Correct Class Research Articles

Explaining and Evaluating Deep Tissue Classification by Visualizing Activations of Most Relevant Intermediate Layers

Abstract Deep Learning-based tissue classification may support pathologists in analyzing digitized whole slide images. However, in such critical tasks, only approaches that can be validated by medical experts in advance to deployment, are suitable. We present an approach that contributes to making automated tissue classification more transparent. We step beyond broadly used visualizations for last layers of a convolutional neural network by identifying most relevant intermediate layers applying Grad-CAM. A visual evaluation by a pathologist shows that these layers assign relevance, where important morphological structures are present in case of correct class decisions. We introduce a tool that can be easily used by medical experts for such validation purposes for any convolutional neural network and any layer. Visual explanations for intermediate layers provide insights into a neural network’s decision for histopathological tissue classification. In future research also the context of the input data must be considered.

Main clinical approaches to orthognathic surgery for class II open bite and genioplasty patients: a brief systematic review

Introduction: Orthognathic surgery (OS) is used to improve the patient's facial appearance and to correct maxillary and mandibular deformities resulting from malocclusions, disease, or trauma. In this context, genioplasty is a procedure to correct an aesthetic and functional deformity of the chin region, improving the contour. Moreover, anterior open bite (AOB) is the lack of vertical contact or negative overbite between the anterior teeth of the superior and inferior arches when the posterior teeth are in occlusion. Objective: The present study aimed to perform a brief systematic review with risk of bias analysis by funnel plot to highlight the main clinical approaches of orthognathic surgery in class II patients with open bites and also concerning genioplasty. Methods: The research was carried out from February 2022 to May 2022 and developed based on Scopus, PubMed, Science Direct, Scielo, and Google Scholar. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 115 articles were found. In total, 57 articles were fully evaluated and 34 were included and evaluated in this study. And of the total of 34 articles, only 11 articles were developed as the main clinical results. A total of 32 articles were excluded because they did not meet the GRADE classification, and 16 were excluded because they were at risk of bias. The symmetric funnel plot does not suggest a risk of bias between the small sample size studies. A retrospective observational study compared 57 patient charts on treatment outcomes for anterior occlusion and vertical skeletal stability after maxillary or mandibular surgery to correct Class II malocclusion with a mild to moderate open bite. After surgery, 87% of Le Fort I patients and 63% of patients with bilateral sagittal split osteotomy had a positive overbite, and at the 6-month follow-up, the percentages were 90% and 74%, respectively. Another study showed Class II patients had significantly greater amounts of lateral and lower translation than class III patients. Also, genioplasty represents one of the most common auxiliary procedures and may be associated with corrective surgery for dentofacial dysmorphisms. However, care must be taken with mental nerve injuries, asymmetries, and intraoperative bleeding are the main immediate complications.

Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) with multi-block data analysis and machine learning for accurate intraregional classification of Barossa Shiraz wine

Authentication of wine can be considered at different scales, with classification according to country, province/state, or appellation/wine producing region. An absorbance-transmission and excitation-emission matrix (A-TEEM) technique was applied for the first time to examine intraregional differences, using Shiraz wines (n = 186) produced during three vintages from five subregions of Barossa Valley and from Eden Valley. Absorption spectra and EEM fingerprints were modelled as a multi-block data set for initial exploration with k-means cluster analysis and principal component analysis, and then with machine learning modelling using extreme gradient boosting discriminant analysis (XGBDA). Whereas some clustering was evident with the initial unsupervised approaches, classification with XGBDA afforded an impressive 100% correct class assignment for subregion and vintage year. Extending the utility and novelty of the A-TEEM approach, predictive models for chemical parameters (alcohol, glucose + fructose, pH, titratable acidity, and volatile acidity) were also validated using A-TEEM data with XGB regression.

Characteristics of cognitive processes of mentally disabled pupils at primary school as the basis for the implementation of a level-differentiated approach in correctional and pedagogic work

The article presents an analysis of the results of studying the cognitive processes of mentally disabled schoolchildren. Based on this analysis, the levels of formation of cognitive processes are identified and characterised. The basis for determining the levels is the number of fully correctly completed tasks. Mentally retarded children in forms I-V revealed low, below average or average levels of formation of cognitive processes. As general manifestations, difficulties in the logical processing of information and lack of drawing conclusions due to the lack of formation of thinking and mental operations can be noted; difficulties in retaining speech information in memory, as well as a reduced ability to regulate their activity were also evident. The remaining manifestations differ depending on the level of formation of cognitive processes. In this regard, the conditions of correctional and pedagogic work with the mentally disabled in correctional classes and at Maths lessons are highlighted.

Logarithmic corrections for jet production at the LHC

Several important processes and analyses at the LHC are sensitive to higher-order perturbative corrections beyond what can currently be calculated at fixed order. One important class of logarithmic corrections are those which appear when the centre-of-mass energy of a QCD collision is much larger than the transverse momenta of the observed jets. We describe the High Energy Jets (HEJ) framework, which includes the dominant high-energy logarithms to provide all-order predictions for several LHC processes including Higgs, W, or Z boson production in association with jets. We will summarise some recent developments, in particular the first matching of HEJ to a NLO calculation.

Comprehensive genomic profiling of EWSR1/FUS::CREB translocation-associated tumors uncovers prognostically significant recurrent genetic alterations and methylation-transcriptional correlates

AbstractTo elucidate the mechanisms underlying the divergent clinicopathologic spectrum of EWSR1/FUS::CREB translocation-associated tumors, we performed a comprehensive genomic analysis of fusion transcript variants, recurrent genetic alterations (mutations, copy number alterations), gene expression, and methylation profiles across a large cohort of tumor types. The distribution of the EWSR1/FUS fusion partners—ATF1, CREB1, and CREM—and exon involvement was significantly different across different tumor types. Our targeted sequencing showed that secondary genetic events are associated with tumor type rather than fusion type. Of the 39 cases that underwent targeted NGS testing, 18 (46%) had secondary OncoKB mutations or copy number alterations (29 secondary genetic events in total), of which 15 (52%) were recurrent. Secondary recurrent, but mutually exclusive, TERT promoter and CDKN2A mutations were identified only in clear cell sarcoma (CCS) and associated with worse overall survival. CDKN2A/B homozygous deletions were recurrent in angiomatoid fibrous histiocytoma (AFH) and restricted to metastatic cases. mRNA upregulation of MITF, CDH19, PARVB, and PFKP was found in CCS, compared to AFH, and correlated with a hypomethylated profile. In contrast, S100A4 and XAF1 were differentially upregulated and hypomethylated in AFH but not CCS. Unsupervised clustering of methylation profiles revealed that CREB family translocation-associated tumors form neighboring but tight, distinct clusters. A sarcoma methylation classifier was able to accurately match 100% of CCS cases to the correct methylation class; however, it was suboptimal when applied to other histologies. In conclusion, our comprehensive genomic profiling of EWSR1/FUS::CREB translocation-associated tumors uncovered mostly histotype, rather than fusion-type associated correlations in transcript variants, prognostically significant secondary genetic alterations, and gene expression and methylation patterns.

Comparison of cephalometric measurements of the Twin Block and A6 appliances in the treatment of Class II malocclusion: a retrospective comparative cohort study.

BackgroundSkeletal Class II malocclusion is a common malocclusion that seriously affects patients’ profile and occlusal function. The key to treatment is to use functional appliances guide the mandible forward. This study aimed to evaluate the clinical efficacy of traditional functional appliance Twin Block (TB) and invisible functional appliance (A6).MethodsIn the retrospective cohort study, 46 patients with Class II Division 1 mandibular retrognathia (23 females, 23 males; mean age 13.66±4.25 years) from the Third Affiliated Hospital of Sun Yat-sen University were selected. They were divided into A6 group and TB group according to the type of appliance guided mandibular forward used in orthodontic treatment (n=23 each; average treatment time 9.82±3.52 months). Lateral cephalometric radiographs were taken before and at the end of each treatment, and paired t-test or paired rank-sum tests were performed when appropriate to detect any statistical significance at the level of α=0.05.ResultsThe baseline characteristics of the two groups of patients were similar. Treatment with both appliances helped correct Class II malocclusion, improve the discrepancy between the maxilla and mandible, reduce the labial inclination of the maxillary anterior teeth, and relieve the deep overbite. A comparison of the treatment effects of the TB and A6 groups showed that the A6 had a better effect when moving Point A backward, and performed better in the abduction of the anterior teeth. TB group has more advantages than A6 group in moving forward point B and improving the nasolabial angle.ConclusionsBoth the A6 and TB can significantly improve Class II malocclusion. A6 showed an obvious advantage in moving Point A backward and adducting the anterior teeth, which better corrects a skeletal Class II malocclusion.

Integrating Semi-Supervised Learning with an Expert System for Vegetation Cover Classification Using Sentinel-2 and RapidEye Data

In complex classification tasks, such as the classification of heterogeneous vegetation covers, the high similarity between classes can confuse the classification algorithm when assigning the correct class labels to unlabelled samples. To overcome this problem, this study aimed to develop a classification method by integrating graph-based semi-supervised learning (SSL) and an expert system (ES). The proposed method was applied to vegetation cover classification in a wetland in the Netherlands using Sentinel-2 and RapidEye imagery. Our method consisted of three main steps: object-based image analysis (OBIA), integration of SSL and an ES (SSLES), and finally, random forest classification. The generated image objects and the related features were used to construct the graph in SSL. Then, an independently developed and trained ES was used in the labelling stage of SSL to reduce the uncertainty of the process, before the final classification. Different spectral band combinations of Sentinel-2 were then considered to improve the vegetation classification. Our results show that integrating SSL and an ES can result in significantly higher classification accuracy (83.6%) compared to a supervised classifier (64.9%), SSL alone (71.8%), and ES alone (69.5%). Moreover, utilisation of all Sentinel-2 red-edge spectral band combinations yielded the highest classification accuracy (overall accuracy of 83.6% with SSLES) compared to the inclusion of other band combinations. The results of this study indicate that the utilisation of an ES in the labelling process of SSL improves the reliability of the process and provides robust performance for the classification of vegetation cover.

The Effect of Orthognathic Surgery on the Hourglass Appearance of the Neck.

In frontal view, the transition from the upper aspect of the neck to the inferior border of the mandible has a subtle hourglass appearance. The aim of this study was to evaluate the effect of the orthognathic surgery on the hourglass appearance of the neck in patients with Class II dentofacial deformity. Twenty-six patients with Class II dentofacial deformity who underwent bilateral sagittal split osteotomy alone or in combination with Le Fort I osteotomy and/or genioplasty were included in this study. The width of the most upper part, the width of the narrowest part, the length of the upper part, the height of the upper part, the depth, and the angle of the hourglass were measured on frontal facial photographs obtained preoperatively and at sixth months postoperatively. The decrease in the width of the narrowest part ( P =0.012) and the right ( P <0.001) and left ( P <0.001) angles of the hourglass were statistically significant. Also, increase in the right ( P =0.036) and the left ( P =0.004) depths of the hourglass were statistically significant. Orthognathic surgery procedures performed to correct Class II dentofacial deformity positively affects the hourglass appearance of the neck, therefore neck esthetics should also be considered in the preoperative evaluation of facial esthetics.

Characterization and Classification of Direct and Commercial Strawberry Beverages Using Absorbance-Transmission and Fluorescence Excitation-Emission Matrix Technique.

The subject of this study was to characterize the absorption and fluorescence spectra of various types of strawberry beverages and to test the possibility of distinguishing between direct juices and pasteurized commercial products on the basis of their spectral properties. An absorbance and transmission excitation–emission matrix (A-TEEMTM) technique was used for the acquisition of spectra. The obtained spectra were analyzed using chemometric methods. The principal component analysis (PCA) revealed differences in both the absorption spectra and excitation–emission matrices (EEMs) of two groups of juices. The parallel factor analysis (PARAFAC) enabled the extraction and characterization of excitation and emission profiles and the relative contribution of four fluorescent components of juices, which were related to various groups of polyphenols and nonenzymatic browning products. Partial least squares–discriminant analysis (PLS-DA) models enabled 100% correct class assignment using the absorption spectra in the visible region, unfolded EEMs, and set of emission spectra with excitation at wavelengths of 275, 305, and 365 nm. The analysis of variable importance in projection (VIP) suggested that the polyphenols and nonenzymatic browning products may contribute significantly to the differentiation of commercial and direct juices. The results of the research may contribute to the development of fast methods to test the quality and authenticity of direct and processed strawberry juices.

Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms.

This study aimed at testing the feasibility of neurosurgical procedures classification into 100+ classes using natural language processing and machine learning. A catboost algorithm and bidirectional recurrent neural network with a gated recurrent unit showed almost the same accuracy of ∼81%, with suggestions of correct class in top 2-3 scored classes up to 98.9%. The classification of neurosurgical procedures via machine learning appears to be a technically solvable task which can be additionally improved considering data enhancement and classes verification.

The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression.

ObjectiveMethods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models. We examined the effect of imbalance correction on the performance of logistic regression models.Material and MethodsPrediction models were developed using standard and penalized (ridge) logistic regression under 4 methods to address class imbalance: no correction, random undersampling, random oversampling, and SMOTE. Model performance was evaluated in terms of discrimination, calibration, and classification. Using Monte Carlo simulations, we studied the impact of training set size, number of predictors, and the outcome event fraction. A case study on prediction modeling for ovarian cancer diagnosis is presented.ResultsThe use of random undersampling, random oversampling, or SMOTE yielded poorly calibrated models: the probability to belong to the minority class was strongly overestimated. These methods did not result in higher areas under the ROC curve when compared with models developed without correction for class imbalance. Although imbalance correction improved the balance between sensitivity and specificity, similar results were obtained by shifting the probability threshold instead.DiscussionImbalance correction led to models with strong miscalibration without better ability to distinguish between patients with and without the outcome event. The inaccurate probability estimates reduce the clinical utility of the model, because decisions about treatment are ill-informed.ConclusionOutcome imbalance is not a problem in itself, imbalance correction may even worsen model performance.

CONTENT OF SOCIAL AND PEDAGOGICAL ASSISTANCE TO STUDENTS-VICTIMS OF BULLYING

The article considers the problem of social and pedagogical assistance to students who are victims of bullying. Its purpose is to analyze the content of social and pedagogical assistance to students who are victims of bullying in an educational institution. Research methods applied: systematization and analysis of scientific and pedagogical literature on the problem of social and pedagogical assistance to victims of bullying; classification of forms and methods of social and pedagogical work with students who are victims of bullying at different stages; generalizations to formulate research conclusions. It is determined that assistance to children victims of bullying is an important area of social and pedagogical work, should be comprehensive and multilevel, not limited to work with a child who has become a victim of school bullying. The stages of social and pedagogical assistance are analyzed: diagnostic; individual assistance to students who are victims of bullying; group work, which is based on the system of interaction of students, class teacher, teachers and class, parents of bullies. It was found that social and pedagogical work with students who are victims of bullying should be carried out using both traditional and innovative methods of work: conversations, individual consultations, correctional and developmental classes, group classes with elements of training, communication hours, etc. Because bullying is a deliberate, group, conscious, and organized process, it cannot be stopped without the intervention of professionals (educators, psychologists, social educators) and parents. This involves purposeful anti-bullying work, which requires the involvement of all available resources of the educational institution and the child's environment.

A Cautionary Note about Having the Right Mixture Model but Classifying the Wrong People

Mixture models can be used for explanation or individual prediction and classification. In practice, researchers are often tempted to make the class membership manifest by classifying cases according to their class of maximum posterior probability and using the “observed” class membership directly or as a variable in follow-up analyses to predict distal outcomes. This study revisits the issue of correct class assignment in latent profile analysis by providing an example where the number of classes is known (3-classes), sampling variability is eliminated, and precise estimates of classification indices are provided. This pseudo-population study design assumes the data-generating mechanism is known and provides a “best-case” scenario for evaluating correct class assignment. We use a variety of classification indices and graphical displays to show that correct classification may be poor despite relatively high entropy and overall correct class assignment metrics (e.g., percent correct). Our study serves as a reminder of the risks associated with trying to make latent class memberships manifest.

Reliable quality assurance of X-ray mammography scanner by evaluation the standard mammography phantom image using an interpretable deep learning model

ObjectiveMammography is the initial examination to detect breast cancer symptoms, and quality control of mammography devices is crucial to maintain accurate diagnosis and to safeguard against degradation of performance. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography. Materials and MethodsA total of 2,208 mammography phantom images were collected for periodic accreditation of the scanner from 1,755 institutions. The dataset was randomly split into training (1,808 images) and testing (400 images) datasets with subgroups (76 images) for the multi-reader study. To develop an interpretable model that contains two deep learning networks in series, five processing steps were performed: mammography phantom detection, phantom object detection, post-processing, score evaluation, and a report with a comment about ambiguous results. ResultsFor phantom detection, the accuracy and mean intersection over union (mIOU) were 1.00 and 0.938 in the test dataset, respectively. During phantom object detection, a total of 6,369 out of 6,400 objects were detected as the correct object class, and the accuracy and mIOU were 0.995 and 0.813, respectively. The predicted score for each object showed a consensus of 97.40% excluding ambiguous points and 59.10% for ambiguous points of the groups. ConclusionsThe interpretable deep learning model using large-scale data from multiple centers shows high performance and reasonable object scoring, successfully validating the reliability and feasibility of mammography phantom image quality management.

Dental, skeletal, and soft-tissue changes in adult orthodontic patients treated with premolar extraction and nonextraction: A cross-sectional study

Various treatment modalities exist to improve and correct Class I malocclusion. This study evaluated the dental, skeletal, and soft-tissue changes in patients treated by premolar extraction (PME) and nonextraction. A cross-sectional study was conducted, including subjects ideally planned for PME. Pretreatment and posttreatment lateral cephalograms were compared among the 2 groups. The American Board of Orthodontics Objective Grading System (ABO-OGS) was used to score the posttreatment casts, and soft-tissue profile silhouettes were judged by a panel of orthodontists to evaluate the improvement among the 2 groups. Comparisons between the 2 groups were performed using the Mann-Whitney U test.Spearman correlation was used to study the correlation between the assessment methodologies. Highly significant differences were observed between the 2 groups for ABO-OGS scores (P≤0.001) and profile silhouette assessment (P≤0.001). Compared with posttreatment cephalometric readings, highly significant differences were seen for the dental variables: SN-U1, IMPA, and interincisal angle (P≤0.001). Statistically significant differences were seen for all soft-tissue cephalometric variables. Highly significant moderate to negative correlation (r= -0.410, P≤0.001) was seen between profile silhouettes and ABO-OGS alignment scores. Highly significant strong negative correlation (r= -0.642, P≤0.001) was seen between profile silhouettes and the lower lip. Dental and soft-tissue changes were highly significant and were found to be in the ideal range when treated with PME. However, PME and nonextraction treatment modalities did not significantly affect the skeletal parameters as seen on the lateral cephalogram.

HEJ 2.1: High-energy resummation with vector bosons and next-to-leading logarithms

We present version 2.1 of the High Energy Jets (HEJ) event generator for hadron colliders. HEJ is a Monte Carlo generator for processes at high energies with multiple well-separated jets in the final state. To achieve accurate predictions, conventional fixed-order perturbative QCD is supplemented with an all-order resummation of large high-energy logarithms. The new version 2.1 now supports processes with final-state leptons originating from a charged or neutral vector boson together with multiple jets, in addition to processes available in earlier versions. Furthermore, the all-order resummation is extended to include an additional gauge-invariant class of subdominant logarithmic corrections. HEJ 2.1 can be obtained from https://hej.hepforge.org. New version program summaryProgram Title:HEJ.CPC Library link to program files:https://doi.org/10.17632/24c9mb6pf9.2Licensing provisions: GPLv2 or later.Programming language: C++.Journal reference of previous version: Comput. Phys. Commun. 245 (2019) 106832.Does the new version supersede the previous version?: YesReasons for the new version: Support for further scattering processes and inclusion of additional corrections.Summary of revisions: This release adds support for the production of two final-state leptons originating from a charged or neutral vector boson together with multiple jets. Resummation is extended to include next-to-leading logarithmic corrections connected to the emission of additional quark-antiquark pairs.Nature of problem: Conventional event generators for high-energy colliders are based on fixed-order perturbation theory, which converges poorly in regions of phase space with large invariant masses between jets. Reliable predictions in these regions are of general interest, and particularly important whenever cuts relevant to weak-boson fusion or weak-boson scattering are applied.Solution method: The perturbative description can be amended by resumming the high-energy logarithms associated with large invariant masses to all orders in perturbation theory. HEJ 2 implements this resummation following the High Energy Jets framework. Using HEJ 2 in conjunction with a conventional event generator allows one to obtain reliable predictions for processes involving multiple jets in high-energy regions of phase space.Additional comments including restrictions and unusual features: The current version implements multi-jet processes with at most a single lepton pair or Higgs boson in the final state. Matching to fixed-order perturbation theory is currently restricted to leading order.

Development of Predictive Classification Models for Whole Cell Antimycobacterial Activity of Benzothiazinones.

Nitrobenzothiazinones (BTZs) are a very potent class of antibiotics against Mycobacterium tuberculosis. However, relationships between their structural properties and whole cell activity remain poorly predictable. Herein, we present the synthesis and antimycobacterial evaluation of a diverse set of BTZs. High potency was predominantly achieved by piperidine and piperazine substitutions, whereupon three compounds were identified as promising candidates, showing preferable metabolic stability. Lack of correlation between potency and calculated binding energies suggested that target inhibition is not the only requirement to obtain suitable antimycobacterial agents. In contrast, prediction of whole cell activity class was successfully accomplished by extensively validated machine learning models. The performance of the superior model was further verified by >70% correct class predictions for a large set of reported BTZs. Our generated model is thus a key prerequisite to streamline lead optimization endeavors, particularly regarding the improvement of overall hit rates in whole cell antimycobacterial assays.

Classifying pollutant flush signals in stormwater using functional data analysis on TSS MV curves

Pollution levels in stormwater vary significantly during rain events, with pollutant flushes carrying a major fraction of an event pollutant load in a short period. Understanding these flushes is thus essential for stormwater management. However, current studies mainly focus on describing the first flush or are limited by predetermined flush categories. This study provides a new perspective on the topic by applying data-driven approaches to categorise Mass Volume (MV) curves for TSS into distinct classes of flush tailored to specific monitoring location. Functional Data Analysis (FDA) was used to investigate the dynamics of MV curves in two large data sets, consisting of 343 measured events and 915 modelled events, respectively. Potential links between classes of MV curves and combinations of rain characteristics were explored through a priori clustering. This yielded correct class assignments for 23-63% of the events using different combinations of MV curve clustering and rainfall characteristics. This suggests that while global rainfall characteristics influence flush, they are not sufficient as sole explanatory variables of different flush phenomena, and additional explanatory variables are needed to assign MV curves into classes with a predictive power that is suitable for e.g. design of stormwater control measures. Our results highlight the great potential of the FDA methodology as a new approach for classifying, describing, and understanding pollutant flush signals in stormwater.

INTERACTION OF FAMILY AND SCHOOL IN THE CONTEXT OF INCLUSIVE EDUCATION

Currently, an inclusive education system is organized in the process of adaptation of children with special educational needs to the environment. This process is very relevant in the world and in the country.The main purpose of the article «interaction of family and school in inclusive education» is to establish close ties between parents raising a child with special educational needs and the staff of an educational secondary school, the organization of trusting and strong relationships with school teachers.The main subject participating in the inclusive education system is the child, the school staff and the parent.A parent is a person who occupies a special place in the education of a child with special educational needs, adaptation of the child to the environment. Psychological and pedagogical assistance to parents is organized by the school staff. The joint activity of specialists (teachers, psychologists, speech therapists, speech pathologists, social educators) has a huge impact on the development of the child.When conducting correctional and developmental classes, parents receive personal information about their child, special consultations are given for parents. Parents should take an active part in the correctional and developmental activities of the child, conduct repeated classes outside of school hours (fixing the studied material at home, medical treatment).