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The economic media discourse depends upon a complex web of metaphors, among which WAR metaphor is worthy of special attention. The data used in this study is comprised of 2566 articles (about 1.2 million words) under the "Economy" column of China Daily published in 2014. Critical Metaphor Analysis (CMA) is used as the analytical framework to investigate WAR metaphor in the economic media discourse. This study is governed by the three steps of CMA including metaphor identification, metaphor interpretation and metaphor explanation. The results show that among the selected 62 lemmas, 40 of them have metaphorical instantiations and more than half of all the metaphorical expressions are nouns. Both social resources and individual resources influence metaphor choice. WAR metaphor has the rhetorical function as persuasion, which constructs the cognitive model of competition in the mind of the readers and arouses their emotions; on the other hand, it hides the cooperative principle of economic activities.

As an interactional encounter between a journalist and one or more newsworthy public figures, an interview program is a special type of discourse that is full of evaluative language. This paper sets out to explore evaluation in interview programs from the perspective of appraisal system. The corpus software used in this study is UAM CorpusTool 3.3--annotating software that is able to automatically annotate the grammatical structure and parts of speech of the text, meanwhile allows manual annotation of linguistic features based on various schemes. The results show that the use of attitudinal resources is closely related to speaker's communication strategies. Invoked evaluation and positive appraisal resources are most frequently used in the interview to establish alignment with the audience. As will be explained below, the design of the program process and other information are also sources of evaluation.

From the 1990s, the multimodal turn in discourse studies makes multimodal discourse analysis a popular topic in linguistics and communication studies. An important approach to applying Systemic Functional Linguistics to non-verbal modes is Visual Grammar initially proposed by Kress and van Leeuwen (1996). Considering that commercial advertisement is an indispensable part of the modern society that bears rich meanings worth discussing, this paper analyzes visual components of the advertisement produced by Tmall for the Double Eleven Shopping Carnival from the perspective of Visual Grammar. By analyzing representational, interactive and compositional meaning presented in the advertisement, this article illustrates how visual components serve as a huge attraction to the viewers and effectively justifies the consumption behavior by appealing to the cultural and social state. It also sheds some light on raising the awareness of consumers by presenting how advertisement producers practice psychological manipulation on the viewers.

A historical perspective on economy metaphor can shed new lights on economic thoughts. Based on the TIME Magazine Corpus (TMC), this paper investigates inflation metaphor over 83 years and compares findings against the economic data over the relatively corresponding period. The results show how inflation, an abstract concept and a normal economic phenomenon, is typically understood and explained in a variety of metaphoric expressions, how different types of metaphor structure and reframe our thinking about inflation, and why the usage of inflation metaphor varied over time. This study highlights the importance of historical perspective and the values of diachronic corpus in researching economy metaphor.

Growth is a concept of particular interest for economic discourse. This paper sets out to explore a small corpus of economic growth, which consists of articles from "The Economist". The corpus software used in this study is a web-based tool Wmatrix, an automatic tagging software able to assign semantic field (domain) tags, and to permit the extraction of key words and key semantic domains by applying the keyness calculation to tag frequency lists. The results show that at 99% confidence (or p < 0.01), the cut-off of 6.63 (log likelihood value) indicates that there are 1051 positive keywords (including multiword expressions) and 80 key semantic domains generated from the corpus. It is evident that BRICs or the emerging economies/markets, in particular China and India, are a big concern regarding economic growth over the past years. A number of examples of possible ways forward in teaching methodology are presented.

Central to argumentative writing is the proper use of epistemic devices (EDs), which distinguish writers' opinions from facts and evaluate the degree of certainty expressed in their statements. Important as these devices are, they turn out to constitute a thorny area for non-native speakers (NNS). Previous research indicates that Chinese EFL learners differ significantly from the native speakers (NS) in marking epistemic modality. One problem of previous studies is that the essay topics are not well controlled, which makes it somewhat ambiguous as to whether the observed linguistic discrepancies are caused by the NNS/NS difference or by the topic differences. This paper sets out to explore much more comparable data from International Corpus Network of Asian Learners of English (ICNALE). The results show that while both NS group and NNS groups are heavily dependent on a restricted range of items, the manipulation of epistemic modality is particularly problematic for the L2 students who employ syntactically simpler constructions and rely on a more limited range of devices, as already discovered in the previous studies. Nevertheless, this study also shows that the most proficient L2 students modify their statements with less certainty markers and more tentative expressions than do their L1 counterparts, and that all learner groups, regardless of their overall language proficiency, use less boosters than L1 writers, which is in sharp contrast with previous studies. The ability to mark epistemic modality has much to do with L2 proficiency. While lower-band students exhibit a heavy reliance on a narrower range of items for strong assertions, higher-band students tend to be more tentative and demonstrate a more native-like use of some Eds. The observed patterns are explained in the light of the inherent properties of English EDs, the imperfect modal instruction and learner factors.

Discourse connectives (DCs) are multi-functional devices used to connect discourse segments and fulfill interpersonal levels of discourse. This study investigates the use of selected 80 DCs within 11 categories in the argumentative essays produced by L1 and L2 university students. The analysis is based on the International Corpus Network of Asian Learners of English (ICNALE) which consists of essays written by native speakers (NS) and non-native speakers (NNS) from 10 countries and regions in Asia. WordSmith Tools were used to generate the quantitative profile of the DCs, while follow-up qualitative analysis in the context of usage provided additional interpretive insights. The total frequency of DCs used by Hong Kong and Singaporean students is significantly less than do L1 writers, mainly because the addictive "and" is by far less frequent in the essays produced by L2 writers. Hong Kong students use much more enumerating, resultive and summative DCs than both L1 writers and L2 writers from Thailand and Singapore. Thai students, on the other hand, employ the causal device "because" much more than both L1 and other L2 writers. Hong Kong and Singaporean students are more formal in tone than L1 and Thai students when using the adversative and resultive DCs. Despite the apparent differences, there are considerable similarities of usage, with "and", "but", "because", "so", "however" and "therefore" occurring among the top 10 most frequently used devices of both L1 and L2 writers, although with strikingly different frequencies. These findings shed light on the pragmatic uses of DCs by L1 and L2 writers as a way to influence the interpretation of the message, and thus succeed in achieving their communicative intentions.

Inflation is often regarded as a dangerous phenomenon which poses a potential threat to economies in the world. It is thus an entity that demands the constant attention of economists, policymakers and the general public. In order to make this abstract entry more concrete and vivid, a number of metaphorical expressions are used to depict inflation. Building on previous studies that relied on researchers' intuition or a small corpus, this paper sets out to investigate the use of inflation metaphor by examining a 450-million-word Corpus of Contemporary American English (COCA). The results show that there exist a considerable number of metaphorical collocates of inflation, which can be grouped into separate yet closely related categories: INFLATION IS FIRE, INFLATION IS DISEASE, INFLATION IS AN ANIMAL, INFLATION IS AN ENEMY, INFLATION IS A RACER, INFLATION IS A MACHINE, and the like. This study shows how these metaphors function in specific contexts, and how they structure and reframe our thinking about inflation and other related economic concepts. These findings have pedagogical implications for both teaching of economics and second language learning of relevant words and phrases.

Background: The role of surgery in metastatic breast cancer (MBC) is currently controversial. Several novel statistical and deep learning (DL) methods promise to infer the suitability of surgery at the individual level. Objective: The objective of this study was to identify the most applicable DL model for determining patients with MBC who could benefit from surgery and the type of surgery required. Methods: We introduced the deep survival regression with mixture effects (DSME), a semi‐parametric DL model integrating three causal inference methods. Six models were trained to make individualized treatment recommendations. Patients who received treatments in line with the DL models' recommendations were compared with those who underwent treatments divergent from the recommendations. Inverse probability weighting (IPW) was used to minimize bias. The effects of various features on surgery selection were visualized and quantified using multivariate linear regression and causal inference. Results: In total, 5269 female patients with MBC were included. DSME was an independent protective factor, outperforming other models in recommending surgery (IPW‐adjusted hazard ratio [HR] = 0.39, 95% confidence interval [CI]: 0.19–0.78) and type of surgery (IPW‐adjusted HR = 0.66, 95% CI: 0.48–0.93). DSME was superior to other models and traditional guidelines, suggesting a higher proportion of patients benefiting from surgery, especially breast‐conserving surgery. The debiased effect of patient characteristics, including age, tumor size, metastatic sites, lymph node status, and breast cancer subtypes, on surgery decision was also quantified. Conclusions: Our findings suggested that DSME could effectively identify patients with MBC likely to benefit from surgery and the specific type of surgery needed. This method can facilitate the development of efficient, reliable treatment recommendation systems and provide quantifiable evidence for decision‐making. [ABSTRACT FROM AUTHOR]

Outphasing power amplifiers (OPAs) can be used in wireless information and power transfer systems due to their excellent conversion efficiency. With the rapid advancement of technology, there is a strong demand for OPAs to achieve wideband and multi-band operations to enhance frequency coverage. This paper provides a comprehensive review of the research progress on the development of broadband and multi-band board-level OPAs. Firstly, the paper introduces the fundamental components of an OPA. Then, the paper delves into a detailed analysis of the load modulation of OPAs. Subsequently, previous works on the circuit topology and design methods for broadband and multi-band OPAs are discussed and compared. The paper highlights various design strategies and their respective advantages and disadvantages. Finally, the paper explores some potential design methods for broadband and multi-band OPAs, discussing emerging technologies and highlighting potential research directions. [ABSTRACT FROM AUTHOR]

Similar to a Doherty power amplifier (DPA), a sequential power amplifier (SPA) is mainly composed of a main amplifier, an auxiliary amplifier and a combiner. However, SPA breaks the bandwidth limitation of the impedance inverter in the DPA, and also simplifies the design procedure. Since the main amplifier has no load modulation, it is easy for the SPA to realize broadband operation and improve the output back-off (OBO) power range. Therefore, SPA has great advantages and potential in expanding bandwidth, improving drain efficiency and expanding the back-off range of a power amplifier simultaneously. This paper describes the evolution and classification of the SPA. First, the basic theory of the SPA is reviewed. Then, some two-way SPAs using coupler and circulator as a power combiner are discussed. Thirdly, the latest popular sequential load modulated balanced amplifier is overviewed. [ABSTRACT FROM AUTHOR]

Utilizing the framework of Extended Conceptual Metaphor Theory (ECMT), this paper presents a corpus-based multi-level comparative study of the economy is human metaphor in English and Chinese economic media discourse. While the results indicate a considerable sharing of their respective conceptual structures of "human body", "human condition" and "human relationship", they do reveal some differences in terms of their preference and the associated metaphorical expressions. The similarities detected can possibly be attributed to the similar body, physiological function and social attributes all human beings share, which then work as the source for drawing inferences about the economy. The differences however are also likely to be derived from the different saliences of human experience which characterize the English and Chinese social-cultural contexts. Another possible explanation for these differing culturally-sourced linguistic metaphors may well be media language and its idiosyncratic stylistic features. [ABSTRACT FROM AUTHOR]

Collaborative crowdsensing (CCS) requires the recruited team to collaborate closely to complete sensing tasks with high quality of service (QoS). The team recruitment of CCS is mainly influenced by the subjective willingness of participants and the objective trust evaluation of the sensing platform; that is, the higher the subjective mutual willingness to work together and the objective mutual trust among participants, the more efficiency with which the CCS tasks will be achieved. However, the existing research lacks comprehensive consideration of mutual willingness and mutual trust among recruited participants. This results in poor QoS. To address this problem, we propose a novel team recruitment method for CCS that jointly considers the willingness and trust to recruit optimal teams. First, we build a graph convolutional network-based willingness-trust network (GCN-WTN) model for CCS to obtain mutual willingness and trust among participants more accurately. Second, we propose a willingness and trust-based team recruitment (WT-TR) method to recruit the optimal teams for CCS. This method introduces the consensus and similarity constraints into the willingness and trust networks to better meet the collaboration needs of CCS. Finally, we implement a recruitment simulation platform for CCS to simulate the team recruitment process and validate the effectiveness of our proposed method. The experimental results show that the teams recruited by the proposed method can significantly improve QoS for CCS. [ABSTRACT FROM AUTHOR]

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement. [ABSTRACT FROM AUTHOR]

Attention convolutional neural networks (ATT-CNNs) have got a huge gain in picture operating and nature language processing. Shortage of interpretability cannot remain the adoption of deep neural networks. It is very conspicuous that is shown in the prediction model of disease aftermath. Biological data are commonly revealed in a nominal grid data structured pattern. ATT-CNN cannot be applied directly. In order to figure out these issues, a novel method which is called the Path-ATT-CNN is proposed by us, making an explicable ATT-CNN model based on united omics data by making use of a recently characterized pathway image. Path-ATT-CNN shows brilliant predictive demonstration difference in primary lung tumor symptom (PLTS) and non-primary lung tumor symptom (non-PLTS) when applied to lung adenocarcinomas (LADCs). The imaginational tool adoption which is linked with statistical analysis enables the status of essential pathways which finally exist in LADCs. In conclusion, Path-ATT-CNN shows that it can be effectively put into use elucidating omics data in an interpretable mode. When people start to figure out key biological correlates of disease, this mode makes promising power in predicting illness. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2007.09.013 Byline: Chunyu Hu (a), Hwangnam Kim (b), Jennifer C. Hou (c) Keywords: Backoff algorithm; IEEE 802.11 MAC; Model; p-Persistent Abstract: Along with the success of IEEE 802.11-compliant WLANs, the distributed coordinated function (DCF) specified as the contention-based medium access mechanism in IEEE 802.11 has been widely used to support applications in new regimes. A thorough understanding of the characteristics of IEEE 802.11 DCF is therefore a research focus. DCF assumes the binary exponential backoff algorithm (BEBA) [D. Bertsekas, R. Gallager, Data Networks, Prentice Hall, 1992]. Several existing models that characterize BEBA as a p-persistent scheme have ignored several subtle protocol details. This has practically constrained the models from being applied in a broader range of configurations, e.g., small contention window, and from being further extended, e.g., 802.11e EDCA. In this paper, we develop an analytical model that captures the subtlety, and faithfully describes the channel activities as governed by DCF. Based on the devised model, we perform a rigorous analysis on the saturation throughput performance in a single-hop WLAN. We show that the stochastic property of the backoff time, r, may substantially impact the system performance. For example, setting the range of r to [1, CW] instead of [0, CW -1], can degrade the system throughput considerably. We also identify, by clearly defining and thus being able to differentiate the two terms, attempt probability and transmission probability, an erroneous extension made to Bianchi's model [G. Bianchi, Performance analysis of the IEEE 802.11 distributed coordination function, IEEE JSAC, 18(3) (2000) 535-547]. All the findings are corroborated by ns-2 simulation. Author Affiliation: (a) Wireless Networking Business Unit, Broadcom Corporation, CA, United States (b) School of Electrical Engineering, Korea University, Seoul 136-713, Republic of Korea (c) Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, United States

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2006.07.017 Byline: Hwangnam Kim (a), Jennifer C. Hou (b), Chunyu Hu (c), Ye Ge (d) Keywords: Wireless local area networks; IEEE 802.11 enhancement and extension; Quality of service; Wireless MAC protocol design and evaluation Abstract: Proliferation of portable, wireless-enabled laptop computers and PDAs, cost-effective deployment of access points, and availability of the license-exempt bands and appropriate networking standards contribute to the conspicuous success of IEEE 802.11 WLANs. In the article, we provide a comprehensive overview of techniques for capacity improvement and QoS provisioning in the IEEE 802.11 protocol family. These techniques represent the R&D efforts both in the research community and the IEEE 802.11 Working Groups. Specifically, we summarize the operations of IEEE 802.11 legacy as well as its extension, introduce several protocol modeling techniques, and categorize the various approaches to improve protocol capacity, to provide QoS (by either devising new MAC protocol components or fine-tuning protocol parameters in IEEE 802.11), and to judiciously arbitrate radio resources (e.g., transmission rate and power). To demonstrate how to adapt QoS provisioning in newly emerging areas, we use the wireless mesh network as an example, discuss the role IEEE 802.11 plays in such a network, and outline research issues that arise. Author Affiliation: (a) School of Electrical Engineering, Korea University, Republic of Korea (b) Department of Computer Science, University of Illinois at Urbana-Champaign, 1304 W. Springfield Avenue, Urbana, IL 61801, United States (c) Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States (d) Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210, United States Article History: Received 2 January 2006; Accepted 3 July 2006 Article Note: (miscellaneous) Responsible Editor: E. Gregori

Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression. In this paper, on the basis of public databases, we study the expression differential mechanism of gene expression of UBE2C in various tumors and the performance of prognosis, clinical features, immunity, methylation, etc. [ABSTRACT FROM AUTHOR]

According to the real-time data of unmanned aerial vehicle (UAV), a multi-layer planning model based on path is constructed, and the algorithm solving process of the model is designed. The optimal path planning in the mission planning is obtained by using the hybrid algorithm of genetic algorithm and tabu search, and the conflict is eliminated on this basis. Through the simulation case, it shows that using UAV to cooperate with missile forces, transmitting combat data in real time, can solve the problem of fuzzy and uncertain battlefield information. Using multi-level programming model can make scientific decision and selection for missile multi wave planning operation path. It can avoid local optimization and result in no output the phenomenon by using genetic algorithm and tabu search hybrid algorithm. [ABSTRACT FROM AUTHOR]

Poly-γ-glutamate (γ-PGA) is a natural macromolecule peptide, and is widely used in the food, medicine, and pharmaceutical industries. In this study, heat- and osmotic shock were used to improve the production of γ-PGA in Bacillus subtilis ZJS18, and its molecular mechanism was explored. The results indicated that the heat- and osmotic shock significantly promoted the production of γ-PGA owing to the stress response of B. subtilis cells to adverse environment. The highest concentrations of γ-PGA reached 14.53 and 15.98 g/l under heat- and osmotic shock, respectively. The activities of five enzymes related to the metabolism of the endogenous glutamate were determined and analyzed. It was found that the activities of glucose-6-phosphate dehydrogenase, isocitrate dehydrogenase, glutamate dehydrogenase and glutamate synthase were significantly altered during heat- and osmotic shock, while the activity of α-ketoglutarate dehydrogenase only showed a little alteration. This study provides a basis for the industrial production and use of γ-PGA, and for understanding its biosynthetic mechanism in B. subtilis ZJS18. [ABSTRACT FROM AUTHOR]

Recognizing activities of daily living is an important research topic for health monitoring and elderly care. However, most existing activity recognition models only work with static and pre-defined sensor configurations. Enabling an existing activity recognition model to adapt to the emergence of new sensors in a dynamic environment is a significant challenge. In this paper, we propose a novel feature incremental learning method, namely the Feature Incremental Random Forest (FIRF), to improve the performance of an existing model with a small amount of data on newly appeared features. It consists of two important components – 1) a mutual information based diversity generation strategy (MIDGS) and 2) a feature incremental tree growing mechanism (FITGM). MIDGS enhances the internal diversity of random forests, while FITGM improves the accuracy of individual decision trees. To evaluate the performance of FIRF, we conduct extensive experiments on three well-known public datasets for activity recognition. Experimental results demonstrate that FIRF is significantly more accurate and efficient compared with other state-of-the-art methods. It has the potential to allow the dynamic exploitation of new sensors in changing environments. [ABSTRACT FROM AUTHOR]

Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging due to two main technical problems: 1) the size of data labeled by doctors is small, and 2) the available data tends to be highly imbalanced (the vast majority tend to be from normal subjects with only a small fraction from subjects with cognitive disorder). In order to effectively deal with these challenges to infer cognitive wellness from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The proposed MOCA first uses the random oversampling iterative random forest based feature selection method to reduce the feature space dimensionality and avoid overfitting, and then adds a bias in the optimization problem of weighted extreme learning machine to achieve good generalization ability in handling imbalanced small-sampling dataset. Experimental results on two real-world datasets including SVD and stroke patients show that MOCA can effectively reduce the rate of misdiagnosis and significantly outperform state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the health-care infrastructure. [ABSTRACT FROM AUTHOR]

Abstract: IEEE 802.11 power save mode (PSM) is a representative of energy-saving protocols which put wireless network interfaces into sleep during idleness. To save energy, part of the performance of IEEE 802.11 is sacrificed attributed to the wake-up latency thus introduced. This paper proposes a complementary mechanism, called link-indexed statistical traffic predictor (LISP) to improve IEEE 802.11 PSM. LISP employs a simple, light-weight traffic prediction method to speed up the delivery of packets along the end-to-end path. By seeking the inherent correlation between ATIM_ACKs and incoming traffic, nodes en route stay awake in the beacon intervals in which packets are anticipated to arrive. As the result, a “freeway” is bridged for packets to rapidly traverse the route. Meanwhile, the number of duty cycles is reduced and more energy is conserved. We have conducted analytical and simulation studies and demonstrated the effectiveness of LISP. The impact of various factors is investigated, including traffic load, number of hops (of routes which connections traverse), ATIM window size and packet size, in both tandem networks and networks of arbitrary topologies. [Copyright &y& Elsevier]

Quality of service (QoS) provisioning is one of the most important criteria in newly emerging UWB WPANs, as they are expected to support a wide variety of applications from time-constrained, multimedia streaming to throughput-hungry, content transfer applications. Recently, the WiMedia Alliance has announced the PHY/MAC specification together with the common radio platform for UWB WPANs. The WiMedia MAC uses the contention-free reservation-based access, which supports isochronous traffic with distributed reservation protocol (DRP), and the contention-based access, which provides service differentiation for best effort and variable-rate real-time traffic with prioritized channel access (PCA), similar to the enhanced distributed coordinated access (EDCA) in IEEE 802.11e. In this paper, we conduct a comprehensive theoretical analysis and show that with the currently recommended parameter setting, EDCA cannot provide adequate QoS. In particular, without responding to the system dynamics (e.g., taking account of the number of active stations in each class), EDCA cannot allocate bandwidth in a deterministic proportional manner and the system bandwidth is underutilized. After identifying the deficiency of EDCA, we propose an enhanced QoS-provisioning framework for the PCA scheme that dynamically controls channel access to adjust allocated bandwidth depending on the change of application's bit rate, i.e., variable-rate real-time traffic. We show that in this framework, 1) variable-rate real-time traffic is adaptively guaranteed of deterministic bandwidth via a contention-based reservation access method; 2) best effort traffic is provided with deterministic proportional QoS; and 3) moreover, the bandwidth utilization in PCA is maximized. By deterministic proportional QoS, we mean that the proposed protocol will maintain the ratio, r_j, of per-station throughput attained by a station of class j to that attained by a station of class 1 at the targeted value, and yet, the real amount of network bandwidth is changing according to the current available network bandwidth. We have also validated and evaluated the QoS provisioning capability and practicality of the proposed PCA framework both via simulation and an empirical study with the Multiband Atheros Driver for WiFi (MADWifi) Linux driver for Wireless LAN devices with the Atheros chipset. [ABSTRACT FROM PUBLISHER]

• This review comprehensively demonstrates the recent applications of deep learning in fundus images. • A knowledge graph is summarized, providing a clear overview for related studies. • Detailed summaries and analyses are provided for each specific task and overall. • State-of-the-art results and newly-released datasets will be released and regularly updated at GitHub. The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus%5fReview to adapt to the rapid development of this field. [ABSTRACT FROM AUTHOR]

• A biosynthetic pathway of myo-inositol was constructed in E. coli. • The initial glucose concentration had an obvious effect on the inositol production. • Higher glucose concentration did not favor the plasmid stability. • The production profile of myo-inositol was investigated in a 1 L shake flask. Myo-inositol is widely used in drug, food and animal feed industries. In this study, the myo-inositol biosynthetic pathway was established in Escherichia coli by introducing the myo-inositol-1-phosphate synthase-encoding gene (INO1) from Saccharomyces cerevisiae SC288. The production of myo-inositol was also investigated in shake flasks. The results showed that gene INO1 was succefully expressed in E. coli as verified by SDS-PAGE analysis. The initial glucose concentrations in M9 media had a positive impact on the production of myo-inositol and acetic acid, OD 600 of cells and plasmid stability. The highest concentration of myo-inositol was obtained at the initial glucose concentration of 30 g/L, which reached 887 mg/L. When the initial concentration of glucose was 40 g/L, a lot of acetic acid was accumulated (8.53 g/L), causing slightly decrease in the concentration of myo-inositol. The recombinant plasmid was more unstable at higher initial concentration of glucose as compared to a lower one. 797 mg/L of myo-inositol was ultimately produced in a 1 L shake flask at the initial glucose concentration of 10 g/L. The maximal OD 600 of cells and the highest concentration of acetic acid were 7.18 and 3.05 g/L, respectively. This study lays good foundation for understanding the biosynthesis of myo-inositol in E. coli and provides the possibility of applying myo-inositol to the industrial production by recombinant E. coli in future. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to comprehensively compare the predictive effect of 10 body fat indexes on MAFLD in different sex, age and BMI subgroups., Patients and Methods: A total of 5403 physical examination data were included and divided into the MAFLD group (N=2632) and non-MAFLD group (N=2771). The differences and correlation of 10 promising indicators between the two groups were compared, including fatty liver index (FLI), hepatic steatosis index (HSI), lipid accumulation product (LAP), visceral fat index (VAI), cardiometabolic index (CMI), body adiposity index (BAI), and triglyceride-glucose index (TyG), waist circumference index (WC), body mass index (BMI), waist to height ratio (WHtR). Logistic regression was used to analyze the risk of MAFLD under different adjustment conditions. The operating characteristic curve of different genders, BMI levels and age subgroups was plotted., Results: Male gender, smoking, alcohol drinking, and higher age are risk factors for MAFLD. In addition to BAI, the other 9 indicators had a high correlation with MAFLD, the area under the curve (AUC) value was >0.7, and the prediction effect was better in females, BMI<24 kg/m2, age <35 years subgroup, among which FLI (AUC: 0.912, 95% CI: 0.905-0.920), LAP (AUC: 0.894, 95% CI: 0.8866-0.903), and HSI (AUC: 0.881, 95% CI: 0.872-0.890) have better prediction effects., Conclusion: Our study confirmed the accuracy of body fat-related indexes in predicting MAFLD in people of different sexes, ages, and BMI levels. Among them, FLI, LAP and HSI have high predictive value and can be utilized as simple and cost-effective tools for screening MAFLD in clinical settings., Competing Interests: The author(s) report no conflicts of interest in this work., (© 2024 Cheng et al.)