Combined chlorhexidine-sodiumfluoride mouthrinse for orthodontic patients: clinical and microbiological study

Background: Orthodontic appliances impede good dental plaque control by brushing. Antimicrobial mouth rinses were suggested to improve this performance. We therefore aimed to investigate the effects of combined mouthrinse containing chlorhexidine (CHX) and sodium fluoride (NaF) on clinical oral hygiene parameters,and plaque bacterial level. Material and Methods: In this double-blind clinical study, 60 fixed orthodontic patients aged 14-25 years were randomly assigned to one of four mouthrinses groups: 1- combined CHX /NaF 2- CHX 0.06% 3- NaF0.05% 4-placebo. Following baseline examination patients were instructed to use the assigned mouthrinse twice daily for 21 days. Bleeding index (BI), modified gingival index (MGI) and plaque index (PI) were determined at the baselineand after three weeks of rinsing. Samples from supragingival plaque were obtained for the assessment of total bacterial, Streptococcus mutans and Lactobacilli colony counts. Data were analyzed by Wilcoxon, Kruskal-Wallis, and Mann-Whitney tests. Results: Clinical parameters; All three active mouth rinses induced significant improvements of BI, MGI, and PI ( P <0.05). Results of CHX/NaF were slightly, but not significantly, better than CHX. CHX/NaF and CHX induced significantly more changes than NaF and placebo. Microbiological measurements; Except placebo, other mouthrinses reduced total bacterial, Streptococcus mutans , and Lactobacilli counts significantly ( P <0.05). CHX/NaF acted against Lactobacilli significantly more than others. Conclusions: Adding CHX0.06%/NaF0.05% combined mouth rinse to daily oral hygiene regimen of orthodontic patients significantly improved oral hygiene status. Effect of this combined mouth rinse on dental plaque Lactobacilli was remarkable. However, large controlled trials could provide more definitive evidence

Overarching Preventive Sympathetic Tripping Approach in Active Distribution Networks Without Telecommunication Platforms and Additional Protective Devices

Nowadays, distributed generation (DG) has made it possible to generate electricity close to the consumption site, resulting in improved efficiency, less environmental pollution, and higher economic profit. These advantages have led to increased penetration of DGs in the distribution system. Protective devices in a distribution system are set by considering the main substation as the only source for feeding short circuit current. However, with the increased influence of DGs as the second main source of short circuit current in distribution systems the short circuit level changes, which leads to false tripping of protective devices, including overcurrent relays. A sympathetic trip, occurring due to a fault in the adjacent feeder, is one of the most serious challenges. This paper analyzes the sympathetic trip in the presence of synchronous based DGs. The equations related to the participation of DGs and upstream network in feeding the short circuit current are obtained. The effect of different parameters on the probability of occurrence of a sympathetic trip is also investigated. Moreover, a novel fast solution is presented for overcoming the sympathetic trip of synchronous based DGs. The proposed method is introduced using the positive-sequence currents of the DGs and main substation. The sympathetic trip is predicted by adopting this prediction index and its occurrence is avoided. The proposed methodology is independent of telecommunication platforms and additional protective devices and can be applied to various short circuits. The method is tested on a network by simulating in DIgSILENT PowerFactory software. Simulation results show the effectiveness of the proposed methodology in predicting and preventing sympathetic trips

Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17

Dark Energy and Matter in 4 Dimensions From an Empty Kaluza-Klein Spacetime

We consider the third order Lovelock equations without the cosmological constant term in an empty $n(\geq 8)$-dimensional Kaluza-Klein spacetime $\mathcal{M}^{4}\times \mathcal{K}^{n-4}$, where $\mathcal{K}^{n-4}$ is a constant curvature space. We show that the emptiness of the higher-dimensional spacetime imposes a constraint on the metric function(s) of 4-dimensional spacetime $\mathcal{M}^{4}$. We consider the effects of this constraint equation in the context of black hole physics, and find a black hole solution in 4 dimensions in the absence of matter field and the cosmological constant (dark energy). This solution has the same form as the 4-dimensional solution introduced in [H. Maeda and N. Dadhich, Phys. Rev. D 74 (2006) 021501(R)] for Gauss-Bonnet gravity in the presence of cosmological constant, and therefore the metric of $\mathcal{M}^{4}$ which satisfies the vacuum Lovelock equations in higher-dimensional Kaluza-Klein spacetime is unique. This black hole solution shows that the curvature of an empty higher-dimensional Kaluza-Klein spacetime creates dark energy and matter with non-traceless energy-momentum tensor in 4 dimensions.Comment: 11 pages, two figure

DeepRetroMoCo:deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Background and purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor.Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing.Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco.Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data

Hyoid bone position in different facial skeletal patterns

Hyoid bone plays a significant role in physiological functions of craniofacial region and it?s position adapts to changes of the head. The purpose of this study was to determine the position of the hyoid bone among subjects with class I, class II and class III skeletal patterns and evaluate the gender differences. One hundred and ten lateral cephalograms (59 females and 51 males) from different skeletal patterns (class I, II and III) were selected. The skeletal patterns were determined according to ANB angle. Using MicroDicom software, different linear and angular measurements (6 variables) was carried out to determine the position of hyoid bone. Intraclass correlation coefficient was used to verify reliability. Descriptive statistics of the variables were calculated and analyzed using two-way ANOVA and Bonferroni statistical methods. The mean distance from the hyoid bone (H) to mandibular plane (MP), to palatal plane (PP), as well as to a third cervical vertebra (C3) was more in males than females (p=0.023, p<0.001, p<0.001 respectively). The mean H to PP distance was significantly more in skeletal class I compared to class III (P=0.01). The mean H to C3 distance was significantly more in skeletal class I compared to class II (P=0.008). The mean angle between H-MP and H-PP did not show any statistical difference among three skeletal classes (p=0.102, P=0.213) and among male and female groups (P=0.172, P=0.904). The hyoid bone is positioned more superior and posterior in females than males and its location differs among different skeletal classes. It is placed more posterior in skeletal class II patterns and more inferior and anterior in skeletal class I patterns

Accuracy of CBCT, Digital Radiography and Cross-Sectioning for the Evaluation of Mandibular Incisor Root Canals

ntroduction: The aim of this study was to compare the accuracy of cone-beam computed tomography (CBCT), digital radiography and tooth sectioning in evaluating root canal morphology of mandibular incisors in an in vitro setting. Methods and Materials: A total of 76 samples were imaged using CBCT, and digital radiography in straight and angled views. The samples were then sectioned at different distances from the apex for further visualization under stereomicroscope. The agreement between the observers was statistically analyzed by kappa correlation coefficient and the chi-square test. Results: The results showed that the majority of the samples had a single canal (Vertucci’s Type I). CBCT analysis reported more frequent multi-canal roots in comparison with the other techniques. In pairwise comparisons, the highest agreement was found between digital radiographic imaging and microscopic cross-sectioning both in terms of canal configuration and the number of root canals. Conclusion: None of the used imaging techniques per se could adequately show the exact internal anatomical configuration in accordance with the gold standard.Keywords: Anatomy; Cone-Beam Computed Tomography; Digital Radiography; Incisor Teet