Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

SIMD Everywhere Optimization from ARM NEON to RISC-V Vector Extensions

Many libraries, such as OpenCV, FFmpeg, XNNPACK, and Eigen, utilize Arm or x86 SIMD Intrinsics to optimize programs for performance. With the emergence of RISC-V Vector Extensions (RVV), there is a need to migrate these performance legacy codes for RVV. Currently, the migration of NEON code to RVV code requires manual rewriting, which is a time-consuming and error-prone process. In this work, we use the open source tool, "SIMD Everywhere" (SIMDe), to automate the migration. Our primary task is to enhance SIMDe to enable the conversion of ARM NEON Intrinsics types and functions to their corresponding RVV Intrinsics types and functions. For type conversion, we devise strategies to convert Neon Intrinsics types to RVV Intrinsics by considering the vector length agnostic (vla) architectures. With function conversions, we analyze commonly used conversion methods in SIMDe and develop customized conversions for each function based on the results of RVV code generations. In our experiments with Google XNNPACK library, our enhanced SIMDe achieves speedup ranging from 1.51x to 5.13x compared to the original SIMDe, which does not utilize customized RVV implementations for the conversions

Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection

The inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for TVR detection. The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority (p < 0.01) compared to prior GLP processing. Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise.Comment: published in IEEE Journal of Translational Engineering in Health & Medicin

Expert-Novice Differences in SMR Activity during Dart Throwing

Cheng M-Y, Hung C-L, Huang C-J, et al. Expert-Novice Differences in SMR Activity during Dart Throwing. Biological Psychology. 2015;110:212-218.Previous evidence suggests that augmented sensorimotor rhythm (SMR) activity is related to the superior regulation of processing cognitive-motor information in motor performance. However, no published studies have examined the relationship between SMR and performance in precision sports; thus, this study examined the relationship between SMR activity and the level of skilled performance in tasks requiring high levels of attention (e.g., dart throwing). We hypothesized that skilled performance would be associated with higher SMR activity. Fourteen dart-throwing experts and eleven novices were recruited. Participants were requested to perform 60 dart throws while EEG was recorded. The 2 (Group: Expert, Novice) x 2 (Time window: –2000 ms to –1000 ms, –1000 ms to 0 ms) ANOVA showed that the dart-throwing experts maintained a relatively higher SMR power than the novices before dart release. These results suggest that SMR might reflect the adaptive regulation of cognitive-motor processing during the preparatory period

Whole-genome DNA methylome analysis of different developmental stages of the entomopathogenic fungus Beauveria bassiana NCHU-157 by nanopore sequencing

The entomopathogenic fungus (EPF), Beauveria bassiana, is an important and commonly used EPF for microbial control. However, the role of DNA methylation has not been thoroughly studied. Therefore, the whole genomic DNA methylome of one promising EPF isolate, B. bassiana NCHU-157 (Bb-NCHU-157), was investigated by Oxford Nanopore Technologies (ONT). First, the whole genome of Bb-NCHU-157 was sequenced by next-generation sequencing (NGS) and ONT. The genome of Bb-NCHU-157 contains 16 contigs with 34.19 Mb and 50% GC content, which are composed of 10,848 putative protein-coding genes. Two putative DNA methyltransferases (DNMTs) were found, including Dim-2 and C-5 cytosine-specific DNA methylases. Both DNMTs showed higher expression levels in the mycelium stage than in the conidia stage, indicating that development of DNA methylation in Bb-NCHU-157 might occur in the mycelium stage. The global methylation level of the mycelium stage (5 mC = 4.56%, CG = 3.33%, CHG = 0.74%, CHH = 0.49%) was higher than that of the conidial stage (5 mC = 2.99%, CG = 1.99%, CHG = 0.63%, CHH = 0.37%) in both the gene and transposable element (TE) regions. Furthermore, the TE regions showed higher methylation frequencies than the gene regions, especially for CHH site methylation, suggesting regulation of genomic stabilization during mycelium development. In the gene regions, high methylation frequencies were found around the transcription start site (TSS) and transcription end site (TES). Moreover, CG and CHG methylation mainly occur in the promoter and intergenic regions, while CHH methylation occurs in the TE region. Among the methylated regions, 371, 661, and 756 differentially DNA methylated regions (DMRs) were hypermethylated in the mycelium in CG, CHG, and CHH, while only 13 and 7 DMRs were hypomethylated in the mycelium in CHG, and CHH, respectively. Genes located in the DMR shared the GO terms, DNA binding (GO: 0003677), and sequence-specific DNA binding (GO: 0043565) for hypermethylation in the mycelium, suggesting that methylation might regulate gene expression from the initial process. Evaluation of the DNA methylome in Bb-NCHU-157 by ONT provided new insight into this field. These data will be further validated, and epigenetic regulation during the development of B. bassiana will be explored

Comparative cardiovascular risk in users versus non-users of xanthine oxidase inhibitors and febuxostat versus allopurinol users

OBJECTIVES: The aim of this study is to determine major adverse cardiovascular events (MACE) and all-cause mortality comparing between xanthine oxidase inhibitors (XOIs) and non-XOI users, and between allopurinol and febuxostat. METHODS: This is a retrospective cohort study of gout patients prescribed anti-hyperuricemic medications between 2013 and 2017 using a territory-wide administrative database. XOI users were matched 1:1 to XOI non-users using propensity scores. Febuxostat users were matched 1:3 to allopurinol users. Subgroup analyses were conducted based on colchicine use. RESULTS: Of the 13 997 eligible participants, 3607 (25.8%) were XOI users and 10 390 (74.2%) were XOI non-users. After propensity score matching, compared with non-users (n = 3607), XOI users (n = 3607) showed similar incidence of MACE (hazard ratio [HR]: 0.997, 95% CI, 0.879, 1.131; P>0.05) and all-cause mortality (HR = 0.972, 95% CI 0.886, 1.065, P=0.539). Febuxostat (n = 276) users showed a similar risk of MACE compared with allopurinol users (n = 828; HR: 0.672, 95% CI, 0.416, 1.085; P=0.104) with a tendency towards a lower risk of heart failure-related hospitalizations (HR = 0.529, 95% CI 0.272, 1.029; P=0.061). Concurrent colchicine use reduced the risk for all-cause mortality amongst XOI users (HR = 0.671, 95% 0.586, 0.768; P<0.001). CONCLUSION: In gout patients, XOI users showed similar risk of MACE and all-cause mortality compared with non-users. Compared with allopurinol users, febuxostat users showed similar MACE and all-cause mortality risks but lower heart failure-related hospitalizations

Intellectual Property Rights and Skills Accumulation: A North-South Model of FDI and Outsourcing

ABSTRACT This study investigates the effects of stronger intellectual property rights (IPR) protection in the South on innovation, skills choice, wage inequality and patterns of production based on a North-South general-equilibrium model with foreign direct investment (FDI) and international outsourcing. We find that stronger IPR protection in the South raises the extent of outsourcing and reduces the extent of FDI. This raises the proportion of Southerners being unskilled and mitigates wage inequality in the South. In the North, stronger Southern IPR protection raises the proportion of Northerners being skilled and wage inequality. The increased welfare due to the lower average price of goods may be offset by damage caused by lower average income. The effects of international specialization, R&amp;D cost and Northern population are also examined