Biology subjects

You, Y.

Publications and source records attributed to You, Y..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Discovering Haematoma-Stimulated Circuits for Secondary Brain Injury after Intraventricular Haemorrhage by Spatial Transcriptome Analysis

Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important yet challenging to study due to disease leisions locations and other complexities. Utilizing the powerful spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular haemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Our data identified three main 3D global pseudospace-time trajectory bundles which represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex induced by experimental intraventricular haematoma stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH. These results are informative in understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injuries and strategizing more effective clinical management regimens. The bioinformatics strategies will also be useful for the study of other CNS diseases.

bioinformatics↗

bioRxiv2022

Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?

Approaches to in silico prediction of protein structures have been revolutionized by AlphaFold2, while those to predict interfaces between proteins are relatively underdeveloped, owing to the overly complicated yet relatively limited data of protein-protein complexes. In short, proteins are 1D sequences of amino acids folding into 3D structures, and interact to form assemblies to function. We believe that such intricate scenarios are better modeled with additional indicative information that reflects their multi-modality nature and multi-scale functionality. To improve binary prediction of inter-protein residue-residue contacts, we propose to augment input features with multi-modal representations and to synergize the objective with auxiliary predictive tasks. (i) We first progressively add three protein modalities into models: protein sequences, sequences with evolutionary information, and structure-aware intra-protein residue contact maps. We observe that utilizing all data modalities delivers the best prediction precision. Analysis reveals that evolutionary and structural information benefit predictions on the difficult and rigid protein complexes, respectively, assessed by the resemblance to native residue contacts in bound complex structures. (ii) We next introduce three auxiliary tasks via self-supervised pre-training (binary prediction of protein-protein interaction (PPI)) and multi-task learning (prediction of inter-protein residue-residue distances and angles). Although PPI prediction is reported to benefit from predicting inter-contacts (as causal interpretations), it is not found vice versa in our study. Similarly, the finer-grained distance and angle predictions did not appear to uniformly improve contact prediction either. This again reflects the high complexity of protein-protein complex data, for which designing and incorporating synergistic auxiliary tasks remains challenging.

bioinformatics↗

bioRxiv2022

Decoding frontotemporal and cell type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs

Abnormalities in temporal and frontal lobes (TL and FL) have been linked to cognition and neuropsychiatric disorders. While structural and functional differences between the brain lobes have been documented in disease, the cellular heterogeneity in FL and TL and its impact to the vulnerability to genetic risk factors for neuropsychiatric disorders is not well studied. We hypothesize that intrinsic cellular-level differences between TL and FL explain the vulnerability of specific cell types to genetic risk factors and psychoactive drugs. To test this, we integrated single-nucleus transcriptome analysis in fresh human FL and TL with data related to genetic susceptibility and gene dysregulation in neuropsychiatric disease, and response to psychoactive drugs. We also investigate how these differences are associated with gene dysregulation in disease brain. Neuronal cell populations were the most vulnerable to psychiatric genetic risk factors, and more specifically parvalbumin interneurons (PVALB neurons). These PVALB-expressed genetic risk factors were mostly upregulated in the TL compared with FL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. We found GRIN2A and HCN1, implicated in schizophrenia by genome-wide association studies, to be significantly upregulated in PVLAB from the TL and in brain cortex from schizophrenia patients. Our analysis provides comprehensive evidence for PVALB neurons as the most vulnerable cell type that is implicated in several psychiatric disorders. PVALB neurons showed the highest vulnerability to psychoactive drug response, which was 3.6-fold higher than the vulnerability to genetic risk factors. In summary, we show high vulnerability of PVALB neurons that is specific to the temporal lobe, implying that differences between TL and FL greatly influence the cell vulnerability to genetic risk factors as well as the response to psychoactive drugs. These findings offer insights into how regional brain differences affect the cell type vulnerabilities in neuropsychiatric disorders.

neuroscience↗

bioRxiv2022

Differential effects of carbon nanotube and graphene on the tomato rhizosphere microbiome

Application of carbonaceous nanomaterials (CNMs) to the soil-plant system can affect plant physiology, with positive results ranging from enhanced seed germination and root system development to improved stress tolerance. The underlying mechanisms are not fully understood. Plant rhizosphere microbiomes at the soil-root interface are strongly influenced by the host plant and play a key role in the plant hosts development and health. Yet few studies have characterized changes in plant rhizosphere microbiomes following applications of CNMs to the soil-plant system. Here we investigated the effects of multi-walled carbon nanotube (CNT) and graphene on microbial communities in the ectorhizosphere of tomato plants versus surrounding bulk soil. Pot experiments were conducted where tomato plants were exposed to CNT or graphene at 200 mg/kg soil for four weeks. Ectorhizosphere and bulk soils were then collected and analyzed for physicochemical properties and microbiome structure and function. While graphene had a limited impact on the tomato rhizosphere microbiome, CNT significantly increased microbial alpha diversity, induced greater divergence of beta diversity, enhanced microbial interactions, and potentially impacted community functions such as aromatic compound degradation, antioxidant synthesis, and redox cofactor biosynthesis. Furthermore, CNT induced stronger and/or unique microbiome alterations in the tomato rhizosphere compared to bulk soil. Our findings reveal the differential modulating effects of two widely-used CNMs on plant rhizosphere microbiomes and highlight an imminent need to understand complex plant root-microbe interplays in the CNM-impacted rhizosphere. These results have implication for realizing the full potential of phytoapplication of CNMs toward improved and sustainable plant production.

microbiology↗

bioRxiv2022

Modelling group heteroscedasticity in single-cellRNA-seq pseudo-bulk data

Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and when not modelled appro-priately, its presence can hamper the detection of differentially expressed genes. Most bulk RNA-seq methods assume equal group variances which will under- and/or over-estimate the true variability in such datasets. We present two methods that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold standard methods that do not account for heteroscedasticity, we show results from simulation studies and various experiments that demonstrate the superior performance of both voomByGroup and voomQWB in error control and power when group variances in pseudo-bulk scRNA-seq data are unequal. We recommend the use of either of these methods over established approaches, with voomByGroup having the advantage of accurate variance estimation since group variance trends can take on different "shapes", whilst voomQWB has the advantage of catering to complex study designs.

bioinformatics↗

bioRxiv2022

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3 or 5 ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.

bioinformatics↗

bioRxiv2022

Cross-Modality and Self-Supervised Protein Embedding for Compound-Protein Affinity and Contact Prediction

MotivationComputational methods for compound-protein affinity and contact (CPAC) prediction aim at facilitating rational drug discovery by simultaneous prediction of the strength and the pattern of compound-protein interactions. Although the desired outputs are highly structure-dependent, the lack of protein structures often makes structure-free methods rely on protein sequence inputs alone. The scarcity of compound-protein pairs with affinity and contact labels further limits the accuracy and the generalizability of CPAC models. ResultsTo overcome the aforementioned challenges of structure naivety and labelled-data scarcity, we introduce cross-modality and self-supervised learning, respectively, for structure-aware and task-relevant protein embedding. Specifically, protein data are available in both modalities of 1D amino-acid sequences and predicted 2D contact maps, that are separately embedded with recurrent and graph neural networks, respectively, as well as jointly embedded with two cross-modality schemes. Furthermore, both protein modalities are pretrained under various self-supervised learning strategies, by leveraging massive amount of unlabelled protein data. Our results indicate that individual protein modalities differ in their strengths of predicting affinities or contacts. Proper cross-modality protein embedding combined with self-supervised learning improves model generalizability when predicting both affinities and contacts for unseen proteins. AvailabilityData and source codes are available at https://github.com/Shen-Lab/CPAC. Contactyshen@tamu.edu Supplementary informationSupplementary data are included.

bioinformatics↗

bioRxiv2022

CIS calibrates GM-CSF signalling strength to regulate macrophage polarization via a STAT5-IRF8 axis

The cytokine granulocyte-macrophage-colony stimulating factor (GM-CSF) possesses the ability to differentiate macrophages (MOs) with opposing functions, namely proinflammatory M1-like and immunosuppressive M2-like. Despite the importance and opposing functional outcomes of these processes, the intrinsic mechanism that regulates the functional polarization of MOs under GM-CSF signaling remains elusive. Here we show that GM-CSF induced MOs polarisation resulted in the expression of the Cytokine-inducible SH2-containing protein (CIS), and that CIS deficiency diverted differentiation of monocytes into immunosuppressive M2-like MOs expression. CIS deficiency resulted in the hyperactivation of the JAK-STAT5 signaling pathway, consequently promoting the downregulation of the transcription factor Interferon Regulatory Factor 8 (IRF8). Loss and gain of function approaches highlighted IRF8 as a critical instructor of the M1-like polarisation program. In vivo, CIS deficiency led to skewing to M2-like macrophages, which induced strong Th2 immune responses characterised by the development of severe experimental asthma. Collectively, we reveal a CIS-censored mechanism interpreting the opposing actions of GM-CSF in MO differentiation and uncovering its role in controlling allergic inflammation.

immunology↗

bioRxiv2022

Comparative transcriptomics unveil distinctive metabolic pathway of phosphonate utilization by diatom Phaeodactylum tricornutum

Phosphonates are important constituents of marine organic phosphorus, however, the bioavailability and catabolism of phosphonates by eukaryotic phytoplankton remain enigmatic. Here, we use diatom Phaeodactylum tricornutum to investigate the bioavailability of phosphonates and elaborate the underlying molecular mechanism. Our results showed that 2-aminoethylphosphonic (2-AEP) can be utilized as alternative phosphorus source. Comparative transcriptomics unveil the 2-AEP utilization comprising two steps, molecular uptake through clathrin-mediated endocytosis and incorporation into the membrane phospholipids in the form of diacylglyceryl-2-AEP (DAG-2-AEP). In the global ocean, we found the prevalence of key genes responsible for vesicle formation (CLTC, AP-2) and DAG-AEP synthesis (PCYT2 and EPT1) in diatom assemblage. In accordance with the observation of elevated transcript abundance in cold waters, our culture experiments showed that cells grown in 2-AEP are more active at lower temperature. This study elucidated a distinctive mechanism of phosphonate utilization by diatom and inspected the ecological implications in adaptive mechanism.

microbiology↗

bioRxiv2021

NanoSplicer: Accurate identification of splice junctions using Oxford Nanopore sequencing

MotivationLong read sequencing methods have considerable advantages for characterising RNA isoforms. Oxford nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilising matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages. ResultsWe developed "NanoSplicer" to identify splice junctions using raw nanopore signal (squiggles). For each splice junction the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using 1. synthetic mRNAs with known splice junctions 2. biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. Our method is implemented in the software package NanoSplicer, available at https://github.com/shimlab/NanoSplicer.

bioinformatics↗

bioRxiv2021

Pattern separation and tuning shift in human sensory cortex underlie fear memory

Animal research has recognized the role of the sensory cortex in fear memory and two key underlying mechanisms--pattern separation and tuning shift. We interrogated these mechanisms in the human sensory cortex in an olfactory differential conditioning study with a delayed (9-day) retention test. Combining affective appraisal and olfactory psychophysics with functional magnetic resonance imaging (fMRI) multivoxel pattern analysis and voxel-based tuning analysis over a linear odor-morphing continuum, we confirmed affective and perceptual learning and memory and demonstrated associative plasticity in the human olfactory (piriform) cortex. Specifically, the piriform cortex exhibited immediate and lasting enhancement in pattern separation (between the conditioned stimuli/CS and neighboring non-CS) and late-onset yet lasting tuning shift towards the CS, especially in anxious individuals. These findings highlight an evolutionarily conserved sensory cortical system of fear memory, which can underpin sensory encoding of fear/threat and confer a sensory mechanism to the neuropathophysiology of anxiety.

neuroscience↗

bioRxiv2021

Benchmarking UMI-based single cell RNA-sequencing preprocessing workflows

Single-cell RNA sequencing (scRNA-seq) technologies and associated analysis methods have undergone rapid development in recent years. This includes methods for data preprocessing, which assign sequencing reads to genes to create count matrices for downstream analysis. Several packaged preprocessing workflows have been developed that aim to provide users with convenient tools for handling this process. How different preprocessing workflows compare to one another and influence downstream analysis has been less well studied. Here, we systematically benchmark the performance of 9 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2 and scruff) using datasets with varying levels of biological complexity generated on the CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. We find that lowly expressed genes are discordant between workflows and observe that some workflows have systematic biases towards particular classes of genomics features. While the scRNA-seq preprocessing workflows compared varied in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produced clustering results that agreed well with the known cell type labels that provided the ground truth in our analysis. In summary, the choice of preprocessing method was found to be less influential than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.

bioinformatics↗

bioRxiv2021

Genomic differentiation within East Asian Helicobacter pylori

The East Asian region, including China, Japan and Korea, accounts for half of gastric cancer deaths. However, different areas have contrasting gastric cancer incidence and the population structure of Helicobacter pylori in this ethnically diverse region is yet unknown. We aimed to investigate genomic differences in H. pylori between these areas to identify sequence polymorphisms associated with increased cancer risk. We analysed 381 H. pylori genomes collected from different areas of the three countries using phylogenetic and population genetic tools to characterize population differentiation. The functional consequences of Single Nucleotide Polymorphisms (SNPs) with a highest fixation index (Fst) between subpopulations were examined by mapping amino-acid changes on 3D protein structure, solved or modelled. 329/381 genomes belonged to the previously identified hspEAsia population indicating that import of bacteria from other regions of the world has been uncommon. Seven sub-regional clusters were found within hspEAsia, related to sub-populations with various ethnicities, geographies and gastric cancer risks. Sub-population-specific amino-acid changes were found in multi-drug exporters (hefC), transporters (frpB-4), outer membrane proteins (hopI), and several genes involved in host interaction, such as catalase, involved in H2O2 entrance, and a flagellin site mimicking host glycosylation. Several of the top hits including frpB-4, hefC, alpB/hopB, and hofC. were also differentiated within the Americas, indicating that a handful of genes may be key to local geographic adaptation. H. pylori within East Asia are not homogeneous but have become differentiated geographically at multiple loci that have facilitated adaptation to local conditions and hosts. This has important implications for further evaluation of these changes in relation to the varying gastric cancer incidence between geographical areas in this region.

microbiology↗

bioRxiv2021

Essential Role of Placenta Derived Interferon Stimulated Gene 20 Against ZIKA Virus Infection

Zika virus is a positive-sense single-stranded RNA virus, which can be transmitted across the placenta and leads to adverse effects on fetal development during pregnancy. The severity of these complications highlights the importance of prevention and treatment. However, no vaccines or drugs are currently available. In this study, we characterized the IFN{beta}-mediated antiviral response in trophoblast cells in order to identify critical components that are necessary for the successful control of viral replication; and determined whether we could use the components of the IFN-induced response as a replacement therapy for ZIKA viral infection during pregnancy. We identified and characterized interferon stimulated gene 20 (ISG20), playing a central role in controlling Zika infection in trophoblast cells, and successfully established a recombinant ISG20-Fc protein that effectively decrease viral titers in vitro and in vivo by maintaining its exonuclease activity and displaying immune modulatory functions. Therefore, rISG20-Fc is a promising anti-viral/immune modulatory approach for the control and prevention of RNA viral infections such as ZIKA virus.

immunology↗

bioRxiv2020

Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery. In this study we consider proteins as multi-modal data including 1D amino-acid sequences and (sequence-predicted) 2D residue-pair contact maps. We empirically evaluate the embeddings of the two single modalities in their accuracy and generalizability of CPAC prediction (i.e. structure-free interpretable compound-protein affinity prediction). And we rationalize their performances in both challenges of embedding individual modalities and learning generalizable embedding-label relationship. We further propose two models involving cross-modality protein embedding and establish that the one with cross interaction (thus capturing correlations among modalities) outperforms SOTAs and our single modality models in affinity, contact, and binding-site predictions for proteins never seen in the training set.

bioinformatics↗

bioRxiv2020

The Biogeography of Fungal Communities Across Different Chinese Wine-producing Regions Associated with Environmental Factors and Spontaneous Fermentation Performance

Chinese Marselan grapes are believed to possess the potential to become a characteristic regional variety, whose quality is internationally recognized. The fermentation-related mycobiota from six climatically diverse Marselan-producing regions in China were analyzed via high-throughput sequencing (HTS), while the influence of environmental factors was evaluated as well. The results implied that the phyla Ascomycota and genus Aureobasidium dominated the fungal communities in 166 Marselan must and ferment samples. Significant differences were detected in the fungal microbiota from the regions, as well as the wineries, while these discrepancies decreased as the fermentation progressed. Moreover, the difference in fungal communities between the wineries exceeded the variation involving the regions, and higher diversity was identified in the types of species than in the compositions. Geoclimatic elements (Gc) and physicochemical indexes (Pi) exerted a significant effect on the fungal must consortium, explaining 58.17% of the taxonomic information. Therefore, a correlation was proposed between the spontaneous fermentation performance, as well as the polyphenol (PP) content, and their association with fungal taxonomic composition. In addition to enriching the knowledge regarding the wine microbiome and its influencing factors, this study may provide a new strategy for harnessing autochthonous "microbial terroir". ImportanceWine microbiome and their metabolism play a crucial role in wine quality. Chinese wine-producing regions are widely distributed with diverse geoclimatic conditions, this study reports a most comprehensive biogeography of fermentation-related fungal communities performed to date, taking the Chinese promising Marselan grape variety as the research object. It reveals significant differences in the fungal microbiota of Marselan must and ferment from different regions and wineries, and higher otherness between species type than that between species composition. The study also reports the main geoclimatic and physicochemical factors shaping fungal flora. Moreover, a correlation was proposed among the spontaneous fermentation performance with fungal taxonomic composition and polyphenol content. Taken together, the results shed light on the wine fungal communities and its influencing factors, which is crucial for vineyard and fermentation microorganism management, and may also provide a new strategy for harnessing autochthonous "microbial terroir" by utilizing spontaneous fermentation.

microbiology↗

bioRxiv2020

Longitudinal single-cell immune profiling revealed distinct innate immune response in asymptomatic COVID-19 patients

Recent studies have characterized the single-cell immune landscape of host immune response of coronavirus disease 2019 (COVID-19), specifically focus on the severe condition. However, the immune response in mild or even asymptomatic patients remains unclear. Here, we performed longitudinal single-cell transcriptome sequencing and T cell/B cell receptor sequencing on 3 healthy donors and 10 COVID-19 patients with asymptomatic, moderate, and severe conditions. We found asymptomatic patients displayed distinct innate immune responses, including increased CD56briCD16- NK subset, which was nearly missing in severe condition and enrichment of a new Th2-like cell type/state expressing a ciliated cell marker. Unlike that in moderate condition, asymptomatic patients lacked clonal expansion of effector CD8+ T cells but had a robust effector CD4+ T cell clonal expansion, coincide with previously detected SARS-CoV-2-reactive CD4+ T cells in unexposed individuals. Moreover, NK and effector T cells in asymptomatic patients have upregulated cytokine related genes, such as IFNG and XCL2. Our data suggest early innate immune response and type I immunity may contribute to the asymptomatic phenotype in COVID-19 disease, which could in turn deepen our understanding of severe COVID-19 and guide early prediction and therapeutics.

immunology↗

bioRxiv2020

Modulation of proteasome activity by curcumin and didemethylcurcumin

Modulation of proteasome function by pharmacological interventions and molecular biology tools is an active area of research in cancer biology and neurodegenerative diseases. Curcumin (diferuloylmethane) is a naturally occurring polyphenol that affects multiple signaling pathways and known to modulate PKC activities. The therapeutic significance of curcumin is often considered to reside in its anti-inflammatory, antioxidant, anti-angiogenic, or anti-apoptotic properties. However, recent research suggests that the therapeutic efficacy of curcumin may be due to its activity as a potent inhibitor of the proteasome. In this study, we show that both curcumin and its synthetic polyphenolic derivative (didemethylcurcumin), CUIII modulated proteasome activity in a biphasic manner. Curcumin and CUIII increased proteasome activity at nanomolar concentrations but inhibited proteasome activity at micromolar concentrations. Curcumin was more effective than CUIII in relative proteasome activity increase at nanomolar concentrations. Also, curcumin was more effective than CUIII in relative proteasome activity inhibition at micromolar concentration. The docking study was conducted on the 20S proteasome catalytic subunit. Estimated Kd values for curcumin and didemethylcurcumin are 0.0054M and 1.3167M, respectively. These values correlate well with the results of the effectiveness of modulation by curcumin compare to CUIII. The small size of CUIII makes its dock to the narrow cavity of the active site, but the binding interaction is not strong compare to curcumin. This study suggests that curcumin and its didemethyl derivative can be used to modulate proteasome activity. This communication implicates the reason why curcumin and its didemethyl derivative can be used to two different disease mechanisms, neurodegeneration, and cancer.

neuroscience↗