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LED and binaural beats sleeping mask as sleep-wake cycle regulations based on brain wave stimulation

Journal of International Dental and Medical Research 2018年第2期11卷 745-750页

作者： Karimasari, Azisya Amalia Widiyanti, Prihartini Sofiah, Amila Wardhani, Priyanka Kusuma Dzulfiqar, Andi Achmad Chabib, Choirul Asmaningtias, Novia Dwi Biomedical Engineering Study program Faculty of Science and Technology Universitas Airlangga Indonesia Institute of Tropical Disease Universitas Airlangga Indonesia Otomatization Information System Faculty of Science and Technology Universitas Airlangga Indonesia

Depression rate has reached 17-27% in Indonesia, while about 5-10% of world population have sleep disorder. Unsurprisingly, one of the symptoms of depression is sleep disorder. Sleeping disorder could be treated by sleeping mask. The material of sleeping Mask is PLA (polylactic acid) in 3D printing. There is Arduino Uno as microcontroller to control alarm for sleep-wake cycle. Device works until the first 30 minutes for blue LED and delta binaural beats and the last 30 minutes for yellow LED and gamma binaural beats.. Blue LED and delta wave of binaural beats can stimulate the delta brainwave which related with deep sleep. While yellow LED and gamma binaural beats could stimulate the gamma brain waves and trigger awakening. This research employs causal quantitative method with experimental primary data collection. Additionally, there are two tests in this research, namely sleep and awakening test. A delta wave stimulation is shown in lead C3-C4 during the sleep test. Stimulated delta wave records 23.42 times higher rate than without stimulation. Thus, the system can actually trigger a deep sleep faster. During the awakening test, the stimulation using gamma binaural beats and yellow light shows that delta brainwave amplitude in lead C3-C4 is lower;while alpha brainwave amplitude in lead O1-O2 is higher. Thus, this system is able to stimulate alpha wave faster than without stimulation. The mask could regulate sleep-wake cycle for alarm device and enhance sleep quality. © 2018 University of Dicle.

关键词： Amplitude Brain wave Depression Sleep disorder Stimulation

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Correction to: Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization

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Health care management science 2023年第4期26卷 747页

作者： Shima Azizi Özge Aygül Brenton Faber Sharon Johnson Renata Konrad Andrew C Trapp Business Analytics and Information Systems Peter J. Tobin College of Business St. John's University Queens NY USA. azizis@stjohns.edu. Data Science Program Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. Biomedical Engineering Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. The Business School Worcester Polytechnic Institute 100 Institute Road Worcester MA USA.

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Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

arXiv

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Preventing bad behavior in academia

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四川生理科学杂志 2024年第7期46卷 1615-1615页

作者： Rayane da Cruz Albino Faculty of Pharmacy Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil. Department of Host-Microbe Interactions St. Jude Children’s Research Hospital Memphis TN USA. School of the Environment The University of Queensland Whiterock QLD Australia. KU Leuven Ghent Belgium. Laboratory for Computational Social Systems Indraprastha Institute of Information Technology Delhi New Delhi India. University Medical College Shantou Guangdong China. Department of Ecoscience Aarhus Universitet Aarhus Denmark. Institute of Environment and Sustainable Development Banaras Hindu University Varanasi Uttar Pradesh India. Department of Medical Parasitology Faculty of Medicine and Health Sciences Sana’a University Sana’a Yemen and Tropical Disease Research Center Faculty of Medicine and Health Sciences University of Science and Technology Sana’a Yemen. Department of Medicine University of Montreal Montreal QC Canada. Electricity Infrastructure and Buildings Division Pacific Northwest National Laboratory Portland OR USA. Department of Biological Sciences University of New Brunswick Saint John Saint John NB Canada. Department of Biomedical Engineering The University of Melbourne Melbourne VIC Australia. Vagelos Molecular Life Sciences Program University of Pennsylvania Philadelphia PA USA. Doctoral Program in Neurobiology and Behavior Columbia University New York NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Engineering University of Cambridge Cambridge Cambridgeshire UK. Department of Geography Liverpool John Moores University Liverpool UK. Department of Biology Stanford University Stanford CA USA. Department of Psychology University of Nicosia Nicosia Cyprus. Department of Global Ecology Carnegie Institution for Science Stanford CA USA. Research and Support Division Stellenbosch University Cape Town South Africa.

We gave young scientists this prompt:Describe one change to scientific policy or culture that would substantially decrease incidents of scientific misconduct or other unethical behavior.

关键词： behavior. prompt culture

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics

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Bioinformatics Advances 2023年第1期3卷

作者： Tsai, Dong-Ming Chang, Ching-Yao Lin, Shih-Ming Kuo, Tien-Chueh Wang, San-Yuan Chen, Guan-Yuan Kuo, Ching-Hua Tseng, Yufeng Jane Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106216 Taiwan The Metabolomics Core Laboratory Centers of Genomic and Precision Medicine National Taiwan University Taipei 100225 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 106216 Taiwan Master Program for Clinical Pharmacogenomics and Pharmacoproteomics School of Pharmacy Taipei Medical University Taipei 110301 Taiwan Forensic Medicine College of Medicine National Taiwan University Taipei 100225 Taiwan School of Pharmacy College of Medicine National Taiwan University Taipei 100225 Taiwan

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Application of deep learning to predict the low serum albumin in new hemodialysis patients

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Nutrition & metabolism 2023年第1期20卷 24页

作者： Cheng-Hong Yang Yin-Syuan Chen Jin-Bor Chen Hsiu-Chen Huang Li-Yeh Chuang Department of Information Management Tainan University of Technology Tainan Taiwan. Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung Taiwan. School of Dentistry Kaohsiung Medical University Kaohsiung Taiwan. Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung Taiwan. Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan. chenjb1019@***. Department of Community Health Chia-Yi Christian Hospital Chia-Yi City Taiwan. 03281@cych.org.tw. Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering I-Shou University Kaohsiung Taiwan. chuang@isu.edu.tw.

BACKGROUND:Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately one-third of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level of patients on HD is strongly correlated with mortality. METHODS:In study, the data sets were obtained from the longitudinal electronic health records of the largest HD center in Taiwan from July 2011 to December 2015, included 1,567 new patients on HD who met the inclusion criteria. Multivariate logistic regression was performed to evaluate the association of clinical factors with low serum albumin, and the grasshopper optimization algorithm (GOA) was used for feature selection. The quantile g-computation method was used to calculate the weight ratio of each factor. Machine learning and deep learning (DL) methods were used to predict the low serum albumin. The area under the curve (AUC) and accuracy were calculated to determine the model performance. RESULTS:Age, gender, hypertension, hemoglobin, iron, ferritin, sodium, potassium, calcium, creatinine, alkaline phosphatase, and triglyceride levels were significantly associated with low serum albumin. The AUC and accuracy of the GOA quantile g-computation weight model combined with the Bi-LSTM method were 98% and 95%, respectively. CONCLUSION:The GOA method was able to rapidly identify the optimal combination of factors associated with serum albumin in patients on HD, and the quantile g-computation with DL methods could determine the most effective GOA quantile g-computation weight prediction model. The serum albumin status of patients on HD can be predicted by the proposed model and accordingly provide patients with better a prognostic care and treatment.

关键词： Deep learning Grasshopper optimization algorithm Hemodialysis Quantile g-computation Serum albumin

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Phase-change metal ink with pH-controlled chemical sintering for versatile and scalable fabrication of variable stiffness electronics

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science advances 2025年第22期11卷 eadv4921页

作者： Simok Lee Gun-Hee Lee Inho Kang Woojin Jeon Semin Kim Yejin Ahn Choong Yeon Kim Do A Kwon Michael D Dickey Steve Park Seongjun Park Jae-Woong Jeong School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea. Medical Research Center Seoul National University Seoul 03080 Republic of Korea. Departments of Cogno-Mechatronics Engineering and Optics and Mechatronics Engineering Pusan National University Busan 46241 Republic of Korea. Graduate School of Semiconductor Technology Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea. KAIST Information and Electronics Research Institute Daejeon 34141 Republic of Korea. Research Laboratory of Electronics Massachusetts Institute of Technology (MIT) Cambridge MA 02139 USA. Department of Mechanical Engineering Massachusetts Institute of Technology (MIT) Cambridge MA 02139 USA. Department of Chemical and Biomolecular Engineering North Carolina State University (NCSU) Raleigh NC 27606 USA. Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea. KAIST Institute for NanoCentury Daejeon 34141 Republic of Korea. Department of Biomedical Sciences College of Medicine Seoul National University Seoul 03080 Republic of Korea. School of Transdisciplinary Innovations Seoul National University Seoul 03080 Republic of Korea. Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul 08826 Republic of Korea. Department of Transdisciplinary Medicine Seoul National University Hospital Seoul 03080 Republic of Korea. KAIST Institute for Health Science and Technology Daejeon 34141 Republic of Korea.

Variable stiffness electronics represent the forefront of adaptive technology, integrating rigid and soft electronics in a single system through dynamic mechanical modulation. While gallium's high modulus tuning ratio and rapid phase transitions make it ideal for transformative electronic systems (TES), its liquid-state instability, high surface tension, and unintended phase transitions during processing pose substantial challenges. Here, we introduce STiffness-Adjustable temperature-Responsive ink (STAR ink), a chemically sinterable gallium composite electronic ink designed to overcome these obstacles. STAR ink enables high-resolution (~50 micrometers) circuit patterning, large-scale batch fabrication, and three-dimensional structure coating at room temperature. Through pH-controlled chemical sintering, STAR ink-based TES exhibits exceptional mechanical tunability (tuning ratio: 1465) and electrical conductivity (2.27 × 10 siemens per meter). Demonstrated applications-from multilayered variable stiffness printed circuit boards (PCBs) matching standard PCBs' complexity to body-temperature responsive neural probe-underscore STAR ink's potential for reconfigurable electronics across consumer electronics and biomedical devices.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

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NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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