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1. You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation

Author

Noroozi, Mehdi, Hadji, Isma, Martinez, Brais, Bulat, Adrian, and Tzimiropoulos, Georgios

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image super-resolution that yields state-of-the-art results using only a single DDIM step. We propose a novel scale distillation approach to train our SR model. Instead of directly training our SR model on the scale factor of interest, we start by training a teacher model on a smaller magnification scale, thereby making the SR problem simpler for the teacher. We then train a student model for a higher magnification scale, using the predictions of the teacher as a target during the training. This process is repeated iteratively until we reach the target scale factor of the final model. The rationale behind our scale distillation is that the teacher aids the student diffusion model training by i) providing a target adapted to the current noise level rather than using the same target coming from ground truth data for all noise levels and ii) providing an accurate target as the teacher has a simpler task to solve. We empirically show that the distilled model significantly outperforms the model trained for high scales directly, specifically with few steps during inference. Having a strong diffusion model that requires only one step allows us to freeze the U-Net and fine-tune the decoder on top of it. We show that the combination of spatially distilled U-Net and fine-tuned decoder outperforms state-of-the-art methods requiring 200 steps with only one single step.

Published
2024

2. Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation

Author

Behrmann, Nadine, Golestaneh, S. Alireza, Kolter, Zico, Gall, Juergen, and Noroozi, Mehdi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to current state-of-the-art frame-level prediction methods, we view action segmentation as a seq2seq translation task, i.e., mapping a sequence of video frames to a sequence of action segments. Our proposed method involves a series of modifications and auxiliary loss functions on the standard Transformer seq2seq translation model to cope with long input sequences opposed to short output sequences and relatively few videos. We incorporate an auxiliary supervision signal for the encoder via a frame-wise loss and propose a separate alignment decoder for an implicit duration prediction. Finally, we extend our framework to the timestamp supervised setting via our proposed constrained k-medoids algorithm to generate pseudo-segmentations. Our proposed framework performs consistently on both fully and timestamp supervised settings, outperforming or competing state-of-the-art on several datasets. Our code is publicly available at https://github.com/boschresearch/UVAST., Comment: ECCV 2022 (Main Conference)

Published
2022

3. Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives

Author

Hoffmann, David T., Behrmann, Nadine, Gall, Juergen, Brox, Thomas, and Noroozi, Mehdi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contrast to the standard InfoNCE loss, which requires a strict binary separation of the training pairs into similar and dissimilar samples, RINCE can exploit information about a similarity ranking for learning a corresponding embedding space. We show that the proposed loss function learns favorable embeddings compared to the standard InfoNCE whenever at least noisy ranking information can be obtained or when the definition of positives and negatives is blurry. We demonstrate this for a supervised classification task with additional superclass labels and noisy similarity scores. Furthermore, we show that RINCE can also be applied to unsupervised training with experiments on unsupervised representation learning from videos. In particular, the embedding yields higher classification accuracy, retrieval rates and performs better in out-of-distribution detection than the standard InfoNCE loss., Comment: AAAI 2022 (Main Track)

Published
2022

5. TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction

Author

Pourheydari, Saber, Bahrami, Emad, Fayyaz, Mohsen, Francesca, Gianpiero, Noroozi, Mehdi, and Gall, Juergen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

While recurrent neural networks (RNNs) demonstrate outstanding capabilities for future video frame prediction, they model dynamics in a discrete time space, i.e., they predict the frames sequentially with a fixed temporal step. RNNs are therefore prone to accumulate the error as the number of future frames increases. In contrast, partial differential equations (PDEs) model physical phenomena like dynamics in a continuous time space. However, the estimated PDE for frame forecasting needs to be numerically solved, which is done by discretization of the PDE and diminishes most of the advantages compared to discrete models. In this work, we, therefore, propose to approximate the motion in a video by a continuous function using the Taylor series. To this end, we introduce TaylorSwiftNet, a novel convolutional neural network that learns to estimate the higher order terms of the Taylor series for a given input video. TaylorSwiftNet can swiftly predict future frames in parallel and it allows to change the temporal resolution of the forecast frames on-the-fly. The experimental results on various datasets demonstrate the superiority of our model., Comment: BMVC 2022

Published
2021

6. Long Short View Feature Decomposition via Contrastive Video Representation Learning

Author

Behrmann, Nadine, Fayyaz, Mohsen, Gall, Juergen, and Noroozi, Mehdi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Self-supervised video representation methods typically focus on the representation of temporal attributes in videos. However, the role of stationary versus non-stationary attributes is less explored: Stationary features, which remain similar throughout the video, enable the prediction of video-level action classes. Non-stationary features, which represent temporally varying attributes, are more beneficial for downstream tasks involving more fine-grained temporal understanding, such as action segmentation. We argue that a single representation to capture both types of features is sub-optimal, and propose to decompose the representation space into stationary and non-stationary features via contrastive learning from long and short views, i.e. long video sequences and their shorter sub-sequences. Stationary features are shared between the short and long views, while non-stationary features aggregate the short views to match the corresponding long view. To empirically verify our approach, we demonstrate that our stationary features work particularly well on an action recognition downstream task, while our non-stationary features perform better on action segmentation. Furthermore, we analyse the learned representations and find that stationary features capture more temporally stable, static attributes, while non-stationary features encompass more temporally varying ones., Comment: ICCV 2021 (Main Conference)

Published
2021

7. Lifetime and past-month substance use and injection among street-based female sex workers in Iran

Author

Roshanfekr, Payam, Khezri, Mehrdad, Karimi, Salah Eddin, Vameghi, Meroe, Ali, Delaram, Ahmadi, Sina, Ahounbar, Elahe, Mahzari, Kambiz, Roshanpajouh, Mohsen, Noroozi, Mehdi, Shokoohi, Mostafa, and Mirzazadeh, Ali

Subjects
Prevention, HIV/AIDS, Substance Misuse, Drug Abuse (NIDA only), Brain Disorders, Development of treatments and therapeutic interventions, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, 5.1 Pharmaceuticals, Good Health and Well Being, Female, HIV Infections, Humans, Iran, Sex Work, Sex Workers, Substance Abuse, Intravenous, Substance-Related Disorders, Female sex workers, Drug use, Injection drug use, Harm reduction, Public Health and Health Services, Substance Abuse
Abstract

BackgroundStreet-based female sex workers (FSWs) are highly at risk of HIV and other harms associated with sex work. We assessed the prevalence of non-injection and injection drug use and their associated factors among street-based FSWs in Iran.MethodsWe recruited 898 FSWs from 414 venues across 19 major cities in Iran between October 2016 and March 2017. Correlates of lifetime and past-month non-injection and injection drug use were assessed through multivariable logistic regression models. Adjusted odds ratios (AOR) and 95% confidence intervals (CI) were reported.ResultsLifetime and past-month non-injection drug use were reported by 60.3% (95% CI 51, 84) and 47.2% (95% CI 38, 67) of FSWs, respectively. The prevalence of lifetime and past-month injection drug use were 8.6% (95% CI 6.9, 10.7) and 3.7% (95% CI 2.6, 5.2), respectively. Recent non-injection drug use was associated with divorced marital status (AOR 2.00, 95% CI 1.07, 3.74), temporary marriage (AOR 4.31 [1.79, 10.40]), had > 30 clients per month (AOR 2.76 [1.29, 5.90]), ever alcohol use (AOR 3.03 [1.92, 6.79]), and history of incarceration (AOR 7.65 [3.89, 15.30]). Similarly, lifetime injection drug use was associated with ever alcohol use (AOR 2.74 [1.20-6.20]), ever incarceration (AOR 5.06 [2.48-10.28]), and ever group sex (AOR 2.44 [1.21-4.92]).ConclusionsNon-injection and injection drug use are prevalent among street-based FSWs in Iran. Further prevention programs are needed to address and reduce harms associated with drug use among this vulnerable population in Iran.

Published
2021

8. Self-labeled Conditional GANs

Author

Noroozi, Mehdi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces a novel and fully unsupervised framework for conditional GAN training in which labels are automatically obtained from data. We incorporate a clustering network into the standard conditional GAN framework that plays against the discriminator. With the generator, it aims to find a shared structured mapping for associating pseudo-labels with the real and fake images. Our generator outperforms unconditional GANs in terms of FID with significant margins on large scale datasets like ImageNet and LSUN. It also outperforms class conditional GANs trained on human labels on CIFAR10 and CIFAR100 where fine-grained annotations or a large number of samples per class are not available. Additionally, our clustering network exceeds the state-of-the-art on CIFAR100 clustering.

Published
2020

9. 3D CNNs with Adaptive Temporal Feature Resolutions

Author

Fayyaz, Mohsen, Bahrami, Emad, Diba, Ali, Noroozi, Mehdi, Adeli, Ehsan, Van Gool, Luc, and Gall, Juergen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips. In this work, we therefore introduce a differentiable Similarity Guided Sampling (SGS) module, which can be plugged into any existing 3D CNN architecture. SGS empowers 3D CNNs by learning the similarity of temporal features and grouping similar features together. As a result, the temporal feature resolution is not anymore static but it varies for each input video clip. By integrating SGS as an additional layer within current 3D CNNs, we can convert them into much more efficient 3D CNNs with adaptive temporal feature resolutions (ATFR). Our evaluations show that the proposed module improves the state-of-the-art by reducing the computational cost (GFLOPs) by half while preserving or even improving the accuracy. We evaluate our module by adding it to multiple state-of-the-art 3D CNNs on various datasets such as Kinetics-600, Kinetics-400, mini-Kinetics, Something-Something V2, UCF101, and HMDB51., Comment: CVPR 2021

Published
2020

10. Unsupervised Video Representation Learning by Bidirectional Feature Prediction

Author

Behrmann, Nadine, Gall, Juergen, and Noroozi, Mehdi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

This paper introduces a novel method for self-supervised video representation learning via feature prediction. In contrast to the previous methods that focus on future feature prediction, we argue that a supervisory signal arising from unobserved past frames is complementary to one that originates from the future frames. The rationale behind our method is to encourage the network to explore the temporal structure of videos by distinguishing between future and past given present observations. We train our model in a contrastive learning framework, where joint encoding of future and past provides us with a comprehensive set of temporal hard negatives via swapping. We empirically show that utilizing both signals enriches the learned representations for the downstream task of action recognition. It outperforms independent prediction of future and past., Comment: Accepted at WACV 2021

Published
2020

14. Boosting Self-Supervised Learning via Knowledge Transfer

Author

Noroozi, Mehdi, Vinjimoor, Ananth, Favaro, Paolo, and Pirsiavash, Hamed

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer this model to a target domain and task. Currently, the most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. In this paper, we present a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific fine-tuned model. This allows us to: 1) quantitatively assess previously incompatible models including handcrafted features; 2) show that deeper neural network models can learn better representations from the same pretext task; 3) transfer knowledge learned with a deep model to a shallower one and thus boost its learning. We use this framework to design a novel self-supervised task, which achieves state-of-the-art performance on the common benchmarks in PASCAL VOC 2007, ILSVRC12 and Places by a significant margin. Our learned features shrink the mAP gap between models trained via self-supervised learning and supervised learning from 5.9% to 2.6% in object detection on PASCAL VOC 2007.

Published
2018

15. Population Size Estimates of Street Children in Iran: Synthesis of Multiple Methods

Author

Vameghi, Meroe, Roshanfekr, Payam, Ali, Delaram, Noroozi, Mehdi, Madani, Saied, McFarland, Willi, and Mirzazadeh, Ali

Subjects
Public Health, Health Sciences, Prevention, Vaccine Related, Pediatric, Adolescent, Child, Child, Preschool, Cities, Data Interpretation, Statistical, Female, Homeless Youth, Humans, Iran, Male, Population Density, Population Surveillance, Street children, Population size estimation, Mapping, Human Movement and Sports Sciences, Public Health and Health Services, Public health
Abstract

We used four methods (direct count, indirect count, wisdom of the crowd, and unique object multiplier) to map and estimate the population size of street children in six major cities in Iran in 2017. In aggregate for the six cities, the number of street children was estimated at 5296 (interquartile range [IQR] 4122-7071) using the median of the four methods. This corresponds to a rate of 16.3 (IQR 12.5-24.5) per 10,000 children age 5-18 years old, or 3.2 (IQR 2.4-5.3) per 10,000 total population. The total number for street children in the country is estimated at 26,000 (IQR 20,239-34,719) children. Results can help policy-makers advocate for resources, plan programs, and evaluate the reach of programs for street children. The maps created through the course of the population size estimation exercise can also guide outreach efforts to provide street children with health and social welfare services.

Published
2019

16. Reflection Separation and Deblurring of Plenoptic Images

Author

Chandramouli, Paramanand, Noroozi, Mehdi, and Favaro, Paolo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we address the problem of reflection removal and deblurring from a single image captured by a plenoptic camera. We develop a two-stage approach to recover the scene depth and high resolution textures of the reflected and transmitted layers. For depth estimation in the presence of reflections, we train a classifier through convolutional neural networks. For recovering high resolution textures, we assume that the scene is composed of planar regions and perform the reconstruction of each layer by using an explicit form of the plenoptic camera point spread function. The proposed framework also recovers the sharp scene texture with different motion blurs applied to each layer. We demonstrate our method on challenging real and synthetic images., Comment: ACCV 2016

Published
2017

17. Representation Learning by Learning to Count

Author

Noroozi, Mehdi, Pirsiavash, Hamed, and Favaro, Paolo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation. We relate transformations of images to transformations of the representations. More specifically, we look for the representation that satisfies such relation rather than the transformations that match a given representation. In this paper, we use two image transformations in the context of counting: scaling and tiling. The first transformation exploits the fact that the number of visual primitives should be invariant to scale. The second transformation allows us to equate the total number of visual primitives in each tile to that in the whole image. These two transformations are combined in one constraint and used to train a neural network with a contrastive loss. The proposed task produces representations that perform on par or exceed the state of the art in transfer learning benchmarks., Comment: ICCV 2017(oral)

Published
2017

18. Motion Deblurring in the Wild

Author

Noroozi, Mehdi, Chandramouli, Paramanand, and Favaro, Paolo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more challenging due to the blur varying spatially and the occlusions between the object. Due to the complexity of the general image model we propose a novel convolutional network architecture which directly generates the sharp image.This network is built in three stages, and exploits the benefits of pyramid schemes often used in blind deconvolution. One of the main difficulties in training such a network is to design a suitable dataset. While useful data can be obtained by synthetically blurring a collection of images, more realistic data must be collected in the wild. To obtain such data we use a high frame rate video camera and keep one frame as the sharp image and frame average as the corresponding blurred image. We show that this realistic dataset is key in achieving state-of-the-art performance and dealing with occlusions.

Published
2017

21. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

Author

Noroozi, Mehdi and Favaro, Paolo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper we study the problem of image representation learning without human annotation. By following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection. To maintain the compatibility across tasks we introduce the context-free network (CFN), a siamese-ennead CNN. The CFN takes image tiles as input and explicitly limits the receptive field (or context) of its early processing units to one tile at a time. We show that the CFN includes fewer parameters than AlexNet while preserving the same semantic learning capabilities. By training the CFN to solve Jigsaw puzzles, we learn both a feature mapping of object parts as well as their correct spatial arrangement. Our experimental evaluations show that the learned features capture semantically relevant content. Our proposed method for learning visual representations outperforms state of the art methods in several transfer learning benchmarks., Comment: ECCV 2016

Published
2016

23. Patterns of HIV Risks and Related Factors among People Who Inject Drugs in Kermanshah, Iran: A Latent Class Analysis

Author

Sharifi, Hamid, Mirzazadeh, Ali, Noroozi, Alireza, Marshall, Brandon DL, Farhoudian, Ali, Higgs, Peter, Vameghi, Meroe, Shahboulaghi, Farahnaz Mohhamadi, Qorbani, Mostafa, Massah, Omid, Armoon, Bahram, and Noroozi, Mehdi

Subjects
Public Health, Health Sciences, Behavioral and Social Science, HIV/AIDS, Drug Abuse (NIDA only), Substance Misuse, Prevention, Infectious Diseases, Prevention of disease and conditions, and promotion of well-being, 3.1 Primary prevention interventions to modify behaviours or promote wellbeing, Infection, Good Health and Well Being, Adolescent, Adult, Aged, Cross-Sectional Studies, HIV Infections, Harm Reduction, Ill-Housed Persons, Humans, Iran, Middle Aged, Needle Sharing, Needle-Exchange Programs, Regression Analysis, Risk-Taking, Sexual Behavior, Substance Abuse, Intravenous, Surveys and Questionnaires, Young Adult, Drug-related risk behaviors, latent class analysis, people who inject drugs, sexual risk behaviors, snowball sampling, Public Health and Health Services, Psychology, Substance Abuse, Public health, Biological psychology, Clinical and health psychology
Abstract

The objective of this study was to explore patterns of drug use and sexual risk behaviors among people who inject drugs (PWID) in Iran. We surveyed 500 PWID in Kermanshah concerning demographic characteristics, sexual risk behaviors, and drug-related risk behaviors in the month prior to study. We used latent class analysis (LCA) to establish a baseline model of risk profiles and to identify the optimal number of latent classes, and we used ordinal regression to identify factors associated with class membership. Three classes of multiple HIV risk were identified. The probability of membership in the high-risk class was 0.33, compared to 0.26 and 0.40 for the low- and moderate-risk classes, respectively. Compared to members in the lowest-risk class (reference group), the highest-risk class members had higher odds of being homeless (OR = 4.5, CI: 1.44-8.22; p = 0.001) in the past 12 months. Members of the high-risk class had lower odds of regularly visiting a needle and syringe exchange program as compared to the lowest-risk class members (AOR = 0.42, CI: 0.2-0.81; p = 0.01). Findings show the sexual and drug-related HIV risk clusters among PWID in Iran, and emphasize the importance of developing targeted prevention and harm reduction programs for all domains of risk behaviors, both sexual and drug use related.

Published
2017

25. Screening Oropharyngeal Dysphagia in Diabetic Patients and Its Potential Impact on Quality of Life.

Author

Aghili, Elhamossadat, Ashtari, Atieh, Bakhtiyari, Jalal, Noroozi, Mehdi, Dehaghani, Shiva Ebrahimian, and Jeddi, Marjan

Subjects
DIABETES complications, CROSS-sectional method, SELF-evaluation, PEARSON correlation (Statistics), PEOPLE with diabetes, DATA analysis, SEX distribution, QUESTIONNAIRES, AGE distribution, DESCRIPTIVE statistics, CHRONIC diseases, QUALITY of life, TYPE 2 diabetes, STATISTICS, MEDICAL screening, DATA analysis software, COMPARATIVE studies, PSYCHOSOCIAL factors, DEGLUTITION disorders
Abstract

Background: Diabetes mellitus is a common chronic disease that affects the entire body, but some of its complications are less recognized. There are underlying mechanisms that suggest a possible co-occurrence of oropharyngeal dysphagia with diabetes. Objectives: The primary goal of this study was to assess the symptoms of oropharyngeal dysphagia resulting from diabetes and explore potential relationships between oropharyngeal dysphagia, sex, age, duration of diabetes, and types of interventions. Additionally, we aimed to investigate how dysphagia might impact the quality of life of individuals with diabetes mellitus. Methods: A total of 268 diabetic patients aged 18 - 65 years participated in this recent cross-sectional study. Participants were specifically selected from individuals with type 2 diabetes mellitus based on inclusion criteria. We collected brief medical histories and demographic information from each patient. Following the medical history, patients were questioned about their swallowing condition, and self-reported signs of dysphagia were recorded. Patients were also asked to complete a Persian version of the Dysphagia Handicap Index (P-DHI) questionnaire, which is used to assess dysphagic patients’ self-assessments. Swallowing function was evaluated using the Mann Assessment of Swallowing Ability (MASA) test. Statistical analyses were performed using SPSS version 16, and Pearson and Spearman correlation coefficients were employed to assess relationships among variables. Results: Our findings revealed that women experienced more severe dysphagia compared to men. Furthermore, the impact of dysphagia on the quality of life was more significant among individuals treated with insulin than among diabetic patients not on insulin therapy (P-value = 0.001). Conversely, there was no significant difference in the quality of life among individuals with varying levels of education (P-value = 0.069). Themost commonly reported symptom by patients on the self-report survey was ”food sticking to the throat during swallowing.” Additionally, patients faced considerable difficulties with the dry mouth subscale of P-DHI and the voice subscale of MASA. Conclusions: Our results suggest that patients with diabetesmay exhibit various symptoms of oropharyngeal dysphagia. Detecting different signs of dysphagia associated with diabetes can guide researchers in developing new screening tools for dysphagia in these patients. Furthermore, our study identified relationships between various variables, aiding therapists in predicting potential signs of dysphagia. For instance, we found a relationship between the duration of diabetes and the occurrence of dysphagia, which can assist therapists in predicting dysphagia in patients with a longer history of diabetes. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Injecting and Sexual Networks and Sociodemographic Factors and Dual HIV Risk among People Who Inject Drugs: A Cross-sectional Study in Kermanshah Province, Iran.

Author

Noroozi, Mehdi, Mirzazadeh, Ali, Noroozi, Alireza, Sharifi, Hamid, Higgs, Peter, Jorjoran-Shushtari, Zahra, Farhoudian, Ali, Fadai, Farbod, Mohhamadi-Shahboulaghi, Farahnaz, Armoon, Bahram, Hajebi, Ahmad, and Massah, Omid

Subjects
Dual risk behaviors, People who inject drugs, Social network, Prevention, Clinical Research, Behavioral and Social Science, Infectious Diseases, HIV/AIDS, Infection, Good Health and Well Being
Abstract

BackgroundFew studies suggest that social network factors, including size of sexual network may associate with drug-related and sexual high-risk behaviors. The objective of this study is to investigate injecting and sexual networks and sociodemographic factors that might be associated with dual HIV risk (DHR) among people who inject drug (PWID).MethodsThe data from a cross-sectional study of 455 PWID that were recruited through peer-referral sampling were used in this study. The data were collected using a structured questionnaire consisted of modules on sociodemographic characteristics, sexual and injection-related risk behaviors during 12 months before the interview. DHR was defined as engaged in both using a syringe previously used by other PWIDs and unprotected sex during last 12 months. Data analysis was performed with descriptive and logistic regression. In final model, we considered variables with P < 0.500 as statistically significant. Finally, reported adjusted odds ratio (AOR) and confidence interval (95% CI) for variables that were significant in the final model.FindingsA total of 455 men who injected drugs participated in this study. The mean age ± standard deviation (SD) was 33.2 ± 7.3 (range 19-58) years. Overall, the prevalence of DHR In the last 12 months, 38% (95% CI: 18.3-51.2%). Multivariate model showed that regular visit to needle, syringe programs (NSPs) reduced odd of HDR to 50% when adjusted for other covariates, but still remained statistically significant (P < 0.050). The odds of reporting DHR was significantly higher in those ≥ 2 sex partners and injection partner (P < 0.010). Odds of DHR was higher (AOR: 2.3) among participants who had more than 2 injection per day but was not statistically significant (P > 0.050).ConclusionDHR was common in PWID in Kermanshah, Iran. Having multiple injecting and sexual partners increased the odds of engaging in dual risk behaviors, but regular visit of NSPs can reduce the DHR among PWID.

Published
2016

29. Impact of HIV Status Notification on Risk Behaviors among Men Who Inject Drugs in Kermanshah, West of Iran.

Author

Noroozi, Alireza, Mirzazadeh, Ali, Farhoudian, Ali, Hajebi, Ahmad, Khankeh, Hamid Reza, Higgs, Peter, Sharifi, Hamid, Armoon, Bahram, and Noroozi, Mehdi

Subjects
Public Health, Health Sciences, Behavioral and Social Science, Clinical Research, Infectious Diseases, Prevention, HIV/AIDS, Good Health and Well Being, Adult, Awareness, Disclosure, HIV Infections, Humans, Iran, Male, Needles, Risk-Taking, Substance Abuse, Intravenous, Surveys and Questionnaires, Syringes, Coarsened exact matching, People who inject drugs, Risk behaviors, Epidemiology
Abstract

BackgroundIt is unclear whether knowing of current HIV status is associated with change in injecting behaviors among people who inject drugs (PWID) in Iran. The objective of the present study was to determine whether awareness of HIV positive status is associated with a reduction in injecting risk behaviors, after matching for socio-demographic characteristics.MethodsFive hundred male PWID were recruited in 2014 from two drop-in centers (DICs) in Kermanshah west of Iran. Trained interviewers collected data on socio-demographic characteristics, HIV testing and drug-related risk behaviors over the last month prior to interview using a structured questionnaire. Our primary exposure of interest was awareness of HIV status, used to group participants into three categories: positive, negative, unaware. We used coarsened exact matching to make the three groups statistically equivalent based on age, place of residence, education and income, and then compared them regarding the proportion of borrowing, lending and reuse of syringes.ResultsMatched sample (n=320) had a mean age ± standard deviation (SD) of 33.5 ±7.6 yr. Overall, 25% (95% CI: 14%, 32%) of participants reported "borrowing a syringe" in the past month and 15% (95% CI: 7%, 22%) of them reported "lending a used syringe" to others in the past month. In comparison to PWID who were unaware of their HIV status, those knew they were HIV positive (OR 1.68, CI95%1.32-2.81) or negative (OR 1.54; 95% CI: 1.28, 2.71) were both more likely to report borrowing syringes in past month.ConclusionsPWID WHO know they are positive for HIV are more likely to borrow another person's syringe, to report reuse of their own used syringes and less likely to report lending their syringes to others. Strategies to scale up HIV testing and counseling for PWID, which also increase awareness of HIV status, may decrease injecting related the risk behaviors.

Published
2016

31. Global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017

Author

Frank, Tahvi D, Carter, Austin, Jahagirdar, Deepa, Biehl, Molly H, Douwes-Schultz, Dirk, Larson, Samantha Leigh, Arora, Megha, Dwyer-Lindgren, Laura, Steuben, Krista M, Abbastabar, Hedayat, Abu-Raddad, Laith Jamal, Abyu, Direslgne Misker, Adabi, Maryam, Adebayo, Oladimeji M, Adekanmbi, Victor, Adetokunboh, Olatunji O, Ahmadi, Alireza, Ahmadi, Keivan, Ahmadian, Elham, Ahmadpour, Ehsan, Ahmed, Muktar Beshir, Akal, Chalachew Genet, Alahdab, Fares, Alam, Noore, Albertson, Samuel B, Alemnew, Birhan Tamene T, Alene, Kefyalew Addis, Alipour, Vahid, Alvis-Guzman, Nelson, Amini, Saeed, Anbari, Zohreh, Anber, Nahla Hamed, Anjomshoa, Mina, Antonio, Carl Abelardo T, Arabloo, Jalal, Aremu, Olatunde, Areri, Habtamu Abera, Asfaw, Ephrem Tsegay, Ashagre, Alebachew Fasil, Asmelash, Daniel, Asrat, Anemaw A, Avokpaho, Euripide F G A, Awasthi, Ashish, Awoke, Nefsu, Ayanore, Martin Amogre, Azari, Samad, Badawi, Alaa, Bagherzadeh, Mojtaba, Banach, Maciej, Barac, Aleksandra, Bärnighausen, Till Winfried, Basu, Sanjay, Bedi, Neeraj, Behzadifar, Masoud, Bekele, Bayu Begashaw, Belay, Saba Abraham, Belay, Yared Belete, Belayneh, Yaschilal Muche, Berhane, Adugnaw, Bhat, Anusha Ganapati, Bhattacharyya, Krittika, Biadgo, Belete, Bijani, Ali, Bin Sayeed, Muhammad Shahdaat, Bitew, Helen, Blinov, Andrew, Bogale, Kassawmar Angaw, Bojia, Hunduma Amensisa, Burugina Nagaraja, Sharath B N, Butt, Zahid A, Cahuana-Hurtado, Lucero, Campuzano Rincon, Julio Cesar, Carvalho, Félix, Chattu, Vijay Kumar, Christopher, Devasahayam J, Chu, Dinh-Toi, Crider, Raquel, Dahiru, Tukur, Dandona, Lalit, Dandona, Rakhi, Daryani, Ahmad, das Neves, José, De Neve, Jan-Walter, Degenhardt, Louisa, Demeke, Feleke Mekonnen, Demis, Asmamaw Bizuneh, Demissie, Dereje Bayissa, Demoz, Gebre Teklemariam, Deribe, Kebede, Des Jarlais, Don, Dhungana, Govinda Prasad, Diaz, Daniel, Djalalinia, Shirin, Do, Huyen Phuc, Doan, Linh Phuong, Duber, Herbert, Dubey, Manisha, Dubljanin, Eleonora, Duken, Eyasu Ejeta, Duko Adema, Bereket, Effiong, Andem, Eftekhari, Aziz, El Sayed Zaki, Maysaa, El-Jaafary, Shaimaa I, El-Khatib, Ziad, Elsharkawy, Aisha, Endries, Aman Yesuf, Eskandarieh, Sharareh, Eyawo, Oghenowede, Farzadfar, Farshad, Fatima, Batool, Fentahun, Netsanet, Fernandes, Eduarda, Filip, Irina, Fischer, Florian, Folayan, Morenike Oluwatoyin, Foroutan, Masoud, Fukumoto, Takeshi, Fullman, Nancy, Garcia-Basteiro, Alberto L, Gayesa, Reta Tsegaye, Gebremedhin, Ketema Bizuwork, Gebremeskel, Gebreamlak Gebremedhn Gebremedhn, Gebreyohannes, Kelali Kalaye, Gedefaw, Getnet Azeze, Gelaw, Belayneh K, Gesesew, Hailay Abrha, Geta, Birhanu, Gezae, Kebede Embaye, Ghadiri, Keyghobad, Ghashghaee, Ahmad, Ginindza, Themba T G, Gugnani, Harish Chander, Guimarães, Rafael Alves, Haile, Michael Tamene, Hailu, Gessessew Bugssa, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hamidi, Samer, Handanagic, Senad, Handiso, Demelash Woldeyohannes, Hanfore, Lolemo Kelbiso, Hasanzadeh, Amir, Hassankhani, Hadi, Hassen, Hamid Yimam, Hay, Simon I, Henok, Andualem, Hoang, Chi Linh, Hosgood, H Dean, Hosseinzadeh, Mehdi, Hsairi, Mohamed, Ibitoye, Segun Emmanuel, Idrisov, Bulat, Ikuta, Kevin S, Ilesanmi, Olayinka Stephen, Irvani, Seyed Sina Naghibi, Iwu, Chinwe Juliana, Jacobsen, Kathryn H, James, Spencer L, Jenabi, Ensiyeh, Jha, Ravi Prakash, Jonas, Jost B, Jorjoran Shushtari, Zahra, Kabir, Ali, Kabir, Zubair, Kadel, Rajendra, Kasaeian, Amir, Kassa, Belete, Kassa, Getachew Mullu, Kassa, Tesfaye Dessale, Kayode, Gbenga A, Kebede, Mihiretu M, Kefale, Adane Teshome, Kengne, Andre Pascal, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khalid, Nauman, Khan, Ejaz Ahmad, Khan, Gulfaraz, Khan, Junaid, Khang, Young-Ho, Khatab, Khaled, Khazaei, Salman, Khoja, Abdullah T, Kiadaliri, Aliasghar A, Kim, Yun Jin, Kisa, Adnan, Kisa, Sezer, Kochhar, Sonali, Komaki, Hamidreza, Koul, Parvaiz A, Koyanagi, Ai, Kuate Defo, Barthelemy, Kumar, G Anil, Kumar, Manasi, Kuupiel, Desmond, Lal, Dharmesh Kumar, Lee, Jane Jean-Hee, Lenjebo, Tsegaye Lolaso, Leshargie, Cheru Tesema, Macarayan, Erlyn Rachelle King, Maddison, Emilie R, Magdy Abd El Razek, Hassan, Magis-Rodriguez, Carlos, Mahasha, Phetole Walter, Majdan, Marek, Majeed, Azeem, Malekzadeh, Reza, Manafi, Navid, Mapoma, Chabila Christopher, Martins-Melo, Francisco Rogerlândio, Masaka, Anthony, Mayenga, Emmanuel Ngassa Laurent, Mehta, Varshil, Meles, Gebrekiros Gebremichael, Meles, Hagazi Gebre, Melese, Addisu, Melku, Mulugeta, Memiah, Peter T N, Memish, Ziad A, Mena, Alemayehu Toma, Mendoza, Walter, Mengistu, Desalegn Tadese, Mengistu, Getnet, Meretoja, Tuomo J, Mestrovic, Tomislav, Miller, Ted R, Moazen, Babak, Mohajer, Bahram, Mohamadi-Bolbanabad, Amjad, Mohammad, Karzan Abdulmuhsin, Mohammad, Yousef, Mohammad Darwesh, Aso, Mohammad Gholi Mezerji, Naser, Mohammadi, Moslem, Mohammadibakhsh, Roghayeh, Mohammadoo-Khorasani, Milad, Mohammed, Jemal Abdu, Mohammed, Shafiu, Mohebi, Farnam, Mokdad, Ali H, Moodley, Yoshan, Moossavi, Maryam, Moradi, Ghobad, Moradi-Lakeh, Maziar, Moschos, Marilita M, Mossie, Tilahun Belete, Mousavi, Seyyed Meysam, Muchie, Kindie Fentahun, Muluneh, Atalay Goshu, Muriithi, Moses K, Mustafa, Ghulam, Muthupandian, Saravanan, Nagarajan, Ahamarshan Jayaraman, Naik, Gurudatta, Najafi, Farid, Nazari, Javad, Ndwandwe, Duduzile Edith, Nguyen, Cuong Tat, Nguyen, Huong Lan Thi, Nguyen, Son Hoang, Nguyen, Trang Huyen, Ningrum, Dina Nur Anggraini, Nixon, Molly R, Nnaji, Chukwudi A, Noroozi, Mehdi, Noubiap, Jean Jacques, Nourollahpour Shiadeh, Malihe, Obsa, Mohammed Suleiman, Odame, Emmanuel Ankrah, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Okoro, Anselm, Oladimeji, Olanrewaju, Olagunju, Andrew T, Olagunju, Tinuke O, Olum, Solomon, Oppong Asante, Kwaku Oppong Asante, Oren, Eyal, Otstavnov, Stanislav S, PA, Mahesh, Padubidri, Jagadish Rao, Pakhale, Smita, Pakpour, Amir H, Patel, Sangram Kishor, Paulos, Kebreab, Pepito, Veincent Christian Filipino, Peprah, Emmanuel K, Piroozi, Bakhtiar, Pourshams, Akram, Qorbani, Mostafa, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Rafay, Anwar, Rafiei, Alireza, Rahim, Fakher, Rahimi-Movaghar, Afarin, Rahimi-Movaghar, Vafa, Rahman, Sajjad ur, Ranabhat, Chhabi Lal, Rawaf, Salman, Reis, Cesar, Renjith, Vishnu, Reta, Melese Abate, Rezai, Mohammad Sadegh, Rios González, Carlos Miguel, Roro, Elias Merdassa, Rostami, Ali, Rubino, Salvatore, Saeedi Moghaddam, Sahar, Safari, Saeed, Sagar, Rajesh, Sahraian, Mohammad Ali, Salem, Marwa R Rashad, Salimi, Yahya, Salomon, Joshua A, Sambala, Evanson Zondani, Samy, Abdallah M, Sartorius, Benn, Satpathy, Maheswar, Sawhney, Monika, Sayyah, Mehdi, Schutte, Aletta Elisabeth, Sepanlou, Sadaf G, Seyedmousavi, Seyedmojtaba, Shabaninejad, Hosein, Shaheen, Amira A, Shaikh, Masood Ali, Shallo, Seifadin Ahmed, Shamsizadeh, Morteza, Sharifi, Hamid, Shibuya, Kenji, Shin, Jae Il, Shirkoohi, Reza, Silva, Diego Augusto Santos, Silveira, Dayane Gabriele Alves, Singh, Jasvinder A, Sisay, Malede Mequanent M, Sisay, Mekonnen, Sisay, Solomon, Smith, Amanda E, Sokhan, Anton, Somayaji, Ranjani, Soshnikov, Sergey, Stein, Dan J, Sufiyan, Mu'awiyyah Babale, Sunguya, Bruno F, Sykes, Bryan L, Tadesse, Birkneh Tilahun, Tadesse, Degena Bahrey, Tamirat, Koku Sisay, Taveira, Nuno, Tekelemedhin, Shishay Wahdey, Temesgen, Habtamu Denekew, Tesfay, Fisaha Haile, Teshale, Manaye Yihune, Thapa, Subash, Tlaye, Kenean Getaneh, Topp, Stephanie M, Tovani-Palone, Marcos Roberto, Tran, Bach Xuan, Tran, Khanh Bao, Ullah, Irfan, Unnikrishnan, Bhaskaran, Uthman, Olalekan A, Veisani, Yousef, Vladimirov, Sergey Konstantinovitch, Wada, Fiseha Wadilo, Waheed, Yasir, Weldegwergs, Kidu Gidey, Weldesamuel, Girmay Teklay T, Westerman, Ronny, Wijeratne, Tissa, Wolde, Haileab Fekadu, Wondafrash, Dawit Zewdu, Wonde, Tewodros Eshete, Wondmagegn, Berhanu Yazew, Yeshanew, Addisu Gize, Yilma, Mekdes Tigistu, Yimer, Ebrahim M, Yonemoto, Naohiro, Yotebieng, Marcel, Youm, Yoosik, Yu, Chuanhua, Zaidi, Zoubida, Zarghi, Afshin, Zenebe, Zerihun Menlkalew, Zewale, Taye Abuhay, Ziapour, Arash, Zodpey, Sanjay, Naghavi, Mohsen, Vollset, Stein Emil, Wang, Haidong, Lim, Stephen S, Kyu, Hmwe Hmwe, and Murray, Christopher J L

Published
2019
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39. ConvNet-Based Depth Estimation, Reflection Separation and Deblurring of Plenoptic Images

Author

Chandramouli, Paramanand, Noroozi, Mehdi, Favaro, Paolo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lai, Shang-Hong, editor, Lepetit, Vincent, editor, Nishino, Ko, editor, and Sato, Yoichi, editor

Published
2017
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42. Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Author

Burstein, Roy, Henry, Nathaniel J., Collison, Michael L., Marczak, Laurie B., Sligar, Amber, Watson, Stefanie, Marquez, Neal, Abbasalizad-Farhangi, Mahdieh, Abbasi, Masoumeh, Abd-Allah, Foad, Abdoli, Amir, Abdollahi, Mohammad, Abdollahpour, Ibrahim, Abdulkader, Rizwan Suliankatchi, Abrigo, Michael R. M., Acharya, Dilaram, Adebayo, Oladimeji M., Adekanmbi, Victor, Adham, Davoud, Afshari, Mahdi, Aghaali, Mohammad, Ahmadi, Keivan, Ahmadi, Mehdi, Ahmadpour, Ehsan, Ahmed, Rushdia, Akal, Chalachew Genet, Akinyemi, Joshua O., Alahdab, Fares, Alam, Noore, Alamene, Genet Melak, Alene, Kefyalew Addis, Alijanzadeh, Mehran, Alinia, Cyrus, Alipour, Vahid, Aljunid, Syed Mohamed, Almalki, Mohammed J., Al-Mekhlafi, Hesham M., Altirkawi, Khalid, Alvis-Guzman, Nelson, Amegah, Adeladza Kofi, Amini, Saeed, Amit, Arianna Maever Loreche, Anbari, Zohreh, Androudi, Sofia, Anjomshoa, Mina, Ansari, Fereshteh, Antonio, Carl Abelardo T., Arabloo, Jalal, Arefi, Zohreh, Aremu, Olatunde, Armoon, Bahram, Arora, Amit, Artaman, Al, Asadi, Anvar, Asadi-Aliabadi, Mehran, Ashraf-Ganjouei, Amir, Assadi, Reza, Ataeinia, Bahar, Atre, Sachin R., Quintanilla, Beatriz Paulina Ayala, Ayanore, Martin Amogre, Azari, Samad, Babaee, Ebrahim, Babazadeh, Arefeh, Badawi, Alaa, Bagheri, Soghra, Bagherzadeh, Mojtaba, Baheiraei, Nafiseh, Balouchi, Abbas, Barac, Aleksandra, Bassat, Quique, Baune, Bernhard T., Bayati, Mohsen, Bedi, Neeraj, Beghi, Ettore, Behzadifar, Masoud, Behzadifar, Meysam, Belay, Yared Belete, Bell, Brent, Bell, Michelle L., Berbada, Dessalegn Ajema, Bernstein, Robert S., Bhattacharjee, Natalia V., Bhattarai, Suraj, Bhutta, Zulfiqar A., Bijani, Ali, Bohlouli, Somayeh, Breitborde, Nicholas J. K., Britton, Gabrielle, Browne, Annie J., Nagaraja, Sharath Burugina, Busse, Reinhard, Butt, Zahid A., Car, Josip, Cárdenas, Rosario, Castañeda-Orjuela, Carlos A., Cerin, Ester, Chanie, Wagaye Fentahun, Chatterjee, Pranab, Chu, Dinh-Toi, Cooper, Cyrus, Costa, Vera M., Dalal, Koustuv, Dandona, Lalit, Dandona, Rakhi, Daoud, Farah, Daryani, Ahmad, Das Gupta, Rajat, Davis, Ian, Davis Weaver, Nicole, Davitoiu, Dragos Virgil, De Neve, Jan-Walter, Demeke, Feleke Mekonnen, Demoz, Gebre Teklemariam, Deribe, Kebede, Desai, Rupak, Deshpande, Aniruddha, Desyibelew, Hanna Demelash, Dey, Sagnik, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Diaz, Daniel, Doshmangir, Leila, Duraes, Andre R., Dwyer-Lindgren, Laura, Earl, Lucas, Ebrahimi, Roya, Ebrahimpour, Soheil, Effiong, Andem, Eftekhari, Aziz, Ehsani-Chimeh, Elham, El Sayed, Iman, El Sayed Zaki, Maysaa, El Tantawi, Maha, El-Khatib, Ziad, Emamian, Mohammad Hassan, Enany, Shymaa, Eskandarieh, Sharareh, Eyawo, Oghenowede, Ezalarab, Maha, Faramarzi, Mahbobeh, Fareed, Mohammad, Faridnia, Roghiyeh, Faro, Andre, Fazaeli, Ali Akbar, Fazlzadeh, Mehdi, Fentahun, Netsanet, Fereshtehnejad, Seyed-Mohammad, Fernandes, João C., Filip, Irina, Fischer, Florian, Foigt, Nataliya A., Foroutan, Masoud, Francis, Joel Msafiri, Fukumoto, Takeshi, Fullman, Nancy, Gallus, Silvano, Gebre, Destallem Gebremedhin, Gebrehiwot, Tsegaye Tewelde, Gebremeskel, Gebreamlak Gebremedhn, Gessner, Bradford D., Geta, Birhanu, Gething, Peter W., Ghadimi, Reza, Ghadiri, Keyghobad, Ghajarzadeh, Mahsa, Ghashghaee, Ahmad, Gill, Paramjit Singh, Gill, Tiffany K., Golding, Nick, Gomes, Nelson G. M., Gona, Philimon N., Gopalani, Sameer Vali, Gorini, Giuseppe, Goulart, Bárbara Niegia Garcia, Graetz, Nicholas, Greaves, Felix, Green, Manfred S., Guo, Yuming, Haj-Mirzaian, Arvin, Haj-Mirzaian, Arya, Hall, Brian James, Hamidi, Samer, Haririan, Hamidreza, Haro, Josep Maria, Hasankhani, Milad, Hasanpoor, Edris, Hasanzadeh, Amir, Hassankhani, Hadi, Hassen, Hamid Yimam, Hegazy, Mohamed I., Hendrie, Delia, Heydarpour, Fatemeh, Hird, Thomas R., Hoang, Chi Linh, Hollerich, Gillian, Rad, Enayatollah Homaie, Hoseini-Ghahfarokhi, Mojtaba, Hossain, Naznin, Hosseini, Mostafa, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Househ, Mowafa, Hsairi, Mohamed, Ilesanmi, Olayinka Stephen, Imani-Nasab, Mohammad Hasan, Iqbal, Usman, Irvani, Seyed Sina Naghibi, Islam, Nazrul, Islam, Sheikh Mohammed Shariful, Jürisson, Mikk, Balalami, Nader Jafari, Jalali, Amir, Javidnia, Javad, Jayatilleke, Achala Upendra, Jenabi, Ensiyeh, Ji, John S., Jobanputra, Yash B., Johnson, Kimberly, Jonas, Jost B., Shushtari, Zahra Jorjoran, Jozwiak, Jacek Jerzy, Kabir, Ali, Kahsay, Amaha, Kalani, Hamed, Kalhor, Rohollah, Karami, Manoochehr, Karki, Surendra, Kasaeian, Amir, Kassebaum, Nicholas J., Keiyoro, Peter Njenga, Kemp, Grant Rodgers, Khabiri, Roghayeh, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khan, Ejaz Ahmad, Khan, Junaid, Khan, Muhammad Shahzeb, Khang, Young-Ho, Khatab, Khaled, Khater, Amir, Khater, Mona M., Khatony, Alireza, Khazaei, Mohammad, Khazaei, Salman, Khazaei-Pool, Maryam, Khubchandani, Jagdish, Kianipour, Neda, Kim, Yun Jin, Kimokoti, Ruth W., Kinyoki, Damaris K., Kisa, Adnan, Kisa, Sezer, Kolola, Tufa, Kosen, Soewarta, Koul, Parvaiz A., Koyanagi, Ai, Kraemer, Moritz U. G., Krishan, Kewal, Krohn, Kris J., Kugbey, Nuworza, Kumar, G. Anil, Kumar, Manasi, Kumar, Pushpendra, Kuupiel, Desmond, Lacey, Ben, Lad, Sheetal D., Lami, Faris Hasan, Larsson, Anders O., Lee, Paul H., Leili, Mostafa, Levine, Aubrey J., Li, Shanshan, Lim, Lee-Ling, Listl, Stefan, Longbottom, Joshua, Lopez, Jaifred Christian F., Lorkowski, Stefan, Magdeldin, Sameh, Abd El Razek, Hassan Magdy, Abd El Razek, Muhammed Magdy, Majeed, Azeem, Maleki, Afshin, Malekzadeh, Reza, Malta, Deborah Carvalho, Mamun, Abdullah A., Manafi, Navid, Manda, Ana-Laura, Mansourian, Morteza, Martins-Melo, Francisco Rogerlândio, Masaka, Anthony, Massenburg, Benjamin Ballard, Maulik, Pallab K., Mayala, Benjamin K., Mazidi, Mohsen, McKee, Martin, Mehrotra, Ravi, Mehta, Kala M., Meles, Gebrekiros Gebremichael, Mendoza, Walter, Menezes, Ritesh G., Meretoja, Atte, Meretoja, Tuomo J., Mestrovic, Tomislav, Miller, Ted R., Miller-Petrie, Molly K., Mills, Edward J., Milne, George J., Mini, G. K., Mir, Seyed Mostafa, Mirjalali, Hamed, Mirrakhimov, Erkin M., Mohamadi, Efat, Mohammad, Dara K., Darwesh, Aso Mohammad, Mezerji, Naser Mohammad Gholi, Mohammed, Ammas Siraj, Mohammed, Shafiu, Mokdad, Ali H., Molokhia, Mariam, Monasta, Lorenzo, Moodley, Yoshan, Moosazadeh, Mahmood, Moradi, Ghobad, Moradi, Masoud, Moradi, Yousef, Moradi-Lakeh, Maziar, Moradinazar, Mehdi, Moraga, Paula, Morawska, Lidia, Mosapour, Abbas, Mousavi, Seyyed Meysam, Mueller, Ulrich Otto, Muluneh, Atalay Goshu, Mustafa, Ghulam, Nabavizadeh, Behnam, Naderi, Mehdi, Nagarajan, Ahamarshan Jayaraman, Nahvijou, Azin, Najafi, Farid, Nangia, Vinay, Ndwandwe, Duduzile Edith, Neamati, Nahid, Negoi, Ionut, Negoi, Ruxandra Irina, Ngunjiri, Josephine W., Thi Nguyen, Huong Lan, Nguyen, Long Hoang, Nguyen, Son Hoang, Nielsen, Katie R., Ningrum, Dina Nur Anggraini, Nirayo, Yirga Legesse, Nixon, Molly R., Nnaji, Chukwudi A., Nojomi, Marzieh, Noroozi, Mehdi, Nosratnejad, Shirin, Noubiap, Jean Jacques, Motlagh, Soraya Nouraei, Ofori-Asenso, Richard, Ogbo, Felix Akpojene, Oladimeji, Kelechi E., Olagunju, Andrew T., Olfatifar, Meysam, Olum, Solomon, Olusanya, Bolajoko Olubukunola, Oluwasanu, Mojisola Morenike, Onwujekwe, Obinna E., Oren, Eyal, Ortega-Altamirano, Doris D. V., Ortiz, Alberto, Osarenotor, Osayomwanbo, Osei, Frank B., Osgood-Zimmerman, Aaron E., Otstavnov, Stanislav S., Owolabi, Mayowa Ojo, P. A., Mahesh, Pagheh, Abdol Sattar, Pakhale, Smita, Panda-Jonas, Songhomitra, Pandey, Animika, Park, Eun-Kee, Parsian, Hadi, Pashaei, Tahereh, Patel, Sangram Kishor, Pepito, Veincent Christian Filipino, Pereira, Alexandre, Perkins, Samantha, Pickering, Brandon V., Pilgrim, Thomas, Pirestani, Majid, Piroozi, Bakhtiar, Pirsaheb, Meghdad, Plana-Ripoll, Oleguer, Pourjafar, Hadi, Puri, Parul, Qorbani, Mostafa, Quintana, Hedley, Rabiee, Mohammad, Rabiee, Navid, Radfar, Amir, Rafiei, Alireza, Rahim, Fakher, Rahimi, Zohreh, Rahimi-Movaghar, Vafa, Rahimzadeh, Shadi, Rajati, Fatemeh, Raju, Sree Bhushan, Ramezankhani, Azra, Ranabhat, Chhabi Lal, Rasella, Davide, Rashedi, Vahid, Rawal, Lal, Reiner Jr, Robert C., Renzaho, Andre M. N., Rezaei, Satar, Rezapour, Aziz, Riahi, Seyed Mohammad, Ribeiro, Ana Isabel, Roever, Leonardo, Roro, Elias Merdassa, Roser, Max, Roshandel, Gholamreza, Roshani, Daem, Rostami, Ali, Rubagotti, Enrico, Rubino, Salvatore, Sabour, Siamak, Sadat, Nafis, Sadeghi, Ehsan, Saeedi, Reza, Safari, Yahya, Safari-Faramani, Roya, Safdarian, Mahdi, Sahebkar, Amirhossein, Salahshoor, Mohammad Reza, Salam, Nasir, Salamati, Payman, Salehi, Farkhonde, Zahabi, Saleh Salehi, Salimi, Yahya, Salimzadeh, Hamideh, Salomon, Joshua A., Sambala, Evanson Zondani, Samy, Abdallah M., Santric Milicevic, Milena M., Jose, Bruno Piassi Sao, Saraswathy, Sivan Yegnanarayana Iyer, Sarmiento-Suárez, Rodrigo, Sartorius, Benn, Sathian, Brijesh, Saxena, Sonia, Sbarra, Alyssa N., Schaeffer, Lauren E., Schwebel, David C., Sepanlou, Sadaf G., Seyedmousavi, Seyedmojtaba, Shaahmadi, Faramarz, Shaikh, Masood Ali, Shams-Beyranvand, Mehran, Shamshirian, Amir, Shamsizadeh, Morteza, Sharafi, Kiomars, Sharif, Mehdi, Sharif-Alhoseini, Mahdi, Sharifi, Hamid, Sharma, Jayendra, Sharma, Rajesh, Sheikh, Aziz, Shields, Chloe, Shigematsu, Mika, Shiri, Rahman, Shiue, Ivy, Shuval, Kerem, Siddiqi, Tariq J., Silva, João Pedro, Singh, Jasvinder A., Sinha, Dhirendra Narain, Sisay, Malede Mequanent, Sisay, Solomon, Sliwa, Karen, Smith, David L., Somayaji, Ranjani, Soofi, Moslem, Soriano, Joan B., Sreeramareddy, Chandrashekhar T., Sudaryanto, Agus, Sufiyan, Mu’awiyyah Babale, Sykes, Bryan L., Sylaja, P. N., Tabarés-Seisdedos, Rafael, Tabb, Karen M., Tabuchi, Takahiro, Taveira, Nuno, Temsah, Mohamad-Hani, Terkawi, Abdullah Sulieman, Tessema, Zemenu Tadesse, Thankappan, Kavumpurathu Raman, Thirunavukkarasu, Sathish, To, Quyen G., Tovani-Palone, Marcos Roberto, Tran, Bach Xuan, Tran, Khanh Bao, Ullah, Irfan, Usman, Muhammad Shariq, Uthman, Olalekan A., Vahedian-Azimi, Amir, Valdez, Pascual R., van Boven, Job F. M., Vasankari, Tommi Juhani, Vasseghian, Yasser, Veisani, Yousef, Venketasubramanian, Narayanaswamy, Violante, Francesco S., Vladimirov, Sergey Konstantinovitch, Vlassov, Vasily, Vos, Theo, Vu, Giang Thu, Vujcic, Isidora S., Waheed, Yasir, Wakefield, Jon, Wang, Haidong, Wang, Yafeng, Wang, Yuan-Pang, Ward, Joseph L., Weintraub, Robert G., Weldegwergs, Kidu Gidey, Weldesamuel, Girmay Teklay, Westerman, Ronny, Wiysonge, Charles Shey, Wondafrash, Dawit Zewdu, Woyczynski, Lauren, Wu, Ai-Min, Xu, Gelin, Yadegar, Abbas, Yamada, Tomohide, Yazdi-Feyzabadi, Vahid, Yilgwan, Christopher Sabo, Yip, Paul, Yonemoto, Naohiro, Lebni, Javad Yoosefi, Younis, Mustafa Z., Yousefifard, Mahmoud, Yousof, Hebat-Allah Salah A., Yu, Chuanhua, Yusefzadeh, Hasan, Zabeh, Erfan, Moghadam, Telma Zahirian, Bin Zaman, Sojib, Zamani, Mohammad, Zandian, Hamed, Zangeneh, Alireza, Zerfu, Taddese Alemu, Zhang, Yunquan, Ziapour, Arash, Zodpey, Sanjay, Murray, Christopher J. L., and Hay, Simon I.

Published
2019
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50. Designing Study Protocol: The Effects of Rhythmic Maternal Movements on Motor Performance of Preterm Infants Hospitalized in a Neonatal Intensive Care Unit: A Randomized Controlled Clinical Trial.

Author

Ravarian, Aida, Soleimani, Farin, Rahmani, Nahid, Sajedi, Firoozeh, Shaabani, Moslem, Noroozi, Mehdi, and Mohseni-Bandpei, Mohammad A.

Subjects
PREMATURE infants, NEONATAL intensive care units, CLINICAL trials, RANDOMIZED controlled trials, RESEARCH protocols, VESTIBULAR apparatus
Abstract

Background: The fetus receives various vestibular stimuli as a result of its mother’s walking and other activities, while premature infants are deprived of these typical stimuli due to hospitalization in a neonatal intensive care unit (NICU). Methods: A single blinded randomized controlled clinical trial was designed to evaluate the effect of rhythmic maternal movements (RMM) on motor performance of infants born preterm with gestational age under 34 weeks. The aim of this plan is to stimulate the vestibular system in infants after preterm birth in hopes of improving motor performance. Seventy preterm infants will be randomized to experimental and control groups. The infants in the experimental group will be given RMM to improve motor development, heart rate and sao2. A neonatal occupational therapist trains mothers to do the RMM with a doll and explains the protocol. RMM is performed twice a day for a week. The Test of Infant Motor Performance (TIMP) is carried out at baseline and after a week of intervention by a blinded assessor. Discussion: This paper designed to study the effects of rhythmic maternal movements on motor performance, heart rate and sao2 of preterm infants hospitalized in a Neonatal Intensive Care Unit. [ABSTRACT FROM AUTHOR]

Published
2024
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