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English as a lingua franca (ELF) refers to ‘any use of English among speakers of different first languages for whom English is the communicative medium of choice, and often the only option’ (Seidlhofer, 2011, p. 7*). ELF research started relatively recently. It was only discussed occasionally in the last century. Landmark changes were the publications of Jenkins (2000*) and Seidlhofer (2001*). These works inspired more research into ELF, as witnessed by a dramatically increased interest in ELF since then, resulting in a large number of journal articles, monographs, edited books (e.g. Mauranen & Ranta, 2009*) and large corpora (e.g. the Vienna-Oxford International Corpus of English, the Corpus of English as a Lingua Franca in Academic Settings, and the Asian Corpus of English). In addition, ELF researchers have launched the annual conference series (International Conference of English as a Lingua Franca), the Journal of English as a Lingua Franca, and the De Gruyter book series Developments in English as a Lingua Franca. These publications move from an initial understanding of ELF as a ‘variety’ or ‘varieties’ to a later conceptualisation of ELF as a dynamic, fluid and variable phenomenon. ELF has become a major focus of discussions and activities among both applied linguists and English language teaching professionals (Jenkins, Cogo, & Dewey, 2011).
To provide scientific, theoretical support for the improvement of medical disaster training, we systematically analyzed the National Disaster Life Support (NDLS) Course and established a training curriculum with feedback based on the current status of disaster medicine in China.
The gray prediction model is applied to long-term forecast research on course effect. In line with the hypothesis, the NDLS course with feedback capability is more scientific and standardized.
The current training NDLS course system is suitable for Chinese medical disasters. After accepting the course training, audiences’ capabilities were enhanced. In the constructed GM (1,1) model prediction, the developing coefficients of the pretest and the posttest are 0.04 and 0.057, respectively. In light of the coefficient, the model is appropriate for the long-term prediction. The predicted results can be used as the basis for constructing training closed-loop optimization feedback. It can indicate that the course system has a good effect as well.
According to the constructed GM model, the NDLS course system is scientific, practical, and operational. The research results can provide reference for relevant departments and be used for the construction of similar training course systems.
Twitter and other social media platforms are often used for sharing interest in products. The identification of purchase decision stages, such as in the AIDA model (Awareness, Interest, Desire, and Action), can enable more personalized e-commerce services and a finer-grained targeting of advertisements than predicting purchase intent only. In this paper, we propose and analyze neural models for identifying the purchase stage of single tweets in a user’s tweet sequence. In particular, we identify three challenges of purchase stage identification: imbalanced label distribution with a high number of non-purchase-stage instances, limited amount of training data, and domain adaptation with no or only little target domain data. Our experiments reveal that the imbalanced label distribution is the main challenge for our models. We address it with ranking loss and perform detailed investigations of the performance of our models on the different output classes. In order to improve the generalization of the models and augment the limited amount of training data, we examine the use of sentiment analysis as a complementary, secondary task in a multitask framework. For applying our models to tweets from another product domain, we consider two scenarios: for the first scenario without any labeled data in the target product domain, we show that learning domain-invariant representations with adversarial training is most promising, while for the second scenario with a small number of labeled target examples, fine-tuning the source model weights performs best. Finally, we conduct several analyses, including extracting attention weights and representative phrases for the different purchase stages. The results suggest that the model is learning features indicative of purchase stages and that the confusion errors are sensible.
Evaluation of Cr, Mn, Fe, Zn and Se in humans is challenged by the potentially high within-individual variability of these elements in biological specimens, which are poorly characterised. This study aimed to evaluate their within-day, between-day and between-month variability in spot samples, first-morning voids and 24-h collections. A total of 529 spot urine samples (including eighty-eight first-morning voids and 24-h collections) were collected from eleven Chinese adult men on days 0, 1, 2, 3, 4, 30, 60 and 90 and analysed for these five elements using inductively coupled plasma-MS. Intraclass correlation coefficients (ICC) were utilised to characterise the reproducibility, and their sensitivity and specificity were analysed to assess how well a single measurement classified individuals’ 3-month average exposures. Serial measurements of Zn in spot samples exhibited fair to good reproducibility (creatinine-adjusted ICC = 0·47) over five consecutive days, which became poor when the samples were gathered months apart (creatinine-adjusted ICC = 0·33). The reproducibility of Cr, Mn, Fe and Se in spot samples was poor over periods ranging from days to months (creatinine-adjusted ICC = 0·01–0·12). Two spot samples were sufficient for classifying 60 % of the men who truly had the highest (top 33 %) 3-month average Zn concentrations; for Cr, Mn, Fe and Se, however, at least three specimens were required to achieve similar sensitivities. In conclusion, urinary Cr, Mn, Fe, Zn and Se concentrations showed a strong within-individual variability, and a single measurement is not enough to efficiently characterise individuals’ long-term exposures.
Alzheimer’s Disease (AD), characterized by deficits in memory and cognition and by behavioral impairment, is a progressive neurodegenerative disorder that influences more than 47 million people worldwide. Currently, no available drug is able to stop AD progression. Therefore, novel therapeutic strategies need to be investigated.
We analyzed the RNA sequencing data (RNA-seq) derived from the Gene Expression Omnibus (GEO) database to identify the differentially expressed mRNAs in AD. The AD mouse model Tg2576 was used to verify the effects of IGF-2. The Morris Water Maze was administered to test the role of IGF-2 in memory consolidation. In addition, we quantified cell apoptosis by the TUNEL assay. The levels of amyloid plaques and the levels of Aβ40 and Aβ42 in the hippocampus were also determined by immunohistochemistry and ELISA, respectively.
RNA-seq analysis revealed that IGF-2 was remarkably reduced in AD. The expression of the upstream genes PI3K and AKT and the downstream gene CREB in the PI3K signaling pathway was significantly increased in the hippocampus of Tg2576 mice cells treated with IGF-2. The Morris water maze test showed that IGF-2 improved memory consolidation in Tg2576 mice. The activity of caspase-3 was decreased in Tg2576 mice treated with IGF-2. Amyloid plaques in the hippocampus were reduced, and the levels of Aβ40 and Aβ42 were decreased. The above effects of IGF-2 on AD were blocked when the PI3K signaling pathway inhibitor wortmannin was added.
IGF-2 attenuates memory decline, oxidative stress, cell apoptosis and amyloid plaques in the AD mouse model Tg2576 by activating the PI3K/AKT/CREB signaling pathway.
This study proposes two multimodal frameworks to classify pathological voice samples by combining acoustic signals and medical records. In the first framework, acoustic signals are transformed into static supervectors via Gaussian mixture models; then, a deep neural network (DNN) combines the supervectors with the medical record and classifies the voice signals. In the second framework, both acoustic features and medical data are processed through first-stage DNNs individually; then, a second-stage DNN combines the outputs of the first-stage DNNs and performs classification. Voice samples were recorded in a specific voice clinic of a tertiary teaching hospital, including three common categories of vocal diseases, i.e. glottic neoplasm, phonotraumatic lesions, and vocal paralysis. Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall (UAR) improvements of 2.02–10.32% and 2.48–17.31%, respectively, compared with systems that use only acoustic signals or medical records. The proposed algorithm also provides higher accuracy and UAR than traditional feature-based and model-based combination methods.
The best first-aid treatment for cardiac arrest patients is Advanced Cardiac Life Support (ACLS) to not only hope to save lives but to also leave minimal sequelae. The American Heart Association (AHA) published updated ACLS guidelines for care in 2015 emphasizing the concept of teamwork in resuscitation. However, the actual use of ACLS is not easy due to stress and unfamiliarity with the process.
Therefore, we want to use the information technology to assist the medical team to implement the ACLS process. This information system can help us to save time and labor, as well as increase precision. In addition to this, data analysis is more convenient, which facilitates the management and supervision of resuscitation quality.
An information system was developed using responsive web design (RWD) website. It can be used on a variety of devices, such as desktops, tablets, or mobile phones, and can be updated simultaneously. The system requires non-synchronous operation to be used in a wireless network environment. When the information system is in operation, the medical personnel can perform the resuscitation actions according to voice prompts, which can periodically remind staff to check rhythm, give correct medication dose, and identify whether defibrillation shock is needed. At the same time, the entire process can be recorded instantly. After the file is uploaded, the medical records are complete at the same time.
After 3 months, the satisfaction of medical staff reached 80.3%, the rate of return of spontaneous circulation (ROSC) of OHCA cases elevated to 45% from 15%, and discharge without neurological sequelae elevated to 33% from 27.4%.
All hospital staff can use this system to assist in the correct implementation of advanced CPR. It improves the quality of resuscitation and reduces the burden on clinical and writing medical records of medical staff.
Previous studies have analyzed brain functional connectivity to reveal the neural physiopathology of bipolar disorder (BD) and major depressive disorder (MDD) based on the triple-network model [involving the salience network, default mode network (DMN), and central executive network (CEN)]. However, most studies assumed that the brain intrinsic fluctuations throughout the entire scan are static. Thus, we aimed to reveal the dynamic functional network connectivity (dFNC) in the triple networks of BD and MDD.
We collected resting state fMRI data from 51 unmedicated depressed BD II patients, 51 unmedicated depressed MDD patients, and 52 healthy controls. We analyzed the dFNC by using an independent component analysis, sliding window correlation and k-means clustering, and used the parameters of dFNC state properties and dFNC variability for group comparisons.
The dFNC within the triple networks could be clustered into four configuration states, three of them showing dense connections (States 1, 2, and 4) and the other one showing sparse connections (State 3). Both BD and MDD patients spent more time in State 3 and showed decreased dFNC variability between posterior DMN and right CEN (rCEN) compared with controls. The MDD patients showed specific decreased dFNC variability between anterior DMN and rCEN compared with controls.
This study revealed more common but less specific dFNC alterations within the triple networks in unmedicated depressed BD II and MDD patients, which indicated their decreased information processing and communication ability and may help us to understand their abnormal affective and cognitive functions clinically.
This paper presents a complete two-step phase-shifting (TSPS) spectral phase interferometry for direct electric-field reconstruction (SPIDER) to improve the reconstruction of ultrafast optical fields. Here, complete TSPS acts as a balanced detection that can not only remove the effect of the dc term of the interferogram, but also reduce measurement noises, and thereby improve the capability of SPIDER to measure the pulses with narrow spectra or complex spectral structures. Some prisms are chosen to replace some environment-sensitive optical components, especially reflective optics to improve operating stability and improve signal-to-noise ratio further. Our experiments show that the available shear can be decreased to 1.5% of the spectral width, which is only about
compared with traditional SPIDER.
In this work, carbon nanotubes (CNTs)-templated binuclear metallophthalocyanines (MTAPcCF3)2C (M = Mn, Fe, Co, Ni, Cu, Zn) assemblies (MTAPcCF3)2C–COOH–CNTs are designed and obtained. Whereafter, the structure and morphology of target products are analyzed by many means such as infrared, X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy. The electrocatalytic performances of lithium-thionyl chloride battery catalyzed by (MTAPcCF3)2C–COOH–CNTs were carried out. The result shows that all catalysts can improve the battery performance including the discharge time and the initial voltage. The catalytic performance of (MTAPcCF3)2C–COOH–CNTs is ordered following the central metal: Mn > Fe > Ni > Co > Cu > Zn. The cell capacity catalyzed by optimal catalyst (MnTAPcCF3)2C–COOH–CNTs can expand to 28.08 mAˑh, with increase by 142.07%, and the (MnTAPcCF3)2C–COOH–CNTs can extend the discharge time to 551.6 s. Besides, the reaction mechanism is presented on the basis of cyclic voltammetry measurements.
BiCuTeO is a potential thermoelectric material owing to its low thermal conductivity and high carrier concentration. However, the thermoelectric performance of BiCuTeO is still below average and has much scope for improvement. In this study, we manipulated the nominal oxygen content in BiCuTeO and synthesized BiCuTeOx (x = 0.94–1.06) bulks by a solid-state reaction and pelletized them by a cold-press method. The power factor was enhanced by varying the nominal oxygen deficiency due to the increased Seebeck coefficient. The thermal conductivity was also reduced due to the decrease in lattice thermal conductivity owing to the small grain size generated by the optimal nominal oxygen content. Consequently, the ZT value was enhanced by ∼11% at 523 K for stoichiometric BiCuTeO0.94 compared to BiCuTeO. Thus, optimal oxygen manipulation in BiCuTeO can enhance the thermoelectric performance. This study can be applied to developing oxides with high thermoelectric performances.
In this study, we investigate a new simple scheme using a planar undulator (PU) together with a properly dispersed electron beam (
beam) with a large energy spread (
) to enhance the free-electron laser (FEL) gain. For a dispersed
beam in a PU, the resonant condition is satisfied for the center electrons, while the frequency detuning increases for the off-center electrons, inhibiting the growth of the radiation. The PU can act as a filter for selecting the electrons near the beam center to achieve the radiation. Although only the center electrons contribute, the radiation can be enhanced significantly owing to the high-peak current of the beam. Theoretical analysis and simulation results indicate that this method can be used for the improvement of the radiation performance, which has great significance for short-wavelength FEL applications.
To revise an abbreviated version of the Silhouettes subtest of the Visual Object and Space Perception (VOSP) battery in order to recognize mild cognitive impairment (MCI) and determine the optimal cutoffs to differentiate among cognitively normal controls (NC), MCI, and Alzheimer’s Disease (AD) in the Chinese elderly.
A cross-sectional validation study.
Huashan Hospital, Shanghai, China.
A total of 591 participants: Individuals with MCI (n = 211), AD (n = 139) and NC (n = 241) were recruited from the Memory Clinic, Huashan Hospital, Shanghai, China.
Baseline neuropsychological battery (including VOSP) scores were collected from firsthand data. An abbreviated version of silhouettes test (Silhouettes-A) was revised from the original English version more suitable for the elderly, including eight silhouettes of animals and seven silhouettes of inanimate objects, with a score ranging from 0 to 15.
Silhouettes-A was an effective test to screen MCI in the Chinese elderly with good sensitivity and specificity, similar to the Montreal cognitive assessment and superior to other single tests reflecting language, spatial, or executive function. However, it had no advantage in distinguishing MCI from AD. The corresponding optimal cutoff scores of Silhouettes-A were 10 for screening MCI and 8 for AD.
Silhouettes-A is a quick, simple, sensitive, and dependable cognitive test to distinguish among NC, MCI, and AD patients.
There is little investigation on the interaction effects of adverse childhood experiences (ACEs) and social support on non-suicidal self-injury (NSSI), suicidal ideation and suicide attempt in community adolescent populations, or gender differences in these effects.
To examine the individual and interaction effects of ACEs and social support on NSSI, suicidal ideation and suicide attempt in adolescents, and explore gender differences.
A school-based health survey was conducted in three provinces in China between 2013–2014. A total of 14 820 students aged 10–20 years completed standard questionnaires, to record details of ACEs, social support, NSSI, suicidal ideation and suicide attempt.
Of included participants, 89.4% reported one or more category of ACEs. The 12-month prevalence of NSSI, suicidal ideation and suicide attempt was 26.1%, 17.5% and 4.4%, respectively; all were significantly associated with increased ACEs and lower social support. The multiple adjusted odds ratio of NSSI in low versus high social support was 2.27 (95% CI 1.85–2.67) for girls and 1.81 (95% CI 1.53–2.14) for boys, and their ratio (Ratio of two odds ratios, ROR) was 1.25 (P = 0.037). Girls with high ACEs scores (5–6) and moderate or low social support also had a higher risk of suicide attempt than boys (RORs: 2.34, 1.84 and 2.02, respectively; all P < 0.05).
ACEs and low social support are associated with increased risk of NSSI and suicidality in Chinese adolescents. Strategies to improve social support, particularly among female adolescents with a high number of ACEs, should be an integral component of targeted mental health interventions.
Written word recognition in Chinese links the perception of individual characters with whole words. With experience in reading, a high-quality word representation can provide top-down influence on the perception of its constituent characters, thus producing a word superiority effect (WSE). In experiments using the Reicher–Wheeler paradigm, we examined the WSE in two-character words for native Chinese readers (Experiment 1) and low-proficiency adult Chinese learners with Thai (Experiment 2a) and Indonesian (Experiment 2b) as native language backgrounds. For native Chinese readers, the WSE was smaller for high-frequency than low-frequency characters, reflecting rapid access to more frequently experienced characters and a consequent reduction of top-down word-level effects. Learners of Chinese, however, showed a strong WSE for both low-frequency and high-frequency characters, reflecting less well-established character representations combined with word-level knowledge sufficient to support character recognition. The results suggest that native Chinese readers develop strong representations at both the character and the word level, while low-proficiency Chinese learners are more dependent on the word level. We discuss the possibility that a word-level emphasis Chinese foreign language instruction is one reason for this pattern.
Research on the risk of stroke following the use of mood stabilisers specific to patients with bipolar disorder is limited.
In this study, we investigated the risk of stroke following the exposure to mood stabilisers in patients with bipolar disorder.
Data for this nationwide population-based study were derived from the Taiwan National Health Insurance Research Database. Among a retrospective cohort of patients with bipolar disorder (n = 19 433), 609 new-onset cases of stroke were identified from 1999 to 2012. A case–crossover study design utilising 14-day windows was applied to assess the acute exposure effect of individual mood stabilisers on the risk of ischaemic, haemorrhagic and other types of stroke in patients with bipolar disorder.
Mood stabilisers as a group were significantly associated with the increased risk of stroke in patients with bipolar disorder (adjusted risk ratio, 1.26; P = 0.041). Among individual mood stabilisers, acute exposure to carbamazepine had the highest risk of stroke (adjusted risk ratio, 1.68; P = 0.018), particularly the ischaemic type (adjusted risk ratio, 1.81; P = 0.037). In addition, acute exposure to valproic acid elevated the risk of haemorrhagic stroke (adjusted risk ratio, 1.76; P = 0.022). In contrast, acute exposure to lithium and lamotrigine did not significantly increase the risk of any type of stroke.
Use of carbamazepine and valproic acid, but not lithium and lamotrigine, is associated with increased risk of stroke in patients with bipolar disorder.
To explore the acceptance and effects of life review on older adults.
A mixed-method study design was utilized in this study.
Four nursing homes located in Fuzhou, China.
Sixty-two older adults from four nursing homes were selected according to the criteria set for this study.
Sixty-two older adults were randomly assigned to either the life review group or the control group, and 55 of them completed the study. Twenty-four participants took part in qualitative interviews after the life review program concluded. The Geriatric Depression Scale–15, Rosenberg Self-Esteem Scale, and Purpose in Life Test were adopted to measure depression, self-esteem, and meaning in life, respectively.
The findings indicated that life review can reduce depressive symptoms and may be effective at improving self-esteem and meaning in life among Chinese elderly people. More importantly, it revealed that cultural factors such as values, beliefs, and attitudes could interfere with participation in a life review.
A culturally sensitive life review program could be an alternative approach to psychotherapy for promoting mental health in older adults.