This research is partially supported by the Institute for Collaborative Biotechnologies (ICB) through contract W911NF-19-D-0001 from the U.S. Army Research Office. In addition to the support from ICB, the study also received support from the Caltech Center for Sensing to Intelligence (S2I) and the Heritage Medical Research Institute. The content of the information does not necessarily reflect the position or policy of the government, and no official endorsement should be inferred.
\n\nThis research is partially supported by the Institute for Collaborative Biotechnologies (ICB) through contract W911NF-19-D-0001 from the U.S. Army Research Office. In addition to the support from ICB, the study also received support from the Caltech Center for Sensing to Intelligence (S2I) and the Heritage Medical Research Institute. The content of the information does not necessarily reflect the position or policy of the government, and no official endorsement should be inferred.
\n\nR.M.M., A.E., J.M., and C.Y.H. conceptualized the project; C.Y.H. and J.M. designed the experiments; C.Y.H., J.M., F.A., T.M., and E.L. conducted the experiments; C.Y.H. performed data analysis; C.Y.H. wrote the manuscript with assistance from T.M.
\n\nThe authors declare no competing financial interest.
\n\nAdditional experimental results including promoter characterization, growth curves in different media, GFP expression dynamics, and arsenic tolerance assays with microscopy images (Figure S1); characterization of the arsenic sensor using fresh spores is detailed, including sporulation under arsenic exposure, dose–response curves, and determination of the limit of detection (Figure S2); a full protocol for protoplast transformation of Bacillus megaterium is included, along with recipes for all required media and buffers; finally, DNA sequences for all plasmid constructs and sensor components used in this study are listed, including the pMM1522 backbone, arsenic-responsive elements (pArs, pArs-ArsR), sfGFP, and alternative promoters (PDF)
", "abstract": "Bacteria innately monitor their environment by dynamically regulating gene expression to respond to fluctuating conditions. Through synthetic biology, we can harness this natural capability to design cell-based sensors. Bacillus megaterium, a soil bacterium, stands out due to its remarkable heavy metal tolerance and sporulation ability, making it an ideal candidate for heavy metal detection with low transportation costs. However, challenges persist: the synthetic biology toolkit for this strain is underdeveloped, and conventional whole-cell sensors necessitate specialized laboratory equipment to read the output. In our study, we have genetically modified B. megaterium for arsenic detection and established a detection threshold below the EPA’s recommendation of 10 ppb for drinking water in both vegetative and spore forms. Additionally, we have integrated both engineered B. megaterium living cells and spores with a complementary metal-oxide-semiconductor (CMOS) chip, providing a proof-of-concept for field-deployable arsenic detection. We show that the limit of detection (LOD) of our integrated sensor is within the range to test arsenic levels in soil and food. As a proof of concept, this work paves the way for the deployment of our sensor in resource-limited settings, ensuring real-time arsenic detection in challenging environments.
", "date": "2025-05-16", "date_type": "published", "publication": "ACS Synthetic Biology", "volume": "14", "number": "5", "publisher": "American Chemical Society", "pagerange": "1615-1624", "issn": "2161-5063", "official_url": "https://authors.library.caltech.edu/records/1xz93-wgt26", "funders": { "items": [ {}, { "grant_number": "W911NF-19-D-0001" }, {}, { "agency": "Heritage Medical Research Institute" } ] }, "local_group": { "items": [ { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" }, { "id": "Heritage-Medical-Research-Institute" }, { "id": "Division-of-Biology-and-Biological-Engineering" }, { "id": "Division-of-Engineering-and-Applied-Science" } ] }, "doi": "10.1021/acssynbio.4c00895", "pmcid": "PMC12090344", "primary_object": { "basename": "a-portable-arsenic-sensor-integrating-bacillus-megaterium-with-cmos-technology.pdf", "url": "https://authors.library.caltech.edu/records/1xz93-wgt26/files/a-portable-arsenic-sensor-integrating-bacillus-megaterium-with-cmos-technology.pdf" }, "related_objects": [ { "basename": "sb4c00895_si_001.pdf", "url": "https://authors.library.caltech.edu/records/1xz93-wgt26/files/sb4c00895_si_001.pdf" } ], "pub_year": "2025", "author_list": "Hu, Chelsea Y.; McManus, John; et al." }, { "id": "https://authors.library.caltech.edu/records/y504c-ftg75", "eprint_status": "archive", "datestamp": "2025-12-28 23:03:14", "lastmod": "2025-12-28 23:03:14", "type": "article", "metadata_visibility": "show", "creators": { "items": [ { "id": "Zacharias-Thomas", "name": { "family": "Zacharias", "given": "Thomas" }, "orcid": "0000-0001-6003-2109" }, { "id": "Gray-Robert", "name": { "family": "Gray", "given": "Robert" }, "orcid": "0000-0001-5980-8774" }, { "id": "Sekine-Ryoto", "name": { "family": "Sekine", "given": "Ryoto" }, "orcid": "0000-0001-6135-8581" }, { "id": "Williams-James", "name": { "family": "Williams", "given": "James" }, "orcid": "0000-0001-9073-5745" }, { "id": "Zhou-Selina", "name": { "family": "Zhou", "given": "Selina" }, "orcid": "0000-0002-9590-2909" }, { "id": "Marandi-A", "name": { "family": "Marandi", "given": "Alireza" }, "orcid": "0000-0002-0470-0050" } ] }, "title": "Energy-Efficient Ultrashort-Pulse Characterization Using Nanophotonic Parametric Amplification", "ispublished": "pub", "full_text_status": "public", "keywords": "ultrafast optics; integrated photonics; ultrashort pulse characterization; thin film lithium niobate; optical parametric amplification; FROG", "note": "© 2025 American Chemical Society.
\n\nDevice nanofabrication was performed at the Kavli Nanoscience Institute (KNI) at Caltech.
\n\nThe authors gratefully acknowledge support from ARO Grant No. W911NF-23-1-0048, NSF Grant No. 1918549, AFOSR Award FA9550-23-1-0755, DARPA Award D23AP00158, the Center for Sensing to Intelligence at Caltech, the Alfred P. Sloan Foundation, and NASA/JPL.
\n\nT.Z. and R.G. contributed equally to this work.
\n\nAdditional details about the experimental scheme, postprocessing, and recovery algorithm (PDF)
", "abstract": "The growth of ultrafast nanophotonic circuits necessitates the development of energy-efficient on-chip pulse characterization techniques. Nanophotonic realizations of Frequency Resolved Optical Gating (FROG), a common pulse characterization technique in bulk optics, have been challenging due to their noncollinear nature and the lack of efficient nonlinear optical processes in the integrated platform. Here, we experimentally demonstrate a novel FROG-based technique compatible with the nanophotonic platform that leverages the high gain-bandwidth of a dispersion-engineered degenerate optical parametric amplifier (DOPA) for energy-efficient ultrashort pulse characterization. We demonstrate on-chip pulse characterization of sub-80 fs, ∼1 fJ pulses using just ∼60 fJ of gate pulse energy, which is several orders of magnitude lower than the gate pulse energy required for characterizing similar pulses in the bulk counterpart. In the future, we anticipate our work will enable the characterization of ultraweak-ultrashort pulses with energies at the single photon level.
", "date": "2025-03-19", "date_type": "published", "publication": "ACS Photonics", "volume": "12", "number": "3", "publisher": "American Chemical Society", "pagerange": "1316-1320", "issn": "2330-4022", "official_url": "https://authors.library.caltech.edu/records/y504c-ftg75", "funders": { "items": [ { "grant_number": "W911NF-23-1-0048" }, { "grant_number": "1918549" }, { "grant_number": "FA9550-23-1-0755" }, { "grant_number": "D23AP00158" }, { "agency": "Center for Sensing to Intelligence, Caltech" }, { "grant_number": "FG-2023-19822" }, {} ] }, "local_group": { "items": [ { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" }, { "id": "Kavli-Nanoscience-Institute" }, { "id": "Division-of-Engineering-and-Applied-Science" } ] }, "doi": "10.1021/acsphotonics.4c02620", "primary_object": { "basename": "ph4c02620_si_001.pdf", "url": "https://authors.library.caltech.edu/records/y504c-ftg75/files/ph4c02620_si_001.pdf" }, "pub_year": "2025", "author_list": "Zacharias, Thomas; Gray, Robert; et al." }, { "id": "https://authors.library.caltech.edu/records/zg888-14w19", "eprint_status": "archive", "datestamp": "2025-01-07 16:19:28", "lastmod": "2025-01-07 16:23:15", "type": "article", "metadata_visibility": "show", "creators": { "items": [ { "id": "Parto-Midya", "name": { "family": "Parto", "given": "Midya" }, "orcid": "0000-0003-2100-5671" }, { "id": "Leefmans-Christian", "name": { "family": "Leefmans", "given": "Christian" } }, { "id": "Williams-James", "name": { "family": "Williams", "given": "James" } }, { "id": "Gray-Robert-M", "name": { "family": "Gray", "given": "Robert M." }, "orcid": "0000-0001-5980-8774" }, { "id": "Marandi-A", "name": { "family": "Marandi", "given": "Alireza" }, "orcid": "0000-0002-0470-0050" } ] }, "title": "Enhanced sensitivity via non-Hermitian topology", "ispublished": "pub", "full_text_status": "public", "keywords": "Optical physics", "note": "© The Author(s) 2025.
\nThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
\n\nThe authors acknowledge support from ARO Grant W911NF-23-1-0048, NSF Grants No. 1846273 and 1918549 and the Center for Sensing to Intelligence at Caltech. The authors wish to thank NTT Research for their financial and technical support.
\n\nThese authors contributed equally: Midya Parto, Christian Leefmans.
\nAll authors contributed to the writing of this manuscript.
\n\nThe data used to generate the plots and results in this paper is available from the corresponding author upon reasonable request.
\n\nA.M. has financial interest in PINC Technologies Inc., which is developing photonic integrated nonlinear circuits. The remaining authors declare no competing interests.
\n\nSupplementary Material (PDF)
", "abstract": "Sensors are indispensable tools of modern life that are ubiquitously used in diverse settings ranging from smartphones and autonomous vehicles to the healthcare industry and space technology. By interfacing multiple sensors that collectively interact with the signal to be measured, one can go beyond the signal-to-noise ratios (SNR) attainable by the individual constituting elements. Such techniques have also been implemented in the quantum regime, where a linear increase in the SNR has been achieved via using entangled states. Along similar lines, coupled non-Hermitian systems have provided yet additional degrees of freedom to obtain better sensors via higher-order exceptional points. Quite recently, a new class of non-Hermitian systems, known as non-Hermitian topological sensors (NTOS) has been theoretically proposed. Remarkably, the synergistic interplay between non-Hermiticity and topology is expected to bestow such sensors with an enhanced sensitivity that grows exponentially with the size of the sensor network. Here, we experimentally demonstrate NTOS using a network of photonic time-multiplexed resonators in the synthetic dimension represented by optical pulses. By judiciously programming the delay lines in such a network, we realize the archetypal Hatano-Nelson model for our non-Hermitian topological sensing scheme. Our experimentally measured sensitivities for different lattice sizes confirm the characteristic exponential enhancement of NTOS. We show that this peculiar response arises due to the combined synergy between non-Hermiticity and topology, something that is absent in Hermitian topological lattices. Our demonstration of NTOS paves the way for realizing sensors with unprecedented sensitivities.
", "date": "2025-01-01", "date_type": "published", "publication": "Light: Science & Applications", "volume": "14", "number": "1", "publisher": "Nature Publishing Group", "pagerange": "6", "issn": "2047-7538", "official_url": "https://authors.library.caltech.edu/records/zg888-14w19", "funders": { "items": [ { "grant_number": "W911NF-23-1-0048" }, { "grant_number": "ECCS-1846273" }, { "grant_number": "CCF-1918549" }, { "grant_number": "Center for Sensing to Intelligence" }, {} ] }, "local_group": { "items": [ { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" } ] }, "doi": "10.1038/s41377-024-01667-z", "primary_object": { "basename": "s41377-024-01667-z.pdf", "url": "https://authors.library.caltech.edu/records/zg888-14w19/files/s41377-024-01667-z.pdf" }, "related_objects": [ { "basename": "41377_2024_1667_MOESM1_ESM.pdf", "url": "https://authors.library.caltech.edu/records/zg888-14w19/files/41377_2024_1667_MOESM1_ESM.pdf" } ], "pub_year": "2025", "author_list": "Parto, Midya; Leefmans, Christian; et al." }, { "id": "https://authors.library.caltech.edu/records/bn3tv-haz45", "eprint_status": "archive", "datestamp": "2026-02-23 23:40:30", "lastmod": "2026-02-23 23:40:30", "type": "article", "metadata_visibility": "show", "creators": { "items": [ { "id": "Wu-Zihui", "name": { "family": "Wu", "given": "Zihui" }, "orcid": "0000-0002-7622-3548" }, { "id": "Yin-Tianwei", "name": { "family": "Yin", "given": "Tianwei" }, "orcid": "0000-0002-4009-414X" }, { "id": "Sun-Yu", "name": { "family": "Sun", "given": "Yu" } }, { "id": "Frost-Robert", "name": { "family": "Frost", "given": "Robert" }, "orcid": "0000-0002-2849-9240" }, { "id": "van-der-Kouwe-Andre", "name": { "family": "van der Kouwe", "given": "Andre" }, "orcid": "0000-0002-2754-6594" }, { "id": "Dalca-Adrian-V", "name": { "family": "Dalca", "given": "Adrian V." }, "orcid": "0000-0002-8422-0136" }, { "id": "Bouman-K-L", "name": { "family": "Bouman", "given": "Katherine L." }, "orcid": "0000-0003-0077-4367" } ] }, "title": "Learning Task-Specific Strategies for Accelerated MRI", "ispublished": "pub", "full_text_status": "public", "keywords": "Compressed sensing MRI; deep learning; end-to-end training; task-specific imaging", "note": "© 2024 IEEE.
\n\nThe authors would like to thank Xinyi Wu for her assistance as a volunteer in testing our learned MRI sequence and collecting data.
\n\nThe work of Zihui Wu was supported in part by Kortschak Fellowship, in part by Amazon AI4Science Fellowship, and in part by Amazon AI4Science Partnership Discovery Grant. This work was supported in part by NSF under Grant 2048237, in part by NIH under Grant 5R01AG064027, Grant 5R01AG070988, Grant R21EB029641, Grant R01HD099846, and Grant R01HD085813, in part by Heritage Medical Research Fellowship, in part by S2I Clinard Innovation Award, and in part by Rockley Photonics.
\n\nThis work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by Mass General Brigham Institutional Review Board under IRB Protocol No. 2012P002376, and performed in line with the Department of Health and Human Services (DHHS) 45 CFR Parts 46 and 164.
\n\nOur code is available at https://github.com/zihuiwu/TACKLE.
\n\nThe associate editor coordinating the review of this manuscript and approving it for publication was Prof. Alejandro F. Frangi.
", "abstract": "© 2024 IEEE.
\n\nThe authors would like to thank Xinyi Wu for her assistance as a volunteer in testing our learned MRI sequence and collecting data.
\nThis work was sponsored by NSF Award 2048237, NIH Projects 5R01AG064027, 5R01AG070988, R21EB029641, R01HD099846, R01HD085813, Heritage Medical Research Fellowship, S2I Clinard Innovation Award, and Rockley Photonics. Z. Wu was sponsored by the Kortschak Fellowship, Amazon AI4Science Fellowship, and Amazon AI4Science Partnership Discovery Grant.
", "abstract": "© 2024 Optica Publishing Group.
\n\nThe authors gratefully acknowledge support from the center for sensing to intelligence at Caltech, and JPL (NASA).
\n\nAir Force Office of Scientific Research (FA9550-23-1-0755); National Science Foundation (1846273).
\n\nData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
\nSee Supplement 1 for supporting content.
\n\nAM and LL have financial interest in PINC Technologies Inc., which is developing photonic integrated nonlinear circuits.
", "abstract": "Traditional absorption spectroscopy has a fundamental difficulty in resolving small absorbance from a strong background due to the instability of laser sources. Existing background-free methods in broadband vibrational spectroscopy help to alleviate this problem but face challenges in realizing either low extinction ratios or time-resolved field measurements. Here, we introduce optical-parametric-amplification-enhanced background-free spectroscopy, in which the excitation background is first suppressed by an interferometer, and then the free-induction decay that carries molecular signatures is selectively amplified. We show that this method can improve the limit of detection in linear interferometry by order(s) of magnitude without requiring lower extinction ratios or a time-resolved measurement, which can benefit sensing applications in detecting trace species.
", "date": "2024-06-01", "date_type": "published", "publication": "Optics Letters", "volume": "49", "number": "11", "publisher": "Optica Publishing Group", "pagerange": "2914-2917", "issn": "0146-9592", "official_url": "https://authors.library.caltech.edu/records/kev6c-xbr35", "funders": { "items": [ { "grant_number": "Center for Sensing to Intelligence" }, {}, { "grant_number": "FA9550-23-1-0755" }, { "grant_number": "ECCS-1846273" } ] }, "local_group": { "items": [ { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" } ] }, "doi": "10.1364/ol.520848", "primary_object": { "basename": "6879873.pdf", "url": "https://authors.library.caltech.edu/records/kev6c-xbr35/files/6879873.pdf" }, "pub_year": "2024", "author_list": "Liu, Mingchen; Gray, Robert M.; et al." }, { "id": "https://authors.library.caltech.edu/records/q6b89-5e166", "eprint_status": "archive", "datestamp": "2024-07-02 21:40:00", "lastmod": "2024-07-02 21:40:00", "type": "article", "metadata_visibility": "show", "creators": { "items": [ { "id": "Zhou-Haowen", "name": { "family": "Zhou", "given": "Haowen" }, "orcid": "0000-0003-0955-4010" }, { "id": "Watson-Mark", "name": { "family": "Watson", "given": "Mark" }, "orcid": "0000-0002-3935-9980" }, { "id": "Bernadt-Cory-T", "name": { "family": "Bernadt", "given": "Cory T" }, "orcid": "0000-0003-1540-9880" }, { "id": "Lin-Steven-Siyu", "name": { "family": "Lin", "given": "Steven (Siyu)" } }, { "id": "Lin-Chieh\u2010yu", "name": { "family": "Lin", "given": "Chieh\u2010yu" } }, { "id": "Ritter-Jon-H", "name": { "family": "Ritter", "given": "Jon H." }, "orcid": "0009-0003-4514-6935" }, { "id": "Wein-Alexander-N", "name": { "family": "Wein", "given": "Alexander" }, "orcid": "0000-0002-8813-3523" }, { "id": "Mahler-Simon", "name": { "family": "Mahler", "given": "Simon" }, "orcid": "0000-0002-9761-445X" }, { "id": "Rawal-Sid", "name": { "family": "Rawal", "given": "Sid" } }, { "id": "Govindan-Ramaswamy", "name": { "family": "Govindan", "given": "Ramaswamy" }, "orcid": "0000-0002-6964-9612" }, { "id": "Yang-Changhuei", "name": { "family": "Yang", "given": "Changhuei" }, "orcid": "0000-0001-8791-0354" }, { "id": "Cote-Richard-J", "name": { "family": "Cote", "given": "Richard J." } } ] }, "title": "AI\u2010guided histopathology predicts brain metastasis in lung cancer patients", "ispublished": "pub", "full_text_status": "public", "keywords": "Pathology and Forensic Medicine", "note": "© 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
\n\nThis study was supported by U01CA233363 and by the Washington University in St. Louis School of Medicine Personalized Medicine Initiative (RJC). HZ, SL, SM and CY are supported by Sensing to Intelligence (S2I) (grant no. 13520296) and Heritage Research Institute for the Advancement of Medicine and Science at Caltech (grant no. HMRI-15-09-01). MW and RG were supported by the National Cancer Institute (grant no. 5R01CA182746).
\nHZ and MW wrote the first draft of the paper. RJC, CY, HZ and MW conceived the experimental design. HZ, SL, SM and CY performed the DL and data analysis in this study. MW, CTB, SR and RJC designed the clinical and pathologic section of the experiments. CTB, CL, JHR and AW provided the clinical evaluation. MW, RG and RJC provided essential data resources. HZ, MW, CTB, SL, SM, SR, RG, CY and RJC contributed to the writing of this paper.
\n\n\n\nThe data that support the findings of this study are openly available in CaltechData at https://doi.org/10.22002/dw66e-mbs82. The code for processing the data is publicly available on GitHub at https://github.com/hwzhou2020/NSCLC_ResNet.
", "abstract": "Brain metastases can occur in nearly half of patients with early and locally advanced (stage I–III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I–III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I–III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met−, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met−) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met−) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy.
\n© 2024 Springer Nature.
\n\nN.B. acknowledges a MedLab Fellowship from ETH Zurich and an Early-Career Fellowship from Collegium Helveticum, Zurich. W.G. acknowledges support from the National Science Foundation Grant 2145802, the American Cancer Society Research Scholar Grant RSG-21-181-01-CTPS and the Center for Sensing to Intelligence at the California Institute of Technology. We thank A. Curtis for language editing.
\nAll authors contributed to the writing of the manuscript. N.B. organized the project and facilitated inclusive discussions among all authors.
\n\nThe authors declare no competing interests.
", "abstract": "A patient-centred system that leverages the analysis of sweat via wearable sensors may better support the management of patients with substance-use disorders.
\n© 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
\n\nThis project has been made possible in part by Caltech’s Center for Sensing to Intelligence, grant number 2020-225832 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation, and National Institutes of Health grants R35 CA220436 (Outstanding Investigator Award) and R01 EB028277.
\n\nAll data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.
\n\nThe authors declare that they have no competing interests.
", "abstract": "Quantum imaging holds potential benefits over classical imaging but has faced challenges such as poor signal-to-noise ratios, low resolvable pixel counts, difficulty in imaging biological organisms, and inability to quantify full birefringence properties. Here, we introduce quantum imaging by coincidence from entanglement (ICE), using spatially and polarization-entangled photon pairs to overcome these challenges. With spatial entanglement, ICE offers higher signal-to-noise ratios, greater resolvable pixel counts, and the ability to image biological organisms. With polarization entanglement, ICE provides quantitative quantum birefringence imaging capability, where both the phase retardation and the principal refractive index axis angle of an object can be remotely and instantly quantified without changing the polarization states of the photons incident on the object. Furthermore, ICE enables 25 times greater suppression of stray light than classical imaging. ICE has the potential to pave the way for quantum imaging in diverse fields, such as life sciences and remote sensing.
\n\u00a9 The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
\n\nThe device nanofabrication was performed at the Kavli Nanoscience Institute (KNI) at Caltech. This work was supported by a NASA Space Technology Graduate Research Opportunities Award. The authors thank NTT Research for their financial and technical support. The authors thank Dr. Mahmood Bagheri for loaning the Mid-IR optical spectrum analyzer. The authors gratefully acknowledge support from ARO grant no. W911NF-23-1-0048, AFOSR award FA9550-20-1-0040, NSF Grant No. 1846273, and 1918549, NASA, and Center for Sensing to Intelligence at Caltech.
\n\nThese authors contributed equally: Arkadev Roy, Luis Ledezma.
A.R., L.L., L.C. and R.G. performed the experiments. L.L. fabricated the chip with help from R.S. A.R., L.L. and R.G. performed the numerical simulations. Q.G., M.L. and R.M.B. contributed to the design, discussions, and debugging. A.R. and A.M. wrote the manuscript with input from all authors. A.M. supervised the project.
\n\nSource data are available for this paper and can be found at the Figshare link. All other data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.
\n\nThe codes that support the findings of this study are available from the corresponding author upon reasonable request.
\n\nL.L., R.M.B., and A.M. are inventors on granted U.S. patent 11,226,538 covering thin-film optical parametric oscillators. L.L., A.M., A.R., R.S., and R.G. are inventors on a U.S. provisional patent application filed by the California Institute of Technology (application number 63/466,188) on 12 May 2023. L.L., A.M., and R.G. are inventors on a U.S. provisional patent application filed by the California Institute of Technology (application number 63/434,015) on 20 December 2022. L.L. and A.M. are involved in developing photonic integrated nonlinear circuits at PINC Technologies Inc. L.L. and A.M. have an equity interest in PINC Technologies Inc. The other authors declare that they have no competing interests.
", "abstract": "Optical frequency comb is an enabling technology for a multitude of applications from metrology to ranging and communications. The tremendous progress in sources of optical frequency combs has mostly been centered around the near-infrared spectral region, while many applications demand sources in the visible and mid-infrared, which have so far been challenging to achieve, especially in nanophotonics. Here, we report widely tunable frequency comb generation using optical parametric oscillators in lithium niobate nanophotonics. We demonstrate sub-picosecond frequency combs tunable beyond an octave extending from 1.5 up to 3.3 \u03bcm with femtojoule-level thresholds on a single chip. We utilize the up-conversion of the infrared combs to generate visible frequency combs reaching 620 nm on the same chip. The ultra-broadband tunability and visible-to-mid-infrared spectral coverage of our source highlight a practical and universal path for the realization of efficient frequency comb sources in nanophotonics, overcoming their spectral sparsity.
", "date": "2023-10-17", "date_type": "published", "publication": "Nature Communications", "volume": "14", "publisher": "Nature Publishing Group", "pagerange": "6549", "issn": "2041-1723", "official_url": "https://authors.library.caltech.edu/records/vdtq9-9rz52", "funders": { "items": [ { "grant_number": "NASA Space Technology Fellowship" }, {}, { "grant_number": "W911NF-23-1-0048" }, { "grant_number": "FA9550-20-1-0040" }, { "grant_number": "ECCS-1846273" }, { "grant_number": "CCF-1918549" } ] }, "local_group": { "items": [ { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" }, { "id": "Kavli-Nanoscience-Institute" } ] }, "doi": "10.1038/s41467-023-42289-0", "pmcid": "PMC10582254", "primary_object": { "basename": "s41467-023-42289-0.pdf", "url": "https://authors.library.caltech.edu/records/vdtq9-9rz52/files/s41467-023-42289-0.pdf" }, "related_objects": [ { "basename": "41467_2023_42289_MOESM1_ESM.pdf", "url": "https://authors.library.caltech.edu/records/vdtq9-9rz52/files/41467_2023_42289_MOESM1_ESM.pdf" } ], "pub_year": "2023", "author_list": "Roy, Arkadev; Ledezma, Luis; et al." }, { "id": "https://authors.library.caltech.edu/records/68h8f-74j58", "eprint_id": 121988, "eprint_status": "archive", "datestamp": "2023-09-28 12:01:52", "lastmod": "2025-11-22 02:20:20", "type": "article", "metadata_visibility": "show", "creators": { "items": [ { "id": "Shen-Cheng", "name": { "family": "Shen", "given": "Cheng" }, "orcid": "0000-0001-7136-4715" }, { "id": "Rawal-Siddarth", "name": { "family": "Rawal", "given": "Siddarth" } }, { "id": "Brown-Rebecca", "name": { "family": "Brown", "given": "Rebecca" } }, { "id": "Zhao-Haowen", "name": { "family": "Zhou", "given": "Haowen" }, "orcid": "0000-0003-0955-4010" }, { "id": "Agarwal-Ashutosh", "name": { "family": "Agarwal", "given": "Ashutosh" } }, { "id": "Watson-Mark-A", "name": { "family": "Watson", "given": "Mark A." }, "orcid": "0000-0002-3935-9980" }, { "id": "Cote-Richard-J", "name": { "family": "Cote", "given": "Richard J." } }, { "id": "Yang-Changhuei", "name": { "family": "Yang", "given": "Changhuei" }, "orcid": "0000-0001-8791-0354" } ] }, "title": "Automatic detection of circulating tumor cells and cancer associated fibroblasts using deep learning", "ispublished": "pub", "full_text_status": "public", "keywords": "Multidisciplinary", "note": "\u00a9 The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. \n\nWe thank all colleagues in the C.Y. and R.J.C. labs for helpful suggestions and feedback. We thank Dr. Dorraya El-Ashry for providing CAF23 cells. We acknowledge Prof. S. Joshua Swamidass from Washington University, St. Louis for helpful advice and experimental design suggestions. This work was supported by the following grants: NIH U01 Funding (U01CA233363), Caltech Center for Sensing to Intelligence (S2I) Funding (13520296), Heritage Research Institute for the Advancement of Medicine and Science at Caltech (HMRI) Funding (HMRI-15-09-01) and Merkin Translational Research Grant 2021. \n\nContributions. C.S. was responsible for planning the project direction, building up imaging system, and developing data preprocessing algorithm as well as DL model. S.R. was responsible for model sample preparation and human annotation on fluorescence images. The project was conceived and supervised by R.J.C. and C.Y. The manuscript was written by C.S. and S.R. and all authors participated in editing the manuscript. \n\nData availability. The datasets generated and/or analysed during the current study are available in the Google Drive repository, https://drive.google.com/drive/folders/1hsxoi5tr3_3e-tldonrWFRbi1B7J4Pcz?usp=sharing. \n\nCode availability. The training and testing code for the ensemble DL mCTC and CAF detection models are available at https://github.com/Scott-Sheen/AI4CTCCAF. \n\nCompeting interests. R.J.C. and S.R. are co-founders and principals at Circulogix Inc. The other authors declare that there are no competing interests.\n\nPublished - 41598-2023-Article-32955.pdf
Supplemental Material - 41598-2023-Article-32955-MOESM1-ESM.docx
", "abstract": "Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our custom system are 99.3% in-focus compared to 89.9% from a top-of-the-line commercial scanner. Then we developed a deep-learning-based method to automatically identify tumor cells serving to mimic CTC (mCTC) and CAFs. Our deep learning method achieved precision and recall of 94% (\u00b1\u20090.2%) and 96% (\u00b1\u20090.2%) for mCTC detection, and 93% (\u00b1\u20091.7%) and 84% (\u00b1\u20093.1%) for CAF detection, significantly better than a conventional computer vision method, whose numbers are 92% (\u00b1\u20090.2%) and 78% (\u00b1\u20090.3%) for mCTC and 58% (\u00b1\u20093.9%) and 56% (\u00b1\u20093.5%) for CAF. Our custom imaging system combined with deep learning cell identification method represents an important advance on CTC and CAF analysis.", "date": "2023-04-07", "date_type": "published", "publication": "Scientific Reports", "volume": "13", "publisher": "Nature Publishing Group", "pagerange": "5708", "id_number": "CaltechAUTHORS:20230627-116746000.5", "issn": "2045-2322", "official_url": "https://resolver.caltech.edu/CaltechAUTHORS:20230627-116746000.5", "funders": { "items": [ { "agency": "NIH", "grant_number": "U01CA233363" }, { "agency": "Center for Sensing to Intelligence (S2I)", "grant_number": "13520296" }, { "agency": "Heritage Medical Research Institute", "grant_number": "HMRI-15-09-01" }, { "agency": "Caltech Merkin Institute for Translational Research" } ] }, "local_group": { "items": [ { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" }, { "id": "Heritage-Medical-Research-Institute" }, { "id": "Richard-Merkin-Institute" } ] }, "doi": "10.1038/s41598-023-32955-0", "pmcid": "PMC10082202", "primary_object": { "basename": "41598-2023-Article-32955.pdf", "url": "https://authors.library.caltech.edu/records/68h8f-74j58/files/41598-2023-Article-32955.pdf" }, "related_objects": [ { "basename": "41598-2023-Article-32955-MOESM1-ESM.docx", "url": "https://authors.library.caltech.edu/records/68h8f-74j58/files/41598-2023-Article-32955-MOESM1-ESM.docx" } ], "pub_year": "2023", "author_list": "Shen, Cheng; Rawal, Siddarth; et al." }, { "id": "https://authors.library.caltech.edu/records/npwrx-d8m85", "eprint_id": 121273, "eprint_status": "archive", "datestamp": "2023-08-22 20:16:33", "lastmod": "2025-11-22 04:02:07", "type": "article", "metadata_visibility": "show", "creators": { "items": [ { "id": "Chen-Yue", "name": { "family": "Chen", "given": "Yue" }, "orcid": "0000-0002-7594-7587" }, { "id": "Zhao-Changhong", "name": { "family": "Zhao", "given": "Changhong" }, "orcid": "0000-0003-0539-8591" }, { "id": "Low-S-H", "name": { "family": "Low", "given": "Steven H." }, "orcid": "0000-0001-6476-3048" }, { "id": "Wierman-A", "name": { "family": "Wierman", "given": "Adam" }, "orcid": "0000-0002-5923-0199" } ] }, "title": "An Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation", "ispublished": "pub", "full_text_status": "public", "keywords": "General Computer Science", "note": "\u00a9 2023 IEEE. \n\nThis work was supported by the Chinese University of Hong Kong (CUHK) Direct Grant for Research under Grant 4055169. The work of Changhong Zhao was supported by the Hong Kong Research Grants Council ECS Award under Grant 24210220. The work of Steven H. Low was supported in part by NSF through ECCS under Grant 1931662, and in part by the Caltech Resnick and S2I Funds.", "abstract": "As the number of prosumers with distributed energy resources (DERs) grows, the conventional centralized operation scheme may suffer from conflicting interests, privacy concerns, and incentive inadequacy. In this paper, we propose an energy sharing mechanism to address the above challenges. It takes into account network constraints and fairness among prosumers. In the proposed energy sharing market, all prosumers play a generalized Nash game. The market equilibrium is proved to have nice features in a large market or when it is a variational equilibrium. To deal with the possible market failure, inefficiency, or instability in general cases, we introduce a price regulation policy to avoid market power exploitation. The improved energy sharing mechanism with price regulation can guarantee the existence and uniqueness of a socially near-optimal market equilibrium. Some advantageous properties are proved, such as the prosumer's individual rationality, a sharing price structure similar to the locational marginal price, and the tendency towards social optimum with an increasing number of prosumers. For implementation, a practical bidding algorithm is developed with a convergence condition. Experimental results validate the theoretical outcomes and show the practicability of our model and method.", "date": "2023-03", "date_type": "published", "publication": "IEEE Transactions on Smart Grid", "volume": "14", "number": "2", "publisher": "IEEE", "pagerange": "1027-1041", "id_number": "CaltechAUTHORS:20230502-727238500.2", "issn": "1949-3053", "official_url": "https://resolver.caltech.edu/CaltechAUTHORS:20230502-727238500.2", "rights": "No commercial reproduction, distribution, display or performance rights in this work are provided.", "funders": { "items": [ { "agency": "Chinese University of Hong Kong", "grant_number": "4055169" }, { "agency": "Research Grants Council of Hong Kong", "grant_number": "24210220" }, { "agency": "NSF", "grant_number": "ECCS-1931662" }, { "agency": "Resnick Sustainability Institute" }, { "agency": "Caltech Center for Sensing to Intelligence (S2I)" } ] }, "local_group": { "items": [ { "id": "Resnick-Sustainability-Institute" }, { "id": "Caltech-Center-for-Sensing-to-Intelligence-(S2I)" } ] }, "doi": "10.1109/tsg.2022.3198721", "pub_year": "2023", "author_list": "Chen, Yue; Zhao, Changhong; et al." } ]