Effects of the Computer Mediated Communication Interaction on Vocabulary Improvement

This study examined the effect of CMC interaction on Iranian EFL learners’ vocabulary improvement. The study was carried out on the basis of a comparative design and tried to compare CMC with face to-face interactions in the Iranian EFL learners in order to see whether the learners’ lexical knowledge improved by the CMC interaction. Participants of the study were advanced learners studying in a language institute. The Oxford placement test was used to determine the Iranian EFL learners’ proficiency level and ensure a homogeneous sample. Then, the participants were randomly assigned to one control group (face-to-face interaction) and one experimental group (CMC interaction) in order to compare the effect of CMC on the learners’ vocabulary improvement. The learners took a pre-test to select 12 target lexical items, treatment activity to perform information-gap task, and two immediate and delayed post-tests for assessing the acquisition of new lexical items. Yahoo Messenger was used to provide the chat communication. The research provided evidence that there was a significant relationship between the use of CMC interaction and face-to-face interaction with regard to improvement in the learners’ vocabulary learning. The result indicated that the learners’ vocabulary learning improved more in CMC interaction in comparison to face-to-face interaction. In addition, there was a significant difference in negotiating the meaning of new lexical items through CMC interaction in comparison to face-to-face interaction. Moreover, the results indicated that in terms of signal, the CMC interaction outperformed face-to-face group

LaTeRF: Label and Text Driven Object Radiance Fields

Obtaining 3D object representations is important for creating photo-realistic simulations and for collecting AR and VR assets. Neural fields have shown their effectiveness in learning a continuous volumetric representation of a scene from 2D images, but acquiring object representations from these models with weak supervision remains an open challenge. In this paper we introduce LaTeRF, a method for extracting an object of interest from a scene given 2D images of the entire scene, known camera poses, a natural language description of the object, and a set of point-labels of object and non-object points in the input images. To faithfully extract the object from the scene, LaTeRF extends the NeRF formulation with an additional `objectness' probability at each 3D point. Additionally, we leverage the rich latent space of a pre-trained CLIP model combined with our differentiable object renderer, to inpaint the occluded parts of the object. We demonstrate high-fidelity object extraction on both synthetic and real-world datasets and justify our design choices through an extensive ablation study

Watch Your Steps: Local Image and Scene Editing by Text Instructions

Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit region implicit in a text instruction. We leverage InstructPix2Pix (IP2P) and identify the discrepancy between IP2P predictions with and without the instruction. This discrepancy is referred to as the relevance map. The relevance map conveys the importance of changing each pixel to achieve the edits, and is used to to guide the modifications. This guidance ensures that the irrelevant pixels remain unchanged. Relevance maps are further used to enhance the quality of text-guided editing of 3D scenes in the form of neural radiance fields. A field is trained on relevance maps of training views, denoted as the relevance field, defining the 3D region within which modifications should be made. We perform iterative updates on the training views guided by rendered relevance maps from the relevance field. Our method achieves state-of-the-art performance on both image and NeRF editing tasks. Project page: https://ashmrz.github.io/WatchYourSteps/Comment: Project page: https://ashmrz.github.io/WatchYourSteps

Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations

Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graphics, vision, and robotics, problems with slow rendering speed and characteristic visual artifacts prevent adoption in many use cases. In this work, we investigate combining an autoencoder (AE) with a NeRF, in which latent features (instead of colours) are rendered and then convolutionally decoded. The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster. Our work is orthogonal to other techniques for improving NeRF efficiency. Further, we can control the tradeoff between efficiency and image quality by shrinking the AE architecture, achieving over 13 times faster rendering with only a small drop in performance. We hope that our approach can form the basis of an efficient, yet high-fidelity, 3D scene representation for downstream tasks, especially when retaining differentiability is useful, as in many robotics scenarios requiring continual learning

SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields

Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3D inpainting. In 3D, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3D inpainting method that addresses these challenges. Given a small set of posed images and sparse annotations in a single input image, our framework first rapidly obtains a 3D segmentation mask for a target object. Using the mask, a perceptual optimizationbased approach is then introduced that leverages learned 2D image inpainters, distilling their information into 3D space, while ensuring view consistency. We also address the lack of a diverse benchmark for evaluating 3D scene inpainting methods by introducing a dataset comprised of challenging real-world scenes. In particular, our dataset contains views of the same scene with and without a target object, enabling more principled benchmarking of the 3D inpainting task. We first demonstrate the superiority of our approach on multiview segmentation, comparing to NeRFbased methods and 2D segmentation approaches. We then evaluate on the task of 3D inpainting, establishing state-ofthe-art performance against other NeRF manipulation algorithms, as well as a strong 2D image inpainter baselineComment: Project Page: https://spinnerf3d.github.i

Psychometric Properties of the Iranian Brief Version of the Transtheoretical Model Instrument in Terms of Hookah Tobacco Smoking Cessation

Background: Transtheoretical model (TTM) has been recognized as a common theoretical model in researches in terms of addictive behaviors. The aim of this study was to examine the psychometric properties of the Persian brief version of the TTM for hookah tobacco smoking cessation in a sample of Iranian rural adults who were in the preparation stage for hookah cessation.Methods: This was a validation study on Iranian rural adult hookah smokers by the TTM instrument. First, to translate the questionnaire items from English to Persian, backward-forward procedure was used. Face and content validity of the instrument items were assessed. Confirmatory factor analysis (CFA) was performed to determine the construct validity of the instrument. For this aim, 300 participants completed the instrument. Cronbach's alpha coefficient and intraclass correlation coefficient (ICC) were calculated to examine the internal consistency and reliability of the subscales of the instrument.Findings: The content validity index (CVI) and content validity ratio (CVR) of the items were ≥ 0.80 and ≥ 0.60, respectively. Based on CFA, the data fitted the TTM model. root mean square error of approximation (RMSEA), the goodness of fit index (GFI), adjusted GFI, and comparative fit index (CFI) were 0.037, 0.960, 0.910, and 0.950, respectively. At this stage, 6 items were deleted. The ICC and Cronbach's alpha of the subscales ranged between 0.60-0.74 and 0.71-0.86, respectively. The final instrument with 29 items was confirmed.Conclusion: The findings suggest that translating Persian brief version of the TTM instrument was a reliable and valid tool to identify the determinants of hookah smoking cessation among Iranian rural adults

Chitosan-based nanoscale systems for doxorubicin delivery:Exploring biomedical application in cancer therapy

Abstract Green chemistry has been a growing multidisciplinary field in recent years showing great promise in biomedical applications, especially for cancer therapy. Chitosan (CS) is an abundant biopolymer derived from chitin and is present in insects and fungi. This polysaccharide has favorable characteristics, including biocompatibility, biodegradability, and ease of modification by enzymes and chemicals. CS‐based nanoparticles (CS‐NPs) have shown potential in the treatment of cancer and other diseases, affording targeted delivery and overcoming drug resistance. The current review emphasizes on the application of CS‐NPs for the delivery of a chemotherapeutic agent, doxorubicin (DOX), in cancer therapy as they promote internalization of DOX in cancer cells and prevent the activity of P‐glycoprotein (P‐gp) to reverse drug resistance. These nanoarchitectures can provide co‐delivery of DOX with antitumor agents such as curcumin and cisplatin to induce synergistic cancer therapy. Furthermore, co‐loading of DOX with siRNA, shRNA, and miRNA can suppress tumor progression and provide chemosensitivity. Various nanostructures, including lipid‐, carbon‐, polymeric‐ and metal‐based nanoparticles, are modifiable with CS for DOX delivery, while functionalization of CS‐NPs with ligands such as hyaluronic acid promotes selectivity toward tumor cells and prevents DOX resistance. The CS‐NPs demonstrate high encapsulation efficiency and due to protonation of amine groups of CS, pH‐sensitive release of DOX can occur. Furthermore, redox‐ and light‐responsive CS‐NPs have been prepared for DOX delivery in cancer treatment. Leveraging these characteristics and in view of the biocompatibility of CS‐NPs, we expect to soon see significant progress towards clinical translation

The expression analysis of IL-6, IL-18, IL-21, IL-23, and TGF-β mRNA in the nasal mucosa of patients with Allergic rhinitis

Background: The profile of inflammatory and suppressing cytokines is important to contribute to the disruption of TH1/ TH2 balance in Allergic rhinitis (AR). Objective: This study aimed to assess the expression levels of IL-6, IL-18, IL-21, IL-23, and TGF-beta in nasal biopsies in AR patients and evaluate its correlation with the severity of AR. Material and method: The study included 30 patients with mild persistent allergic rhinitis (MPAR), patients with moder- ate-to-severe (M/S) PAR, and 30 healthy individuals. The biopsies of nasal inferior turbinate mucosa were collected from each participant. The expression of IL-6, IL-18, IL-21, IL-23, and TGF-beta was evaluated by the quantitative real-time polymerase chain reaction. The degree of eosinophil infiltration into the nasal mucosa, blood eosinophils, and total serum IgE level were also measured. Result: The expression of IL-6, IL-18, and IL-23 in patients with AR significantly increased compared to the control group. Conversely, the gene expression of the TGF-beta declined in the M/S PAR group rather than the AR-group. The data did not show a significant difference in the expression of the IL-21 gene between AR+ and AR-groups. Conclusion: We suggested that inflammatory cytokines including IL-6, IL-18, and IL-23 may be involved in the severity of AR and associated with markers of inflammation

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019