Optimizing Deep Neural Networks for Single Cell Segmentation

Analysis of live-cell imaging experiments at the resolution of single cells provides exciting insights into the inner workings of biological systems. Advances in biological imaging and computer vision allow for segmentation of natural images with a high degree of accuracy. However, automation of the segmentation pipeline at the single cell resolution remains a challenging task. Complex deep learning models require large, well-annotated datasets that are rarely available in biology. In this research, we explore various methods that optimize state of the art deep learning frameworks, despite limited resources. We trained a large permutation of models to quantify their capacity and to measure the effects of temporal information, spatial awareness and transfer learning on model performance. We find that, although training set size is most impactful in improving model accuracy, we can leverage techniques like spatial awareness and transfer learning to compromise for the lack of data. These insights show that, with an abundance of data, light-weight models can be as performant as their heavy-weight counterparts in cellular analysis

A Mixed-Methods Study of Secondary Student and Teacher Attitudes to Mobile Education Apps in Lagos, Nigeria

With the advent of smartphones, laptops, and other various portable devices, the ability to incorporate technology into the classroom has increased dramatically in the last few decades. This study evaluates the perceptions and attitudes of both students and teachers in relation to mobile apps that assist in classroom learning. The research used a mixed-methods approach that collected demographic information and conducted qualitative interviews to determine the perceptions of mobile apps to students and teachers. Cross-sectional data was collected from participants and analyzed for associations. 43 students and 6 teachers were recruited and interviewed. The participants were asked about their thoughts on mobile educational apps, and their interviews were audio recorded and transcribed. Inductive thematic analysis was used to analyze the data. For students, themes were centered on barriers to educational app adoption, barriers to continued use of education apps, tracking progress, credibility, and goal setting/reminders. For teachers, themes identified that influenced mobile app use, and criteria used for mobile app selection was identified. Future research should aim to assess quantitative improvements in mobile educational app use within the classroom

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Characterizing drug-metabolizing enzymes and transporters that are bona fide CAR-target genes in mouse intestine

Intestine is responsible for the biotransformation of many orally-exposed chemicals. The constitutive androstane receptor (CAR/Nr1i3) is known to up-regulate many genes encoding drug-metabolizing enzymes and transporters (drug-processing genes/DPGs) in liver, but less is known regarding its effect in intestine. Sixty-day-old wild-type and Car−/− mice were administered the CAR-ligand TCPOBOP or vehicle once daily for 4 days. In wild-type mice, Car mRNA was down-regulated by TCPOBOP in liver and duodenum. Car−/− mice had altered basal intestinal expression of many DPGs in a section-specific manner. Consistent with the liver data (Aleksunes and Klaassen, 2012), TCPOBOP up-regulated many DPGs (Cyp2b10, Cyp3a11, Aldh1a1, Aldh1a7, Gsta1, Gsta4, Gstm1-m4, Gstt1, Ugt1a1, Ugt2b34, Ugt2b36, and Mrp2–4) in specific sections of small intestine in a CAR-dependent manner. However, the mRNAs of Nqo1 and Papss2 were previously known to be up-regulated by TCPOBOP in liver but were not altered in intestine. Interestingly, many known CAR-target genes were highest expressed in colon where CAR is minimally expressed, suggesting that additional regulators are involved in regulating their expression. In conclusion, CAR regulates the basal expression of many DPGs in intestine, and although many hepatic CAR-targeted DPGs were bona fide CAR-targets in intestine, pharmacological activation of CAR in liver and intestine are not identical

Data from: Neonatal activation of the xenobiotic-sensors PXR and CAR results in acute and persistent down-regulation of PPARα-signaling in mouse liver

Safety concerns have emerged regarding the potential long-lasting effects due to developmental exposure to xenobiotics. The pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are critical xenobiotic-sensing nuclear receptors that are highly expressed in liver. The goal of this study was to test our hypothesis that neonatal exposure to PXR- or CAR-activators not only acutely but also persistently regulates the expression of drug-processing genes (DPGs). A single dose of the PXR-ligand PCN (75 mg/kg), CAR-ligand TCPOBOP (3 mg/kg), or vehicle (corn oil) was administered intraperitoneally to 3-day-old neonatal wild-type mice. Livers were collected 24 h post-dose or from adult mice at 60 days of age, and global gene expression of these mice was determined using Affymetrix Mouse Transcriptome Assay 1.0. In neonatal liver, PCN up-regulated 464 and down-regulated 449 genes, whereas TCPOBOP up-regulated 308 and down-regulated 112 genes. In adult liver, there were 15 persistently up-regulated and 22 persistently down-regulated genes following neonatal exposure to PCN, as well as 130 persistently up-regulated and 18 persistently down-regulated genes following neonatal exposure to TCPOBOP. Neonatal exposure to both PCN and TCPOBOP persistently down-regulated multiple Cyp4a members, which are prototypical-target genes of the lipid-sensor PPARα, and this correlated with decreased PPARα-binding to the Cyp4a gene loci. RT-qPCR, western blotting, and enzyme activity assays in livers of wild-type, PXR-null, and CAR-null mice confirmed that the persistent down-regulation of Cyp4a was PXR and CAR dependent. In conclusion, neonatal exposure to PXR- and CAR-activators both acutely and persistently regulates critical genes involved in xenobiotic and lipid metabolism in liver

CEL files

This zip file contains 18 CEL files, named with the ages (3-day-old and 60-day-old) and treatments (corn oil [CO], TCPOBOP, PCN)

Transcriptome profiles in mouse liver

C57BL/6J mice were administered with corn oil, TCPOBOP, PCN at 3-day-old age, the livers were collected after 24 hours or at 60-day-old age. This excel data sheet contains 18 columns. Three biological replicates for each treatment. The row contains all the transcripts