Nan Ding

With the rapid development of sequencing technologies towards higher throughput and lower cost, sequence data are generated at an unprecedentedly explosive rate. To provide an efficient and easy-to-use platform for managing huge sequence data, here we present Genome Sequence Archive (GSA; http://bigd.big.ac.cn/gsa or http://gsa.big.ac.cn), a data repository for archiving raw sequence data. In compliance with data standards and structures of the International Nucleotide Sequence Database Collaboration (INSDC), GSA adopts four data objects (BioProject, BioSample, Experiment, and Run) for data organization, accepts raw sequence reads produced by a variety of sequencing platforms, stores both sequence reads and metadata submitted from all over the world, and makes all these data publicly available to worldwide scientific communities. In the era of big data, GSA is not only an important complement to existing INSDC members by alleviating the increasing burdens of handling sequence data deluge, but also takes the significant responsibility for global big data archive and provides free unrestricted access to all publicly available data in support of research activities throughout the world.

Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pre-trained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train a large, 4-billion parameter ViT (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design.

Circulating microRNA expression levels can serve as diagnostic/prognostic biomarkers in several types of malignant tumors; however, to our knowledge, there have been reports describing their value in cervical squamous cell carcinoma (SCC). In this study, we used hybridization arrays to compare the microRNA expression profiles in cervical squamous cell carcinomas (SCC) samples among patients with lymph node metastasis (LNM) or without LNM; 89 microRNAs were found to fit our inclusion criteria. Using quantitative PCR (qPCR), we examined the expression levels of these microRNAs in cervical cancer tissue, as well as in serum from patients and healthy women. We compared the expression levels between patients with LNM (n=40) and those without LNM (n=40) and healthy controls (n=20). Using regression analysis, we generated a comprehensive set of marker microRNAs and drew the fitted binormal receiver operating characteristic (ROC) curves to access the predictive value. We identified 6 serum microRNAs that can predict LNM in cervical SCC patients; these microRNAs were miR-1246, miR-20a, miR-2392, miR-3147, miR-3162-5p and miR-4484. The area under the curve (AUC) of the comprehensive set of serum microRNAs predicting LNM was 0.932 (sensitivity, 0.856; specificity, 0.850). The predictive value of the serum microRNAs was inferior to that in tissue (AUC 0.992; sensitivity, 0.967; specificity, 0.950; P=0.018). We compared the LNM predictive value of serum microRNAs and SCC antigen (SCC-Ag) by drawing fitted binormal ROC curves However, serum microRNA analysis is by far superior to serum SCC‑Ag analysis (AUC 0.713; sensitivity, 0.612; specificity, 0.700; P<0.0001). Serum microRNAs are a good predictor of LNM with clinical value in early-stage cervical SCC.

BACKGROUND: The increase in multidrug-resistant tuberculosis (MDR-TB) severely hampers tuberculosis prevention and control in China, a country with the second highest MDR-TB burden globally. The first nationwide drug-resistant tuberculosis surveillance program provides an opportunity to comprehensively investigate the epidemiological/drug-resistance characteristics, potential drug-resistance mutations, and effective population changes of Chinese MDR-TB. METHODS: We sequenced 357 MDR strains from 4600 representative tuberculosis-positive sputum samples collected during the survey (70 counties in 31 provinces). Drug-susceptibility testing was performed using 18 anti-tuberculosis drugs, representing the most comprehensive drug-resistance profile to date. We used 3 statistical and 1 machine-learning methods to identify drug-resistance genes/single-nucleotide polymorphisms (SNPs). We used Bayesian skyline analysis to investigate changes in effective population size. RESULTS: Epidemiological/drug-resistance characteristics showed different MDR profiles, co-resistance patterns, preferred drug combination/use, and recommended regimens among 7 Chinese administrative regions. These factors not only reflected the serious multidrug co-resistance and drug misuse but they were also potentially significant in facilitating the development of appropriate regimens for MDR-TB treatment in China. Further investigation identified 86 drug-resistance genes/intergenic regions/SNPs (58 new), providing potential targets for MDR-TB diagnosis and treatment. In addition, the effective population of Chinese MDR-TB displayed a strong expansion during 1993-2000, reflecting socioeconomic transition within the country. The phenomenon of expansion was restrained after 2000, likely attributable to the advances in diagnosis/treatment technologies and government support. CONCLUSIONS: Our findings provide an important reference and improved understanding of MDR-TB in China, which are potentially significant in achieving the goal of precision medicine with respect to MDR-TB prevention and treatment.