搜索结果：ValuePair

Transfer learning is a technique utilized in deep learning applications to transmit learned inference to a different target domain. The approach is mainly to solve the problem of a few training datasets resulting in model overfitting, which affects model performance. The study was carried out on publications retrieved from various digital libraries such as SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar, which formed the Primary studies. Secondary studies were retrieved from Primary articles using the backward and forward snowballing approach. Based on set inclusion and exclusion parameters, relevant publications were selected for review. The study focused on transfer learning pretrained NLP models based on the deep transformer network. BERT and GPT were the two elite pretrained models trained to classify global and local representations based on larger unlabeled text datasets through self-supervised learning. Pretrained transformer models offer numerous advantages to natural language processing models, such as knowledge transfer to downstream tasks that deal with drawbacks associated with training a model from scratch. This review gives a comprehensive view of transformer architecture, self-supervised learning and pretraining concepts in language models, and their adaptation to downstream tasks. Finally, we present future directions to further improvement in pretrained transformer-based language models.

Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.

The synthesis of melatonin (MLT) physiologically decreases during aging. Treatment with MLT has shown anxiolytic, hypnotic, and analgesic effects, but little is known about possible age-dependent differences in its efficacy. Therefore, we studied the effects of MLT (20 mg/kg, intraperitoneal) on anxiety-like behavior (open field (OFT), elevated plus maze (EPMT), three-chamber sociability, and marble-burying (MBT) tests), and the medial prefrontal cortex (mPFC)-dorsal hippocampus (dHippo) circuit in adolescent (35-40 days old) and adult (three-five months old) C57BL/6 male mice. MLT did not show any effect in adolescents in the OFT and EPMT. In adults, compared to vehicles, it decreased locomotor activity and time spent in the center of the arena in the OFT and time spent in the open arms in the EPMT. In the MBT, no MLT effects were observed in both age groups. In the three-chamber sociability test, MLT decreased sociability and social novelty in adults, while it increased sociability in adolescents. Using local field potential recordings, we found higher mPFC-dHippo synchronization in the delta and low-theta frequency ranges in adults but not in adolescents after MLT treatment. Here, we show age-dependent differences in the effects of MLT in anxiety paradigms and in the modulation of the mPFC-dHippo circuit, indicating that when investigating the pharmacology of the MLT system, age can significantly impact the study outcomes.

BACKGROUND: No effective pharmacological or non-pharmacological interventions exist for patients with long COVID. We aimed to describe recovery 1 year after hospital discharge for COVID-19, identify factors associated with patient-perceived recovery, and identify potential therapeutic targets by describing the underlying inflammatory profiles of the previously described recovery clusters at 5 months after hospital discharge. METHODS: The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID-19 across the UK. Recovery was assessed using patient-reported outcome measures, physical performance, and organ function at 5 months and 1 year after hospital discharge, and stratified by both patient-perceived recovery and recovery cluster. Hierarchical logistic regression modelling was performed for patient-perceived recovery at 1 year. Cluster analysis was done using the clustering large applications k-medoids approach using clinical outcomes at 5 months. Inflammatory protein profiling was analysed from plasma at the 5-month visit. This study is registered on the ISRCTN Registry, ISRCTN10980107, and recruitment is ongoing. FINDINGS: 2320 participants discharged from hospital between March 7, 2020, and April 18, 2021, were assessed at 5 months after discharge and 807 (32·7%) participants completed both the 5-month and 1-year visits. 279 (35·6%) of these 807 patients were women and 505 (64·4%) were men, with a mean age of 58·7 (SD 12·5) years, and 224 (27·8%) had received invasive mechanical ventilation (WHO class 7-9). The proportion of patients reporting full recovery was unchanged between 5 months (501 [25·5%] of 1965) and 1 year (232 [28·9%] of 804). Factors associated with being less likely to report full recovery at 1 year were female sex (odds ratio 0·68 [95% CI 0·46-0·99]), obesity (0·50 [0·34-0·74]) and invasive mechanical ventilation (0·42 [0·23-0·76]). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate with cognitive impairment, and mild, relating to the severity of physical health, mental health, and cognitive impairment at 5 months. We found increased inflammatory mediators of tissue damage and repair in both the very severe and the moderate with cognitive impairment clusters compared with the mild cluster, including IL-6 concentration, which was increased in both comparisons (n=626 participants). We found a substantial deficit in median EQ-5D-5L utility index from before COVID-19 (retrospective assessment; 0·88 [IQR 0·74-1·00]), at 5 months (0·74 [0·64-0·88]) to 1 year (0·75 [0·62-0·88]), with minimal improvements across all outcome measures at 1 year after discharge in the whole cohort and within each of the four clusters. INTERPRETATION: The sequelae of a hospital admission with COVID-19 were substantial 1 year after discharge across a range of health domains, with the minority in our cohort feeling fully recovered. Patient-perceived health-related quality of life was reduced at 1 year compared with before hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials. FUNDING: UK Research and Innovation and National Institute for Health Research.

Abstract Leaf compounds may contribute to plant defense against Cronartium rusts. Secondary compounds are either natural or induced in leaves. We studied the variation of compounds in leaves of six alternate hosts of Cronartium pini and two of C. ribicola that represented either susceptible or resistant species to these rusts. Extracts from the plant leaves were analyzed using LC-MSMS (liquid chromatography tandem mass spectrometry) and compounds were compared between susceptible and resistant species of the same plant genera to identify significant differences between resistant and susceptible species. Also, LC–MS (liquid chromatography mass spectrometry) with external calibration was used to quantify 12 candidate compounds known from the literature. Among these compounds, the most abundant significant ones in C. pini -resistant Melampyrum pratense were chlorogenic acid and quercitrin, in Veronica chamaedrys ferulic acid, quercitrin and luteolin and in Impatiens glandulifera quercitrin, ferulic acid, kaempferol, rutin and hyperoside. In C. ribicola -resistant Ribes rubrum the most abundant significant compounds were caffeic acid, p-coumaric acid and quercitrin. Among all extracted leaf compounds, concentrations of three compounds were over 1000 times greater in rust-resistant M. pratense , three compounds in V. chamaedrys , eight compounds in I. glandulifera , and one compound in R. rubrum than in rust-susceptible species. Among the compounds, the most promising possibly linked to rust resistance were chlorogenic acid and quercitrin.

Eighty-five sperm samples were cryopreserved and SYBR14/PI, MitoTracker Deep Red FM, FITC-PSA/PI and chlortetracycline were used for imaging flow cytometry evaluation of sperm viability, mitochondrial membrane potential (MMP), acrosome integrity and sperm capacitation, respectively. Sperm motility was also registered. Sperm motility (46.1 ± 7.7 vs. 24.1% ± 6.5%), sperm viability (49.8 ± 11.5 vs. 32.3% ± 9.6%) and high MMP (49.8% ± 12.4% vs. 34.9% ± 9.9%) decreased significantly (p < .05) during cryopreservation process, in contrast to acrosome-reacted in viable spermatozoa (1.0% ± 1.6% vs. 1.0% ± 1.0%) and sperm capacitation (10.0 ± 9.8 vs. 8.2% ± 12.4%) that were similar (p > .05) before and after cryopreservation. Positive correlations were found between sperm motility versus high MMP (r = .63), sperm motility versus sperm viability (r = .67) and sperm viability versus high MMP (r = .88). In conclusion, cryopreservation of alpaca spermatozoa is related to a decrease in sperm motility, sperm viability and high MMP, meanwhile acrosome integrity and sperm capacitation are not affected.

OBJECTIVES: The cut off values for serum high sensitivity C-reactive protein (hsCRP), ferritin, interleukin 6 (IL-6) and plasma D-dimer could be of profound help in detecting COVID-19 patients at risk of adverse outcomes. Therefore, the aim of the present study is to determine the cut off values of the serum hsCRP, ferritin, IL-6 and plasma D-dimer in predicting mortality in COVID-19 patients. METHODS: Four hundred RT-PCR confirmed cases of COVID-19 were sub divided into two groups based on their outcome during hospitalisation. Group I consisted of survivors and Group II consisted of non-survivors. The survivors were further divided into three sub-groups: mild, moderate and severe based on the severity of infection. The laboratory data of serum hsCRP, ferritin, IL-6 and plasma D-dimer for all these patients was retrieved from the Medical Record Section of the Hospital. RESULTS: Mean serum hsCRP, ferritin, IL-6 and plasma D-dimer levels were significantly higher in non-survivors as compared to survivors of COVID-19. The levels of these biomarkers correlated with the severity of COVID-19 illness. ROC curve analysis revealed that plasma D-dimer is having a better predictive value as compared to other parameters in predicting mortality in COVID-19. CONCLUSIONS: The serum hsCRP, ferritin, IL-6 and plasma D-dimer levels could be used in risk stratification of COVID-19 patients. The optimum cut off given by the current study could be considered in predicting adverse outcome in these patients. Amongst the many studied biomarkers, plasma D-dimer might be the best early biomarker to predict mortality in COVID-19 patients.

PURPOSE: To identify the global and regional distribution of peripapillary vessel density (pVD) and try to find out the relationships between pVD and the visual field mean sensitivity (VFMS) in healthy myopic eyes. DESIGN: Prospective cross-sectional study. METHODS: Two hundred and twenty-two participants (393 eyes) with myopia (myopic refractive error < - 0.5 diopters) from two clinical centers were recruited in this study and were divided into 4 groups according to the spherical equivalent (SE): Group1:- 0.5D ≥ SE > - 6.00D, Group2: - 6.00D ≥ SE > - 8.00D, Group3:- 8.00D ≥ SE > - 10.00D, Group4:SE ≤ -10.00D.The pVD assessed with optical coherence tomography angiography (OCTA) was quantified in 8 sectors. Peripapillary retinal nerve fibre layer (pRNFL) imaging was performed with SD-OCT. Visual field (VF) tests were performed with the 30-2 SITA standard program on the Humphrey 750i Visual Field Analyzer and were grouped into 8 regions that matched the structure. RESULTS: The pRNFL had no significant difference in all groups (p = 0.422). The average pVD were significantly lower in group 4 (47.61 ± 6.58) than in group 2 and 3 (51.49 ± 3.21, 50.48 ± 3.43 respectively) (p < 0.05). While both pVD in group2 and 3 were statistically lower than group1 (52.77 ± 2.86). The average VFMS was significantly lower in group 4 (901.85 ± 386.54) than other three groups (1169.15 ± 328.94, 1081.77 ± 338.83, 1076.89 ± 358.18, for group1,2,3 respectively). The pVD and VFMS were positively correlated in group3 (r = 0.184) and group4 (r = 0.476) (p < 0.05). Linear regression analysis demonstrated that VFMS were positively associated with pVD especially in temporal and nasal quadrants in myopic eyes. CONCLUSIONS: The pVD shows a significant positive correlation with VFMS in highly myopic eyes with SE ≤ - 8.00D. We suggest that pVD measurement by OCTA could be a sensitive and useful method for monitoring myopic functional change.

CONTEXT: The Star Excursion Balance Test (SEBT) is a widely accepted method of assessing dynamic postural stability. The Y Balance Test (YBT) is a commercially available device for measuring balance that uses 3 (anterior, posteromedial, and posterolateral) of the 8 SEBT directions and has been advocated as a method for assessing dynamic balance. To date, no studies have compared reach performance in these tests in a healthy population. OBJECTIVE: To determine whether any differences exist between reach distance performance for the anterior, posteromedial, and posterolateral directions of the SEBT and the YBT. DESIGN: Descriptive laboratory study. SETTING: University motion analysis laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 20 healthy active male participants (age = 22.50 ± 3.05 years, height = 1.78 ± 0.82 m, weight = 79.48 ± 11.32 kg, body mass index = 24.96 ± 2.56 kg/m²). INTERVENTION(S): Participants carried out 3 trials in each reach direction on each leg on the SEBT and the YBT a minimum of 1 week apart. MAIN OUTCOME MEASURE(S): The means of the 3 trials in each direction on each leg on both tests were calculated. Data were collected after 4 practice trials in each direction. Paired t tests and Bland-Altman plots were used to compare reach distances between the SEBT and the YBT. RESULTS: Participants reached farther in the anterior direction on the SEBT than on the YBT. No differences were observed in the posteromedial and posterolateral directions. CONCLUSIONS: Differing postural-control strategies may be used to complete these tasks. This finding has implications for the implementation and interpretation of these dynamic balance tests.

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of taskoriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states and achieves stateof-the-art performance on the WoZ and DSTC2 state tracking tasks. GLAD obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ, outperforming prior work by 3.7% and 5.5%. On DSTC2, our model obtains 74.5% joint goal accuracy and 97.5% request accuracy, outperforming prior work by 1.1% and 1.0%.

BACKGROUND: Many patients with cerebral infarction suffer from symptoms such as pain, fatigue, and depression. The aim of this study was to evaluate these symptoms in relation to health-related quality of life (HRQoL) on long-term follow-up. MATERIALS AND METHODS: All surviving stroke patients admitted to the Stroke Unit, Haukeland University Hospital, Norway between February 2006 and November 2008 were sent a questionnaire, including a visual analog pain scale, Fatigue Severity Scale, Depression Subscale of Hospital Anxiety and Depression Scale, Barthel Index, and three measures of HRQoL--15D, EuroQol, and EuroQol Visual Analogue Scale--at least 6 months after stroke onset. Cox regression survival analysis, including EQ-5D, was performed by November 2009. RESULTS: The questionnaire was returned by 328 patients. All three symptoms were reported by 10.1% of the patients, and 26% reported two symptoms. There was a significant association between worse HRQoL scores and an increasing number of cooccurring symptoms for all three HRQoL scores. Fatigue, depression, pain, functional state, and sleeping disorder on follow-up accounted for 58%-83% of the variability in HRQoL, depending on which HRQoL scale was used. Cox regression analysis showed that mortality was associated with a low EuroQol score (P = 0.016). CONCLUSION: Pain, fatigue, and depression were common symptoms among these stroke patients and, to a large extent, they determined the patients' HRQoL. Low HRQoL was associated with increased mortality.

暂无摘要（点击查看原文获取完整内容）

Neural networks have been proposed to classify remotely sensed and ancillary GIS data. In this paper, the backpropagation algorithm is critically evaluated, using as an example, the mapping of a eucalypt forest on the far south coast of New South Wales, Australia. A GIS database was combined with Landsat thematic mapper data, and 190 plots were field sampled in order to train the neural network model and to evaluate the resulting classifications. The results show that the neural network did not accurately classify GIS and remotely sensed data at the forest type level (Anderson Level III), though conventional classifiers also perform poorly with this type of problem. Previous studies using neural networks have classified more general (e.g., Anderson Level I, II) landcover types at a higher accuracy than those obtained here, but mapped land cover into more general themes. Given the poor classification results and the difficulties associated with the setting up of suitable parameters for the neural-network (backpropagation) algorithm, it is concluded that the neural-network approach does not offer significant advantages over conventional classification schemes for mapping eucalypt forests from Landsat TM and ancillary GIS data at the Anderson Level III forest type level.

暂无摘要（点击查看原文获取完整内容）

We introduce "asynchronized concurrency (ASCY)," a paradigm consisting of four complementary programming patterns. ASCY calls for the design of concurrent search data structures (CSDSs) to resemble that of their sequential counterparts. We argue that ASCY leads to implementations which are portably scalable: they scale across different types of hardware platforms, including single and multi-socket ones, for various classes of workloads, such as read-only and read-write, and according to different performance metrics, including throughput, latency, and energy. We substantiate our thesis through the most exhaustive evaluation of CSDSs to date, involving 6 platforms, 22 state-of-the-art CSDS algorithms, 10 re-engineered state-of-the-art CSDS algorithms following the ASCY patterns, and 2 new CSDS algorithms designed with ASCY in mind. We observe up to 30% improvements in throughput in the re-engineered algorithms, while our new algorithms out-perform the state-of-the-art alternatives.

We present a suite of programs, named CING for Common Interface for NMR Structure Generation that provides for a residue-based, integrated validation of the structural NMR ensemble in conjunction with the experimental restraints and other input data. External validation programs and new internal validation routines compare the NMR-derived models with empirical data, measured chemical shifts, distance- and dihedral restraints and the results are visualized in a dynamic Web 2.0 report. A red-orange-green score is used for residues and restraints to direct the user to those critiques that warrant further investigation. Overall green scores below ~20 % accompanied by red scores over ~50 % are strongly indicative of poorly modelled structures. The publically accessible, secure iCing webserver ( https://nmr.le.ac.uk ) allows individual users to upload the NMR data and run a CING validation analysis.

We simplify previous work in the development of algorithms for the generation of referring expressions while at the same time taking account of psycholinguistic findings and transcript data. The result is a straightforward algorithm that is computationally tractable, sensitive to the preferences of human users, and reasonably domain-independent. We provide a specification of the resources a host system must provide in order to make use of the algorithm, and describe an implementation used in the IDAS system.

This study concerns with the major objective of foreign language teaching: vocabulary acquisition. The modern trends of teaching and the vast advance of technology enable teachers to use online and mobile applications in a very wide range. The real effect of using such a method need to be measured. Accordingly, this experimental-design study investigated the effect of Quizlet, a rapidly growing application with an online and mobile phone version, on vocabulary acquisition. Two groups of low-level EFL learners at Prince Sattam Bin Abdulaziz University in Saudi Arabia ( N = 42) participated in the study. Each group underwent a pretest and a posttest to assess their acquisition of the assigned vocabulary lessons that were extracted from their syllabus. After using Quizlet for vocabulary learning for a month, the experimental group participants show a significant improvement in vocabulary posttest. Accordingly, the study acknowledges and recommends using the application at university level

Positive Psychological Coaching is receiving increasing attention within the organizational field because of its potential benefits for employees' development and well-being (Passmore and Oades, 2014). The main aim of this study was to test the impact of a Positive Psychological Micro-Coaching program on non-executive workers' psychological capital, and analyze how goal-related self-efficacy predicts goal attainment during the coaching process. Following a control trial design, 60 non-executive employees (35 in the experimental group and 25 in the waiting-list control group) from an automotive industry company participated in a Positive Psychological Micro-Coaching program over a period of 5 weeks. The intervention was grounded in the strengths-based approach and focused on setting a specific goal for personal and professional growth. The program consisted of a group session, three individual coaching sessions, and individual inter-session monitoring. Pre, post, and 4-month follow up measurements were taken to assess the impact on the study variables. Our results reveal that psychological capital increased significantly at post and follow-up times compared to baseline levels. In addition, results confirmed that goal-related self-efficacy predicted goal attainment during the micro-coaching process. Practical implications suggest that short-term positive psychological coaching is a valuable method for developing personal resources, such as psychological capital and to facilitate the goal achievement in non-executive employees, in order to reach work-related goals.