Miracle-dz/UniFAR-FTUs huggingface dataset:
Quetiapine XR efficacy and tolerability as monotherapy and as adjunctive treatment to conventional antidepressants in the acute and maintenance treatment of major depressive disorder: a review of registration trials

Results from pivotal registration trials in major depressive disorder cohere with outcomes from effectiveness studies indicating that the majority of individuals receiving single-agent pharmacotherapy fail to achieve and sustain symptomatic remission. Several factors provided the impetus for this review: suboptimal efficacy with existing pharmacotherapy for major depressive disorder, quetiapine XR efficacy in the acute and maintenance treatment of bipolar depression, emerging pharmacodynamic evidence that quetiapine XR (and/or its metabolites) uniquely engages monoaminergic systems salient to symptom relief in depressive syndromes, the increasing use of second-generation antipsychotics in the treatment of major depressive disorder and the recent FDA review of quetiapine XR in major depressive disorder. Studies reviewed herein are pivotal registration trials that evaluated the acute and maintenance efficacy and tolerability of quetiapine XR (as monotherapy and as adjunctive treatment) in major depressive disorder. In addition, we also review recent investigations characterizing the pharmacodynamic effect of quetiapine's principal active metabolite, norquetiapine. All studies were obtained from AstraZeneca (Wilmington, DE, USA) and have been presented at national/international scientific meetings. Taken together, extant studies demonstrated that quetiapine XR (50 – 300 mg) provides rapid and sustained symptomatic improvement in the acute and maintenance treatment of major depressive disorder. Quetiapine XR may also offer advantages relative to duloxetine in time to onset of antidepressant action. The major limitations of quetiapine XR use in major depressive disorder relate to weight gain and disrupted glucose/lipid homeostasis as well as sedation/somnolence. Quetiapine XR has tolerability advantages compared with duloxetine on measures of sexual dysfunction. The data from the studies reviewed herein also indicate that quetiapine XR poses a low risk for extrapyramidal side effects in middle-aged and elderly individuals with major depressive disorder.
Comparison of creatinine index and geriatric nutritional risk index for nutritional evaluation of patients with hemodialysis

Introduction: Malnutrition is prevalent in hemodialysis (HD) patients, and the risk of mortality is strongly correlated with malnutrition. Current methods of nutritional evaluation are mostly subjective, time‐consuming, and cumbersome. Creatinine index (CI) and geriatric nutritional risk index (GNRI) are very simple and objective methods to assess the nutritional status of HD patients. The present study compares the performance of CI and GNRI as nutritional risk assessment tools.
Effect of budesonide and montelukast in asthmatic children exposed to relevant allergens

Background: Montelukast has been shown to be effective in controlling the increase in exhaled NO in asthmatic children re‐exposed to house dust mite (HDM). This study compared the effect of low dose inhaled budesonide and oral montelukast in preventing the expected relapse of airway inflammation and reactivity in a group of 24 mild asthmatic children allergic to HDM after a brief period of exposure to relevant allergens.
Does Stock Split Influence to Liquidity and Stock Return? (Empirical Evidence in the Indonesian Capital Market)

Research on stock splits has frequently been undertaken. The results vary, but fundamentally can be classified into two groups. First, the stock split is purely ? cosmetic? Second, the stock split has a real effect on stocks. The difference between these opinions raises controversy. The purpose of this study is to examine whether stock splits influence stock liquidity and return of an individual stock as well as in a group of stocks as a portfolio. Overall, the results of this study show that stock splits did influence stock price, trading volume and bid-ask spread but did not influence stock risk and abnormal return from the point of view of an individual stock as well as in a group of stocks as a portfolio. The test of a relationship between bid? ask spread and stock price, trading volume and stock risk for each stock shows that all three variable did not significantly affect the bid? ask spread. On the other hand, the test of a relationship in a portfolio reveals that only stock prices significantly affect the bid-ask spread.
Employee Characteristics and Job Satisfaction

The effect of employee characteristics on job satisfaction is an area that has been given consideration by scholars without an agreement. It is becoming undoubted that this relationship varies from firm to firm based on working conditions. The purpose of this study is to determine the effect of employee characteristics on job satisfaction of private security guards in Western Kenya. Using a sample of 385, findings reveal that pay, education, years of experience, expectations and gender have significant effect on job satisfaction while age, marital status and professional training do not significantly affect job satisfaction. The findings thus contrast other studies suggesting that for different organizations, different employee characteristics affect job satisfaction.
Variability of attention bias in socially anxious adolescents: differences in fixation duration toward adult and adolescent face stimuli

ABSTRACT Prior research on attention bias in anxious youth, often utilising a visual dot probe task, has yielded inconsistent findings, which may be due to how bias is assessed and/or variability in the phenomenon. The present study utilises eye gaze tracking to assess attention bias in socially anxious adolescents, and explores several methodological and within-subject factors that may contribute to variability in attention bias. Attention bias to threat was measured in forty-two treatment-seeking adolescents (age 12–16 years) diagnosed with Social Anxiety Disorder. Bias scores toward emotional stimuli (vigilant attention) and bias scores away from emotional stimuli (avoidant attention) were explored. Bias scores changed between vigilance and avoidance within individuals and over the course of stimulus presentation. These differences were not associated with participant characteristics nor with self-reported social anxiety symptoms. However, clinician rated severity of social anxiety, explained a significant proportion of variance in the bias scores for adult, but not the adolescent, stimuli. Variability in attention bias among socially anxious adolescents is common and varies as a function of stimulus duration and type. Results may inform stimulus selection for future research.
Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems

Fog computing is identified as a key enabler for using various emerging applications by battery powered and computationally constrained devices. In this paper, we consider devices that aim at improving their performance by choosing to offload their computational tasks to nearby devices or to an edge cloud. We develop a game theoretical model of the problem and use a variational inequality theory to compute an equilibrium task allocation in static mixed strategies. Based on the computed equilibrium strategy, we develop a decentralized algorithm for allocating the computational tasks among nearby devices and the edge cloud. We use the extensive simulations to provide insight into the performance of the proposed algorithm and compare its performance with the performance of a myopic best response algorithm that requires global knowledge of the system state. Despite the fact that the proposed algorithm relies on average system parameters only, our results show that it provides a good system performance close to that of the myopic best response algorithm.
Managers’ Turnover in the Public Sector—The Role of Psychosocial Working Conditions

ABSTRACT An important challenge for public organizations is to attract and retain skilled managers. The present study explores how profiles of psychosocial working conditions, assessed by the combination of managerial-specific job demands and job resources, longitudinally predict managers’ turnover intentions and actual turnover in Swedish municipalities. Considerable effects of managers’ psychosocial working conditions on turnover intentions but not on actual turnover were found. Thus, poor working conditions may result in psychologically detached managers in public organizations, which may have considerable and costly effects on both the organizations and the managers, in terms of decreased commitment, performance, and impaired health.
Annotating Zero Anaphora for Question Answering

We constructed a large annotated dataset of zero pronouns that correspond to adjuncts marked by -de (translated to English as in , at , by or with ) in Japanese. Adjunct zero anaphora resolution plays an important role in extracting information such as location and means from a text. To our knowledge, however, there have been no large-scale dataset covering them. In this paper, focusing on the application of zero anaphora resolution to question answering (QA), we proposed two annotation schemes. The ﬁrst scheme was designed to efﬁciently collect zero anaphora instances that are useful in QA. Instead of directly annotating zero anaphora, annotators evaluated QA instances whose correctness hinges on zero anaphora resolution. Over 20,000 instances of zero anaphora were collected with this scheme. We trained a multi-column convolutional neural network with the annotated data, achieving an average precision of 0.519 in predicting the correctness of QA instances of the same type. In the second scheme, zero anaphora is annotated in a more direct manner. A model trained with the results of the second annotation scheme performed better than the ﬁrst scheme in identifying zero anaphora for sentences randomly sampled from a corpus, suggesting a tradeoff between application-speciﬁc and general-purpose annotation schemes.
Burden of impaired sleep quality on work productivity in functional dyspepsia

Background Impaired sleep quality is common, and can reduce work productivity in patients with functional dyspepsia (FD). Objective The objective of this article is to evaluate whether there is a direct association between the presence of FD and the severity of impaired sleep quality, and to calculate the economic loss due to the decreased work productivity associated with sleep quality. Methods In Study 1, using a web-based survey completed by workers with and without FD, we evaluated impaired sleep quality, work and daily productivity, and the severity of reflux and bowel symptoms. In Study 2, the association between the presence of FD and the severity of impaired sleep quality was validated in a hospital-based cohort. Results In both Study 1 and 2, although impaired sleep quality was more frequent in participants with FD than in those without FD, the independent association between the presence of FD and the severity of impaired sleep quality was not observed after adjustment for the severity of reflux and bowel symptoms. FD participants with impaired sleep quality reported additional economic loss of 53,500 Japanese yen/month. Conclusion Although the association between impaired sleep quality and FD was indirect, concomitant impaired sleep quality could worsen economic loss.
Sequence-Based Deep Learning Architectures for Visual Odometry An Empirical Evaluation of Sequence-Based Deep Learning Architectures for Visual Odometry

Background. Many applications in 3D environments require the knowledge of an agent’s position in that environment, including a wide range of important basic tasks such as environment navigation, mapping, and planning. One method to estimate position is visual odometry, where the egomotion of a camera is estimated by processing the relative movement between video frames recorded by the camera. Previous methods to do visual odometry relied on a mixture of feature matching and hand-coded heuristics to estimate egomotion, while some newer methods tried to learn it end-to-end using deep learning. Within the deep learning literature on egomotion, only several sequence-based models have been tried such as LSTMs or convolutional networks, but comparing between these models and other new ones (such as transformer networks) hasn’t been done thoroughly. Aim. The aim of this project is to evaluate the current effectiveness of several state-of-the-art deep learning architectures on the task of visual odometry within the KITTI odometry dataset. Data. The KITTI odometry dataset is a set of videos collected using a self-driving car sensor platform for the purposes of estimating egomotion accurately from video. It consists of 21 sequences with sequence lengths of between 271 to 4981 RGB images, with around 41,000 images in total. 11/21 of the sequences have public ground-truth egomotion available, collected using a state-of-the-art localization system within the vehicle. Methods. To prevent overfitting due to the low number of data samples (11 sequences total, 3 training sequences) we use pre-trained image motion featurization models which were trained on external data and are fixed during training of the sequence models. After featurization, we evaluate several sequenced-based models on the KITTI dataset: temporal convolutions, LSTM recurrent networks and attention-based transformer networks. Results. We evaluate the models using relative-frame RMSE and global-frame absolute trajectory error (ATE). Relative Translational (Rotational) RMSE is proportional to the true training loss used, and measures L2 error between the predicted position (quaternion) and the groundtruth position (quaternion). ATE measures global error after aligning the estimated trajectory with the ground-truth using a scaling, rotation and translation transformation. Conclusions. In conclusion, we evaluated a variety of learning-based sequential models on the task of visual odometry in KITTI and our results show that temporal residual networks obtain slightly lower ATE than the other models tested, suggesting that most of the models are only utilizing temporally local information and the benefits of long-temporal-horizon sequential models is not fully realized. The lack of a large visual odometry dataset thus presents a significant challenge for learning-based models to reach state-of-the-art performance.
SEASONAL VARIATION IN SEMEN QUANTITY AND QUALITY TRAITS OF IRANIAN CROSSBRED RAMS

Evaluation of the seasonal changes in seminal traits of 10 crossbred rams (2-6 year old) was the main purpose of the present study. The crossbred rams consisted of 5 Ghezel×Baluchi (GH×BL) and 5 ArkharMerino×Ghezel (AM×GH). Semen was evaluated for volume, total spermatozoa per ejaculate (TSE), spermatozoa concentration (SC), semen colour, wave motion, sperm progressive motility, percentage of live and abnormal spermatozoa, semen pH, methylene blue reduction time (MBRT) and semen index. Semen index of GH×BL and AM×GH rams in autumn was greater than in spring. Best semen quality was observed in late summer and first autumn month. In AM×GH rams the highest values of TSE, SC and semen colour were recorded during winter. However, there were no significant differences between autumn and summer months except for the semen colour (P<0.05). In both the genetic groups the highest and the lowest values of wave motion, progressive motility and live spermatozoa were observed during autumn and spring, respectively. The data showed that the optimal performance of the crossbred rams has been obtained in late summer and the beginning autumn. Seasonal variations of semen characteristics were observed for all of the seminal traits except for the wave motion and MBRT. In conclusion, there were seasonal variations between the crosses in seminal traits and the semen has the capability and quality to be used for AI in breeding programmes throughout the year.
Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain–computer interfaces (BCIs), in order to boost classification performance, is still limited. In this paper, we propose a classification framework for MI data by introducing a new temporal representation of the data and also utilizing a convolutional neural network (CNN) architecture for classification. The new representation is generated from modifying the filter-bank common spatial patterns method, and the CNN is designed and optimized accordingly for the representation. Our framework outperforms the best classification method in the literature on the BCI competition IV-2a 4-class MI data set by 7% increase in average subject accuracy. Furthermore, by studying the convolutional weights of the trained networks, we gain an insight into the temporal characteristics of EEG.
An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.
Genome-Wide Comparative Functional Analyses Reveal Adaptations of Salmonella sv. Newport to a Plant Colonization Lifestyle

Outbreaks of salmonellosis linked to the consumption of vegetables have been disproportionately associated with strains of serovar Newport. We tested the hypothesis that strains of sv. Newport have evolved unique adaptations to persistence in plants that are not shared by strains of other Salmonella serovars. We used a genome-wide mutant screen to compare growth in tomato fruit of a sv. Newport strain from an outbreak traced to tomatoes, and a sv. Typhimurium strain from animals. Most genes in the sv. Newport strain that were selected during persistence in tomatoes were shared with, and similarly selected in, the sv. Typhimurium strain. Many of their functions are linked to central metabolism, including amino acid biosynthetic pathways, iron acquisition, and maintenance of cell structure. One exception was a greater need for the core genes involved in purine metabolism in sv. Typhimurium than in sv. Newport. We discovered a gene, papA, that was unique to sv. Newport and contributed to the strain’s fitness in tomatoes. The papA gene was present in about 25% of sv. Newport Group III genomes and generally absent from other Salmonella genomes. Homologs of papA were detected in the genomes of Pantoea, Dickeya, and Pectobacterium, members of the Enterobacteriacea family that can colonize both plants and animals.
Correlates of domestic violence perpetration reporting among recently-married men residing in slums in Pune, India

Domestic violence (DV) is prevalent in low-income and slum-dwelling communities in India. To date, the focus of DV prevention in resource-poor settings has largely been with women. We herein aim to identify correlates of DV perpetration to help inform future primary prevention efforts that focus on behavioral change in men. Utilizing a cross-sectional design, potential correlates of DV perpetration were explored among a geographically-clustered random sample of 100 recently-married men residing in slums in Pune, India. In multivariable regression, DV perpetration was associated with less time spent alone in the relationship post-marriage (standardized β = -0.230, p<0.01), not attaining the “husband ideal” (standardized β = -0.201, p<0.05), poor resilience (standardized β = -0.304, p < .01), having limited definitions of behaviors constituting DV (standardized β = -0.217, p<0.05), and reporting greater jealousy if the participant’s spouse were to talk to men outside the family (standardized β = 0.272, p<0.01). The identified correlates should inform components of future DV primary prevention interventions that target men as potential perpetrators or the couple as a unit.
The clinical efficacy of ultrasound-guided percutaneous microwave ablation for rib metastases with severe intractable pain: a preliminary clinical study

Purpose: To retrospectively evaluate the clinical efficacy of ultrasound-guided percutaneous microwave ablation (US-PMWA) for patients with rib metastases that caused severe intractable pain. Materials and methods: From Jan 2016 to Apr 2018, 9 rib metastases from 7 solid tumor patients were treated with US-PMWA. The visual analogue scale (VAS), daily opiate intake doses, local tumor control and complications were recorded and analyzed. Results: The follow-up period ranged from 6 to 33 months (median: 16 months). The procedures were successfully performed in all of the patients by one ablation. The ablation power ranged from 30 to 60 W, and the ablation time was 610.0±317.5 s. The mean preablation VAS pain score was 8.1±0.7, whereas the mean VAS pain score at 72 h postablation was 3.3±0.5 (P<0.001). All of the patients needed to apply oral and/or intravenous injection opiates to relieve severe intractable pain before ablation, with daily opiate intake doses of 61.4±30.8 mg. After ablation, five patients did not need to apply any opiate treatments 72 h after ablation, and only two patients needed oral opiates (daily opiate intake doses: 30 mg and 20 mg). Recurrence was detected in three lesions at 6, 11 and 9 months after ablation, with the maximum diameter observed being more than 4 cm. All of the patients were alive during the follow-up period. No minor or major complications occurred. Conclusion: US-PMWA appears to be feasible, convenient, safe and effective in the palliative management of refractory pain caused by rib metastases. This treatment can improve the quality of life of patients and may also achieve promising local control of tumors.