Research on College Students' Innovation and Entrepreneurship Education from The Perspective of Artificial Intelligence Knowledge-Based Crowdsourcing

Based on the practical process of innovation and entrepreneurship education for college students in the author's university, this study analyzes and deconstructs the key concepts of AI knowledge-based crowdsourcing on the basis of literature research, and analyzes the objective fitting needs of combining AI knowledge-based crowdsourcing with college students' innovation and entrepreneurship education practice through a survey and research of a random sample of college students, and verifies that college students' knowledge and application of AI knowledge-based crowdsourcing in the learning and practice of innovation and entrepreneurship The study also verifies the awareness and application of AI knowledge-based crowdsourcing knowledge by university students in the learning and practice of innovation and entrepreneurship

Semiconductor Electronic Label-Free Assay for Predictive Toxicology.

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology

Leveraging writing systems changes for deep learning based Chinese affective analysis

Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text

Statin pretreatment is protective despite an association with greater coronary artery disease burden in patients presenting with a first ST-elevation myocardial infarction.

The relationship of chronic pre-event statin use with coronary disease severity at the time of presentation with a first acute ST-elevation myocardial infarction (STEMI) is unknown. A retrospective review was performed of consecutive patients presenting with STEMI and without a prior history of vascular disease, divided into those whom had been treated with statins before presentation (n=50) and those whom were not pretreated (n=231). Patients pretreated with statins were more likely to have left main (24.0% vs 8.3%; P=.001) or 3-vessel disease (44.0% vs 25.1%; P=.007) vs untreated patients. After matching for risk factors, a trend toward higher likelihood of 3-vessel disease persisted in the statin pretreatment group (47.6% vs 28.6%; P=.07). Significantly lower peak troponin-I levels (87.8 mg/dL vs 134.5 mg/dL; P=.006) were found in patients pretreated with statins, suggesting that statin pretreatment is protective in patients with STEMI despite the presence of greater disease burden. This finding supports the concept that statin therapy alters the natural history of coronary artery disease development leading to a first STEMI and is cardioprotective in those patients who experience a first STEMI

An efficient synthesis of Vildagliptin intermediates

Efficient and high yielding methods for the preparation of vildagliptin 1 intermediate of (S)-1-(2-chloroacetyl) pyrrolidine-2-carbonitrile 2 and 3-amino-1-adamantane alcohol 3 respectively have been described. (S)-1-(2-Chloroacetyl) pyrrolidine-2-carbonitrile 2 has been synthesized from l-proline 2a via chloroacetyl chloride, performed with acetonitrile in the presence of sulfuric acid via one-pot reactions. 3-Amino-1-adamantane alcohol 3 has been prepared from amantadine hydrochloride via oxidation by sulfuric acid/nitric acid and boric acid as catalyst, and has been subjected to ethanol extraction. The overall yield is about 95%.

An efficient synthesis of Vildagliptin intermediates

1128-1131Efficient and high yielding methods for the preparation of vildagliptin 1 intermediate of (S)-1-(2-chloroacetyl) pyrrolidine-2-carbonitrile 2 and 3-amino-1-adamantane alcohol 3 respectively have been described. (S)-1-(2-Chloroacetyl) pyrrolidine-2-carbonitrile 2 has been synthesized from L-proline 2a via chloroacetyl chloride, performed with acetonitrile in the presence of sulfuric acid via one-pot reactions. 3-Amino-1-adamantane alcohol 3 has been prepared from amantadine hydrochloride via oxidation by sulfuric acid/nitric acid and boric acid as catalyst, and has been subjected to ethanol extraction. The overall yield is about 95%

Global Proteomic Analysis of the Resuscitation State of Vibrio parahaemolyticus Compared With the Normal and Viable but Non-culturable State

Vibrio parahaemolyticus is a common pathogen which has become a major concern of seafood products. The bacteria in the viable but non-culturable (VBNC) state are unable to form colonies on growth media, but under appropriate conditions they can regain culturability. In this study, V. parahaemolyticus was induced into VBNC state at low temperature and oligotrophic condition, and was resuscitated to culturable state. The aim of this study is to explore the comparative proteomic profiles of the resuscitation state compared with the VBNC state and the exponential phase of V. parahaemolyticus using isobaric tags for relative and absolute quantitation (iTRAQ) technique. The differentially expressed proteins (DEPs) were subjected to GO functional annotations and KEGG pathway analysis. The results indicated that a total of 429 proteins were identified as the significant DEPs in the resuscitation cells compared with the VBNC cells, including 330 up-regulated and 99 down-regulated DEPs. Meanwhile, the resuscitation cells displayed 25 up-regulated and 36 down-regulated DEPs (total of 61 DEPs) in comparison with the exponential phase cells. The remarkable DEPs including ribosomal proteins, ABC transporters, outer membrane proteins and flagellar proteins. GO annotation showed that the 429 DEPs were classified into 37 GO terms, of which 17 biological process (BP) terms, 9 cellular component (CC) terms and 11 molecular function (MF) terms. The up-regulated proteins presented in all GO terms except two terms of developmental process and reproduction. The 61 DEPs were assigned to 23 GO terms, the up- and down-regulated DEPs were both mainly involved in cellular process, establishment of localization, metabolic process and so on. KEGG pathway analysis revealed that the 429 DEPs were assigned to 35 KEGG pathways, and the pathways of ribosome, glyoxylate and dicarboxylate metabolism were significantly enriched. Moreover, the 61 DEPs located in 26 KEGG pathways, including the significantly enriched KEGG pathways of ABC transporters and two-component system. This study would contribute to a better understanding of the molecular mechanism underlying the resuscitation of the VBNC state of V. parahaemolyticus

Predicting per-lesion local recurrence in locally advanced non-small cell lung cancer following definitive radiation therapy using pre- and mid-treatment metabolic tumor volume

Background: We evaluated whether pre- and mid-treatment metabolic tumor volume (MTV) predicts per lesion local recurrence (LR) in patients treated with definitive radiation therapy (RT, dose≥60 Gy) for locally advanced non-small cell lung cancer (NSCLC). Methods: We retrospectively reviewed records of patients with stage III NSCLC treated from 2006 to 2018 with pre- and mid-RT PET-CT. We measured the MTV of treated lesions on the pre-RT (MTVpre) and mid-RT (MTVmid) PET-CT. LR was defined per lesion as recurrence within the planning target volume. Receiver operating characteristic (ROC) curves, cumulative incidence rates, and uni- and multivariable (MVA) competing risk regressions were used to evaluate the association between MTV and LR. Results: We identified 111 patients with 387 lesions (112 lung tumors and 275 lymph nodes). Median age was 68 years, 69.4% were male, 46.8% had adenocarcinoma, 39.6% had squamous cell carcinoma, and 95.5% received concurrent chemotherapy. Median follow-up was 38.7 months. 3-year overall survival was 42.3%. 3-year cumulative incidence of LR was 26.8% per patient and 11.9% per lesion. Both MTVpre and MTVmid were predictive of LR by ROC (AUC = 0.71 and 0.76, respectively) and were significantly associated with LR on MVA (P = 0.004 and P = 7.1e-5, respectively). Among lesions at lower risk of LR based on MTVpre, higher MTVmid was associated with LR (P = 0.001). Conclusion: Per-lesion, larger MTVpre and MTVmid predicted for increased risk of LR. MTVmid was more highly predictive of LR than MTVpre and if validated may allow for further discrimination of high-risk lesions at mid-RT informing dose painting strategies

PromptTTS 2: Describing and Generating Voices with Text Prompt

Speech conveys more information than just text, as the same word can be uttered in various voices to convey diverse information. Compared to traditional text-to-speech (TTS) methods relying on speech prompts (reference speech) for voice variability, using text prompts (descriptions) is more user-friendly since speech prompts can be hard to find or may not exist at all. TTS approaches based on the text prompt face two challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompt for speech. In this work, we introduce PromptTTS 2 to address these challenges with a variation network to provide variability information of voice not captured by text prompts, and a prompt generation pipeline to utilize the large language models (LLM) to compose high quality text prompts. Specifically, the variation network predicts the representation extracted from the reference speech (which contains full information about voice) based on the text prompt representation. For the prompt generation pipeline, it generates text prompts for speech with a speech understanding model to recognize voice attributes (e.g., gender, speed) from speech and a large language model to formulate text prompt based on the recognition results. Experiments on a large-scale (44K hours) speech dataset demonstrate that compared to the previous works, PromptTTS 2 generates voices more consistent with text prompts and supports the sampling of diverse voice variability, thereby offering users more choices on voice generation. Additionally, the prompt generation pipeline produces high-quality prompts, eliminating the large labeling cost. The demo page of PromptTTS 2 is available online\footnote{https://speechresearch.github.io/prompttts2}.Comment: Demo page: https://speechresearch.github.io/prompttts