Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the American Board of Anesthesiology Examination.

BACKGROUND: Over the past decade, artificial intelligence (AI) has expanded significantly with increased adoption across various industries, including medicine. Recently, AI-based large language models such as Generative Pretrained Transformer-3 (GPT-3), Bard, and Generative Pretrained Transformer-3 (GPT-4) have demonstrated remarkable language capabilities. While previous studies have explored their potential in general medical knowledge tasks, here we assess their clinical knowledge and reasoning abilities in a specialized medical context. METHODS: We studied and compared the performance of all 3 models on both the written and oral portions of the comprehensive and challenging American Board of Anesthesiology (ABA) examination, which evaluates candidates' knowledge and competence in anesthesia practice. RESULTS: Our results reveal that only GPT-4 successfully passed the written examination, achieving an accuracy of 78% on the basic section and 80% on the advanced section. In comparison, the less recent or smaller GPT-3 and Bard models scored 58% and 47% on the basic examination, and 50% and 46% on the advanced examination, respectively. Consequently, only GPT-4 was evaluated in the oral examination, with examiners concluding that it had a reasonable possibility of passing the structured oral examination. Additionally, we observe that these models exhibit varying degrees of proficiency across distinct topics, which could serve as an indicator of the relative quality of information contained in the corresponding training datasets. This may also act as a predictor for determining which anesthesiology subspecialty is most likely to witness the earliest integration with AI. CONCLUSIONS: GPT-4 outperformed GPT-3 and Bard on both basic and advanced sections of the written ABA examination, and actual board examiners considered GPT-4 to have a reasonable possibility of passing the real oral examination; these models also exhibit varying degrees of proficiency across distinct topics.

PMechDB: A Public Database of Elementary Polar Reaction Steps.

Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb.

Deep Learning Assisted Imaging Methods to Facilitate Access to Ophthalmic Telepathology.

Purpose: To investigate the use of super-resolution imaging techniques to enable telepathology using low-cost commercial cameras. Design: Experimental study. Participants: A total of 139 ophthalmic pathology slides obtained from the Ophthalmic Pathology service at the University of California, Irvine. Methods: Denoising Diffusion Probabilistic Model (DDPM) was developed to predict super-resolution pathology slide images from low-resolution inputs. The model was pretrained using 150 000 images randomly sampled from the ImageNet dataset. Patch aggregation was used to generate large images with DDPM. The performance of DDPM was evaluated against that of generative adversarial networks (GANs) and Robust UNet, which were also trained on the same dataset. Main Outcome Measures: The performance of models trained to generate super-resolution output images from low-resolution input images can be evaluated by using the mean squared error (MSE) and Structural Similarity Index Measure (SSIM), as well as subjective grades provided by expert pathologist graders. Results: In total, our study included 110 training images, 9 validation images, and 20 testing images. The objective performance scores were averaged over patches generated from 20 test images. The DDPM-based approach with pretraining produced the best results, with an MSE score of 1.35e-5 and an SSIM score of 0.8987. A qualitative analysis of super-resolution images was conducted by expert 3 pathologists and 1 expert ophthalmic microscopist, and the average accuracy of identifying the correct ground truth images ranged from 25% to 70% (with an average accuracy of 46.5%) for widefield images and 25% to 60% (with an average accuracy of 38.25%) for individual patches. Conclusions: The DDPM-based approach with pretraining is assessed to be effective at super-resolution prediction for ophthalmic pathology slides both in terms of objective and subjective measures. The proposed methodology is expected to decrease the reliance on costly slide scanners for acquiring high-quality pathology slide images, while also streamlining clinical workflow and expanding the scope of ophthalmic telepathology. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Medical Informatics Operating Room Vitals and Events Repository (MOVER): a public-access operating room database.

Objectives: Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository (MOVER). Materials and Methods: This first release of MOVER includes adult patients who underwent surgery at the University of California, Irvine Medical Center from 2015 to 2022. Data for patients who underwent surgery were captured from 2 different sources: High-fidelity physiological waveforms from all of the operating rooms were captured in real time and matched with electronic medical record data. Results: MOVER includes data from 58 799 unique patients and 83 468 surgeries. MOVER is available for download at https://doi.org/10.24432/C5VS5G, it can be downloaded by anyone who signs a data usage agreement (DUA), to restrict traffic to legitimate researchers. Discussion: To the best of our knowledge MOVER is the only freely available public data repository that contains electronic health record and high-fidelity physiological waveforms data for patients undergoing surgery. Conclusion: MOVER is freely available to all researchers who sign a DUA, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

Reanalysis of primate brain circadian transcriptomics reveals connectivity-related oscillations.

Research shows that brain circuits controlling vital physiological processes are closely linked with endogenous time-keeping systems. In this study, we aimed to examine oscillatory gene expression patterns of well-characterized neuronal circuits by reanalyzing publicly available transcriptomic data from a spatiotemporal gene expression atlas of a non-human primate. Unexpectedly, brain structures known for regulating circadian processes (e.g., hypothalamic nuclei) did not exhibit robust cycling expression. In contrast, basal ganglia nuclei, not typically associated with circadian physiology, displayed the most dynamic cycling behavior of its genes marked by sharp temporally defined expression peaks. Intriguingly, the mammillary bodies, considered hypothalamic nuclei, exhibited gene expression patterns resembling the basal ganglia, prompting reevaluation of their classification. Our results emphasize the potential for high throughput circadian gene expression analysis to deepen our understanding of the functional synchronization across brain structures that influence physiological processes and resulting complex behaviors.

Spatiotemporal Mapping of Brain Cilia Reveals Region-Specific Oscillation of Length and Orientation.

In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-hour period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia at 45 degree intervals, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified circadian rhythms of cilia length in five brain regions: nucleus accumbens core, somatosensory cortex, and three hypothalamic nuclei. Our findings present novel insights into the complex relationship between cilia dynamics, circadian rhythms, and brain function, highlighting cilia crucial role in the brain's response to environmental changes and regulation of time-dependent physiological processes.

Combining Allograft Adipose and Fascia Matrix as an Off-the-Shelf Scaffold for Adipose Tissue Engineering Stimulates Angiogenic Responses and Activates a Proregenerative Macrophage Profile in a Rodent Model.

OBJECTIVE: Bioscaffolds for treating soft tissue defects have limitations. As a bioscaffold, allograft adipose matrix (AAM) is a promising approach to treat soft tissue defects. Previously, we revealed that combining superficial adipose fascia matrix with AAM, components of the hypodermis layer of adipose tissue, improved volume retention, adipogenesis, and angiogenesis in rats 8 weeks after it was implanted compared with AAM alone. Here, we modified the fascia matrix and AAM preparation, examined the tissue over 18 weeks, and conducted a deeper molecular investigation. We hypothesized that the combined matrices created a better scaffold by triggering angiogenesis and proregenerative signals. METHODS: Human AAM and fascia matrix were implanted (4 [1 mL] implants/animal) into the dorsum of male Fischer rats (6-8 weeks old; ~140 g) randomly as follows: AAM, fascia, 75/25 (AAM/fascia), 50/50, and 50/50 + hyaluronic acid (HA; to improve extrudability) (n = 4/group/time point). After 72 hours, as well as 1, 3, 6, 9, 12, and 18 weeks, graft retention was assessed by a gas pycnometer. Adipogenesis (HE), angiogenesis (CD31), and macrophage infiltration (CD80 and CD163) were evaluated histologically at all time points. The adipose area and M1/M2 macrophage ratio were determined using ImageJ. RNA sequencing (RNA-seq) and bioinformatics were conducted to evaluate pathway enrichments. RESULTS: By 18 weeks, the adipose area was 2365% greater for 50/50 HA (281.6 ± 21.6) than AAM (11.4 ± 0.9) (P < 0.001). The M1/M2 macrophage ratio was significantly lower for 50/50 HA (0.8 ± 0.1) than AAM (0.9 ± 0.1) at 6 weeks (16%; P < 0.05). This inversely correlated with adipose area (r = -0.6; P > 0.05). The RNA-seq data revealed that upregulated adipogenesis, angiogenesis, and macrophage-induced tissue regeneration genes were temporally different between the groups. CONCLUSIONS: Combining the fascia matrix with AAM creates a bioscaffold with an improved retention volume that supports M2 macrophage-mediated angiogenesis and adipogenesis. This bioscaffold is worthy of further investigation.

Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the ABA Exam.

Over the past decade, Artificial Intelligence (AI) has expanded significantly with increased adoption across various industries, including medicine. Recently, AI's large language models such as GPT-3, Bard, and GPT-4 have demonstrated remarkable language capabilities. While previous studies have explored their potential in general medical knowledge tasks, here we assess their clinical knowledge and reasoning abilities in a specialized medical context. We study and compare their performances on both the written and oral portions of the comprehensive and challenging American Board of Anesthesiology (ABA) exam, which evaluates candidates' knowledge and competence in anesthesia practice. In addition, we invited two board examiners to evaluate AI's answers without disclosing to them the origin of those responses. Our results reveal that only GPT-4 successfully passed the written exam, achieving an accuracy of 78% on the basic section and 80% on the advanced section. In comparison, the less recent or smaller GPT-3 and Bard models scored 58% and 47% on the basic exam, and 50% and 46% on the advanced exam, respectively. Consequently, only GPT-4 was evaluated in the oral exam, with examiners concluding that it had a high likelihood of passing the actual ABA exam. Additionally, we observe that these models exhibit varying degrees of proficiency across distinct topics, which could serve as an indicator of the relative quality of information contained in the corresponding training datasets. This may also act as a predictor for determining which anesthesiology subspecialty is most likely to witness the earliest integration with AI.

Liver and muscle circadian clocks cooperate to support glucose tolerance in mice.

Physiology is regulated by interconnected cell and tissue circadian clocks. Disruption of the rhythms generated by the concerted activity of these clocks is associated with metabolic disease. Here we tested the interactions between clocks in two critical components of organismal metabolism, liver and skeletal muscle, by rescuing clock function either in each organ separately or in both organs simultaneously in otherwise clock-less mice. Experiments showed that individual clocks are partially sufficient for tissue glucose metabolism, yet the connections between both tissue clocks coupled to daily feeding rhythms support systemic glucose tolerance. This synergy relies in part on local transcriptional control of the glucose machinery, feeding-responsive signals such as insulin, and metabolic cycles that connect the muscle and liver. We posit that spatiotemporal mechanisms of muscle and liver play an essential role in the maintenance of systemic glucose homeostasis and that disrupting this diurnal coordination can contribute to metabolic disease.

MOVER: Medical Informatics Operating Room Vitals and Events Repository.

Artificial Intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository, which includes data from 58,799 unique patients and 83,468 surgeries collected from the UCI Medical Center over a period of seven years. MOVER is freely available to all researchers who sign a data usage agreement, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

Cell-specific vulnerability to metabolic failure: the crucial role of parvalbumin expressing neurons in creatine transporter deficiency.

Mutations in the solute carrier family 6-member 8 (Slc6a8) gene, encoding the protein responsible for cellular creatine (Cr) uptake, cause Creatine Transporter Deficiency (CTD), an X-linked neurometabolic disorder presenting with intellectual disability, autistic-like features, and epilepsy. The pathological determinants of CTD are still poorly understood, hindering the development of therapies. In this study, we generated an extensive transcriptomic profile of CTD showing that Cr deficiency causes perturbations of gene expression in excitatory neurons, inhibitory cells, and oligodendrocytes which result in remodeling of circuit excitability and synaptic wiring. We also identified specific alterations of parvalbumin-expressing (PV+) interneurons, exhibiting a reduction in cellular and synaptic density, and a hypofunctional electrophysiological phenotype. Mice lacking Slc6a8 only in PV+ interneurons recapitulated numerous CTD features, including cognitive deterioration, impaired cortical processing and hyperexcitability of brain circuits, demonstrating that Cr deficit in PV+ interneurons is sufficient to determine the neurological phenotype of CTD. Moreover, a pharmacological treatment targeted to restore the efficiency of PV+ synapses significantly improved cortical activity in Slc6a8 knock-out animals. Altogether, these data demonstrate that Slc6a8 is critical for the normal function of PV+ interneurons and that impairment of these cells is central in the disease pathogenesis, suggesting a novel therapeutic venue for CTD.

RMechDB: A Public Database of Elementary Radical Reaction Steps.

We introduce RMechDB, an open-access platform for aggregating, curating, and distributing reliable data about elementary radical reaction steps for computational radical reaction modeling and prediction. RMechDB contains over 5,300 elementary radical reaction steps, each with a single transition state at or around room temperature. These elementary step reactions are manually curated plausible arrow-pushing steps for organic radical reactions. The steps were taken from a variety of sources. Over 2,000 mechanistic steps were extracted from textbooks and/or constructed from research publications. Another 3,000 were taken from gas-phase atmospheric reactions of isoprene and other organic molecules on the MCM (Master Chemical Mechanism) Web site. Reactions are encoded in the SMIRKS format with accurate atom mapping and annotations for arrow-pushing mechanisms. At its core, RMechDB consists of a database schema with an online interactive search interface and a request portal for downloading the raw form of elementary step reactions with their metadata. It also offers an interface for submitting new reactions to RMechDB and expanding the data set through community contributions. Although there are several applications for RMechDB, it is primarily designed as a central platform of radical elementary steps with a unified and structured representation. We believe that this open access to this data and platform enables the extension of data-driven models for chemical reaction predictions and other chemoinformatics predictive tasks.

Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning.

Purpose: To evaluate the potential for artificial intelligence-based video analysis to determine surgical instrument characteristics when moving in the three-dimensional vitreous space. Methods: We designed and manufactured a model eye in which we recorded choreographed videos of many surgical instruments moving throughout the eye. We labeled each frame of the videos to describe the surgical tool characteristics: tool type, location, depth, and insertional laterality. We trained two different deep learning models to predict each of the tool characteristics and evaluated model performances on a subset of images. Results: The accuracy of the classification model on the training set is 84% for the x-y region, 97% for depth, 100% for instrument type, and 100% for laterality of insertion. The accuracy of the classification model on the validation dataset is 83% for the x-y region, 96% for depth, 100% for instrument type, and 100% for laterality of insertion. The close-up detection model performs at 67 frames per second, with precision for most instruments higher than 75%, achieving a mean average precision of 79.3%. Conclusions: We demonstrated that trained models can track surgical instrument movement in three-dimensional space and determine instrument depth, tip location, instrument insertional laterality, and instrument type. Model performance is nearly instantaneous and justifies further investigation into application to real-world surgical videos. Translational Relevance: Deep learning offers the potential for software-based safety feedback mechanisms during surgery or the ability to extract metrics of surgical technique that can direct research to optimize surgical outcomes.

Brain histone beta-hydroxybutyrylation couples metabolism with gene expression.

Little is known about the impact of metabolic stimuli on brain tissue at a molecular level. The ketone body beta-hydroxybutyrate (BHB) can be a signaling molecule regulating gene transcription. Thus, we assessed lysine beta-hydroxybutyrylation (K-bhb) levels in proteins extracted from the cerebral cortex of mice undergoing a ketogenic metabolic challenge (48 h fasting). We found that fasting enhanced K-bhb in a variety of proteins including histone H3. ChIP-seq experiments showed that K9 beta-hydroxybutyrylation of H3 (H3K9-bhb) was significantly enriched by fasting on more than 8000 DNA loci. Transcriptomic analysis showed that H3K9-bhb on enhancers and promoters correlated with active gene expression. One of the most enriched functional annotations both at the epigenetic and transcriptional level was "circadian rhythms''. Indeed, we found that the diurnal oscillation of specific transcripts was modulated by fasting at distinct zeitgeber times both in the cortex and suprachiasmatic nucleus. Moreover, specific changes in locomotor activity daily features were observed during re-feeding after 48-h fasting. Thus, our results suggest that fasting remarkably impinges on the cerebral cortex transcriptional and epigenetic landscape, and BHB acts as a powerful epigenetic molecule in the brain through direct and specific histone marks remodeling in neural tissue cells.

Tryptophan metabolism is a physiological integrator regulating circadian rhythms.

OBJECTIVE: The circadian clock aligns physiology with the 24-hour rotation of Earth. Light and food are the main environmental cues (zeitgebers) regulating circadian rhythms in mammals. Yet, little is known about the interaction between specific dietary components and light in coordinating circadian homeostasis. Herein, we focused on the role of essential amino acids. METHODS: Mice were fed diets depleted of specific essential amino acids and their behavioral rhythms were monitored and tryptophan was selected for downstream analyses. The role of tryptophan metabolism in modulating circadian homeostasis was studied using isotope tracing as well as transcriptomic- and metabolomic- analyses. RESULTS: Dietary tryptophan depletion alters behavioral rhythms in mice. Furthermore, tryptophan metabolism was shown to be regulated in a time- and light- dependent manner. A multi-omics approach and combinatory diet/light interventions demonstrated that tryptophan metabolism modulates temporal regulation of metabolism and transcription programs by buffering photic cues. Specifically, tryptophan metabolites regulate central circadian functions of the suprachiasmatic nucleus and the core clock machinery in the liver. CONCLUSIONS: Tryptophan metabolism is a modulator of circadian homeostasis by integrating environmental cues. Our findings propose tryptophan metabolism as a potential point for pharmacologic intervention to modulate phenotypes associated with disrupted circadian rhythms.

The hidden link between circadian entropy and mental health disorders.

The high overlapping nature of various features across multiple mental health disorders suggests the existence of common psychopathology factor(s) (p-factors) that mediate similar phenotypic presentations across distinct but relatable disorders. In this perspective, we argue that circadian rhythm disruption (CRD) is a common underlying p-factor that bridges across mental health disorders within their age and sex contexts. We present and analyze evidence from the literature for the critical roles circadian rhythmicity plays in regulating mental, emotional, and behavioral functions throughout the lifespan. A review of the literature shows that coarse CRD, such as sleep disruption, is prevalent in all mental health disorders at the level of etiological and pathophysiological mechanisms and clinical phenotypical manifestations. Finally, we discuss the subtle interplay of CRD with sex in relation to these disorders across different stages of life. Our perspective highlights the need to shift investigations towards molecular levels, for instance, by using spatiotemporal circadian "omic" studies in animal models to identify the complex and causal relationships between CRD and mental health disorders.

CircadiOmics: circadian omic web portal.

Circadian rhythms are a foundational aspect of biology. These rhythms are found at the molecular level in every cell of every living organism and they play a fundamental role in homeostasis and a variety of physiological processes. As a result, biomedical research of circadian rhythms continues to expand at a rapid pace. To support this research, CircadiOmics (http://circadiomics.igb.uci.edu/) is the largest annotated repository and analytic web server for high-throughput omic (e.g. transcriptomic, metabolomic, proteomic) circadian time series experimental data. CircadiOmics contains over 290 experiments and over 100 million individual measurements, across >20 unique tissues/organs, and 11 different species. Users are able to visualize and mine these datasets by deriving and comparing periodicity statistics for oscillating molecular species including: period, amplitude, phase, P-value and q-value. These statistics are obtained from BIO_CYCLE and JTK_CYCLE and are intuitively aggregated and displayed for comparison. CircadiOmics is the most up-to-date and cutting-edge web portal for searching and analyzing circadian omic data and is used by researchers around the world.

The central clock suffices to drive the majority of circulatory metabolic rhythms.

Life on Earth anticipates recurring 24-hour environmental cycles via genetically encoded molecular clocks active in all mammalian organs. Communication between these clocks controls circadian homeostasis. Intertissue communication is mediated, in part, by temporal coordination of metabolism. Here, we characterize the extent to which clocks in different organs control systemic metabolic rhythms, an area that remains largely unexplored. We analyzed the metabolome of serum from mice with tissue-specific expression of the clock gene Bmal1. Having functional hepatic and muscle clocks can only drive a minority (13%) of systemic metabolic rhythms. Conversely, limiting Bmal1 expression to the central pacemaker in the brain restores rhythms to 57% of circulatory metabolites. Rhythmic feeding imposed on clockless mice resulted in a similar rescue, indicating that the central clock mainly regulates metabolic rhythms via behavior. These findings explicate the circadian communication between tissues and highlight the importance of the central clock in governing those signals.

Weakly Supervised Polyp Segmentation in Colonoscopy Images Using Deep Neural Networks.

Colorectal cancer (CRC) is a leading cause of mortality worldwide, and preventive screening modalities such as colonoscopy have been shown to noticeably decrease CRC incidence and mortality. Improving colonoscopy quality remains a challenging task due to limiting factors including the training levels of colonoscopists and the variability in polyp sizes, morphologies, and locations. Deep learning methods have led to state-of-the-art systems for the identification of polyps in colonoscopy videos. In this study, we show that deep learning can also be applied to the segmentation of polyps in real time, and the underlying models can be trained using mostly weakly labeled data, in the form of bounding box annotations that do not contain precise contour information. A novel dataset, Polyp-Box-Seg of 4070 colonoscopy images with polyps from over 2000 patients, is collected, and a subset of 1300 images is manually annotated with segmentation masks. A series of models is trained to evaluate various strategies that utilize bounding box annotations for segmentation tasks. A model trained on the 1300 polyp images with segmentation masks achieves a dice coefficient of 81.52%, which improves significantly to 85.53% when using a weakly supervised strategy leveraging bounding box images. The Polyp-Box-Seg dataset, together with a real-time video demonstration of the segmentation system, are publicly available.

Bioinformatics and Systems Biology of Circadian Rhythms: BIO_CYCLE and CircadiOmics.

Circadian rhythms are fundamental to biology and medicine and today these can be studied at the molecular level in high-throughput fashion using various omic technologies. We briefly present two resources for the study of circadian omic (e.g. transcriptomic, metabolomic, proteomic) time series. First, BIO_CYCLE is a deep-learning-based program and web server that can analyze omic time series and statistically assess their periodic nature and, when periodic, accurately infer the corresponding period, amplitude, and phase. Second, CircadiOmics is the larges annotated repository of circadian omic time series, containing over 260 experiments and 90 million individual measurements, across multiple organs and tissues, and across 9 different species. In combination, these tools enable powerful bioinformatics and systems biology analyses. The are currently being deployed in a host of different projects where they are enabling significant discoveries: both tools are publicly available over the web at: http://circadiomics.ics.uci.edu/ .