SPONSORSHIP IN ESPORTS WELCOME TO THE BRAVE NEW WORLD

In the past few decades, eSports and competitive gaming are experiencing huge developments and growing tremendouslyin itspopularity (Newzoo, 2019; PwC, 2019). Same as any other fast-growing industry, eSports has attracted significant capital investments and marketing practices. As predicted by Newzoo (2019), the market revenue of eSports will reach 1,790 million dollars by 2022, andthe sponsorships would likely be the largestcontributor. Unlike sponsorshipin traditional sports, various features of eSport make the sponsorship in conjunction with the event very unique and different from the traditionalsportssponsorship: the target market, the categories of sponsors, the form of sponsorship, etc. This study intends to compare eSport to the traditionalsports, find out the unique characteristics of eSports, and discuss a recent eSport sponsorshipin the 2018 League of Legends World Championship: where eSport was featuredas a “brave new world” full of opportunities and risks for sponsorshipby using a case study method. Inthe end, the implications, future of eSport sponsorship, and limitations of the research will be explored

Prognostic value of right ventricular free wall strain in patients with sepsis

BackgroundRight ventricular systolic dysfunction (RVSD) in patients with sepsis is an area of growing interest, but its prognostic significance remains unclear and additional tools are needed to improve our understanding. Right ventricular free wall strain (RV-FWS) is a relatively new parameter to assess RV function. This study aimed to investigate the potential correlation between impaired RV-FWS and prognostic outcomes in patients with sepsis.MethodsWe prospectively assessed right ventricular function in patients with sepsis within the initial 24 h of their hospital admission. RV-FWS, right ventricular global strain (RV-GS), fractional area change (FAC), and tricuspid annular plane systolic excursion (TAPSE) were examined. RVSD was defined as impaired RV-FWS. Moreover, the association between RVSD and 30-day mortality rate was assessed.ResultsThis study included 89 patients. Among them, 27 (30.3%) succumbed to their illness within 30 days. The nonsurviving patients demonstrated significantly lower absolute RV-FWS (−19.7% ± 2.4% vs. −21.1% ± 2.1%, P = 0.008) and RV-GS (−17.7% ± 1.2% vs. −18.4% ± 1.4%, P = 0.032) values than the surviving patients. However, TAPSE and FAC values were not significantly different between the two groups. The optimal cutoff values for RV-FWS, RV-GS, FAC, and TAPSE were −19.0%, −17.9%, 36.5%, and 1.55 cm, respectively. Kaplan–Meier survival curves revealed that patients with impaired RV-FWS and RV-GS demonstrated lower 30-day survival rates, and the predictive performance of RV-FWS (hazard ratio [HR]: 3.97, 95% confidence interval [CI]: 1.85–8.51, P < 0.001) was slightly higher than FAC and TAPSE. However, multivariable Cox regression analysis revealed no association between impaired RV-FWS and mortality outcomes (HR: 1.85, 95% CI: 0.56–6.14, P = 0.316).ConclusionsImpaired RV-FWS is not associated with short-term mortality outcomes, and RV strain imaging is of limited value in assessing the prognosis of sepsis

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions

BackgroundS-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists’ assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation.MethodsThe US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two.ResultsThere were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group (Z = 3.882, p = 0.0001) and the S-Detect group (Z = 3.861, p = 0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions when comparing conventional US and S-Detect techniques.ConclusionsThe addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules

A Close Look at Multi-tenant Parallel CNN Inference for Autonomous Driving

Part 2: AIInternational audienceConvolutional neural networks (CNNs) are widely used in vision-based autonomous driving, i.e., detecting and localizing objects captured in live video streams. Although CNNs demonstrate the state-of-the-art detection accuracy, processing multiple video streams using such models in real-time imposes a serious challenge to the on-car computing systems. The lack of optimized system support, for example, could lead to a significant frame loss due to the high processing latency, which is unacceptable for safety-critical applications. To alleviate this problem, several optimization strategies such as batching, GPU parallelism, and data transfer modes between CPU/GPU have been proposed, in addition to a variety of deep learning frameworks and GPUs. It is, however, unclear how these techniques interact with each other, which particular combination performs better, and under what settings. In this paper, we set out to answer these questions. We design and develop a Multi-Tenant Parallel CNN Inference Framework, MPInfer, to carefully evaluate the performance of various parallel execution modes with different data transfer modes between CPU/GPU and GPU platforms. We find that on more powerful GPUs such as GTX 1660, it achieves the best performance when we adopt parallelism across CUDA contexts enhanced by NVIDIA Multi-Process Service (MPS), with 147.06 FPS throughput and 14.50 ms latency. Meanwhile, on embedded GPUs such as Jetson AGX Xavier, pipelining is a better choice, with 46.63 FPS throughput and 35.09 ms latency

Appraisal Concerns for the Clinical Research and Development of Drugs for Rare Diseases

The incidence of each of the rare disease is very low. The complexity and diagnosis difficulty of the rare disease lead to the difficulties in the clinical research and development (R&D) of drugs for rare diseases. There is an urgent clinical need for the drug development of rare diseases in China. Encouraging R&D of new drugs, particularly the innovative drugs with China's own independent intellectural property is the basis for solving the predicament in drug shortage in China.. In order to further improve the efficiency of clinical R&D of drugs for rare diseases, the National Medical Products Administration (NMPA), Center for Drug Evaluation (CDE) issued Technical Guidance for Clinical Research and Development of Drugs for Rare Diseases. This is the first guidance for rare diseases in China that is drafted from the standpoint of the clinical technology research and development.The guidance is the scientifitc thinking and framework for the drug developing enterprises to research and develop drugs for rare disease efficiently and appropriately by following drug developing protocols and relating to the special features of rare disease.This paper presents the concepts and rationale in the guidance for the appraisal of rare disease drug research and development

Enhanced rock weathering increased soil phosphorus availability and altered root phosphorus‐acquisition strategies

International audienceAbstract Enhanced rock weathering (ERW) has been proposed as a measure to enhance the carbon (C)‐sequestration potential and fertility of soils. The effects of this practice on the soil phosphorus (P) pools and the general mechanisms affecting microbial P cycling, as well as plant P uptake are not well understood. Here, the impact of ERW on soil P availability and microbial P cycling functional groups and root P‐acquisition traits were explored through a 2‐year wollastonite field addition experiment in a tropical rubber plantation. The results show that ERW significantly increased soil microbial carbon‐use efficiency and total P concentrations and indirectly increased soil P availability by enhancing organic P mobilization and mineralization of rhizosheath carboxylates and phosphatase, respectively. Also, ERW stimulated the activities of P‐solubilizing ( gcd , ppa and ppx ) and mineralizing enzymes ( phoADN and phnAPHLFXIM ), thus contributing to the inorganic P solubilization and organic P mineralization. Accompanying the increase in soil P availability, the P‐acquisition strategy of the rubber fine roots changed from do‐it‐yourself acquisition by roots to dependence on mycorrhizal collaboration and the release of root exudates. In addition, the direct effects of ERW on root P‐acquisition traits (such as root diameter, specific root length, and mycorrhizal colonization rate) may also be related to changes in the pattern of belowground carbon investments in plants. Our study provides a new insight that ERW increases carbon‐sequestration potential and P availability in tropical forests and profoundly affects belowground plant resource‐use strategies