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The interactive information in blockchain architecture establishes an effective communication channel between users and enterprises, enabling them to communicate in a comprehensive and effective manner. Therefore, taking blockchain interactive information as the research object, this paper explores how the intervention of official information on investors affects the stock price movement and then makes predictions on stock prices according to the emotional tendency of interactive information. With the contextual information fusion, a sentiment computing model based on a convolutional neural network is established to extract and quantify the emotional features of blockchain interactive information. Combined with investors’ emotional features, the stock price prediction model based on long short-term memory is proposed. The experiment results show that the accuracy of the model has been improved by incorporating the intervened emotional features, thereby proving that information clarification can have a positive effect on the stock price.

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.

Candida auris is a globally emerging yeast that causes outbreaks in health care settings and is often resistant to one or more classes of antifungal medications (1). Cases of C. auris with resistance to all three classes of commonly prescribed antifungal drugs (pan-resistance) have been reported in multiple countries (1). C. auris has been identified in the United States since 2016; the largest number (427 of 911 [47%]) of confirmed clinical cases reported as of October 31, 2019, have been reported in New York, where C. auris was first detected in July 2016 (1,2). As of June 28, 2019, a total of 801 patients with C. auris were identified in New York, based on clinical cultures or swabs of skin or nares obtained to detect asymptomatic colonization (3). Among these patients, three were found to have pan-resistant C. auris that developed after receipt of antifungal medications, including echinocandins, a class of drugs that targets the fungal cell wall. All three patients had multiple comorbidities and no known recent domestic or foreign travel. Although extensive investigations failed to document transmission of pan-resistant isolates from the three patients to other patients or the environment, the emergence of pan-resistance is concerning. The occurrence of these cases underscores the public health importance of surveillance for C. auris, the need for prudent antifungal prescribing, and the importance of conducting susceptibility testing on all clinical isolates, including serial isolates from individual patients, especially those treated with echinocandin medications. This report summarizes investigations related to the three New York patients with pan-resistant infections and the subsequent actions conducted by the New York State Department of Health and hospital and long-term care facility partners.

Considering the deficiencies in the existing emotional lexicons like too many manual interventions, lack of scalability and ignorance of dependency parsing in emotional computing, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public event-oriented Weibo emotional lexicon; then, it proposes a Weibo emotion computing method based on dependency parsing and designs an emotion binary tree based on dependency parsing, and dependency-based emotion calculation rules; and at last, through an experiment, it shows that this emotional lexicon has a wider coverage and higher accuracy than the existing ones, and it also performs a public opinion evolution analysis on an actual public event and the empirical results show that the algorithm is feasible and effective.

For the current online reviews sentiment classification method, there are some problems such as serious text sparseness and coarse granularity of sentiment calculation. In this paper, the emotion in online reviews is divided into four categories: happiness, hope, disgust, and anxiety. Based on the combination of cognitive evaluation theory and sentiment analysis, a novel approach that combines a well-known techniques to sentiment classification, ie, support vector machine and the latent semantic analysis, was proposed. Based on the approach, this paper explored the influence of these four kinds of emotions on the helpfulness of online reviews, examined the moderating effects of emotion on the helpfulness of online reviews under the two types of products. The experimental results showed that this model could effectively conduct multi-emotion fine-grained computing for online reviews, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that happiness and disgust emotion had significant positive impact on the helpfulness of online reviews, while on the other hand anxiety emotion had significant negative influence. The algorithm and its empirical conclusions provide useful theoretical basis and reference for the company to optimize marketing strategy and improve customer relationship under web 2.0.

Crowd Funding is a behavior that raises funds for projects or venture capital by generating a small amount of money from a large number of people. It started late in China, but has rapidly been developeing. At present, the research on crowdfunding in China is still at an early stage and lacks deep-level exploration Those researches mostly focus on the meaning, characteristics, and operating methods of crowd Funding. Therefore, this paper takes crowd Funding literature as the research object, using CiteSpace to map, describe and counts the related keywords and cited references in the target domain. Starting with the cited relations among the existing studies of core database, focusing on weighted documents, this papaer analyzes the relationship of cooccurrence in crowd funding papers, and conductes research hotspots, trends and knowledge structures in the "Crowd Funding" field. The results show that the business economy is the largest research category in crowdfunding, followed by computer science and government laws and regulations, and there are also studies about crowdfunding from the perspective of the environmental ecology science, public management and social science.

Background New York State (NYS) is experiencing a continuing outbreak of Candida auris, first identified in 2016. Patients who are colonized asymptomatically with C. auris can progress to bloodstream infection (BSI). Methods Colonized patients with positive nares or axilla/groin C. auris cultures were followed prospectively. Laboratories, hospitals and skilled nursing facilities reported C. auris clinical infections to the NYS Department of Health. Patient demographics, clinical history, hospital admission, procedures, and outcomes data were obtained using a standardized case report form. Patient-days were determined from date of first positive colonization to date of first positive clinical isolate, death, or March 30, 2018, whichever was first. Results Between September 28, 2016 and March 30, 2018, 187 C. auris colonized patients were identified. Of these, seven progressed to BSI during at least 24,781 patient days of follow-up (median: 98 patient-days, range 0–548 days.) The median time from date of first colonization to date of BSI was 86 days (range 3–310 days). The median patient age at time of colonization was 71 years (range 57–89 years). Between colonization and BSI, patients had a median of five admissions in healthcare facilities (range 1–12). All patients had central neurologic disease, gastrostomy tubes, chronic wounds, and vascular lines at time of BSI. All patients had a positive culture for one or more other multi-drug resistant organism within 90 days of a positive C. auris culture, and all received antibiotics in the 30 days before BSI. Six (86%) patients received mechanical ventilation and had tracheostomies. Five (71%) patients had diabetes. Four (57%) had vascular lines replaced in the 30 days before BSI onset. Two (29%) cases had gastrostomy tube replacement between colonization and BSI. One patient died a week after C. auris BSI; a second died 4 months later. Conclusion In NYS, 4% of C. auris colonized patients developed BSI, a rate of 0.3 BSI per 1,000 patient-days. BSI patients have portals of entry such as indwelling medical devices and wounds. Neurologic disease and diabetes may be risk factors for BSI. Meticulous aseptic technique for invasive procedures, device and wound care may help prevent C. auris BSI in colonized patients. Disclosures All authors: No reported disclosures.

Background As of March 25 2018, 151 clinical cases of C. auris were diagnosed in NYS. We conducted point prevalence surveys (PPS) and environmental surveys (ES) to detect surveillance cases and assess the burden of environmental contamination in NYS healthcare facilities from September 12, 2016. Methods A PPS was defined as culturing ≥2 individuals at a healthcare facility that diagnosed, cared for, or was near a facility with a C. auris case. ES involved environmental swabbing in facilities where cases resided or were admitted. Cultures and polymerase chain reaction (PCR) were performed at the NYS Wadsworth Center. Results As of March 25, 2018, 81 PPS or ES had been conducted at 55 facilities. From these PPS, a total of 144 (6.1%) individuals were positive for C. auris by culture; 125 were PCR positive. The rates of culture positive C. auris identified patients varied by facility type: hospitals (38/767, 5.0%), long-term care facilities (LTCF) (88/1,404, 6.3%), long-term acute care (1/35, 2.9%), and co-located hospital and LTCF (17/138, 12.3%). The majority of the LTCF C. auris culture-positive cases (80/82) were identified in facilities that cared for ventilated patients. Rates in LTCF caring for ventilated patients were nearly 10 times as high as other LTCF [86/1,121 (7.7%) vs. 2/284 (0.7%)]. ES identified 86 (3.0%) samples positive by culture and 257 (8.9%) by PCR. Thirty-seven (67%) of the 55 facilities had at least one positive environmental sample by PCR or culture; many of these positive samples were from surfaces or equipment deemed to be “clean.” Over 1,900 person-hours were needed to conduct onsite PPS and ES that collected >4,200 human and >2,800 environmental samples and identified opportunities for improving basic infection prevention and environmental cleaning. Ten facilities, including the co-located hospital and LTCF, had multiple positive PPS or ES. Conclusion PPS conducted over 17 months detected many colonized individuals and C. auris in facility environments, likely indicating a silent reservoir for this organism beyond clinical cases, especially in LTCFs. Serial PPS and ES can help improve C. auris detection and inform subsequent infection prevention and control interventions. However, these efforts are resource intensive and can divert resources from other activities. Disclosures All authors: No reported disclosures.

In this paper the issue of respiratory complications following acute spinal cord injury with reference to the area of high dependency care is considered. It will deal with the pathophysiology behind acute spinal cord injury and its effect on the respiratory system, while discussing the interventions used to prevent these complications. A multitude of therapeutic interventions in the care of respiratory complications has been identified. And positioning, chest physiotherapy and assisted coughing techniques will be considered in this paper.