Exploration of Cultivating Business English Talents of Maritime University—Taking Zhejiang Ocean University as an Example

With the construction of Zhoushan Free Trade Zone and settlement of Boeing project, internationalization has brought new opportunities to Zhoushan's foreign trade industries, vigorously promoted Zhoushan's marine economy into rapid development, and put forward higher request to the cultivation of Business English talents. But based on the current social reality, there exist few international Business English talents who both have the ability of language and operation. Zhoushan's marine industries' development is entering a new era of internationalization. If we can cultivate Business English talents who both have the ability of language and operation, we will be sure to break through the bottleneck of Zhoushan's marine industries towards the world. Based on current situation of Business English talents training, this thesis takes Zhejiang Ocean University as an example to make positive contribution to exploring innovative talents training modes of Business English major in maritime universities and promoting the development of Zhoushan's marine economy. This thesis can be divided into 3 parts. Part 1 introduces the background, significance and current situation on cultivating Business English talents of maritime universities. Part 2 is an experimental research on current situation of cultivating Business English talents in Zhejiang Ocean University. And some suggestions on cultivating Business English talents of maritime universities are mentioned in part 3

Displaced or Augmented? How does Artificial Intelligence Affect Our Jobs: Evidence from LinkedIn

With the rapid advances of artificial intelligence (AI), increasingly more job tasks can be automated. Despite the AI hype, we know little about the extent to which AI may destroy or augment the career of different occupations of professionals. Although most existing literature focused on creating AI automation scores for each occupation, AI may automate non-critical tasks for many occupations which indirectly increases the productivity and value creation of jobs. Therefore, we develop a novel method to estimate the AI automation scores for core and supplemental work activities of all major occupations and analyze how employees’ human capital characteristics may lead to different results: being augmented or displaced by AI. Particularly, skills accumulated from prior work experiences and excellent educational background can reduce the automation risks. Additionally, professionals with major in computing, law, and medicine are more likely to be augmented since only their supplemental work activities may be automated

FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons

With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory goals to optimize. To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level. This approach introduces an adversary that evaluates the fairness of a model besides its prediction accuracy on the main task, and performs joint-optimization to achieve a balanced result. In this paper, we noticed that when performing backward propagation based training, such contradictory phenomenon has shown on individual neuron level. Based on this observation, we propose FairNeuron, a DNN model automatic repairing tool, to mitigate fairness concerns and balance the accuracy-fairness trade-off without introducing another model. It works on detecting neurons with contradictory optimization directions from accuracy and fairness training goals, and achieving a trade-off by selective dropout. Comparing with state-of-the-art methods, our approach is lightweight, making it scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron can effectively improve all models' fairness while maintaining a stable utility

CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models

Incidence of patients with bone metastases at diagnosis of solid tumors in adults: a large population-based study

Background: Bones are one of the most common metastatic sites for solid malignancies. Bone metastases can significantly increase mortality and decrease the quality of life of cancer patients. In the United States, around 350,000 people die each year from bone metastases. This study aimed to analyze and update the incidence and prognosis of bone metastases with solid tumors at the time of cancer diagnosis and its incidence rate for each solid cancer.Methods: We used the Surveillance, Epidemiology, and End Results (SEER) database to find patients diagnosed with solid cancers originating from outside the bones and joints between 2010 and 2016. Data were stratified by age, sex, and race. Patients with a tumor in situ or with an unknown bone metastases stage were excluded. We then selected most of the sites where cancer often occurred, leaving 2,207,796 patients for the final incidence analysis. For the survival analysis, patients were excluded if they were diagnosed at their autopsy or on their death certificate, or had unknown follow-ups. The incidence of bone metastases and overall survival was compared between patients with different primary tumor sites.Results: We identified 2,470,634 patients, including 426,594 patients with metastatic disease and 113,317 patients with bone metastases, for incidence analysis. The incidence of bone metastases among the metastatic subset was 88.74% in prostate cancer, 53.71% in breast cancer, and 38.65% in renal cancer. In descending order of incidence, there were patients with other cancers in the genitourinary system (except for renal, bladder, prostate, and testicular cancer) (37.91%), adenocarcinoma of the lung (ADC) (36.86%), other gynecologic cancers (36.02%), small- cell lung cancer (SCLC) (34.56%), non-small cell lung cancer not otherwise specified and others [NSCLC (NOS/others)] (33.55%), and bladder (31.08%) cancers. The rate of bone metastases is 23.19% in SCLC, 22.50% in NSCLC (NOS/others), 20.28% in ADC, 8.44% in squamous cell carcinoma of the lung (SCC), and 4.11% in bronchioloalveolar carcinoma [NSCLC (BAC)]. As for the digestive system, the overall bone metastases rate was 7.99% in the esophagus, 4.47% in the gastric cancer, 4.42% in the hepatobiliary cancer, 3.80% in the pancreas, 3.26% in other digestive organs, 1.24% in the colorectum, and 1.00% in the anus. Overall, the incidence rate of bone metastases among the entire cohort in breast and prostate cancer was 3.73% and 5.69%, respectively.Conclusions: The results of this study provide population-based estimates for the incidence rates of patients with bone metastases at initial diagnosis of their solid tumor. The findings can help clinicians to early detect bone metastases by bone screening to anticipate the occurrence of symptoms and favorably improve the prognosis

Plant Microbiome and Mycorrhizal Fungi

In this paper, the research results on the synergy between mycorrhizal fungi and plant microorganisms in China and abroad were summarized. The purpose of this paper was to elaborate the effects of the synergy mechanism between mycorrhizal fungi and plant microorganisms on crop growth and stress resistance, soil physical and chemical properties, and soil microbial diversity and to analyze the contribution of the interaction between mycorrhizal fungi and plant microorganisms in agriculture and forestry, so as to provide theoretical basis for the further preparation of composite microbial agents, the healthy and green improvement of crop yield, and the ecological restoration of forestry stress resistance. The main directions of future research in this field were also analyzed

Specificity and function of T cell subset identities using single‐cell sequencing

Abstract T cells, with numerous classifications, are vital components of the adaptive immune system, which function to maintain homeostasis to protect against pathogens. With the rapid development of single‐cell RNA sequencing (scRNA‐seq), the specificity and functions of high‐resolution characterizations and identities of T cells are continuously explored and discovered. The exact T cell identities provide new insights for deeply understanding the heterogeneity of T cells and for the identification of previously unrecognized cell subsets. The accuracy and specificity of T cell cluster and annotation are critical and important in scRNA‐seq analyses, even though the characters and numbers of T cell marker gene panels (MGPs) are to be furthermore improved and uncovered. In order to initiate the discussion on identities of T cell subsets/clusters and impacts of identity specificity in the understanding of immune function, the present review systematically summarized the T cell identities of MGPs and functional characteristics of distinct T cell identities in the scRNA‐seq analysis. We also discussed the critical gene differences among panels across T cell subsets, cell functional states, tissue types, and diseases, with a special focus on the significance and potential values of T cell MGP accuracy and specificity in clinical applications. We hope that the precise knowledge of T cell subsets/clusters benefit decision designs and makings of biomarker discoveries and therapeutic strategies to improve the outcomes of patients

A General Framework of Remote Sensing Epipolar Image Generation

Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo properties, in this paper, we propose a general framework to generate epipolar images for both in-track and cross-track stereo images. We first investigate the theoretical epipolar constraints of single-sensor and multi-sensor images and then introduce the proposed framework in detail. Considering large elevation changes in mountain areas, the publicly available digital elevation model (DEM) is applied to reduce the initial offsets of two stereo images. The left image is projected into the image coordinate system of the right image using the rational polynomial coefficients (RPCs). By dividing the raw images into several blocks, the epipolar images of each block are parallel generated through a robust feature matching method and fundamental matrix estimation, in which way, the horizontal disparity can be drastically reduced while maintaining negligible vertical disparity for epipolar blocks. Then, stereo matching using the epipolar blocks can be easily implemented and the forward intersection method is used to generate the digital surface model (DSM). Experimental results on several in-track and cross-track images, including optical-optical, SAR-SAR, and SAR-optical pairs, demonstrate the effectiveness of the proposed framework, which not only has obvious advantages in mountain areas with large elevation changes but also can generate high-quality epipolar images for flat areas. The generated epipolar images of a ZiYuan-3 pair in Songshan are further utilized to produce a high-precision DSM

Forward single‐cell sequencing into clinical application: Understanding of cancer microenvironment at single‐cell solution

Abstract Single‐cell RNA sequencing (scRNA‐seq) is considered an important approach to understand the molecular mechanisms of cancer microenvironmental functions and has the potential for clinical and translational discovery and development. The recent concerns on the impact of scRNA‐seq for clinical practice are whether scRNA can be applied as a routine measurement of clinical biochemistry to assist in clinical decision‐making for diagnosis and therapy. Pushing single‐cell sequencing into clinical application is one of the important missions for clinical and translational medicine (CTM), although there still are a large number of challenges to be overcome. The present Editorial as one of serials aims at overviewing the history of scRNA‐seq publications in CTM, sharing the understanding and consideration of the cancer microenvironment at the single‐cell solution and emphasising the objective of translating scRNA‐seq into clinical application. The dynamic characteristics and patterns of single‐cell identity, regulatory networks, and intercellular communication play decisive roles in the properties of the microenvironment, malignancy and migrative capacity of cancer cells, and defensive capacity of immune cells. The microenvironmental single‐cell transcriptomic profiles and cell clusters defined by scRNA‐seq have great value for exploring the molecular mechanisms of diseases and predicting cell sensitivities to therapy and patient prognosis