Biology subjects

Gong, W.

Publications and source records attributed to Gong, W..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Angiopoietin-like 4 promotes the proliferation and migration of epidermal stem cells and contributes to the re-epithelialization of cutaneous wounds

Proliferation and migration of epidermal stem cells (EpSCs) are essential for epithelialization during skin wound healing. Angiopoietin-like 4 (ANGPTL4) has been reported to play an important role in wound healing, but the mechanisms involved are not fully understood. Here we investigate the contribution of ANGPTL4 to full-thickness wound re-epithelialization and the underlying mechanisms using Angptl4 knockout mice. Immunohistochemical staining reveals that ANGPTL4 is significantly upregulated in the basal layer cells of the epidermis around the wound during cutaneous wound healing. ANGPTL4 deficiency impairs wound healing. H & E staining shows that ANGPTL4 deficiency significantly reduces the thickness, length and area of regenerated epidermis postwounding. Immunohistochemical staining for markers of EpSCs (6 integrin and {beta}1 integrin) and cell proliferation (PCNA) shows that the number and proliferation of EpSCs in the basal layer of the epidermis are reduced in ANGPTL4-deficient mice. In vitro studies show that ANGPTL4 deficiency impedes EpSC proliferation, causes cell cycle arrest at the G1 phase and reduced the expression of cyclins D1 and A2, which can be reversed by ANGPTL4 overexpression. ANGPTL4 deletion suppresses EpSC migration, which is also rescued by ANGPTL4 overexpression. Overexpression of ANGPTL4 in EpSCs accelerates cell proliferation and migration. Collectively, our results indicate that ANGPTL4 promotes EpSCs proliferation by upregulating cyclins D1 and A2 expression and accelerating cell cycle transition from G1 to S phase, and ANGPTL4 promotes skin wound re-epithelialization by stimulating EpSC proliferation and migration. Our study reveals a novel mechanism underlying EpSC activation and re-epithelialization during cutaneous wound healing.

cell biology↗

bioRxiv2022

A cryptic transactivation domain of EZH2 binds AR and AR's splice variant promoting oncogene activation and tumorous transformation

Enhancer of Zeste Homolog 2 (EZH2) and androgen receptor (AR) are crucial chromatin regulators involved in the development and/or progression of prostate tumor, including advanced castration-resistant prostate cancer (CRPC). To sustain prostate tumorigenicity, EZH2 establishes noncanonical biochemical interaction with AR for mediating oncogene activation, in addition to its canonical role as a transcriptional repressor and enzymatic subunit of Polycomb Repressive Complex 2 (PRC2). However, the molecular basis underlying non-canonical activities of EZH2 in prostate cancer remains elusive and therapeutic strategies for targeting EZH2:AR-mediated oncogene activation activities are also lacking. Here, we report that a cryptic transactivation domain of EZH2 (EZH2TAD) binds both AR and AR spliced variant 7 (AR-V7, an AR variant enriched in CRPC), mediating assembly and/or recruitment of transactivation-related machineries at genomic sites that lack PRC2 binding. Such noncanonical targets of EZH2:AR/AR-V7:(co)activators are enriched for the clinically-relevant oncogenes. We also show that EZH2TAD is required for the chromatin recruitment of EZH2, for EZH2-mediated oncogene activation, and for CRPC growth in vitro and in vivo. To completely block EZH2s multifaceted oncogenic activities in prostate cancer, we employed MS177, a recently developed proteolysis targeting chimera (PROTAC) of EZH2. Strikingly, MS177 achieved on-target depletion of both EZH2s canonical (EZH2:PRC2) and noncanonical (EZH2TAD:AR/AR-V7:coactivators) complexes in prostate tumor, eliciting much more potent antitumor effects than the catalytic inhibitors of EZH2. Overall, this study reports previously unappreciated requirements of EZH2TAD for mediating EZH2s noncanonical (co)activator recruitment and gene-activation functions in prostate tumor and suggests EZH2-targeting PROTACs as potentially attractive therapeutics for the treatment of aggressive prostate tumors that rely on the circuits wired by EZH2 and AR.

cancer biology↗

bioRxiv2022

Synechococcus nitrogen gene loss in iron-limited ocean regions

Synechococcus are the most abundant cyanobacteria in high latitude regions and are responsible for an estimated 17% of annual marine primary productivity. Despite their biogeochemical importance, Synechococcus populations have been unevenly sampled across the ocean, with most studies focused on low-latitude strains. In particular, the near absence of Synechococcus genomes from high-latitude, High Nutrient Low Chlorophyll (HNLC) regions leaves a gap in our knowledge of picocyanobacterial adaptation to iron limitation and their influence on carbon, nitrogen, and iron cycles. We examined Synechococcus populations from the subarctic North Pacific, a well-characterized HNLC region, with quantitative metagenomics. Assembly with short and long reads produced two near complete Synechococcus metagenome-assembled genomes (MAGs). Quantitative metagenome-derived abundances of these populations matched well with flow cytometry counts, and the Synechococcus MAGs were estimated to comprise >99% of the Synechococcus at Station P. Whereas the Station P Synechococcus MAGs contained multiple genes for adaptation to iron limitation, both genomes lacked genes for uptake and assimilation of nitrate and nitrite, suggesting a dependence on ammonium, urea, and other forms of recycled nitrogen leading to reduced iron requirements. A global analysis of Synechococcus nitrate reductase abundance in the TARA Oceans dataset found nitrate assimilation genes are also lower in other HNLC regions. We propose nitrate and nitrite assimilation gene loss in Synechococcus represents an adaptation to severe iron limitation in high-latitude regions where ammonium availability is higher. Our findings have implications for models that quantify the contribution of cyanobacteria to primary production and subsequent carbon export. SignificanceThe cyanobacterium Synechococcus is a major contributor to ocean primary production and biogeochemistry. Here, we used quantitative metagenomics to assemble and enumerate two Synechococcus genomes from an iron-limited, High Nutrient Low Chlorophyll region. We show these genomes represent the majority of Synechococcus cells at the site and are the first known Synechococcus unable to assimilate either nitrate or nitrite. This gene loss is likely due to the high iron quota of these proteins and predominant availability of recycled forms of nitrogen. Synechococcus loss of nitrate assimilation affects their role in elemental cycles (e.g., carbon, nitrogen, and iron), limits their potential for carbon export, and enhances our understanding of Synechococcus evolution in response to nutrient limitation and competition.

ecology↗

bioRxiv2022

SeATAC: a tool for exploring the chromatin landscape and the role of pioneer factors

The position of the nucleosome and chromatin packaging in eukaryotic genomes govern gene regulation and cellular functions. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. However, there is no method that is specifically designed for detecting differential chromatin accessibility using ATAC-seq datasets. In this study, we developed a bioinformatics tool called SeATAC, that used a conditional variational autoencoder (CVAE) model to learn the latent representation of ATAC-seq V-plots, and to estimate the statistically differential chromatin accessibility. We demonstrated that SeATAC outperformed MACS2 and NucleoATAC on four separate tasks including: (1) detection of differential V-plots; (2) definition of nucleosome positions; (3) detection of nucleosome changes and (4) designation of transcriptional factor binding sites (TFBS) with differential chromatin accessibility. By applying SeATAC to several pioneer factor induced differentiation or reprogramming ATAC-seq datasets, we found that induction of these pioneer factors not only relaxed the closed chromatin but also decreased the chromatin accessibility of 20% - 30% of their target sites. These two groups of TF binding sites were characterized by different genomic distribution and histone marks. Here, we present SeATAC as a novel tool to accurately reveal the genomic regions with differential chromatin accessibility from ATAC-seq data.

bioinformatics↗

bioRxiv2022

Novel Murine models of Mania and Depression

Neuropathological mechanisms triggering manic syndrome or manic episodes in bipolar disorder remain poorly characterised, as the research progress is severely limited by the paucity of appropriate animal models. Here we developed a novel manic mice model by combining a series of chronic unpredictable rhythm disturbances (CURD), which include disruption of circadian rhythm, sleep deprivation, exposure to cone light, with subsequent interference of followed spotlight, stroboscopic illumination, high temperature stress, noise disturbance and foot shock. To validate this novel manic model, we used multiple behavioural and cell biology approaches comparing the CURD-model with healthy controls and depressed mice. The depression model was created by an exposure to an improved chronic unpredictable mild stress, which we defined as chronic unpredictable mild restraint (CUMR). A novel manic mice model induced by environmental stressors and free from genetic or pharmacological interventions will benefit research into pathological mechanisms of mania.

animal behavior and cognition↗

bioRxiv2022

Two-photon patterned photostimulation with low-power, high-efficiency and reliable single-cell optogenetic control

Two-photon optogenetics enables selectively stimulating individual cells for manipulating neuronal ensembles. As the general photostimulation strategy, the patterned two-photon excitation has enabled millisecond-timescale activation for single or multiple neurons, but its activation efficiency is suffered from high laser power due to low beam-modulation efficiency. Here, we develop a high- efficiency beam-shaping method based on the Gerchberg-Saxton (GS) algorithm with spherical-distribution initial phase (GSSIP) to reduce the patterned two-photon excitation speckles and intensity. It can well control the phase of shaped beams to attain speckle-free accurate patterned illumination with an improvement of 44.21% in the modulation efficiency compared with that of the traditional GS algorithm. A combination of temporal focusing and the GSSIP algorithm (TF-GSSIP) achieves patterned focusing through 500-m-thickness mouse brain slices, which is 2.5 times deeper than the penetration depth of TF-GS with the same signal-to-noise ratio (SNR). With our method, the laser power can be reduced to only 55.56% of that with traditional method (the temporal focusing with GS, TF-GS) to reliably evoke GCaMP6s response in C1V1-expressing cultured neurons with single-cell resolution. Besides, the photostimulation efficiency is remarkably increased by 80.19% at the same excitation density of 0.27 mW/m2. This two-photon stimulation method with low-power, reliable and patterned illumination may pave the way for analyzing neural circuits and neural coding and decoding mechanism.

neuroscience↗

bioRxiv2021

Total virome characterizations of game animals in China reveals a spectrum of emerging viral pathogens

Game animals are wildlife species often traded and consumed as exotic food, and are potential reservoirs for SARS-CoV and SARS-CoV-2. We performed a meta-transcriptomic analysis of 1725 game animals, representing 16 species and five mammalian orders, sampled across China. From this we identified 71 mammalian viruses, with 45 described for the first time. Eighteen viruses were considered as potentially high risk to humans and domestic animals. Civets (Paguma larvata) carried the highest number of potentially high risk viruses. We identified the transmission of Bat coronavirus HKU8 from a bat to a civet, as well as cross-species jumps of coronaviruses from bats to hedgehogs and from birds to porcupines. We similarly identified avian Influenza A virus H9N2 in civets and Asian badgers, with the latter displaying respiratory symptoms, as well as cases of likely human-to-wildlife virus transmission. These data highlight the importance of game animals as potential drivers of disease emergence. HighlightsO_LI1725 game animals from five mammalian orders were surveyed for viruses C_LIO_LI71 mammalian viruses were discovered, 18 with a potential risk to humans C_LIO_LICivets harbored the highest number of potential high risk viruses C_LIO_LIA species jump of an alphacoronavirus from bats to a civet was identified C_LIO_LIH9N2 influenza virus was detected in a civet and an Asian badger C_LIO_LIHumans viruses were also identified in game animals C_LI

zoology↗

bioRxiv2021

Supervised Phenotype Discovery from Multimodal Brain Imaging

Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.

neuroscience↗

bioRxiv2021

NBI-921352, a First-in-Class, Na V 1.6 Selective, Sodium Channel Inhibitor That Prevents Seizures in Scn8a Gain-of- Function Mice, and Wild-Type Mice and Rats

NBI-921352 (formerly XEN901) is a novel sodium channel inhibitor designed to specifically target NaV1.6 channels. Such a molecule provides a precision-medicine approach to target SCN8A-related epilepsy syndromes (SCN8A-RES), where gain-of-function (GoF) mutations lead to excess NaV1.6 sodium current, or other indications where NaV1.6 mediated hyper-excitability contributes to disease (Gardella and Moller, 2019; Johannesen et al., 2019; Veeramah et al., 2012). NBI-921352 is a potent inhibitor of NaV1.6 (IC50 0.051 {micro}M), with exquisite selectivity over other sodium channel isoforms (selectivity ratios of 756X for NaV1.1, 134X for NaV1.2, 276X for NaV1.7, and >583X for NaV1.3, NaV1.4, and NaV1.5). NBI-921352 is a state-dependent inhibitor, preferentially inhibiting activated (inactivated or open) channels. The state dependence leads to potent stabilization of inactivation, inhibiting NaV1.6 currents, including resurgent and persistent NaV1.6 currents, while sparing the closed/rested channels. The isoform-selective profile of NBI-921352 led to a robust inhibition of action-potential firing in glutamatergic excitatory pyramidal neurons, while sparing fast-spiking inhibitory interneurons, where NaV1.1 predominates. Oral administration of NBI-921352 prevented electrically induced seizures in a Scn8a GoF mouse, as well as in wild-type mouse and rat seizure models. NBI-921352 was effective in preventing seizures at lower brain and plasma concentrations than commonly prescribed sodium channel inhibitor antiseizure medicines (ASMs) carbamazepine, phenytoin, and lacosamide. NBI-921352 was well tolerated at higher multiples of the effective plasma and brain concentrations than those ASMs. NBI-921352 is entering phase II proof-of-concept trials for the treatment of SCN8A-developmental epileptic encephalopathy (SCN8A-DEE) and adult focal-onset seizures.

pharmacology and toxicology↗

bioRxiv2021

Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

Modelling and predicting individual differences in task-evoked FMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble leaner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of tfMRI scans, suggesting that it has potential to supplement traditional task localisers.

neuroscience↗

bioRxiv2021

Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal carcinoma

BackgroundPytoptosis is essential for tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the heterogeneity of pyroposis and its relationship with the tumor microenvironment (TME) remain unclear. The aim of the present study was to identify proptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures. MethodsFirst, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model establsihed by LASSO regression was verified by ICGC database. Then, the clinical significance, functional status, immune infiltration, cell-cell communication, genomic alteration and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model. ResultsTwo heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype, and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in C2 subgroup was lower than that in C1 subgroup. Pyroptosis related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via Pyroptosis related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while C2 was characterized by more significant alterations in SPEN and ROS1 mutation. Finally, we constructed and validated a robust and promising signature based on the pyroptosis-related risk score for assessing the prognosis in ccRCC. ConclusionWe identified two heterogeneous pyroptosis subtypes and 5 reliable risk signatures to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

molecular biology↗

bioRxiv2021

Elongation factor ELOF1 drives transcription-coupled repair and prevents genome instability

Correct transcription is crucial for life. However, DNA damage severely impedes elongating RNA Polymerase II (Pol II), causing transcription inhibition and transcription-replication conflicts. Cells are equipped with intricate mechanisms to counteract the severe consequence of these transcription-blocking lesions (TBLs). However, the exact mechanism and factors involved remain largely unknown. Here, using a genome-wide CRISPR/cas9 screen, we identified elongation factor ELOF1 as an important new factor in the transcription stress response upon DNA damage. We show that ELOF1 has an evolutionary conserved role in Transcription-Coupled Nucleotide Excision Repair (TC-NER), where it promotes recruitment of the TC-NER factors UVSSA and TFIIH to efficiently repair TBLs and resume transcription. Additionally, ELOF1 modulates transcription to protect cells from transcription-mediated replication stress, thereby preserving genome stability. Thus, ELOF1 protects the transcription machinery from DNA damage by two distinct mechanisms.

cell biology↗

bioRxiv2021

Spatially coordinated heterochromatinization of distal short tandem repeats in fragile X syndrome

Short tandem repeat (STR) instability is causally linked to pathologic transcriptional silencing in a subset of repeat expansion disorders. In fragile X syndrome (FXS), instability of a single CGG STR tract is thought to repress FMR1 via local DNA methylation. Here, we report the acquisition of more than ten Megabase-sized H3K9me3 domains in FXS, including a 5-8 Megabase block around FMR1. Distal H3K9me3 domains encompass synaptic genes with STR instability, and spatially co-localize in trans concurrently with FMR1 CGG expansion and the dissolution of TADs. CRISPR engineering of mutation-length FMR1 CGG to normal-length preserves heterochromatin, whereas cut-out to pre-mutation-length attenuates a subset of H3K9me3 domains. Overexpression of a pre-mutation-length CGG de-represses both FMR1 and distal heterochromatinized genes, indicating that long-range H3K9me3-mediated silencing is exquisitely sensitive to STR length. Together, our data uncover a genome-wide surveillance mechanism by which STR tracts spatially communicate over vast distances to heterochromatinize the pathologically unstable genome in FXS. One-Sentence SummaryHeterochromatinization of distal synaptic genes with repeat instability in fragile X is reversible by overexpression of a pre-mutation length CGG tract.

genomics↗

bioRxiv2021

Activity-induced gene expression and long-range enhancer-promoter contacts in cohesin-deficient neurons

Cohesin and CTCF are major drivers of 3D genome organization, but their role in neurons is still emerging. Here we show a prominent role for cohesin in the expression of genes that facilitate neuronal maturation and homeostasis. Unexpectedly, we observed two major classes of activity-regulated genes with distinct reliance on cohesin in primary cortical neurons. Immediate early genes remained fully inducible by KCl and BDNF, and short-range enhancer-promoter contacts at the Immediate early gene Fos formed robustly in the absence of cohesin. In contrast, cohesin was required for full expression of a subset of secondary response genes characterised by long-range chromatin contacts. Cohesin-dependence of constitutive neuronal genes with key functions in synaptic transmission and neurotransmitter signaling also scaled with chromatin loop length. Our data demonstrate that key genes required for the maturation and activation of primary cortical neurons depend cohesin for their full expression, and that the degree to which these genes rely on cohesin scales with the genomic distance traversed by their chromatin contacts.

genomics↗

bioRxiv2021

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the models utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data. HighlightsO_LIWe introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data C_LIO_LIsPROFUMO hierarchically estimates fMRI networks for the population and every individual C_LIO_LIWe characterised high dimensional resting state fMRI networks from UK Biobank C_LIO_LIModel outperforms ICA and dual regression for estimation of individual-specific network topography C_LIO_LIWe demonstrate the models utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity C_LI

neuroscience↗

bioRxiv2020

Aggregation and immobilisation of membrane proteins interplay with local lipid order in the plasma membrane of T cells

The quest for understanding of numerous vital membrane-associated cellular processes, such as signalling, has largely focussed on the spatiotemporal orchestration and reorganisation of the identified key proteins, including their binding and aggregation. Despite strong indications of the involvement of membrane lipid heterogeneities, historically often termed lipid rafts, their roles in many processes remain controversial and mechanisms elusive. Taking activation of T lymphocytes as an example, we here investigate membrane properties around the key proteins - in particular the T cell receptor (TCR), its main kinase Lck, and phosphatase CD45. We determine their partitioning and co-localisation in passive cell-derived model membranes (i.e. giant plasma-membrane vesicles, GPMVs), and explore their mobility and local lipid order in live Jurkat T cells using fluorescence correlation spectroscopy and spectral imaging with polarity-sensitive membrane probes. We find that upon aggregation and partial immobilisation, the TCR changes its preference towards more ordered lipid environments, which can in turn passively recruit Lck. We observe similar aggregation-induced local membrane ordering and recruitment of Lck also by CD45, as well as by a membrane protein of antigen-presenting cells, CD86, which is not supposed to interact with Lck directly. This highlights the involvement of lipid-mediated interactions and suggests that the cellular membrane is poised to modulate the frequency of protein encounters according to their aggregation state and alterations of their mobility, e.g. upon ligand binding.

biophysics↗

bioRxiv2020

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

bioinformatics↗

bioRxiv2020

Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subjects predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subjects age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal.

neuroscience↗