AI Assistant in Online Pharmacy

Artificial intelligence (AI) has been increasingly popular in diagnosing diseases and recommending drugs in digital healthcare platforms. Leveraging the introduction of an AI-powered medical assistant to one drug category in an online pharmacy platform, we investigate how the adoption of AI affects users’ purchase behaviors using a difference-in-differences design. We find that the adoption of the AI assistant significantly increases users’ purchases in the platform, even for drugs not recommended by the AI assistant. Furthermore, we find that the positive effect of the AI assistant adoption is stronger for early technology adopters, inexperienced users, and users with higher privacy concerns, likely because these users tend to perceive higher value from AI. Finally, our mediation analysis shows that the AI feature increases users’ purchases by increasing their engagement levels in the platform. Our results have important implications for designing and evaluating AI features in online platforms

Image Data Augmentation for Deep Learning: A Survey

Deep learning has achieved remarkable results in many computer vision tasks. Deep neural networks typically rely on large amounts of training data to avoid overfitting. However, labeled data for real-world applications may be limited. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data. As an effective way to improve the sufficiency and diversity of training data, data augmentation has become a necessary part of successful application of deep learning models on image data. In this paper, we systematically review different image data augmentation methods. We propose a taxonomy of reviewed methods and present the strengths and limitations of these methods. We also conduct extensive experiments with various data augmentation methods on three typical computer vision tasks, including semantic segmentation, image classification and object detection. Finally, we discuss current challenges faced by data augmentation and future research directions to put forward some useful research guidance

Insights into lignocellulose degradation: comparative genomics of anaerobic and cellulolytic Ruminiclostridium-type species

Mesophilic, anaerobic, and cellulolytic Ruminiclostridium-type bacterial species can secrete an extracellular, multi-enzyme machinery cellulosome, which efficiently degrades cellulose. In this study, we first reported the complete genome of Ruminiclostridium papyrosolvens DSM2782, a single circular 5,027,861-bp chromosome with 37.1% G + C content, and compared it with other Ruminiclostridium-type species. Pan-genome analysis showed that Ruminiclostridium-type species share a large number of core genes to conserve basic functions, although they have a high level of intraspecific genetic diversity. Especially, KEGG mapping revealed that Ruminiclostridium-type species mainly use ABC transporters regulated by two-component systems (TCSs) to absorb extracellular sugars but not phosphotransferase systems (PTSs) that are employed by solventogenic clostridia, such as Clostridium acetobutylicum. Furthermore, we performed comparative analyses of the species-specific repertoire of CAZymes for each of the Ruminiclostridium-type species. The high similarity of their cohesins suggests a common ancestor and potential cross-species recognition. Additionally, both differences between the C-terminal cohesins and other cohesins of scaffoldins and between the dockerins linking with cellulases and other catalytic domains indicate a preference for the location of cellulosomal catalytic subunits at scaffoldins. The information gained in this study may be utilized directly or developed further by genetic engineering and optimizing enzyme systems or cell factories for enhanced biotechnological biomass deconstruction and biofuel production

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur

Generation of Embryonic Origin-Specific Vascular Smooth Muscle Cells from Human Induced Pluripotent Stem Cells

Vascular smooth muscle cells (VSMCs), a highly mosaic tissue, arise from multiple distinct embryonic origins and populate different regions of our vascular network with defined boundaries. Accumulating evidence has revealed that the heterogeneity of VSMC origins contributes to region-specific vascular diseases such as atherosclerosis and aortic aneurysm. These findings highlight the necessity of taking into account lineage-dependent responses of VSMCs to common vascular risk factors when studying vascular diseases. This chapter describes a reproducible, stepwise protocol for the generation of isogenic VSMC subtypes originated from proepicardium, second heart field, cardiac neural crest, and ventral somite using human induced pluripotent stem cells. By leveraging this robust induction protocol, patient-derived VSMC subtypes of desired embryonic origins can be generated for disease modeling as well as drug screening and development for vasculopathies with regional susceptibility

Generation of Quiescent Cardiac Fibroblasts Derived from Human Induced Pluripotent Stem Cells.

Myocardial fibrosis is a hallmark of cardiac remodeling, which can progressively lead to heart failure, a leading cause of death worldwide. The effector cells of fibrosis in the heart are cardiac fibroblasts (CFs). There is currently no effective therapeutic strategy clinically available to specifically attenuate maladaptive responses of CFs. Large-scale applications such as high-throughput drug screening are difficult due to the limited availability of human primary CFs, thus limiting the development of future treatments. Here, we describe a robust induction protocol that can be used to generate a scalable, consistent, genetically defined source of quiescent CFs from human induced pluripotent stem cells for cardiac fibrosis modeling, drug discovery, and tissue engineering

Generation of Vascular Smooth Muscle Cells From Induced Pluripotent Stem Cells: Methods, Applications, and Considerations.

The developmental origin of vascular smooth muscle cells (VSMCs) has been increasingly recognized as a major determinant for regional susceptibility or resistance to vascular diseases. As a human material-based complement to animal models and human primary cultures, patient induced pluripotent stem cell iPSC-derived VSMCs have been leveraged to conduct basic research and develop therapeutic applications in vascular diseases. However, iPSC-VSMCs (induced pluripotent stem cell VSMCs) derived by most existing induction protocols are heterogeneous in developmental origins. In this review, we summarize signaling networks that govern in vivo cell fate decisions and in vitro derivation of distinct VSMC progenitors, as well as key regulators that terminally specify lineage-specific VSMCs. We then highlight the significance of leveraging patient-derived iPSC-VSMCs for vascular disease modeling, drug discovery, and vascular tissue engineering and discuss several obstacles that need to be circumvented to fully unleash the potential of induced pluripotent stem cells for precision vascular medicine

Generation of Vascular Smooth Muscle Cells From Induced Pluripotent Stem Cells: Methods, Applications, and Considerations.

The developmental origin of vascular smooth muscle cells (VSMCs) has been increasingly recognized as a major determinant for regional susceptibility or resistance to vascular diseases. As a human material-based complement to animal models and human primary cultures, patient induced pluripotent stem cell iPSC-derived VSMCs have been leveraged to conduct basic research and develop therapeutic applications in vascular diseases. However, iPSC-VSMCs (induced pluripotent stem cell VSMCs) derived by most existing induction protocols are heterogeneous in developmental origins. In this review, we summarize signaling networks that govern in vivo cell fate decisions and in vitro derivation of distinct VSMC progenitors, as well as key regulators that terminally specify lineage-specific VSMCs. We then highlight the significance of leveraging patient-derived iPSC-VSMCs for vascular disease modeling, drug discovery, and vascular tissue engineering and discuss several obstacles that need to be circumvented to fully unleash the potential of induced pluripotent stem cells for precision vascular medicine

Characterization of new microsatellite markers based on the transcriptome sequencing of Clematis finetiana

Abstract Background Clematis is the biggest genus in the family Ranunculaceae with about 300 species. Clematis is also a globally important commercial group of flowers, especially in the United States and European countries. Their petals with different colors and shapes make the genus the “Queen of the Vines”. However, the genomic information and phylogeny of Clematis based on existing molecular studies are limited. In this paper, new microsatellites (SSR) markers were identified from the transcriptome data of C. finetiana obtained using the Illumina paired-end sequencing technology. Results Sequences on a total of 71,900 high-quality unigenes with the mean length of 865 bp were produced in this study. There were 6192unigenes annotated and classified into 49 functional sub-groups in three main ontology categories in GO (Gen Ontology) database,14,022 unigenes mapped to COGs (Clusters of Orthologous Groups) database and classified into 25 functional categories, and 21,494 unigenes obtained and divided into 128 pathways of KEGG (Kyoto Encyclopedia of Genes) Database. A total of 7532 SSRs were discovered from 6337 unigenes. We randomly tested 210 primer pairs, of which 52 primer pairs were able to generate specific products, and 19 possessed polymorphism in the 13 wild populations of six species from Clematis, which were used as a test material. Conclusions The dataset of C. finetiana transcriptome and the identified new SSR markers will promote genetic research and breeding effort in Clematis