced fluorescence (LIF) technique was used to generate spectral signatures of endogenous fluorophores relevant to the tissue molecular composition changes in human brain glioma tumors. The goal is to study the changes of fluorescence emission spectra from endogenous fluorophores in human brain glioma of different grades, and to find new biomarkers for prognostic optical molecular pathological diagnosis. Two hundred and thirty-seven (237) native fluorescence spectra from 61 subjects were measured using LabRAM HR Evolution micro photoluminescence (PL) system for four grades of glioma tumors in ex-vivo. The differences of four grades of glioma tumors were identified by the characteristic fluorophores fingerprints under the excitation laser wavelength at UV 325nm. To our best knowledge, this is the first report for human brain study using this technique. The fluorescence peaks of biomarkers with major contribution were found, including tryptophan, collagen, elastin, reduced nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD) and phospholipids that play important roles in the cellular energy metabolism and glycolysis pathway. The ratios of peak intensities and the peak positions in fluorescence spectra of may be used to diagnose human brain diseases or to guide biopsy during surgical resection. © 2019 SPIE.
Keywords: Amino acids; Biomarkers; Biopsy; Brain; Cellular energy; Cellular energy metabolism; Emission spectroscopy; FAD; Fluorescence; Fluorescence microscopy; Fluorescence spectroscopy; Fluorophores; Grading; Human brain; Human brain glioma tissues; Laser excitation; Laser induced fluorescence spectroscopy; Laser optics; Laser produced plasmas; Metabolism; NADH; Native fluorescence; Native laser-induced fluorescence spectroscopy; Native-fluorescence; Nucleotides; Optical molecular pathology; Pathology; Phospholipids; Tryptophan; Tumors; dept.phy

K2 discovers a busy bee: an unusual transiting neptune found in the beehive cluster - Obermeier, Christian; Henning, Thomas; Schlieder, Joshua E.; Crossfield, Ian J. M.; Petigura, Erik A.; Howard, Andrew W.; Sinukoff, Evan; Isaacson, Howard; Ciardi, David R.; David, Trevor J.; Hillenbrand, Lynne A.; Beichman, Charles A.; et al. (The Astronomical Journal, 2016)
Open clusters have been the focus of several exoplanet surveys, but only a few planets have so far been discovered. The Kepler spacecraft revealed an abundance of small planets around small cool stars, therefore, such cluster members are prime targets for exoplanet transit searches. Kepler's new mission, K2, is targeting several open clusters and star-forming regions around the ecliptic to search for transiting planets around their low-mass constituents. Here, we report the discovery of the first transiting planet in the intermediate-age (800 Myr) Beehive cluster (Praesepe). K2-95 is a faint (Kp = 15.5 mag) dwarf from K2's Campaign 5 with an effective temperature of 3471 ±124 K, approximately solar metallicity and a radius of 0.402± 0.050.R⊕ We detected a transiting planet with a radius of3.47+0.78 -0.53R⊕ and an orbital period of 10.134 days. We combined photometry, medium/high-resolution spectroscopy, adaptive optics/speckle imaging, and archival survey images to rule out any false-positive detection scenarios, validate the planet, and further characterize the system. The planet's radius is very unusual as M-dwarf field stars rarely have Neptune-sized transiting planets. The comparatively large radius of K2-95b is consistent with the other recently discovered cluster planets K2-25b (Hyades) and K2-33b (Upper Scorpius), indicating systematic differences in their evolutionary states or formation. These discoveries from K2 provide a snapshot of planet formation and evolution in cluster environments and thus make excellent laboratories to test differences between field-star and cluster planet populations. © 2016. The American Astronomical Society. All rights reserved..
Keywords: dept.phy; eclipses; peer-reviewed; stars: individual (K2-95); stars: low-mass; techniques: photometric; techniques: spectroscopic

197 candidates and 104 validated planets in k2's first five fields - Crossfield, Ian J M; Ciardi, David R; Petigura, Erik A; Sinukoff, Evan; Schlieder, Joshua E; Schlieder, Joshua E; Howard, Andrew W; Beichman, Charles A; Isaacson, Howard; Dressing, Courtney D; Christiansen, Jessie L; Fulton, Benjamin J; et al. (Astrophysical Journal Supplement Series, 2016)
We present 197 planet candidates discovered using data from the first year of the NASA K2 mission (Campaigns 0-4), along with the results of an intensive program of photometric analyses, stellar spectroscopy, high-resolution imaging, and statistical validation. We distill these candidates into sets of 104 validated planets (57 in multi-planet systems), false positives, and 63 remaining candidates. Our validated systems span a range of properties, with median values of RP= 2.3 R⊕, P = 8.6 days, Teff = 5300 K, and Kp = 12.7mag. Stellar spectroscopy provides precise stellar and planetary parameters for most of these systems. We show that K2 has increased by 30% the number of small planets known to orbit moderately bright stars (1-4 R R⊕, Kp = 9-13 mag). Of particular interest are planets smaller than 2 R⊕, orbiting stars brighter than Kp = 11.5 mag, 5 receiving Earth-like irradiation levels, and several multi-planet systems - including 4 planets orbiting the M dwarf K2-72 near mean-motion resonances. By quantifying the likelihood that each candidate is a planet we demonstrate that our candidate sample has an overall false positive rate of 15%-30%, with rates substantially lower for small candidates (<2R⊕) and larger for candidates with radii >8 R⊕ and/or with P<3 days. Extrapolation of the current planetary yield suggests that K2 will discover between 500 and 1000 planets in its planned four-year mission, assuming sufficient follow-up resources are available. Efficient observing and analysis, together with an organized and coherent follow-up strategy, are essential for maximizing the efficacy of planet-validation efforts for K2, TESS, and future large-scale surveys. © 2016. The American Astronomical Society. All rights reserved.
Keywords: catalogs; dept.phy; planets and satellites: fundamental parameters; planets and satellites: general; techniques: high angular resolution; techniques: photometric; techniques: spectroscopic