Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies  are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and the accompanying text files provide basic patient information . Several pediatric radiologists annotated dataset images by placing lines, bounding boxes, or polygons to mark pathologies like fractures or periosteal reactions. They also tagged general image characteristics. This dataset is publicly available to encourage computer vision research.  This is also true in the pediatric population. Distal radius and ulna fractures account for the majority of pediatric wrist injuries with an incidence peak in adolescence5. In some parts of the world, access to specialist reporting is considerable restricted, if not unavailable6.Pediatric surgeons in training or emergency physicians often interpret trauma radiographs, sometimes without being backed-up by experienced pediatric radiologists. Even in developed countries, shortages of radiologists were reported, posing a risk to patient care10. Further modalities like ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) can be helpful in case of uncertainties after completed clinical examination and radiography. They increase sensitivity and specificity. However, some fractures are even occult in these modalities13.Studies indicated various amounts of diagnostic error in reading emergency X-rays, up to a percentage of 26%17. There is also a limited number of studies dealing with automated detection of wrist fractures in adults22. Computer vision applications typically rely on large numbers of training samples24. Large publicly available datasets enable researchers around the globe to compare their algorithms in a standardized way, yet, there are still limited options accessible to date25.Computer vision algorithms were found to be useful in many medical fields, mainly when large amounts of data can be analyzed. First positive experiences with the detection of pathologies in trauma X-rays were recently publishedGraz”, “Pediatric”, “Wrist”, and “Digital X-ray”. The image collection is de-identified, distributed in an accessible file format , and publicly available. It is accompanied by various annotations as well as image and object tags established by human experts.We present a comprehensively annotated pediatric wrist trauma radiography dataset (GRAZPEDWRI-DX) for machine learning. The acronym is composed of the terms “We constructed the GRAZPEDWRI-DX dataset from image data (pediatric wrist radiographs), natural language (report texts), and human expert annotations . Annotations were established directly based onto the X-ray contents and with the aid of the corresponding free text reports, finally combined to the whole dataset. The whole process of dataset construction is presented as a flowchart in Fig. The project (No. EK 31-108 ex 18/19) was approved by the local ethics committee of the Medical University of Graz (IRB00002556). Requirement for oral or written patient consent was waived due to the retrospective project design. All study-related methods were conducted in accordance with the Declaration of Helsinki and all relevant guidelines and regulations.27. Apart from the pixel information, DICOM images contain meta data (DICOM header) that rely on tight rules. Some of the meta data are mandatory, while others are optional. The DICOM standard is updated regularly28. Since there are still some barriers with the use of DICOM, we decided to convert the images to the more widespread Portable Network Graphics (PNG) format. PNG images offer a lossless compression of the pixel data and can be stored in more than 8 Bit grayscale format. Radiography DICOMs typically contain 12-bit, sometimes 16-bit of grayscale information. DICOM images served as input and were read with the “pydicom” package29. The PNG format does not allow to store 12 bits per channel, so we normalized the grayscale values of all DICOMs to 16-bit, stretching the grayscale histogram from initially 0 to 4,095 (12-bit) to 0 to 65,535 (16-bit) with the “cv2” module. Afterwards, PNGs were saved to the hard disk.10,643 wrist radiography studies of 6,091 unique pediatric patients  were retrieved as Digital Imaging and Communications in Medicine (DICOM) images from the local Picture Archiving and Communication System (PACS). DICOM is the standard medical image format which enables permanent storage and exchange between different modalities and institutions30 cryptographic hashes of “Institution” and “Patient ID”, afterwards hashed with “SHA-3-256”31. The resulting hash is the same for each individual patient. The second part of the file name consisted of the acquisition time’s Unix timestamp, subtracted by a 9-integer long hash of the “Patient ID”, keeping the original study intervals while irreversibly masking the original examination times. The series number and image number (zero-padded to two digits) represented the next part of the filename, which was followed by a region token, the laterality and projection, and finally sex and age . An example file name would be: „e07bd2cecaefda782adff762c134e7f26f1b72dec670cb6c36d42ce40e3e30fb_0020600143_01-01_WRI-L1_F010-3.png“. For this specific publication, we replaced the ascending patient hashes by continuous numbers from zero onwards, forced to four digits by zero-padding, and provided only full years of age. Moreover, we subtracted the Unix timestamps by an arbitrary number a second time, and replaced series number and image number by ascending study IDs, e.g. „4229_0020600143_02_WRI-L1_F010.png“, explained in Fig. De-identification and image conversion procedure was performed in batch by a custom-coded Python script. PNGs do not allow to store meta data as a standard feature. To retain sufficient information for processing and analysis of the PNG images, we created the file names based onto DICOM header information. The first part of the filename was constructed from concatenated strings of “Blake2b”Any additional layers or overlays in the original DICOM were irreversibly discarded during the image conversion and de-identification process.After DICOM conversion including removal of the header information, PNG images can still contain identifying text in their pixel matrix, usually referred to as “Burned In Annotation”. These text passages, written into the image pixels can contain sensitive information about the patient, the radiological technologists or even the referring clinician. We found our DICOM headers routinely inaccurate regarding the presence of the “Burned In Annotation” attribute. Automated text-recognition methods based on the Tesseract optical character recognition software were not reliable enough to enable unvalidated masking of potentially sensitive information. We therefore screened all images manually and masked related images with black boxes in IrfanView (n = 97). In contrast to other publicly available datasets, we also masked radiographer abbreviations.Board-certified pediatric radiologists  with experiences between 6 and 29 years in musculoskeletal radiology validated all images annotations performed on the Supervisely  artificial intelligence online platform. A dedicated server hosted the web-based image database, allowing for collaborate labeling. Clients accessed the server with a web browser over the Internet. Apart from the mentioned validating radiologists, local radiologists, visiting colleagues, and medical students helped to progress the dataset with different shares of labeling times. All annotations were executed between March 2018 and February 2022.The annotators manually placed objects with dedicated tools. Amongst others, we annotated fractures, metal implants, periosteal reactions, or bone lesions with bounding boxes, polygons, or lines. Image tags were manually set to represent features of each image, when appropriate. Collected objects Fig. . and imaAll available radiology reports were manually read by the authors and classified either into fracture or no fracture. They served as basis for labeling fractures in the annotation period. The German natural language reports are not delivered together with the dataset as 1) we were not able assure full anonymity, 2) they might not be correctly linked to the corresponding image occasionally due to pooling of images of different date and/or body regions into single free text reports. However, we are willing to provide the reports for research purposes on request.32. We grant free access to the dataset, without the need for user registration. The dataset is distributed in ZIP archives with a total size of 15.2 Gigabytes (GB), containing its original folder structure displayed below.Data are provided for download on Figshare (10.6084/m9.figshare.14825193)33 format , in YOLOv534 format (“yolov5” folder), as well as image tags in comma separated values (CSV) format. The whole project structure is shared by a ZIP file.We provide ready-to-use data in proprietary Supervisely project (“supervisely” folder) format, in PASCAL Visual Object Classes (VOC)The root folder contains a CSV file (“dataset.csv”) with all filenames, basic patient information and image tags. The “notebooks” folder holds three pieces of code, one for previewing annotations, and one for splitting files based on two columns of a CSV file, and one image post-processing. They are released as Jupyter notebooks. The “images” folder contains all wrist radiographs in 16-bit PNG format. The “supervisely” folder has a single “meta.json” file and is structured into a “wrist” subfolder that consists of an “ann” subfolder with json annotations, and an empty “img” subfolder. This “img” subfolder needs to be populated from the “images” folder, if needed. The “pascalvoc” folder contains Extensible Markup Language (XML) data files for object detection. This folder also does not include image files, which need to be copied from the “images” folder, if necessary. Every annotation file is associated with a corresponding image file, featuring the same file basename and only variable file extension. The “yolov5” folder contains the labels as text files (TXT) in the dedicated YOLOv5 format. It comes with a “meta.yaml” file for basic settings and object mappings.An annotated example study is provided in Fig. 36. “softtissue” refers to an unspecific soft tissue swelling, usually due to trauma. “text” marks any text passage or single letters visible, most commonly laterality indicators “L” and “R”.A total number of 67,771 objects were annotated in the dataset, splitting into the different categories as specifically detailed in Table 37 in images with acute or subacute fractures, but not in healed or remodeled fractures. Images might contain AO classifications without a labeled fracture object (bounding box) in cases, where a fracture is known to be present but not clearly visualized in the respective projection. Table The annotators set 74,459 image tags in the 20,327 images. They included AO/OTA (“Arbeitsgemeinschaft für Osteosynthesefragen” /”Orthopaedic Trauma Association”) classifications (2018 version)Top 5 most common AO classification ratings in the dataset are “23r-M/2.1” , “23r-M/3.1; 23u-M/2.1” , “23r-M/3.1; 23u-E/7” , “23r-M/3.1; 23u-M/2.1” , and “23-M/3.1” , followed by a number of 102 further AO classifications or combinations that can be retrieved from the accompanying CSV file. Multiple fracture types within the same image are separated by semicolons.“image_conversion.ipynb”).Image data is of high quality, delivered in full pixel resolution and complete grayscale spectrum. We did not apply any post-processing apart from histogram normalization (stretching) the usually 12-bit input spectrum to 16-bit due to meet PNG image format requirements. Note that there is no loss of information due this reversible procedure Fig. .. Postpr39. Images in question are tagged “diagnosis_uncertain” . However, it must be kept in mind that annotation errors might not be confined to these images, but could be present within the whole dataset. Particularly AO classification codes are not always unambiguous. Images that are considered normal might contain occult fractures, while we believe that studies rated to be pathologic, are accurately tagged in the majority of cases.All studies and their annotations were reviewed by experienced pediatric radiologists at least twice. Still, users need to consider inevitable inaccuracies, discrepancies, and errors in labeling due to the restricted diagnostic sensitivity of X-ray studiesst percentile 0.22, 25th percentile 0.63, 50th percentile = median 0.73, 75th percentile 0.80, 99th percentile 0.94 for the blinded raters. An IoU of 1 resembles perfectly overlapping bounding boxes, while 0 would be no overlap at all. Thus, inter-rater bounding box agreement was moderate. However, the whole dataset was labeled in an iterative continuous fashion, where labels were little-by-little adjusted multiple times by different annotators. So labeling quality is considered more accurate than the IoU level alone might indicate.We assessed “fracture” labeling agreement between the validating pediatric radiologists with intersection over union (IoU) measurements in a subsample of 100 randomly chosen overlapping images, resulting in a mean IoU 0.70 , and a validation set of 4,000 images. A Windows PC equipped with an Nvidia GeForce RTX 2060 SUPER  ran 50 epochs of a COCO40 pre-trained “YOLOv5m” model with an input size of 640 pixels and a batch size of 16 samples. Standard hyperparameters were used. Python version was 3.9.5. Table We trained a state-of-the-art neural network for object detection, namely YOLOv5https://creativecommons.org/licenses/by/4.0/). Users are prohibited to attempt re-identification of personal patient information.The dataset is made freely available for any purpose. The data provided within this work are free to copy, share or redistribute in any medium or format. The data might be adapted, remixed, transformed, and built upon. The dataset is licensed under a Creative Commons “Attribution 4.0 International” license (CC BY 4.0) . It incorporates options to apply sharpening and contrast enhancing in terms of intensity rescaling and contrast limited adaptive histogram equalization (CLAHE), enabled by the Python “scikit-image (skimage)” package41.Computer vision algorithms commonly rely on input images with 8 bits per channel for training and inference, particularly when using transfer learning bases on large image archives. The dataset contains a Python script that enables image conversion from the 16-bit source images to 8-bit samples  from 12,173 patients25. The LERA dataset accumulated lower extremity radiographs of 182 patients42. Both lack comprehensive labeling that we provide within the current dataset.The GRAZPEDWRI-DX dataset enables assessment of a variety of research questions around the injured pediatric wrist. To our knowledge, there are no related pediatric datasets publicly available. In adults, there is a limited number of musculoskeletal radiography collections available to the community. The Stanford University datasets MURAWe might release further revisions of the dataset annotations in the future.


This study investigates the volatile compounds of green tea produced with different leaves from spring, summer, and autumn in high−latitude region. A total of 95 volatile compounds were identified by gas chromatography–mass spectrometry (GC–MS). Spring, summer and autumn green tea contained 68, 72 and 82 volatile compounds, respectively. Principal component analysis (PCA), partial least squares−discrimination analysis (PLS−DA), and hierarchical cluster analysis (HCA) classified the samples and showed the difference. And 32 key characteristic components were screened out based on variable importance in the projection (VIP) values higher than 1.0. The characteristic volatile compounds of spring green tea including 18 components, such as geranylacetone, phenethyl alcohol, geraniol, β−ionone, jasmone, 1−octen−3−ol and longifolene. 13 components such as 2−methylfuran, indole, 1−octanol, D−limonene and ethanethiol were the key compounds in summer green tea. And 2,4,6−trimethylstyrene was the major differential volatile compounds in autumn green tea. The results increase our knowledge of green tea in different seasons and provide a theoretical basis for production control of green tea. Green tea, classified as unfermented tea, accounts for 60% of China’s annual tea production, and is widely appreciated by consumers owing to its unique aroma ,2. AromaPrevious studies have examined volatile compounds in green tea, and components such as linalool nonanal, (Z)−3−hexenyl hexanoate, β−ionone, geraniol, and cis−jasmone were identified as major volatiles ,12. ChinThe sensory quality (aroma and taste) of tea is greatly affected by the climatic changes of different seasons ,22, and Shandong Province is located at the north latitude of 34–38°, belonging to the high latitude tea area. Due to its unique climate and geological conditions, with large temperature difference between day and night, Shandong green tea possesses an excellent aroma quality. Tea plants grown in the Shandong area are plucked late because of the low temperature in early spring, but the harvesting period is long. The quality characteristics of green tea produced in the three seasons of spring, summer and autumn are different and have their own distinctive aroma profile. However, there has been no systematic evaluation of the characteristic volatile compounds of Shandong green tea. Hence, the aroma of green tea from different seasons in the high−latitude region needs to be elucidated.Multivariate statistical analysis methods such as principal component analysis (PCA) and partial least squares−discrimination analysis (PLS−DA) have been proven to be efficient and rapid methods to classify food samples and highlight their differences ,29,30,31In this study, headspace solid phase microextraction (HS−SPME) combined with gas chromatography−mass spectrometry (GC−MS) were employed to investigate the characteristic differences between the green teas produced in different seasons. These methods have been widely applied in the identification of volatile compounds in foods with high accuracy and efficiency ,37. Our The tea variety Longjing−changye, one of the main varieties cultivated in the high latitude tea area, was selected as the tea source for the experiment. The fresh tea leaves (one bud and two leaves) were collected from Laoshan area  in May, July, and September of 2018, respectively. All samples were prepared using identical processing techniques. First, fresh leaves were withered for 3 h under natural conditions, and then the withered leaves were fixed at approximately 220–240 °C. Fixed leaves were rolled lightly for 15 min, then rolled heavily for 30 min, finally rolled lightly for 20 min. After rolling, each tea leaf was toasted using a drum at 110 °C for 30 min, finally green tea was obtained by redrying at 90 °C for 1 h. All tea samples were stored at −80 °C until analyzed.According to the China Sensory Review Standards (GB−T/23776−2018), all samples were evaluated by five experienced experts  who were trained and authenticated by professional organizations ,39. The The extraction of green tea volatile compounds was conducted by reported HS−SPME method with minor modifications ,41. BrieThe GC−MS analytical procedure was based on previous study . Chromat6−C25) with information from National Institute of Standards and Technology (NIST) library. RI was calculated with the retention time of each compound according to previous literature [The raw data acquired by GC–MS were first deconvolved using Agilent Mass Hunter Qualitative Analysis software . Volatile compounds were tentatively identified by comparing their mass spectra (MS) and the practical retention indices . The key volatile compounds responsible for each sample were screened by variable influences in projection (VIP) > 1.0 [All identified compounds were used for statistical analysis. One−way ANOVA (Duncan’s multiple range tests) was used for data analysis by SPSS 25.0 software . P) > 1.0 . A hieraThe sensory evaluation results of green teas are shown in The volatile components of all samples were detected by HS−SPME/GC−MS. A total of 95 compounds were tentatively identified, including 40 hydrocarbons, 17 alcohols, 10 esters, 8 aldehydes, 5 ketones, 4 phenols, 2 sulfur compounds, and 9 other compounds in three green teas .A Venn diagram was performed to visualize the distribution of compounds in green tea during the three seasons B. The nuThe PCA model was established based on the relative content of volatile components. As shown in 2Y = 0.995, Q2 = 0.991) indicated the robustness of the model. Then, the effect of modeling was evaluated by the method of substitution test. The low intercepts  was obtained through 200 times cross−validations, which demonstrated that there was no overfitting phenomenon, and this model was reliable  was adopted to compare the volatile profiles of green tea in three seasons. In reliable C.Variable importance in the projection (VIP) can quantify the contribution of each variable of PLS−DA to classification. It is generally considered that the variable with VIP value greater than 1.0 plays a key role in classification ,43. In tHierarchical cluster analysis revealed the distribution of 32 key differential compounds among spring tea, summer tea and autumn tea. In Differences in the content and composition of volatile compounds result in different types of tea aroma. In our study, 32 key compounds were identified based on multivariate statistical analysis. Geraniol (Sweet), β−ionone , jasmone , and 1−octen−3−ol  had higher content in spring tea, which might be key source of the chestnut−like characteristic of spring tea ,44,45. PThe aroma quality is affected by the harvest season, cultivar, origin, manufacturing process ,49, of tSeason is an important factor affecting the aroma of tea. In this study, according to the sensory evaluation results, spring tea, summer tea, and autumn tea showed chestnut−like, clean, and floral aroma, respectively, and the aroma score was ranked as spring tea > summer tea > autumn tea. 32 key compounds were identified. Among them, 18 volatile compounds including geranylacetone, phenethyl alcohol, geraniol, β−ionone, jasmone, 1−octen−3−ol, longifolene were the key compounds in spring tea; 13 volatile compounds including 2−methylfuran, indole, 1−octanol, D−limonene, ethanethiol were key compounds in summer tea; the key component of autumn tea was 2,4,6−trimethylstyrene. This study enriched the aroma theory of green tea from the high latitude region and provided scientific basis for quality control of green tea production.


ACTN1, ACTA2, and PALLD) were highly expressed in RCM CFs, whereas several tubulin genes  were down-regulated. These results implies that the signaling pathways associated with cytoskeletal elements alter the rheological properties of RCM CFs, particularly those related to CF–cardiomyocyte interactions, thereby leading to diastolic cardiac dysfunction in RCM.Restrictive cardiomyopathy (RCM) is a rare disease characterized by increased ventricular stiffness and preserved ventricular contraction. Various sarcomere gene variants are known to cause RCM; however, more than a half of patients do not harbor such pathogenic variants. We recently demonstrated that cardiac fibroblasts (CFs) play important roles in inhibiting the diastolic function of cardiomyocytes via humoral factors and direct cell–cell contact regardless of sarcomere gene mutations. However, the mechanical properties of CFs that are crucial for intercellular communication and the cardiomyocyte microenvironment remain less understood. In this study, we evaluated the rheological properties of CFs derived from pediatric patients with RCM and healthy control CFs via atomic force microscopy. Then, we estimated the cellular modulus scale factor related to the cell stiffness, fluidity, and Newtonian viscosity of single cells based on the single power-law rheology model and analyzed the comprehensive gene expression profiles via RNA-sequencing. RCM-derived CFs showed significantly higher stiffness and viscosity and lower fluidity compared to healthy control CFs. Furthermore, RNA-sequencing revealed that the signaling pathways associated with cytoskeleton elements were affected in RCM CFs; specifically, cytoskeletal actin-associated genes ( Restrictive cardiomyopathy (RCM) is characterized by normal ventricular wall thickness and motion with low ventricular extensibility, leading to diastolic dysfunction. Pediatric RCM has a very poor prognosis and a 2-year transplant free survival rate of ~40% , 2. In aThe heart is mainly composed of cardiomyocytes and cardiac fibroblasts (CFs), both of which closely interact via direct cell–cell adhesion, extracellular matrix (ECM), and paracrine factors. Previous studies demonstrate that both cardiomyocytes and CFs play an important role in cardiac function maintenance and development , 9. In aThree independent primary culture lines of patient-derived CFs  were obtained at ventricular assist device implantation or heart transplantation, as previously described . Then, tGenomic DNA was extracted from patients’ peripheral blood samples, and custom-targeted gene enrichment and DNA library preparation were performed using a Nextera Capture Custom Enrichment kit . DNA samples were analyzed via targeted next-generation sequencing of 257 genes related to cardiomyopathies, and sequenced using the Illumina MiSeq platform, generating approximately two million 150-bp paired-end reads for each sample (Q30 ≥ 90%), as previously described . GeneticWe used a customized AFM attached to an upright optical microscope  similar to that reported previously , 17, 18.f = 61, 83, 177, 203, 263, 427, 563, and 611 Hz, and an amplitude of 6 nm using a piezoactuator. The amplitude and phase shift of cantilever displacement with respect to the reference modulation signal at each frequency were acquired using a multiple lock-in amplifier based on a LabVIEW-FPGA program  and set the temperature at 30°C to reduce the evaporation of the culture medium and minimize the change in the chemical composition of the medium. We confirmed that the mean value of the complex shear modulus G*of cells exhibited no remarkable change over the duration of AFM experiments (~1 h).In the frequency-domain AFM rheological measurement, the sample stage approached the cantilever at a constant speed until it reached the maximum loading force of 500 pN, and the cantilever was vibrated with a sinusoidal modulation signal with multiple frequencies, such as G*, we used the Sneddon’s model [F*  is the loading force with a small amplitude indentation oscillation, δ1*, around an operating indentation, δ0, and E0 is the Young’s modulus at zero frequency obtained from the approach force curve [θ is the half-opening angle of the conical indenter, which was set to 17.5° [v to be 0.5. The frequency-dependent Young’s modulus E1* is given by 2(1+ν)G*. Eliminating the hydrodynamic drag force Fd* given by Fd*/δ1* = ib(0)f, where i is the imaginary unit, and b(h) is a viscous drag factor that depends on the separation distance h between the cell surface and the probe [G* of the cells as follows:G’ and G” represent the storage and loss moduli of the cell, respectively, and F1* = 4tanθ · E1*δ0δ1*/{π(1 − v2)}. The b(0) value was determined by extrapolating the b (h) values measured at various separation distances at f = 100 Hz.In order to estimate ’s model , approxice curve , 23–25. to 17.5° . We assuhe probe , we obtaG’ and G” as a function of f were fitted to the power-law structural damping model with additional Newtonian viscosity [α is the power-law exponent showing between 0 for solid and 1 for liquid. g(α) is Γ(1 − α) cos(πα/2), with Γ denoting the gamma function. G0 is the scale factor of the modulus at a frequency scale factor f0, which was arbitrarily set to 1 Hz. The hysteresivity η(α) is equivalent to tan(πα/2) and μ is the Newtonian viscous damping coefficient. It is reasonable to assume that the Newtonian viscous damping coefficient of living cells should not be a negative value, thus in our analysis, we defined a constraint that μ is kept at not having a negative value.AFM data were analyzed using Igor Pro software . For each cell, iscosity , 27–29 gt-test.Total RNA was extracted from each CF line and sequenced using the Illumina HiSeq 2500 platform, as previously described . Then, sCells were fixed using 4% paraformaldehyde/phosphate-buffered saline (PBS), and were then permeabilized by 0.1% Triton-X/PBS for 10 min. For staining actin filaments and microtubules, cells were incubated overnight at 4°C with Alexa Fluor 488-conjugated phalloidin  and anti-tubulin primary antibody , followed by blocking with 1% bovine serum albumin. Alexa Fluor 532 secondary antibody  was applied at room temperature for 30 min, then nuclei were stained with Hoechst33342 . The stained cell samples were imaged using a laser scanning confocal microscope .This study was approved by the Research Ethics Committee of Osaka University (nos. 15211 and 442). Written informed consent was obtained from the parents of the minors included in this study before obtaining heart specimens and blood samples.t-test was performed to compare three individuals of RCM group and three individuals of healthy control. Upon confirming normal distribution by Shapiro-Wilk test, unpaired two-tailed t-test was performed to compare AFM data from all cells between RCM and healthy control groups. Otherwise, Mann-Whitney U test was performed. Statistical significance was set at P < 0.05.The statistical analyses were performed using JMP Pro 14 software. Unpaired two-tailed TNNI3 missense variants, which were reported to cause RCM, and no pathological variants were observed in RCM 3. Left ventricle end diastolic pressure (LVEDP) was elevated in all RCM patients. Furthermore, histological analysis of cardiac fibrosis revealed no significant difference in the fibrotic area, as previously described [The summary of patients’ clinical profiles is presented in escribed .G’ and G” of all CFs derived from three patients with RCM and three healthy controls, respectively, as a function of frequency, measured by AFM. The standard deviation represents the cell-to-cell variation of G* at frequencies in the same group. The frequency dependence of G* in both RCM and healthy CFs followed the single power-law rheology (PLR) model described in G0, α, and μ, which are the cell modulus scale factor, power-law exponent, and Newtonian viscous damping coefficient, respectively. Studies have reported that the number (ensemble) distribution of complex modulus G* in single cells exhibits a log-normal distribution [G0 and α of cells show log-normal and normal (Gaussian) distributions, respectively [G0 in a logarithmic scale and α in a linear scale. We confirmed that Shapiro-Wilk test showed that logG0 and α of all cells measured by AFM were normally distributed whereas μ was not normal.For each cell measured by AFM, we quantified the power-law rheological parameters such as ribution , 30, 31,ectively , 32–35. G0, α, and μ between three individuals in RCM and healthy CFs with unpaired t-test. As a result, the G0, which is related to cell stiffness [P = 0.0153; α was significantly decreased in RCM CFs compared to those in healthy CFs  while α was significantly lower in RCM CFs (P<0.001), as compared to those in healthy CFs.All of the data acquired from AFM are provided in ACTN1, ACTA2, and PALLD) were significantly upregulated in RCM CFs, whereas that of several tubulin genes  were significantly down-regulated  the power-law exponent, α, and (C) the Newtonian viscous damping coefficient, μ, in CFs derived from patients with restrictive cardiomyopathy  and healthy controls . *P<0.001 by unpaired two-tailed t-test and **P<0.001 by Mann-Whitney U test.The dots represent all data of (A) the cell modulus scale factor, (PDF)Click here for additional data file.


There were significantly (p < 0.05) higher concentrations of dry matter, ether extract, hemicellulose, and total carbohydrate in corn distiller’s grain than in the other three types of distiller’s grain. In addition, red distiller’s grain showed a higher (p < 0.05) gas production rate constant (c) and ruminal outflow rate, as well as higher (p < 0.05) concentrations of total phenol, total anthocyanins and 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity, than the other three types of distiller’s grains. In contrast, red distiller’s grain displayed the lowest (p < 0.05) immediately soluble fraction (a) and half the time of maximum gas production relative to the other samples. In particular, the levels of methane (%) in white distiller’s grain and glutinous rice distiller’s grain were greater (p < 0.05) than that in red distiller’s grain. Moreover, the ammonia nitrogen content in red distiller’s grain was greater (p < 0.05) than that in white distiller’s grain and corn distiller’s grain. In contrast, red distiller’s grain exhibited a lower (p < 0.05) level of ruminal fluid acetic acid relative to that found in white distiller’s grain and corn distiller’s grain. Taken together, the results showed that red distiller’s grain and glutinous rice distiller’s grain could be used as protein feed, red distiller’s grain had higher levels of total phenols and total anthocyanins and a high DPPH scavenging activity; corn distiller’s grain might be considered as an alternative energy source feed, and white distiller’s grain exhibited higher total gas production.Distiller’s grain is rich in natural active ingredients and can be used as an excellent antioxidant feed for goats. The current study aimed to assess the feeding value of four different types of distiller’s grains with an in vitro gas production trial. The chemical composition, total phenols, total anthocyanins, dry matter degradability, methane, hydrogen, and rumen fermentation parameters were evaluated. The results indicated that red distiller’s grain and glutinous rice distiller’s grain had higher ( Currently, the development of new unconventional functional feed is a research hotspot because the results can provide references and lessons for exploiting and utilizing regeneration feed. Improving the utilization and transformation ratio from grain to livestock products, broadening the available feed resources, and developing unconventional feeds are urgently required . With thThe production of methane is the main factor in fermentation energy loss in ruminants, and it undoubtedly contributes to the global greenhouse effect. Reducing methane production is very helpful in improving the energy efficiency of the diet and protecting the environment. Accordingly, it is necessary to find an effective way to decrease methane production in ruminant animals . One stuDistiller’s grain is a byproduct of the liquor industry that is favoured by farmers because of its low production cost and high nutritional value . PreviouThe samples consisted of white distiller’s grain , red distiller’s grain , glutinous rice distiller’s grain , and corn distiller’s grain . All the samples were dried at 65 °C in a vacuum oven for 72 h and were ground and passed through a 1 mm sieve prior to the experiment for further analysis.The dry matter (DM), crude protein (CP), ether extract (EE), and ash of distiller’s grains were detected using the method described by the Association of Official Analytical Chemists . Neutralg for 10 min at 4 °C . The sample extract was analysed using the Folin–Ciocalteu method, and the optical density (OD) value was assayed at 725 nm using a microplate reader . Finally, the amount of total phenols was calculated through the standard curve and expressed values on a DM basis (x%).The total phenolic content of distiller’s grain was analyzed as per Slinkard and Singleton . BrieflyThe total anthocyanin content was determined according to the pH-differential method as described by Yang and Zhai . BrieflyThe 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity was detected according to the method of Tian et al. . BrieflyCH4) and k were calculated according to Wang et al. [H2) and k were calculated according to Wang et al. [The design concept, detailed components, and operation methods of anaerobic fermentation equipment in vitro were those used by Wang et al. . Brieflyg et al. ; hydrogeg et al. .Two Xiangdong black goats equipped with a rumen cannula were used to provide ruminal fluid. The feed was prepared according to National Research Council guidelines  with a cg and 4 °C for 10 min, and then 1 mL of supernatant was mixed with 0.1 mL of 25% meta-phosphoric acid for future analysis of ammonia nitrogen and volatile fatty acids. The remaining samples were filtered through a piece of 300 mesh nylon gauze, placed in an aluminium box, and dried at 105 °C until reaching a constant weight; the dry matter degradability (DMD) was then calculated.The rumen fermentation was stopped at 48 h, and then the pH was immediately determined using a pH meter . Next, 2 mL of rumen fermentation solution was centrifuged at 12,000× Ammonia nitrogen was assayed according to the method of Weatherburn . Briefly2 as eluent for the flame ionization detector; the injection temperature was 250 °C and the split ratio was 50:1, with an injection volume of 1 μL; the temperature program comprised an initial temperature of 60 °C for 2 min, 20 °C/min increase to 220 °C, and maintenance at this temperature for 1 min.The volatile fatty acids were analysed via GC, and the peak area of individual volatile fatty acids was detected. We then calculated the concentration according to the standard curve. The GC conditions were as follows: separation of volatile fatty acids was performed using a DB-FFAP column (30 m × 250 μm × 0.25 μm), the carrier gas was nitrogen gas, and the flow rate was 0.8 mL/min; 30 mL of hydrogen and 350 mL of oxygen gas were as combustion gas and 40 mL of Nijn = μ + Si + Tj + (S*T)ij + εijn, where Yijn is the observation, μ is the overall mean, Si is the effect of distiller’s grain, Tj is the effect of incubation time, (S*T)ij is the effect of interaction between distiller’s grain and incubation time, and εijn is the random error with mean 0 and variance σ2. The significant difference level was set at p < 0.05.All observations were analysed using SAS System Version 9.1.3 . The anaerobic bottle was considered as the experimental unit. There were 3 replicates in each distiller’s grain. Data on chemical composition, gas production kinetics, DMD, and rumen fermentation parameters were analysed by one-way analysis of variance (ANOVA). Data on the total gas production, methane, and hydrogen were noted according to a two-factorial ANOVA model: Yp < 0.05) level of ADF relative to the other distiller’s grains. Red distiller’s grain showed the highest (p < 0.05) OM relative to the other samples. Glutinous rice distiller’s grain had a significantly (p < 0.05) higher content of CP, whereas it displayed a lower (p < 0.05) CHO content compared to the other three samples. There were significantly (p < 0.05) higher concentrations of DM, EE, hemicellulose, and CHO in corn distiller’s grain than in the other three distiller’s grain samples.As shown in p < 0.05) greater than those of the other three distiller’s grains (p < 0.05) higher than that in white distiller’s grain, whereas it did not differ significantly (p > 0.05) from that found in corn distiller’s grain. Similarly, the DPPH scavenging activity in red distiller’s grain was significantly (p < 0.05) higher than that in the other samples.The levels of bioactive substances for total phenols and total anthocyanins of red distiller’s grain were significantly (s grains . Moreovep < 0.05) total gas production of all the samples (p < 0.05) influenced by the substrate and incubation time. Additionally, white distiller’s grain showed significantly higher (p < 0.05) levels of gas production from the immediately soluble fraction (a) and IFRD, whereas it had significantly lower (p < 0.05) values of ruminal outflow rate (k) and DMD relative to the other samples (p < 0.05) gas production rate constant (c) and k, as well as the lowest (p < 0.05) immediately soluble fraction (a) and HMGP among all the samples. The gas production from the insoluble fraction (b) and potential extent of gas production (a+b) of glutinous rice distiller’s grain were significantly (p < 0.05) lower than those of other substrates. Furthermore, the b, a+b, HMGP, and DMD of corn distiller’s grain were greater (p < 0.05) than those of white distiller’s grain, red distiller’s grain, and glutinous rice distiller’s grain.Corn distiller’s grain showed the highest ( samples . The tot samples . Red disp < 0.05) up to 10 h of incubation time and then dropped throughout the experiment. In addition, the level of hydrogen production (mL/g) increased (p < 0.05) over the whole experimental period, and the hydrogen production (mL/g) in white distiller’s grain was greater (p < 0.05) than that of the other samples.As shown in p > 0.05), whereas they were greater (p < 0.05) than those of red distiller’s grain and corn distiller’s grain (p < 0.05) values of k than the other two samples. Corn distiller’s grain showed the highest (p < 0.05) gas volume of methane (VCH4) and VfCH4 among all four samples. For hydrogen, no significant difference (p > 0.05) was observed in hydrogen (%) among all distiller’s grains. Corn distiller’s grain displayed significantly (p < 0.05) higher levels of gas volume of hydrogen (VH2) and VfH2 than were found in the other distiller’s grains.The levels of methane (%) of white distiller’s grain and glutinous rice distiller’s grain did not differ (’s grain . In contp < 0.05) pH value relative to the other distiller’s grains (p < 0.05) than that of the white distiller’s grain and corn distiller’s grain. White distiller’s grain had a higher (p < 0.05) percentage of acetic acid relative to the other distiller’s grains. In contrast, the propionic acid content in corn distiller’s grain was significantly higher (p < 0.05) than that in the other distiller’s grains. However, corn distiller’s grain showed a lower (p < 0.05) ratio of acetic acid to propionic acid. In addition, glutinous rice distiller’s grain had higher (p < 0.05) levels of butyric acid, isobutyrate, valerate, and isovalerate than the other three distiller’s grains.Glutinous rice distiller’s grain displayed a higher  corn distiller’s grain showed a relatively high CHO concentration and might be considered an alternative energy source feed; (2) red distiller’s grain has an abundance of total phenols and total anthocyanins and shows a high level of DPPH scavenging activity, which could reduce methane production; (3) glutinous rice distiller’s grain had a relatively high content of CP and could be used as a protein feedstuff; and (4) white distiller’s grain was superior to other distiller’s grains in terms of improving gas production. Further studies are needed to understand the mechanism of active constituents of active substances in distiller’s grain inhibiting rumen methane with in vivo experiments.


TARDBP and RBFOX1, and their associated genes are involved in synaptic function. Our data is shared through an online resource F3UTER (https://astx.shinyapps.io/F3UTER/). Overall, our data improves 3’UTR annotation and provides additional insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.There is growing evidence for the importance of 3’ untranslated region (3’UTR) dependent regulatory processes. However, our current human 3’UTR catalogue is incomplete. Here, we develop a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3’UTRs. We identify unannotated 3’UTRs associated with 1,563 genes across 39 human tissues, with the greatest abundance found in the brain. These unannotated 3’UTRs are significantly enriched for RNA binding protein (RBP) motifs and exhibit high human lineage-specificity. We find that brain-specific unannotated 3’UTRs are enriched for the binding motifs of important neuronal RBPs such as  3’ untranslated regions (3’UTRs) play a crucial role in regulating gene expression, but our 3’UTR catalogue is incomplete. Here, the authors develop a machine learning-based framework to predict previously unannotated 3’UTRs in 39 human tissues. There is evidence to suggest that these RNA-based regulatory processes may be particularly important in large, polarised cells such as neurons. Recent studies have shown that transcripts which are highly expressed in neurons have both significantly longer 3’UTRs and higher 3’UTR diversity4. Furthermore, it has been shown that thousands of mRNA transcripts localise within subcellular compartments of neurons and undergo regulated local translation, allowing neurons to rapidly react to local extracellular stimuli7. Thus, there has been growing interest in the impact of 3’UTR usage on neuronal function in health and disease.The 3’UTRs of protein-coding messenger RNAs (mRNAs) play a crucial role in regulating gene expression at the post-transcriptional level. They do so by providing binding sites for 11, there is evidence to suggest that our current catalogue is incomplete14. Large-scale 3’end RNA-sequencing (RNA-seq) has identified a large number of novel polyadenylation (poly(A)) sites, many of which are located outside of annotated exons13. These insights are complemented by increasing recognition of the functional importance of transcriptional activity outside of known exons, particularly in human brain tissues17. This raises the possibility of developing new approaches for 3’UTR identification seeded from short-read RNA-seq data, but this is challenging, as evidenced by the shortcomings of transcript assembly tools to accurately annotate 3’UTRs20 and the subsequent development of alternative polyadenylation profiling tools21. The latter also has limitations, including the dependence on a priori gene annotations  or assembled transcripts  and the focus on identifying novel poly(A) sites within or in the vicinity of annotated 3’UTRs. This hinders their application to other transcriptionally active intergenic regions and so to the identification of unannotated 3’UTRs. Deep learning-based methods to predict poly(A) sites directly from DNA sequences e.g., APARENT28, DeepPASTA29, OmniPolyA30 and Leung et al.31, can alleviate this problem, but these tools do not incorporate transcriptomic data and only work on sequences of 206 nucleotides in length (with APARENT as an exception), which makes them unsuitable to identify tissue-specific and full-length unannotated 3’UTRs.However, despite ongoing efforts to identify and characterise 3’UTRs in the human genomehttps://astx.shinyapps.io/F3UTER/), which can be queried to visualise unannotated 3’UTR predictions and the omic features used to predict them.In this study, we present a machine learning-based framework, named F3UTER, which leverages both genomic and tissue-specific transcriptomic features to identify unannotated 3’UTRs. We apply F3UTER to RNA-seq data from Genotype-Tissue Expression Consortium (GTEx) to predict hundreds of unannotated 3’UTRs across a wide range of human tissues, with the highest prevalence discovered in the brain. We provide evidence to suggest that these unannotated 3’UTR sequences are functionally significant and have higher human lineage specificity than expected by chance. More specifically, we found brain-specific unannotated 3’UTRs were enriched for genes involved in synaptic function and interact with neuronal RBPs implicated in neurodegenerative and neuropsychiatric disorders. We release our data in an online platform, F3UTER  as previously reported by Zhang and colleagues15. These ERs were identified through annotation-independent expression analysis of RNA-seq data generated by GTEx with ER calling performed separately for 39 human tissues, including 11 non-redundant human brain regions. We focused on the subset of ERs most likely to be 3’UTRs, namely intergenic ERs which lie within 10 kb of a protein-coding gene ('Methods'). We found that these intergenic ERs were significantly higher in number  Fig. , suggest35, only some intergenic ERs are likely to be generated by unannotated 3’UTRs. This prompted us to develop a method to distinguish 3’UTRs from other transcribed genomic elements (non-3’UTRs) using short-read RNA-seq data. To achieve this aim, we first constructed a training set of known 3’UTRs (positive examples) and non-3’UTRs (negative examples) from Ensembl human genome annotation (v94). We obtained 17,719 3’UTRs and a total of 162,249 non-3’UTRs, consisting of five genomic classes: 21,798 5’UTRs, 130,768 internal coding exons (ICE), 3718 long non-coding RNAs (lncRNAs), 3819 non-coding RNAs (ncRNAs) and 2146 pseudogenes (Methods). For each of the positive and negative examples, we constructed a set of 41 informative omic features, which were broadly categorised as either genomic or transcriptomic in nature. Features calculated from genomic data included poly(A) signal (PAS) occurrence, DNA sequence conservation, mono-/di-nucleotide frequency, transposon occurrence and DNA structural properties. Features calculated from transcriptomic data included entropy efficiency of the mapped reads , and percentage difference  between the reads mapped at the boundaries ('Methods'). To gain a better understanding of these features, we performed a univariate analysis to individually inspect the relationship between each feature and the genomic classes in our training dataset . Overall, while the genomic and transcriptomic features used had overlapping distributions amongst some genomic classes, each feature was significantly different when compared across all the genomic classes Finding 3’ Untranslated Expressed Regions  and calculated metrics such as accuracy, sensitivity, specificity, kappa, area under the ROC curve (AUROC) and area under the precision-recall curve (AUPRC). F3UTER achieved a mean accuracy of 0.96, sensitivity of 0.92, specificity of 0.96, kappa of 0.78, AUROC of 0.98 Fig.  and AUPRMus musculus), fruit fly (Drosophila melanogaster) and zebrafish (Danio rerio). For each species, known 3’UTR and known non-3’UTR regions from Ensembl were used as the validation data for F3UTER (Methods and Supplementary Table M. musculus and D. rerio) and midgut (for D. melanogaster)  and D. melanogaster , as these species are far apart from humans on the evolutionary tree. Overall, this analysis demonstrates that F3UTER can accurately classify 3’UTRs in M. musculus, and can potentially be used on data from other non-human species.We next investigated whether F3UTER, a model trained on features derived from the human genome, can classify 3’UTRs in non-human species. For this analysis, we selected three commonly used model organisms: mouse (38 consisting of both RNA-seq data and paired 3’-seq in four types of B cells (namely CD5 + B cells (CD5), germinal center B cells (GCB), memory B cells (MB) and naive B cells (NB); Supplementary Table We evaluated the performance of F3UTER using an independent dataset from Singh et al.15. Then, we used F3UTER to predict unannotated 3’UTRs in these ER datasets, and compared these predictions to intergenic poly(A) clusters detected using paired 3’-seq  sites within the ERs ('Methods'). If at least one of the predicted poly(A) site contained in an ER was within 30 nucleotides (nt) of a poly(A) cluster in the corresponding 3’-end data, it was considered a true positive. Whereas if an ER without any predicted poly(A) site overlapped a poly(A) cluster, it was considered a false negative. On average across the four cell types, APARENT, GETUTR and TAPAS achieved a PPV of 25%, 15% and 10%, and a FOR of 14%, 16% and 14%, respectively  sites, namely APARENT15 in 39 tissues using RNA-seq data provided by GTEx. Similar to the ER datasets produced from Singh et al. data, we focused on confident 3’UTR predictions with a prediction probability of >0.6  of our unannotated 3’UTRs (covering 188 kb of genomic space and associated with 99 distinct genes) were previously detected by Miura et al. in the corresponding tissues . An unannotated 3’UTR was categorised as annotated if at least 50% of the unannotated 3’UTR overlapped with an annotated 3’UTR from the reference annotations. Of the 7528 predicted unannotated 3’UTRs across the 39 tissues, 1060  were annotated in at least one of the reference annotations . Consistent with previous reports demonstrating higher RBP-binding densities in known 3’UTRs relative to other genomic regions42, we found that 3’UTR predictions were enriched for RBP-binding motifs compared to non-3’UTR predictions , highly brain-specific (n = 1571), shared (n = 2951) and ambiguous (n = 2698)  to obtain more granular information, we found that genes associated with unannotated 3’UTRs were more significantly enriched for biological processes in the postsynapse  atlas. Other similar examples included the genes, C19orf12, SCN2A, RTN2 and OPA1  Fig. . Moreoveentclass1pt{minimaAPP Fig.  and onlyTARDBP, an RNA binding protein implicated in both frontotemporal dementia and amyotrophic lateral sclerosis47. Of the 80 gene targets that we identified for TARDBP through unannotated 3’UTRs, up to 41 had a TARDBP-binding site in their existing 3’UTR annotations, based on either computational scanning of TARDBP motif . By using shared 3’UTR predictions as the negative control, we removed RBPs associated with non-brain tissues and so identified RBP binding of greatest relevance to human brain function. This analysis identified eight RBPs with significantly enriched binding in the brain-specific unannotated 3’UTRs  and demonstrate that F3UTER can be successfully applied to human genomic regions from any tissue with existing bulk RNA-seq data. In fact, even though intergenic ERs in the four types of B cells were generated using ≤4 samples, we were able to validate 35% of the unannotated 3’UTR predictions using 3’-end sequencing data, showing that F3UTER can be a useful tool even for small RNA-seq datasets. Furthermore, it should be noted that F3UTER does not depend on ER datasets as input, but instead any set of interesting human genomic regions can be used. We note that F3UTER can also be applied to data from the mouse genome, which is evident from its high accuracy to classify known 3’UTRs in mice. Given the continued popularity and high availability of short-read RNA-seq data across tissues, cell types and disease states, we believe that (1) F3UTER could be applied more broadly to predict unannotated 3’UTRs and improve our understanding of 3’UTR diversity and usage, and (2) the set of omic features devised within this study could form the basis for other predictive models aimed at increasing the accuracy of human transcriptomic annotation.We focus on F3UTER-predicted 3’UTRs in the human brain, which we find to be most prevalent when comparing predictions across all 39 human tissues. We believe that the higher frequency of incomplete 3’UTR annotation in human brain could be attributed to several factors including: (1) higher transcript diversity with many rare isoforms expressed in this tissue; (2) high cellular heterogeneity complicating detection of tissue-/cell-type-specific transcripts; (3) historically lower availability of human brain samples; and (4) reliance on post-mortem tissues, which suffer from RNA degradation resulting in decreased accuracy of transcript identification.TARDBP and RBFOX1. Furthermore, genes linked to unannotated brain-specific 3’UTRs were significantly enriched for those involved in synaptic function, and although not a significant enrichment, some were already associated with rare neurogenetic and adult-onset neurodegenerative disorders.While we find that collectively the unannotated 3’UTRs predicted by F3UTER were significantly enriched for RBP binding and exhibited high human lineage specificity, the latter was primarily driven by brain-specific 3’UTR predictions. Overall, these findings suggest that predicted 3’UTRs are likely to be functionally important in the human genome. Moreover, these findings provide some explanation for the difficulties of identifying 3’UTRs through cross-species analyses particularly when considering brain-specific transcripts. Interestingly, we find that brain-specific unannotated 3’UTRs were enriched for binding of RBPs already implicated in neurological disorders, such as 51. Although increasing the use of single-nuclei RNA-seq, together with long-read RNA-seq will provide further insights into alternative 3’UTR usage and will impact the field considerably, these technologies still have significant limitations for the identification of rare transcripts. Therefore, we believe that F3UTER, which can effectively utilise existing short-read RNA-seq datasets, will be of interest to a wide range of researchers. Furthermore, we release our results through an online resource (F3UTER: https://astx.shinyapps.io/F3UTER/) which allows users to both easily query unannotated 3’UTRs and inspect the omic features driving the classifier’s prediction for an ER of interest.Taken together, our results demonstrate that F3UTER not only improved 3’UTR annotation, but also identified unannotated 3’UTRs in the human brain which provided additional insights into the mRNA–RBP interactome with implications for our understanding of neurological and neurodevelopmental diseases. With this in mind, we note the growing interest in the role of 3’UTR-based mechanisms in translational regulation within complex, large, polarised cell types such as neurons15 in 39 GTEx (v6) tissues, comprising of 11 non-redundant brain tissues and 28 non-brain tissues . Each ER was associated to the gene which connected to the ER via a junction read. In cases where no junction read was present, the nearest expressed gene (rpkm > 0.1) in the corresponding tissue was assigned to the ER. From this dataset, we selected intergenic ERs which were associated with protein-coding genes and were located within 10 kb of their associated genes, resulting in 240,529 ERs. Based on the location of intergenic ERs with respect to their associated genes, i.e., whether upstream or downstream, we annotated their orientation as 5’  or 3’  respectively. The total genomic space covered by these intergenic ERs was calculated by adding the length of all ERs in each tissue. To further remove ERs which were unlikely to be 3’UTRs, we selected 3’ intergenic ERs with a length ≤2 kb—which is the third quartile limit of known 3’UTR exon lengths. We also removed short ERs with length ≤40 nt for which feature calculation can be problematic. This resulted in a set of 94,922 intergenic 3’ ERs across all 39 tissues, and this set was used as input to F3UTER. Since the GTEx RNA-seq data used was un-stranded, the detected intergenic ERs had no strand information, and therefore, strand attribute was not used for orientation and filtering of intergenic ERs.We collected the set of intergenic ERs identified by Zhang and colleaguesIn this 3’ intergenic ER dataset, 5% of the ERs were connected to genes via junction reads, with a median distance of 2.5 kb between them. The remaining ERs were associated to genes based on proximity, i.e., the nearest expressed gene in the corresponding tissue, with a median distance of 2 kb between them. Overall, in the complete dataset , ERs were located at a median distance of 2 kb from their associated genes, with the majority of them (62%) located within 3 kb. We associated all ERs to a total of 5716 distinct genes across the 39 GTEx tissues, of which 834 genes (15%) were associated with at least one ER via junction reads. Amongst the ER-gene associations based on proximity, 93% of the nearest expressed genes were also the nearest gene. We found this percentage to be the same in the complete dataset. We identified that 446 genes associated with ERs via proximity  overlapped another protein-coding gene on the same strand. Based on this we estimated that only 9% of the total ER-gene associations could be affected by overlapping genes.For positive examples, we used known 3’UTRs, while for negative examples, we used regions in the genome which are known to be non-3’UTRs, namely 5’UTRs, internal coding exons (ICEs), lncRNAs, ncRNAs and pseudogenes. Ensembl human genome annotation (v94 GTF) was used to extract the genomic coordinates of these different genomic classes. For all classes in our training dataset, firstly, we selected high confidence annotations at the transcript level with transcript support level (TSL) = 1. Secondly, we collapsed and combined multiple transcripts associated with a single gene to make a consensus 'meta-transcript' per gene. This merged all the overlapping regions emerging from the same gene. Finally, we extracted exons with width >= 40 (nt) from these meta-transcripts to serve as learning examples.lncRNA: “non_coding”, “3prime_overlapping_ncRNA”, “antisense”, “lincRNA”, “sense_intronic”, “sense_overlapping”, “macro_lncRNA”ncRNA: “miRNA”, “misc_RNA”, “rRNA”, “snRNA”, “snoRNA”, “vaultRNA”pseudogene: “pseudogene”, “processed_pseudogene”, “unprocessed_pseudogene”, “transcribed_processed_pseudogene”, “transcribed_unitary_pseudogene”, “transcribed_unprocessed_pseudogene”, “translated_processed_pseudogene”, “unitary_pseudogene”, “unprocessed_pseudogene”, “TR_V_pseudogene”, “TR_J_pseudogene”, “rRNA_pseudogene”, “polymorphic_pseudogene”, “IG_V_pseudogene”, “IG_pseudogene”, “IG_J_pseudogene”, “IG_C_pseudogene”To capture regions of 3’UTR exons, 5’UTR exons and ICEs, transcripts from protein-coding genes were selected. For ICE examples, transcripts with at least three coding exons were further selected  and their first and last coding exons were removed to capture ICEs. To capture lncRNA, ncRNA and pseudogene exons, we selected annotations from the GTF file with the following gene biotypes:For each region in the training dataset, we calculated several genomic and transcriptomic-based features. Transcriptomic features were used to account for tissue-specific properties of transcribed elements in the genome.Poly(A) signals : Previous studies have shown that 3’UTR sequences of most mammalian genes contain the consensus AAUAAA motif (or a close variant) 10–30 nt upstream of the poly(A) site8. These motif sites are recognised and bound by the cleavage and polyadenylation specificity factor (CPSF), and are referred to as polyadenylation signals (PASs). PASs are an important characteristic of 3’UTRs and are involved in the regulation of the polyadenylation process8. We used 12 commonly occurring PASs 53 to construct a consensus position weight matrix (PWM). Each region was scanned for potential PWM matches and a binary outcome was reported i.e., whether the region contains a potential PAS or not. The “searchSeq” function (with min.score= “95%”) from the R package “TFBSTools” (v1.24.0)54 was used to detect PWM matches.Mono- and di-nucleotide frequency (n = 20): The sequence composition in 3’UTRs, especially near the poly(A) sites has been shown to be important for polyadenylation52. The frequency probability of each mono-nucleotide  and di-nucleotide pair  was calculated as the number of nucleotide occurrences divided by the length of the region.DNA sequence conservation (n = 1): Sequences of non-protein-coding transcripts and untranslated regions are poorly conserved compared to protein-coding sequences56. For every genomic position, we extracted the phastCons score of the human genome (hg38) across seven species pre-computed by the UCSC genome browser, and averaged it across the region to calculate mean sequence conservation score for each region.Transposons (n = 1): Previous studies have revealed that transposons are highly enriched within lncRNAs compared to protein-coding genes and other non-coding elements58. These transposable elements are considered to be the functional domains of lncRNAs. We calculated the total fraction of region covered with transposons—LINEs, SINEs, LTRs, DNA and RC transposons. The hg38 genomic coordinates of the transposable elements (Dfam v2.0) were downloaded from http://www.repeatmasker.org/species/hg.html.DNA structural properties (n = 16): The underlying sequence composition of a DNA molecule plays an important role in determining its structure. As a result, similar DNA sequences have a tendency to have similar DNA structures59. We calculated 16 properties of DNA structures which can be predicted from a nucleotide sequence based on previous experiments. To quantitatively measure a structural property from a nucleotide sequence, we used pre-compiled conversion tables downloaded from http://bioinformatics.psb.ugent.be/webtools/ep3/?conversion60. Depending on the structural property, we extracted scores for each di-nucleotide or tri-nucleotide occurrence in the sequence from the conversion tables, and averaged the scores across the region.Entropy efficiency (n = 1): We measured the uniformity of read coverage across a region using entropy efficiency, as described in Gruber et al.36. The entropy efficiency (EE) of a region (x) was calculated as, Percentage difference (n = 1): We calculated the percentage difference (PD) between the read counts at the boundaries of a region. For read counts r1 and r2 measured at the boundaries of a region x, PD was calculated as: Z-test for continuous and categorical variables, respectively, to identify features with significant differences across all the genomic classes. We used UMAP37 to visualise all the features in two-dimensional space. The UMAP analysis was performed using the R package “umap” (v0.2.7.0) with default parameters. The clusters were visualised as a 2D density and a scatter plot. Each data point was labelled and coloured according to its genomic class.For univariate analysis, we performed non-parametric Kruskal–Wallis test and two-sided proportion n = 179,968), and columns represented the quantified features (n = 41). The features were scaled and centred in R using the preProcess function of R 'Caret' package (v6.0-85)61. The elastic net multinomial logistic regression model was trained using the 'glmnet' R package (v4.0-2)62 with the following parameters: family =  'multinomial', alpha = 0.5, nlambda = 25 and maxit = 10,000. The random forest multinomial classifier was trained within Caret using the “randomForest” package (v4.6-14)63 with default parameters . We performed a fivefold cross-validation (repeated 20 times) to evaluate the performance of these multinomial classifiers, where the model was trained on 80% of the data (training dataset) and tested on 20% of the remaining data . Downsampling of the data was employed to correct for imbalance in the sample size of the classes. For each cross-validation run, we produced a confusion matrix for each prediction class using the Caret’s confusionMatrix function and computed the false-positive and negative rates. In addition, we report model’s overall Cohen’s kappa, which estimates the accuracy of a model compared to the expected accuracy and is a more accurate measure of performance for imbalanced datasets. These metrics were averaged across all the cross-validation runs for reporting purposes. With the elastic net logistic regression model, we found that known 3’UTRs were most likely to be misclassified as lncRNAs (4.98%), followed by ICEs (2.46%) and pseudogenes (0.88%). On the other hand, false-positive 3’UTR predictions, which totalled 44%, were predominantly composed of known ICEs (17.23%) and 5’UTRs (16.06%).To perform multivariate analysis, a feature matrix was generated where rows represented regions from the training dataset (We designed F3UTER as a binary classifier to categorise an ER into a 3’UTR (positive) or a non-3’UTR (negative). This random forest classifier was implemented in R using Caret as the machine learning framework and 'randomForest' as the machine learning algorithm within Caret. The random forest classifier was trained using the default parameters . We performed a fivefold cross-validation (repeated 20 times) to evaluate the performance of the F3UTER. For each cross-validation run, we calculated the performance metrics such as accuracy, kappa, sensitivity, specificity, ROC curve and precision-recall curve, using the caret’s confusionMatrix function. Variable importance was measured using mean decrease in accuracy and Gini coefficient, as natively reported by random forest. The Gini coefficient measures the contribution of variables towards homogeneity of nodes in the random forest tree. These metrics were averaged across all the cross-validation runs for reporting purposes. For bias-variance trade-off analysis, we trained F3UTER on sequentially increasing sample size of training data , hence sequentially increasing the complexity of the model. For each sample size value, a fraction of the training data was randomly selected, and a fivefold cross-validation was performed which captured all the performance metrics for both the training and validation datasets. This process was repeated 20 times for each sample size. To make 3’UTR predictions on ER datasets, the classifier with the highest kappa statistic was selected from the cross-validation process.Mus musculus (GRCm38): v102; Drosophila melanogaster (BDGP6.32): v104; Danio rerio (GRCz10): v91). The processing of all genomic classes and the calculation of omic features for each region in the validation data was done in a similar way to F3UTER’s training data. Since F3UTER was trained on PASs found in mammals, each test region was scanned for the same set of PASs. PhastCons scores for M. musculus (mm10–60wayEuarchontoGlire) and D. melanogaster (dm6–27way) were downloaded from the UCSC genome browser. The genomic coordinates of transposable elements for all three non-human species were downloaded from http://www.repeatmasker.org/species/hg.html. Since pre-computed phastCons scores were not available for D. rerio, and pre-computed tables of DNA structural properties were not available for all three non-human species, we constructed two additional F3UTER models for this analysis: (1) trained without DNA structural properties and sequence conservation—applied to validation data from D. rerio; and (2) trained without DNA structural properties—applied to validation data from M. musculus and D. melanogaster.For validation data in each species, we extracted known 3’UTR and known non-3’UTR  regions from Ensembl (64 (v2.5). The reads were allowed to map to a maximum of one locus (--outFilterMultimapNmax 1). The resulting aligned reads were then converted into bigwig signals for calculating coverage over test regions. We also used kallisto65 (v0.45.0) to quantify gene abundance in transcripts per million (TPM). Known 3’UTR and non-3’UTR regions associated with genes not expressed  were removed from the validation data. This resulted in a total of 190,310, 108,786 and 47,517 test regions in D. rerio, M. musculus and D. melanogaster, respectively , germinal center B cells (GCB), memory B cells (MB) and naive B cells (NB)) was used for validating 3’UTR predictions (Supplementary Table 15—which is compiled into a R package (ODER: https://github.com/eolagbaju/ODER). Multiple replicates were used for ER calling. Candidate 3’ intergenic ERs were processed and selected in the same way as ERs from GTEx. This resulted in 726 ERs in CD5 cells, 1158 ERs in GCB cells, 624 ERs in MB cells and 985 ERs in NB cells. Omic features required by F3UTER were then calculated for these 3’ intergenic ERs.Previously published RNA-seq and its corresponding 3’-end sequencing data from Singh et al.13. Poly(A) site clusters from multiple replicates were unified and collapsed to produce a non-overlapping set. These poly(A) site clusters were compared to Ensembl human genome annotation (v92) to identify sites which occur within the intergenic regions. F3UTER was applied to 3’ intergenic ERs produced from Singh et al. data and the resulting predictions were compared to intergenic poly(A) site clusters to calculate their overlap. Predictions with at least a 1 base overlap with a poly(A) site were considered to be overlapping. Positive predictive value (PPV) was calculated as the number of 3’UTR predictions overlapping a poly(A) site divided by the total number of 3’UTR predictions (66 (v2.29.2), the locations of 3’UTR predictions were shuffled in the intergenic genomic space on the same chromosome, hence generating random intergenic ERs with length, size and chromosome distribution similar to 3’UTR predictions in B cells. To shuffle the locations within the intergenic space, we excluded the genomic space covered by genes  and intergenic ERs (both 3’ and 5’) in the respective B-cells data. The overlap between these randomly generated intergenic ERs and poly(A) sites was then calculated, and this process was repeated 10,000 times to produce a distribution of expected overlap. The p value was calculated as z-score was calculated as Processed poly(A) site clusters for 3’-end data associated with the RNA-seq samples from Singh et al. were downloaded from the poly(A) atlas22 was included in this benchmarking study because it was identified as the top-performing method for predicting poly(A) sites in a previous benchmarking study21 comparing all alternative polyadenylation profiling tools using RNA-seq data. We also included APARENT28  in our benchmarking analysis. This is because we noted that Chen and colleagues21 did not consider 3’UTR identification tools based on deep learning applied to DNA sequences.F3UTER was compared to other alternative polyadenylation profiling tools using two metrics: PPV and FOR. TAPAS28 is a deep neural network trained to predict alternative polyadenylation in human 3’UTRs. We used APARENT’s poly(A) detection model  downloaded from APARENT’s GitHub repository (https://github.com/johli/aparent), which can predict the locations of poly(A) sites from DNA sequence. APARENT was applied on the DNA sequence of ER datasets from Singh et al. using default parameters. For each ER sequence, APARENT either predicted one or more poly(A) site(s), or no poly(A) site was predicted. ER sequences in which no poly(A) site was predicted by APARENT were considered to be non-3’UTR predictions.APARENT (APA REgression NeT)39 predicts poly(A) cleavage sites in 3’UTRs using RNA-seq data. GETUTR’s source code (v2.0.0) was downloaded from http://big.hanyang.ac.kr/GETUTR/download.htm. GETUTR requires a refFlat reference file from which the genomic coordinates of 3’UTRs are extracted, and RNA-seq mapped reads in BAM format. We provided ERs to GETUTR as 3’UTRs in refFlat files and RNA-seq mapped reads from multiple replicates were pooled together. GETUTR was run with default parameters. For each ER, GETUTR either predicted one or more poly(A) site(s), or no poly(A) site was predicted. It should be noted that in many cases, GETUTR also predicted poly(A) sites outside the genomic coordinates of the input ERs. However, we followed a lenient approach and if such predicted poly(A) site overlapped with a true poly(A) site from 3’-end data, it was considered a true positive. ERs in which no poly(A) site was predicted by GETUTR were considered to be non-3’UTR predictions.GETUTR 22 is a programme to detect novel alternative polyadenylation sites from RNA-seq data. TAPAS’s model to find novel alternative polyadenylation sites (APA_sites_detection) was downloaded from its GitHub repository: https://github.com/arefeen/TAPAS. Similar to GETUTR, TAPAS requires a reference annotation file in refFlat format, and a coverage file for RNA-seq mapped reads. We provided ERs to TAPAS as 3’UTRs in refFlat files and coverage was calculated using pooled RNA-seq mapped reads. TAPAS was run with default parameters. For each ER, TAPAS either predicted one or more novel poly(A) site(s), or the original end of the ER was output. ERs for which the original end was output were considered to be non-3’UTR predictions.TAPAS 3 were extracted from the supplementary files provided with their study. These coordinates were mapped to hg19 and therefore were converted to hg38 for comparison. We considered all categories of predictions annotated by Miura et al.  in 11 human tissues  corresponding to our set of GTEx tissues.A feature matrix of 3’ intergenic ERs was generated in each tissue. F3UTER was applied to each matrix to categorise intergenic ERs into 3’UTR (prediction probability n = 7337). On the other hand, for an ER which overlapped (≥1 nt) ERs from other tissues, we calculated the proportion of brain tissues in which the ER was detected. If more than 75% of the tissues were brain-related, the ER was labelled as “highly brain-specific” . From the remaining data, ERs detected in at least five tissues, with their start and end coordinates within a 10-nt window, were labelled as “shared” . All the remaining ERs which did not fall in any of the above categories were labelled as “ambiguous” .To explore the biological relevance of 3’UTR predictions, they were categorised into four groups based on their tissue-specificity: absolute tissue-specific, highly brain-specific, shared and ambiguous. To do such categorisation, the genomic coordinates of ER predictions were compared across the 39 tissues. An ER which did not overlap any other ER across the tissues was labelled as “absolute tissue-specific” or present in only one tissue (n = 739). For 3’UTRs expressed in multiple tissues, we calculated the proportion of brain tissues in which the 3’UTR was expressed. If more than 75% of the tissues were brain-related, the 3’UTR was labelled as 'highly brain-specific' (n = 733). From the remaining data, 3’UTRs which were expressed in at least five tissues were labelled as 'shared' . To scan genomic regions for RBP binding, we extracted the DNA sequence (hg38) of regions in predicted 3’UTRs, predicted non-3’UTRs and known 3’UTRs. The set of negative control sequences was obtained by shuffling the predicted 3’UTR sequences while preserving the di-nucleotide frequency, using the universalmotif R package .For the set of known 3’UTRs, we selected annotated 3’UTRs from Ensembl v94 which were “expressed” (mean coverage ≥5) in at least one of the 39 GTEx tissues. To do this, we calculated mean coverage of each annotated 3’UTR region using the GTEx RNA-seq data (v6). Annotated 3’UTRs with mean coverage of less than 5 reads in all 39 tissues were removed, resulting in 16,407 known 3’UTRs. We further categorised this known set of 3’UTRs into three groups: absolute tissue-specific, highly brain-specific and shared. 3’UTRs which were expressed in only one tissue were labelled as 'absolute tissue-specific' 67. Curated RBP motifs from the CISBP-RNA database (n = 97) were retrieved from the MEME motif database (Homo_sapiens.dna_encoded_cisbp_rna). We used FIMO68 with a uniform background to scan query sequences for potential RBP motif matches. For each RBP motif and query sequence pair, we calculated normalised counts as the number of motif matches (with 69 with default parameters to compare binding enrichment of RBPs between highly brain-specific (query) and shared 3’UTR predictions (control). RBP motifs with an enrichment TARDBP and RBFOX1 binding. The existing 3’UTRs of genes associated with these unannotated 3’UTRs were scanned for TARDBP and RBFOX1 motifs in a similar way using FIMO. Binding sites of TARDBP identified using iCLIP technology were extracted from the POSTAR2 database70. We downloaded all the curated RBP-binding sites in the human genome from POSTAR2, and selected TARDBP sites in the brain, which resulted in 133,037 binding sites from multiple iCLIP experiments. These binding sites were then collapsed and unified, resulting in a set of 118,157 non-redundant TARDBP-binding sites.The position weight matrices (PWMs) of RBP-binding motifs in humans were collected from the ATtRACT database44, were used to quantify the occurrence of CNCRs within unannotated 3’UTRs. We extracted the CNC score for each 10-nt window and averaged it across the query region to calculate a mean CNC score for each query region. Regions for which the CNC score could not be calculated due to gaps in the data were removed from the analysis, resulting in 15,279 known 3’UTRs and 6522 predicted 3’UTRs.The CNC scores, as reported by Chen et al.71. All genes expressed in brain were used as the background . The list of brain expressed genes was obtained from the SynGO portal45. GO terms attaining an enrichment q value  <0.05 were considered significant. Similarly, SynGO45 was used to identify enriched GO terms  associated with synaptic function, using brain expressed genes as the background. To calculate enrichment of genes associated with rare neurogenetic disorders, OMIM72 genes related to neurological disorders were used (1948 genes). The list of genes associated with adult-onset neurodegenerative disorders was extracted from Genomic England PanelApp (254 green labelled genes)73. A hypergeometric test was used to calculate the enrichment using brain expressed genes as the ‘gene universe’.To investigate molecular functions and biological processes significantly associated with a gene list, we performed GO enrichment analysis in R using the clusterProfiler package (v3.14.3)r) was calculated using the rstatix package (v0.6.0).All data processing, statistical analysis, and plotting were conducted in R (version 3.6.2) software. All statistical tests were two-sided. The Wilcoxon effect size : https://www.ensembl.org/index.htmlEnsembl GTF: https://www.gencodegenes.org/human/Gencode GTF (comprehensive gene annotation): https://genome.ucsc.edu/cgi-bin/hgTablesUCSC Table Browser: https://hgdownload.soe.ucsc.edu/downloads.htmlphastCons scores: http://www.repeatmasker.orgTransposable elements: http://bioinformatics.psb.ugent.be/webtools/ep3/?conversionDNA structural properties: https://www.polyasite.unibas.ch/atlasPoly(A) site atlas: https://attract.cnic.es/ATtRACT database: https://meme-suite.org/meme/db/motifsMEME motif database: http://lulab.life.tsinghua.edu.cn/postar2/index.phpPOSTAR2: https://www.syngoportal.org/SynGO: https://www.omim.org/OMIM: https://nhsgms-panelapp.genomicsengland.co.uk/Genomics England PanelApp: Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Investigations on gender variations in the risk factors of cognitive impairment are required to promote future precision medicine among older adults, as well as to contribute to a better understanding of the “male–female health-survival paradox”. With this study, we aimed to investigate the effects of “diet–smoking–gender” three-way interactions on cognitive impairments among Chinese older adults. We conducted a 16-year prospective cohort study among 15,953, 15,555, 16,849, 9716, 7116, and 13,165 older adults from the 2002, 2005, 2008–2009, 2011–2012, 2014, and 2017–2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), respectively. Cognitive impairment was measured by the Mini-Mental State Examination (MMSE). The dietary diversity score (DDS) was calculated using the CLHLS food frequency questionnaire. Generalized estimating equations (GEE) were used to assess the “diet–smoking–gender” three-way interaction effects on cognitive impairment across the six waves of CLHLS. We found that higher dietary diversity was associated with lower probability of cognitive impairment among older adults . However, smoking behavior may negatively influence the protective effect of higher dietary diversity on cognitive function among females . Our findings imply that we should take gender differences and lifestyle behaviors into consideration in implementing dietary interventions to improve cognitive function among older adults. Cognitive impairment is an age-related deterioration in cognitive functions with increasing difficulties with language and memory ,2. With Dietary represents a possible preventative measure against cognitive impairment . The cumPrevious studies have also shown that smoking has adverse effects on health ,16. TherMoreover, the phenomenon of the “male–female health-survival paradox”, which states that females experience higher rates of health conditions but live longer than males, has been identified by many scholars ,28. MaleTherefore, in the current study, we aimed to investigate the effect of “diet–smoking–gender” three-way interactions on cognitive impairments among Chinese older adults aged 65 to 105 years old based on the 2002–2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), the largest dataset of oldest–old (with comparable young–old) cohorts in the world.The study data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Details of the survey design and participants have been described elsewhere ,33. BrieCognitive impairment (yes vs. no) was assessed by the Chinese version of the Mini-Mental State Examination (MMSE) , which hAccording to previous literature ,37,38, wSmoking behavior was measured by the question of “Did you smoke in the past?”. If the answer to the question was “Yes”, then it was coded as 1; otherwise, it was coded as 0. Only smoking behavior in the past was considered because for two reasons. First, the DDS was measured according to past dietary frequencies around age 60. Second, there might be a reverse causation effect on current smoking behavior caused by cognitive condition.We considered the following variables as covariates, all of which have been noted as major factors contributing to cognitive impairment in Chinese older adults ,40: age,iY is the status of cognitive impairment (yes vs. no); iP is the probability of cognitive impairment; iN, iB, and iS are the DDS, smoking frequency, and sex of the ith individual, respectively; iN × iB, iN × iS, and iB × iS are the three 2-way interaction items among iN, iB, and iS, respectively; iBiSiN is the 3-way interaction item; jiX is a vector of covariates of the ith individual; iP/(1 − iP) is the odds ratio (OR); and iu is the error term.The following model was used to explore the “diet–smoking–gender” 3-way interaction effects on cognitive impairment:iBiSiN 3-way interaction effect on cognitive impairment exists or not. However, it is not possible to directly evaluate the effect of specific combinations of diet, smoking, and gender on cognitive impairment and its significance based on Model (1). Considering the difficulty to construct integrated binary variables based on continuous variable iN with dummies iB and iS, we transformed the continuous DDS into a dichotomous variable (low DDS vs. high DDS). Then, we used the following Model (2) to perform analyses of the integrated effects among diet, smoking, and gender on cognitive function of older adults:N,B,SZ is a series of dummy that which represent the combinations of the statuses of the dietary diversity score , ever smoker , and gender ; and jiX is a vector of covariates of the ith individual. There are 8 (=2 × 2 × 2) combinations of the N, B, and gender statuses, and we used N=0,B=0,S=0Z  as the reference group. The coefficients N,B,Sγ measure the odds ratio (OR) of the health outcome for the corresponding dummy variables, N,B,SZ.Model (1) shows whether a significant N,B,SZ is a series of dummy variables that represent the combinations of the statuses of eating frequency of meat , ever smoker , and gender ; and jiX is a vector of covariates of the ith individual. We used N=0,B=0,S=0Z  as the reference group.Furthermore, we investigated the effect of each food involved in the DDS on cognitive function separately. We constructed 7 dichotomous variables of past dietary frequency, including meat, fish or seafood, eggs, beans, sugar, tea, and garlic. For each food, we classified answer categories of “not every week but at least once per month”, “not every month but occasionally”, and “rarely or never” into “low frequency” and answer categories of “almost every day” and “not every day but at least once per week” into “high frequency”. Taking “meat” as an example, we utilized Model (2) to perform an analyses of the integrated effects of meat, smoking, and gender on cognitive function of older adults, where Generalized estimating equations (GEE) were used in both Models (1) and (2) to assess the “diet–smoking–gender” 3-way interaction effects on cognitive impairment across the six waves of the CLHLS. As an expansion of generalized linear model (GLM), GEE can effectively model repeated categorical variables from longitudinal studies. We classified cognitive impairment as a dichotomous variable  as the dependent variable, so we used logit models and reported the odds ratios (ORs) and 95% confidence intervals (CIs) obtained from the model-estimated robust standard errors. Exchangeable correlation structure was used to account for subject-level repeated measures. We also tried the continuous variable of “cognitive function score (0–30)” as the dependent variable and obtained similar results (data not shown). All statistical analyses were performed using Stata version 14.0 .(1)Among all samples, high DDS was negatively associated with cognitive impairment , and males had better cognitive function  compared with females.(2)Among all samples, there was a significant “high DDS–smoking” interaction effect  on cognitive impairment. Regarding never-smoking participants, OR1 of cognitive impairment for high DDS versus low DDS was 0.92. Regarding participants who smoked in the past, OR2 of cognitive impairment for high DDS versus low DDS was 0.92 × 1.11 = 1.02 > 1.0 > OR1. Compared with never-smoking participants with low DDS, OR3 of cognitive impairment for participants who smoked in the past with high DDS was 0.92 × 0.92 × 1.11 = 0.94 > OR1. OR1, OR2, and OR3 indicate that “smoking in the past” may decrease the protective effect of high DDS on cognitive function.(3)Among all samples, there was a significant “high DDS–smoking–gender” three-way interaction effect  on cognitive impairment.(4)Among males, “high DDS–smoking” two-way interaction effects on cognitive function were not significant, which indicates that high DDS was always a protective factor with respect to cognitive function among males, regardless of their smoking behavior in the past.(5)Among females, there was a significant “high DDS–smoking” interaction effect  on cognitive impairment. Regarding never-smoking females, OR4 of cognitive impairment for high DDS versus low DDS was 0.92. Regarding females who smoked in the past, OR5 of cognitive impairment for high DDS versus low DDS was 0.92 × 1.26 = 1.16 > 1.0 > OR4. Compared with never-smoking females with low DDS, OR6 of cognitive impairment for females who smoked in the past with high DDS was 0.92 × 0.87 × 1.26 = 1.01 > 1.0 > OR4. OR4, OR5, and OR6 indicate that “smoking in the past” may offset the protective effect of high DDS on cognitive function among females.N = 0, 1), ever smoker , and gender  and used the combination “low DDS, non-smoker, female” as the reference group. Among males, the two curves were relatively parallel, with a decreasing trend, which suggests that there was no interaction between DDS and smoking behavior see . “SmokinFurthermore, we also observed that among females with high DDS, non-smokers had lower risk of experiencing cognitive impairment than ever smokers. However, among females with low DDS, non-smokers had a significantly higher risk of experiencing cognitive impairment than ever smokers.(1)Among all samples, we found that a high dietary frequency of “meat” , “fish or seafood” , “beans” , “tea” , and “garlic”  were associated with a lower risk of cognitive impairment.(2)Among all samples, there was a significant “high dietary frequency–smoking–gender” three-way interaction effect on cognitive impairment with “meat” or “fish or seafood” as the diet dichotomous variable.(3)Among males, the “high dietary frequency–smoking” interaction effect on cognitive impairment with “meat” as the diet dichotomous variable was not significant, which indicates that high dietary frequency of “meat” was always a protective factor with respect to cognitive function among males, regardless of their smoking behavior in the past. Regarding “fish or seafood”, there was a significant “high dietary frequency–smoking” interaction effect  on cognitive impairment. For never-smoking males, the OR of cognitive impairment for high dietary frequency versus low dietary frequency was 0.99. For males who smoked in the past, the OR of cognitive impairment for high dietary frequency versus low dietary frequency was 0.99 × 0.89 = 0.88. Compared with never-smoking males with low dietary frequency, the OR of cognitive impairment for males who smoked in the past with high dietary frequency was 0.99 × 1.02 × 0.89 = 0.90. These results indicate that high dietary frequency of “fish or seafood” was a protective factor with respect to cognitive function among both never-smoking males and males who smoked in the past.(4)Among females, regarding “meat”, there was a significant “high dietary frequency–smoking” interaction effect  on cognitive impairment. For never-smoking females, the OR of cognitive impairment for high dietary frequency versus low dietary frequency was 0.93. For females who smoked in the past, the OR of cognitive impairment for high dietary frequency versus low dietary frequency was 0.93 × 1.17 = 1.09 > 0.93. Compared with never-smoking females with low dietary frequency, the OR of cognitive impairment for females who smoked in the past with high dietary frequency was 0.93 × 0.90 × 1.17 > 0.93. These estimates indicate that “smoking in the past” may offset the protective effect of high dietary frequency of “meat” on cognitive function among females. Similarly, the results also indicate that “smoking in the past” may offset the protective effect of high dietary frequency of “fish or seafood” on cognitive function among females.In the current study, we examined the effect of “diet–smoking–gender” three-way interactions on cognitive impairments based on analyses of the large datasets of a longitudinal study among Chinese older adults. We found that higher dietary diversity was associated with a lower probability of cognitive impairment among older adults. However, smoking in the past may negatively influence the protective effect of higher dietary diversity on cognitive function. Among females, smoking in the past offset the protective effect of higher dietary diversity on cognitive function. However, dietary diversity was always a protective factor with respect to cognitive function among males.Previous studies reported that there was a positive association between higher dietary diversity and cognitive function ,13,41,42Furthermore, we also found interactions between smoking in the past and higher dietary diversity on cognitive impairment; smoking behavior may negatively influence the effect of higher dietary diversity on cognitive function. We hypothesized the underlying mechanism of the interactions between smoking behavior and higher dietary diversity on cognitive function is that nicotine intake in smokers might have an adverse effect on the release of some neurotransmitters , and sucFinally, we observed that among females with high dietary diversity, those who had smoked in the past had a higher risk of experiencing cognitive impairment than never smokers. In contrast, among females with low dietary diversity, those who had smoked in the past had a lower risk of experiencing cognitive impairment than never smokers. Previous studies have also shown conflicting results with respect to the association between smoking in the past and cognitive function among older adults ,53. SomeTo the best of our knowledge, this is the first study exploring the effect of “diet–smoking–gender” three-way interactions on cognitive impairment among older adults. Strengths of this study also include the large study size with a nationally representative sample of Chinese older adults and the longitudinal design of the six waves from 2002 to 2018. However, there are still some limitations of the current study. First, we measured the dietary diversity only due to the availability of questions in CLHLS. We recommend further studies to investigate the frequency and amount of nutrient intake to provide more comprehensive dietary suggestions. Second, we could not examine the hypothesized underlying mechanism of “diet–smoking–gender” three-way interactions with respect to cognitive impairments, and we recommend future experimental studies to investigate this mechanism. Third, there might be bidirectional associations between dietary diversity or smoking and cognitive function, and the cognitive function of older adults might influence their dietary diversity or smoking behavior. However, we only adopted the past DDS at around 60 years and smoking in the past in the current study in order to reduce the possible effect of reverse causation on current DDS or current smoking by cognitive condition.We found a “diet–smoking–gender” three-way interaction with respect to cognitive impairment among older adults, which shows that smoking behavior may offset the protective effect of higher dietary diversity on cognitive function but may not influence the effect of higher dietary diversity on cognitive function among males. Our findings imply that we should take gender differences and lifestyle behaviors into consideration before implementing dietary interventions to promote cognitive function among older adults. We recommend policymakers pay more attention to gender differences among older adults and consider strategies to support the development of personalized precision diet interventions. However, due to the complexity of diet and the unclear mechanism of cumulative effects of diverse diet components on cognitive function, as well as their interactions with gender and smoking, our results are far from conclusive, and more studies are needed to provide scientific evidence for the future development of precision diet interventions. We recommend further investigation on potential gender differences in interactions between smoking behavior and diet with respect to cognitive function, as well as the underlying mechanisms.