Kjerburch5164

Cephalometric analysis is a fundamental examination which is widely used in orthodontic diagnosis and treatment planning. Its key step is to detect the anatomical landmarks in lateral cephalograms, which is time-consuming in traditional manual way. To solve this problem, we propose a novel approach with a cascaded three-stage convolutional neural networks to predict cephalometric landmarks automatically. In the first stage, high-level features of the craniofacial structures are extracted to locate the lateral face area which helps to overcome the appearance variations. Next, we process the aligned face area to estimate the locations of all landmarks simultaneously. At the last stage, each landmark is refined through a dedicated network using high-resolution image data around the initial position to achieve more accurate result. Elexacaftor We evaluate the proposed method on several anatomical landmark datasets and the experimental results show that our method achieved competitive performance compared with the other methods.Biological nitrogen fixation (BNF), performed by diazotrophic prokaryotes, is responsible for reducing dinitrogen (N2) present in the biosphere into biologically available forms of nitrogen. Paenibacillus brasilensis PB24 is a diazotrophic Gram-positive bacterium and is considered ecologically and industrially important because it is able to produce antimicrobial substances and 2,3-butanediol. However, the genetics and regulation of its nitrogen fixing (nif) genes have never been assessed so far. Therefore, the present study aimed to (i) identify the structural and regulatory genes related to BNF in the PB24 genome, (ii) perform comparative genomics analysis of the nif operon among different Paenibacillus species and (iii) study the expression of these genes in the presence and absence of NH4. Strain PB24 showed a nif operon composed of nine genes (nifBHDKENXhesAV), with a conserved synteny (with small variations) among the Paenibacillus species evaluated. BNF regulatory genes, glnK and amtB (encoding GlnK signal transduction protein and AmtB transmembrane protein, respectively) and glnR and glnA genes (encoding the transcription factor GlnR and glutamine synthetase) were found in the PB24 genome. Primers were designed for qPCR amplification of the nitrogenase structural (nifH, nifD and nifK) and regulatory (glnA and amtB) BNF genes. The structural gene expression in PB24 was up- and downregulated in the absence and presence of NH4, respectively. The gene expression levels indicated a GlnR-mediated repression of genes associated with ammonium import (amtBglnK) and BNF (nif genes). Additionally, the regulatory mechanism of GlnR in P. brasilensis PB24 differed from the other Paenibacillus evaluated, considering the different distribution of binding sites recognized by GlnR.

Rapid diagnosing is crucial for controlling malaria. Various studies have aimed at developing machine learning models to diagnose malaria using blood smear images; however, this approach has many limitations. This study developed a machine learning model for malaria diagnosis using patient information.

To construct datasets, we extracted patient information from the PubMed abstracts from 1956 to 2019. We used two datasets a solely parasitic disease dataset and total dataset by adding information about other diseases. We compared six machine learning models support vector machine, random forest (RF), multilayered perceptron, AdaBoost, gradient boosting (GB), and CatBoost. In addition, a synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem.

Concerning the solely parasitic disease dataset, RF was found to be the best model regardless of using SMOTE. Concerning the total dataset, GB was found to be the best. However, after applying SMOTE, RF performed the best. Considering the imbalanced data, nationality was found to be the most important feature in malaria prediction. In case of the balanced data with SMOTE, the most important feature was symptom.

The results demonstrated that machine learning techniques can be successfully applied to predict malaria using patient information.

The results demonstrated that machine learning techniques can be successfully applied to predict malaria using patient information.

Opioid misuse (OM) is a major health problem in the United States, and can lead to addiction and fatal overdose. We sought to employ natural language processing (NLP) and machine learning to categorize Twitter chatter based on the motive of OM.

We collected data from Twitter using opioid-related keywords, and manually annotated 6988 tweets into three classes-No-OM, Pain-related-OM, and Recreational-OM-with the No-OM class representing tweets indicating no use/misuse, and the Pain-related misuse and Recreational-misuse classes representing misuse for pain or recreation/addiction. We trained and evaluated multi-class classifiers, and performed term-level k-means clustering to assess whether there were terms closely associated with the three classes.

On a held-out test set of 1677 tweets, a transformer-based classifier (XLNet) achieved the best performance with F

-score of 0.71 for the Pain-misuse class, and 0.79 for the Recreational-misuse class. Macro- and micro-averaged F

-scores over all classes were 0.82 and 0.92, respectively. Content-analysis using clustering revealed distinct clusters of terms associated with each class.

While some past studies have attempted to automatically detect opioid misuse, none have further characterized the motive for misuse. Our multi-class classification approach using XLNet showed promising performance, including in detecting the subtle differences between pain-related and recreation-related misuse. The distinct clustering of class-specific keywords may help conduct targeted data collection, overcoming under-representation of minority classes.

Machine learning can help identify pain-related and recreational-related OM contents on Twitter to potentially enable the study of the characteristics of individuals exhibiting such behavior.

Machine learning can help identify pain-related and recreational-related OM contents on Twitter to potentially enable the study of the characteristics of individuals exhibiting such behavior.