The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

The Dynamic Changes of Brassica napus Seed Microbiota across the Entire Seed Life in the Field.

The seed microbiota is an important component given by nature to plants, protecting seeds from damage by other organisms and abiotic stress. However, little is known about the dynamic changes and potential functions of the seed microbiota during seed development. In this study, we investigated the composition and potential functions of the seed microbiota of rapeseed (Brassica napus). A total of 2496 amplicon sequence variants (ASVs) belonging to 504 genera in 25 phyla were identified, and the seed microbiota of all sampling stages were divided into three groups. The microbiota of flower buds, young pods, and seeds at 20 days after flowering (daf) formed the first group; that of seeds at 30 daf, 40 daf and 50 daf formed the second group; that of mature seeds and parental seeds were clustered into the third group. The functions of seed microbiota were identified by using PICRUSt2, and it was found that the substance metabolism of seed microbiota was correlated with those of the seeds. Finally, sixty-one core ASVs, including several potential human pathogens, were identified, and a member of the seed core microbiota, Sphingomonas endophytica, was isolated from seeds and found to promote seedling growth and enhance resistance against Sclerotinia sclerotiorum, a major pathogen in rapeseed. Our findings provide a novel perspective for understanding the composition and functions of microbiota during seed development and may enhance the efficiency of mining beneficial seed microbes.

Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy.

OBJECTIVE: To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies. DESIGN: A nested case-control design evaluated the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location. SETTING: Three university hospitals. PATIENTS: A total of 218 individuals with pain and/or bleeding in early pregnancy: 75 had an ongoing intrauterine gestation; 68 had ectopic pregnancies (EPs); and 75 had miscarriages. INTERVENTIONS: Serum levels of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for the identification of a nonviable pregnancy (ongoing intrauterine pregnancy vs. miscarriage or EP) and an EP (EP vs. ongoing intrauterine pregnancy or miscarriage). MAIN OUTCOME MEASURES: The predicted classification using each model was compared with the actual diagnosis, and sensitivity, specificity, positive predictive value, negative predictive value, conclusive classification, and accuracy were calculated. RESULTS: Models using classification regression tree analysis using 3 (pregnancy-specific beta-1-glycoprotein 3 [PSG3], chorionic gonadotropin-alpha subunit, and pregnancy-associated plasma protein-A) biomarkers were able to predict a maximum sensitivity of 93.3% and a maximum specificity of 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of 3 (soluble fms-like tyrosine kinase-1, PSG3, and tissue factor pathway inhibitor 2) biomarkers achieved a maximum sensitivity of 98.5% and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously, the conclusive classification increased to 72.7% with an accuracy of 95.9%. The predictive ability of the biomarkers in the random forest produced similar test characteristics when using 11 predictive biomarkers. CONCLUSION: We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers choriogonadotropin alpha, pregnancy-associated plasma protein-A, and PSG3 can be used to predict viability, and soluble fms-like tyrosine kinase-1, tissue factor pathway inhibitor 2, and PSG3 can be used to predict pregnancy location.

A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is essential for the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to assess the status of several established biomarkers, including ER, PGR, HER2 and KI67. Biomarker assessment can also be facilitated by computational pathology image analysis methods, which have made numerous substantial advances recently, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections from the same tumour. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients.

Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline.

Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.

Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE.

STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers.

The influence mechanism of hydrogeochemical environment and sulfur and nitrogen cycle on arsenic enrichment in groundwater: A case study of Hasuhai basin, China.

Hydro-biogeochemical processes control the formation and evolution of high arsenic (As) groundwater. However, the effects of nitrogen and sulfur cycles in groundwater on As migration and transformation are not well understood. Thus, twenty-one groundwater samples were collected from the Hasuhai basin. Hydrochemistry and geochemical modeling were used to analyze the geochemical processes associated with nitrogen and sulfur cycles. An arsenic speciation model (AM) and a sulfide-As model (SAM) were constructed to verify the existence of As species and the formation mechanism of thioarsenate. A hydrous ferric oxide (Hfo)-As adsorption model (HAM) and a competitive adsorption model (CAM) were used to reveal the adsorption and desorption mechanisms of As. The results showed that high arsenic groundwater (As > 10 µg/L) was mainly distributed under reductive conditions, and the highest concentration was 231.5 µg/L. The modeling results revealed that sulfides were widely involved in the geochemical cycle of As, with H3AsO3 and H2AsO3- accounting for >70 % of the total As, and thioarsenate accounting for 30 %. S/As < 2.5 and S/Fe < l control the formation of thioarsenate. With the high correlation of NH4+, TFe, sulfide, and TAs, the co-mobilization of N and S cycles may facilitate As enrichment in groundwater. A weak alkaline reduction environment triggered by the decomposition of organic matter was the main factor leading to the transfer of As from the aquifer to the groundwater. This research contributes to the development of high-As groundwater, and the findings are of general significance for drinking water in the Hasuhai Basin.

Fair Canonical Correlation Analysis.

This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.

Lentinus edodes Polysaccharides Alleviate Acute Lung Injury by Inhibiting Oxidative Stress and Inflammation.

Acute lung injury (ALI) is a kind of lung disease with acute dyspnea, pulmonary inflammation, respiratory distress, and non-cardiogenic pulmonary edema, accompanied by the mid- and end-stage characteristics of COVID-19, clinically. It is imperative to find non-toxic natural substances on preventing ALI and its complications. The animal experiments demonstrated that Lentinus edodes polysaccharides (PLE) had a potential role in alleviating ALI by inhibiting oxidative stress and inflammation, which was manifested by reducing the levels of serum lung injury indicators (C3, hs-CRP, and GGT), reducing the levels of inflammatory factors (TNF-α, IL-1ß, and IL-6), and increasing the activities of antioxidant enzymes (SOD and CAT) in the lung. Furthermore, PLE had the typical characteristics of pyran-type linked by ß-type glycosidic linkages. The conclusions indicated that PLE could be used as functional foods and natural drugs in preventing ALI.

Pharmacokinetics and Main Metabolites of Anwulignan in Mice.

Anwulignan is a representative component of Chinese traditional medicine Schisandra sphenanthera, with strong pharmacological activities. However, there are few reports on its pharmacokinetics and metabolites in the body. In this study, a metabolomic method based on UHPLC-Q-Orbitrap-MS was used to study the pharmacokinetics of anwulignan in the blood, organs, urine, and feces samples of mice after the intragastric administration of anwulignan (10 mg/kg). The pharmacokinetic parameters were calculated, and the distribution characteristics and main metabolites of anwulignan in the body of mice were analyzed. The results showed that the retention time of anwulignan in the body of mice was longer (t 1/2 = 7.1 h), and anwulignan was widely distributed in the body (Vz/F = 32.81 L/kg), especially in the liver. The order of anwulignan concentration in the tissues of mice from high to low was the liver > heart > brain > kidney > lung > spleen. Anwulignan was mainly excreted through the digestive tract in the form of its prototype and metabolites, indicating that it might experience an enterohepatic circulation. A total of seven metabolites were identified, and the demethylation, hydroxylation, dehydroxylation, and demethoxylation were considered to be the main metabolic ways of anwulignan in the body of mice.

Under-Oil Autonomously Regulated Oxygen Microenvironments: A Goldilocks Principle-Based Approach for Microscale Cell Culture.

Oxygen levels in vivo are autonomously regulated by a supply-demand balance, which can be altered in disease states. However, the oxygen levels of in vitro cell culture systems, particularly microscale cell culture, are typically dominated by either supply or demand. Further, the oxygen microenvironment in these systems is rarely monitored or reported. Here, a method to establish and dynamically monitor autonomously regulated oxygen microenvironments (AROM) using an oil overlay in an open microscale cell culture system is presented. Using this method, the oxygen microenvironment is dynamically regulated via the supply-demand balance of the system. Numerical simulation and experimental validation of oxygen transport within multi-liquid-phase, microscale culture systems involving a variety of cell types, including mammalian, fungal, and bacterial cells are presented. Finally, AROM is applied to establish a coculture between cells with disparate oxygen demands-primary intestinal epithelial cells (oxygen consuming) and Bacteroides uniformis (an anaerobic species prevalent in the human gut).

Study on the Hepatoprotection of Schisandra chinensis Caulis Polysaccharides in Nonalcoholic Fatty Liver Disease in Rats Based on Metabolomics.

The aim of this study was to investigate the hepatoprotection of Schisandra chinensis Caulis polysaccharides (SCPs) in the nonalcoholic fatty liver disease (NAFLD) induced by high-fat diet (HFD) in rats. A total of 30 Wistar rats were randomly divided into the control group (CON), model group (MOD), and Schisandra chinensis caulis polysaccharide (SCP) group. Except for those in the CON group, the other rats were fed with high-fat diet for 4 weeks to establish an NAFLD model. From the 5th week, rats in the SCP group were given SCP solution (100 mg kg-1) by gavage for 6 weeks, and those in the CON and MOD groups were given an equal volume of distilled water in the same way. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) levels in serum, the malondialdehyde (MDA) level, glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD) activities in the liver tissue were detected. The small molecular metabolites in the blood of rats were determined by the metabolomics method of ultra-high-performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap-MS/MS) combined with multivariate analysis. The enrichment analysis and pathway analysis of the different metabolites were carried out. The therapeutic mechanism of SCP in NAFLD rats was verified by western blot. The results showed that the levels of AST, ALT, TG, TC, and LDL-C in the serum of rats in the SCP group were significantly lower, and the levels of HDL-C were significantly higher than those in the MOD group. The screening and analysis of the metabolic pathways showed that SCP could alleviate the development of NAFLD by regulating the expression of UDP-glucose pyrophosphorylase (UGP2), UDP-glucose 6-dehydrogenase (UGDH), acetyl CoA carboxylase (ACC), and fatty acid synthase (FAS) in the liver of NAFLD rats. This study may provide a theoretical basis for the development and utilization of SCP.

Study on the effect of active components of Schisandra chinensis on liver injury and its mechanisms in mice based on network pharmacology.

The aim of this study was to analyze the active components of Schisandra chinensis on liver injury and its mechanism in mice by network pharmacology. The active components of S. chinensis were found through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and their corresponding targets were predicted. The targets of liver injury were searched through Therapeutic Targets Database (TTD), DisGeNET and drugbank databases, and the Venn diagram was constructed to obtain the action targets. The "drug-active component-target" network and protein-protein interaction network (PPI) were constructed by using STRING database and Cytoscape software, and the key targets were further screened by the enrichment analysis of relevant KEGG pathways. Finally, a CCl4-induced mouse liver injury model was established to verify the efficacy and related targets of S. chinensis and clarify its mechanism. Eight active components and 56 related targets of S. chinensis were screened out based on their oral bioavailability (OB) and drug likeness (DL). Five targets of S. chinensis related to liver injury were found by using the Venn diagram. The key targets, namely Ptgs2 and Nos2 genes, were further screened out by constructing a PPI network, and Schisandrol B (SCB) was considered the key component most closely related to the liver injury in S. chinensis. The results indicate that SCB may play a role in the treatment of the CCl4-induced liver injury by down-regulating the expression of iNOS and COX-2, and regulating the expression of NF-κB and IL-17 signaling pathway to inhibit the expression of proinflammatory factors.

Liver and Brain Protective Effect of Sulfated Polysaccharides from Residue of Lion's Mane Medicinal Mushroom, Hericium erinaceus (Agaricomycetes), on D-Galactose-Induced Aging Mice.

This work describes the preparation of sulfated Hericium erinaceus residue polysaccharides (SHRPs) and investigates their antioxidant and antiaging effects on D-galactose-induced aging mice. The results indicated that the degree of SO42- substitution was 0.47 ± 0.22 for SHRPs. In vitro analysis and in vivo animal experiments manifested that SHRPs could alleviate aging properties by scavenging radicals, elevating enzyme activities, and reducing malondialde-hyde content, thus improving serum biochemical indices and enhancing immunological activity. Liver and brain injuries of mice could be remitted by SHRP interventions. Structural characteristics of SHRPs in this work were elucidated by Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry, and the results suggest that SHRPs could be used as an effective dietary supplement and functional food for prevention of aging and age-related diseases.

Editing homologous copies of an essential gene affords crop resistance against two cosmopolitan necrotrophic pathogens.

Sclerotinia sclerotiorum and Botrytis cinerea are typical necrotrophic pathogens that can attack more than 700 and 3000 plant species, respectively, and cause huge economic losses across numerous crops. In particular, the absence of resistant cultivars makes the stem rot because of S. sclerotiorum the major threat of rapeseed (Brassica napus) worldwide along with Botrytis. Previously, we identified an effector-like protein (SsSSVP1) from S. sclerotiorum and a homologue of SsSSVP1 on B. cinerea genome and found that SsSSVP1 could interact with BnQCR8 of rapeseed, a subunit of the cytochrome b-c1 complex. In this study, we found that BnQCR8 has eight homologous copies in rapeseed cultivar Westar and reduced the copy number of BnQCR8 using CRISPR/Cas9 to improve rapeseed resistance against S. sclerotiorum. Mutants with one or more copies of BnQCR8 edited showed strong resistance against S. sclerotiorum and B. cinerea. BnQCR8-edited mutants did not show significant difference from Westar in terms of respiration and agronomic traits tested, including the plant shape, flowering time, silique size, seed number, thousand seed weight and seed oil content. These traits make it possible to use these mutants directly for commercial production. Our study highlights a common gene for breeding of rapeseed to unravel the key hindrance of rapeseed production caused by S. sclerotiorum and B. cinerea. In contrast to previously established methodologies, our findings provide a novel strategy to develop crops with high resistance against multiple pathogens by editing only a single gene that encodes the common target of pathogen effectors.

Protective effects of sulfated polysaccharides from Lentinula edodes on the lung and liver of MODS mice.

In this work, the effects of sulfated polysaccharides from Lentinula edodes (SPLE) on zymosan (ZYM)-induced multiple organ dysfunction syndrome (MODS) mice were investigated. Using the MODS mice model, biochemical works have already shown that in mice treated with SPLE, the lung parameters of GGT, C3 and hs-CRP were down-regulated and the hepatic parameters of TC, TG, ALT and AST, HDLC, LDL-C and VLDL-C were improved, the serum levels of CK, Cr and Amy were decreased, and the levels of inflammatory factors such as TNF-α, IL-1ß, IL-6 and IL-10 were also reduced, the activity of antioxidant enzymes SOD and CAT enhanced, and the content of MDA was reduced. In addition, histopathology of the lung and liver confirmed the beneficial effects of SPLE on MODS mice, indicating that SPLE played a role in protecting the organ function of MODS mice. In addition, SPLE was characterized as a sulfated ß-glucan linked by ß-type glycosidic bonds. These conclusions indicated that SPLE had effective antioxidant and anti-inflammatory activities, and could be used as a functional food and medicine to prevent MODS.

A deep learning based multiscale approach to segment the areas of interest in whole slide images.

This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). We propose a multi-scale image processing method based on an automatic end-to-end deep neural network algorithm for the segmentation of cancerous areas. A seven-level gaussian pyramid representation of the histopathological image was built to provide the texture information at different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsampling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and the weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scale approach which achieved better scores than state-of-the-art methods.

Characterization and Hepatoprotections of Ganoderma lucidum Polysaccharides against Multiple Organ Dysfunction Syndrome in Mice.

BACKGROUND: The liver is one of the most commonly affected organs in multiple organ dysfunction syndrome (MODS). In recent years, there have been many studies on Ganoderma lucidum polysaccharides (GLP), but the role of GLP in MODS is still unclear. The purpose of this work was to explore the antioxidant, anti-inflammatory, and protective effects of GLP on the liver in MODS model mice. METHODS: The characteristic properties of GLP were processed by physicochemical analysis. The MODS models were successfully established with intraperitoneal injection of zymosan in Kunming strain mice. The antioxidant, anti-inflammatory, and hepatoprotective effects of GLP were processed both in vitro and in vivo by evaluating the oxidative parameters, inflammatory factors, and liver pathological observations. RESULTS: The characterization analysis revealed that GLP was a pyranose mainly composed of glucose with the molecular weights (Mw) of 8309 Da. The experimental results proved that GLP had potential hepatoprotection possibly by improving the antioxidant status (scavenging excessive oxygen radicals, increasing the antioxidant enzyme activities, and reducing the lipid peroxide), alleviating the inflammatory response (reducing the inflammatory factor levels), and guaranteeing the liver functions. CONCLUSIONS: This research suggested that GLP had the potential to be developed as a natural medicine for the treatment of multiple organ failure.

Molecular Mechanism of the Regulatory Effect of Schisandrol A on the Immune Function of Mice Based on a Transcription Factor Regulatory Network.

The molecular mechanism of the regulatory effed of schizandrol A (SA) on the immune function of cyclophosphamide-induced immunosuppressive mice was explored in this study. On the basis of 1619 differentially expressed genes related to the regulatory effect of SA on the immune function of mice screened in our previous study, transcription factors and their corresponding target genes were screened in the Transcriptional Regulatory Element Database (TRED), and a transcription factor target gene regulatory network was constructed. The key nodes of the network were statistically analyzed to clarify the role of transcription factors in the regulatory network. The correlation of network genes with diseases was analyzed with an online annotation tool through the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, the key factors related to the regulatory effect of SA on the immune function of mice were screened and verified by animal experiments and the detection of related protein expression by western blot analysis. The results showed that SA could alleviate the immunosuppression induced by cyclophosphamide in mice and regulate the protein expression of Jun, Trp53, and Creb1 in the spleen tissue of mice, together with the transcription factors Atf4 and E2f2. SA may thus play a role in the alleviation of some immunity-related diseases (such as cancer) by regulating the immune function of mice through multiple genes and their transcription factors.