Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%

Evaluation of the efficacy and tolerability of miglitol in Chinese patients with type 2 diabetes mellitus inadequately controlled by diet and sulfonylureas

The objective of this study is to examine the efficacy and tolerability of miglitol with respect to improving glycemic control in Chinese patients with type 2 diabetes mellitus inadequately controlled by diet and sulfonylurea treatment. This was a randomized, double-blinded, placebo-controlled, multicenter study. A total of 105 patients were randomized to receive 24 weeks of treatment with miglitol (n = 52; titrated from 50 mg to 100 mg 3 times daily) or placebo (n = 53). Concomitant sulfonylurea treatment and diet remained unchanged. The primary endpoint was change in glycated hemoglobin (HbA1c) from baseline at 24 weeks. Secondary endpoints were changes in fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and postprandial serum insulin (PSI). The miglitol treatment group showed significantly greater reductions in HbA1c and PPG levels compared with the placebo group. With respect to adverse events, abdominal discomfort, diarrhea, and hypoglycemia occurred with similar frequency in both groups. Results of this study indicate that miglitol significantly improves metabolic control in Chinese patients with type 2 diabetes mellitus. Miglitol is safe and well tolerated, with the exception of abdominal discomfort. Therefore, miglitol may be a useful adjuvant therapy for Chinese patients with type 2 diabetes mellitus inadequately controlled by diet and sulfonylurea treatment

Tracing neuronal circuits in transgenic animals by transneuronal control of transcription (TRACT)

Comprender los cálculos que tienen lugar en los circuitos cerebrales requiere identificar cómo las neuronas de esos circuitos están conectadas entre sí. Describimos una técnica llamada TRACT (control de transcripción neuronal) basada en la proteólisis intramembrana inducida por ligando para revelar conexiones monosinápticas que surgen de las neuronas de interés marcadas genéticamente. En esta estrategia, las neuronas que expresan un ligando artificial (neuronas "donadoras") se unen y activan un receptor artificial de ingeniería genética en sus parejas sinápticas (neuronas "receptoras"). Tras la unión del ligando-receptor en las sinapsis, el receptor se escinde en su dominio transmembrana y libera un fragmento de proteína que activa la transcripción en las parejas sinápticas. Al usar TRACT en Drosophila, hemos confirmado la conectividad entre las neuronas receptoras olfativas y sus objetivos postsinápticos, y hemos descubierto nuevas conexiones potenciales entre las neuronas en el circuito circadiano. Nuestros resultados demuestran que el método TRACT se puede utilizar para investigar la conectividad de los circuitos neuronales en el cerebro.Understanding the computations that take place in brain circuits requires identifying how neurons in those circuits are connected to one another. We describe a technique called TRACT (TRAnsneuronal Control of Transcription) based on ligand-induced intramembrane proteolysis to reveal monosynaptic connections arising from genetically labeled neurons of interest. In this strategy, neurons expressing an artificial ligand (‘donor’ neurons) bind to and activate a genetically-engineered artificial receptor on their synaptic partners (‘receiver’ neurons). Upon ligand-receptor binding at synapses the receptor is cleaved in its transmembrane domain and releases a protein fragment that activates transcription in the synaptic partners. Using TRACT in Drosophila we have confirmed the connectivity between olfactory receptor neurons and their postsynaptic targets, and have discovered potential new connections between neurons in the circadian circuit. Our results demonstrate that the TRACT method can be used to investigate the connectivity of neuronal circuits in the brain.• National Institute of Health (USA). Beca UO 1109147, para Carlos LoispeerReviewe

Putative tumour-suppressor gene DAB2 is frequently down regulated by promoter hypermethylation in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Human Disabled-2 (DAB2), is a multi-function signalling molecule that it is frequently down-regulated in human cancers. We aimed to investigate the possible tumour suppressor effect of DAB2 in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>We studied the expression of DAB2 in NPC cell lines, xenografts and primary tumour samples. The status of promoter methylation was assessed by methylation specific PCR and bisulfite sequencing. The functional role of DAB2 in NPC was investigated by re-introducing DAB2 expression into NPC cell line C666-1.</p> <p>Results</p> <p>Decrease or absent of <it>DAB2 </it>transcript was observed in NPC cell lines and xenografts. Loss of DAB2 protein expression was seen in 72% (33/46) of primary NPC as demonstrated by immunohistochemistry. Aberrant <it>DAB2 </it>promoter methylation was detected in 65.2% (30/46) of primary NPC samples by methylation specific PCR. Treatment of the DAB2 negative NPC cell line C666-1 with 5-aza-2'-deoxycytidine resulted in restoration of DAB2 expression in a dose-dependent manner. Overexpression of DAB2 in NPC cell line C666-1 resulted in reduced growth rate and 35% reduction in anchorage-dependent colony formation, and inhibition of serum-induced c-Fos expression compared to vector-transfected controls. Over expression of DAB2 resulted in alterations of multiple pathways as demonstrated by expression profiling and functional network analysis, which confirmed the role of DAB2 as an adaptor molecule involved in multiple receptor-mediated signalling pathways.</p> <p>Conclusions</p> <p>We report the frequent down regulation of DAB2 in NPC and the promoter hypermethylation contributes to the loss of expression of DAB2. This is the first study demonstrating frequent DAB2 promoter hypermethylation in human cancer. Our functional studies support the putative tumour suppressor effect of DAB2 in NPC cells.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis