Classification of type 1 and type 2 graphs

1996 ◽  
pp. 53-95
Author(s):  
Hian-Poh Yap

The challenges of diagnosing diabetes in childhood

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mairi Pucci ◽  
Marco Benati ◽  
Claudia Lo Cascio ◽  
Martina Montagnana ◽  
Giuseppe Lippi
Keyword(s):  
Diabetes Mellitus ◽  
Children And Young People ◽  
Narrative Literature ◽  
Diagnosis And Classification ◽  
Narrative Literature Review ◽  
Optimal Approach ◽  
Diagnostic Step

AbstractDiabetes is one of the most prevalent diseases worldwide, whereby type 1 diabetes mellitus (T1DM) alone involves nearly 15 million patients. Although T1DM and type 2 diabetes mellitus (T2DM) are the most common types, there are other forms of diabetes which may remain often under-diagnosed, or that can be misdiagnosed as being T1DM or T2DM. After an initial diagnostic step, the differential diagnosis among T1DM, T2DM, Maturity-Onset Diabetes of the Young (MODY) and others forms has important implication for both therapeutic and behavioral decisions. Although the criteria used for diagnosing diabetes mellitus are well defined by the guidelines of the American Diabetes Association (ADA), no clear indications are provided on the optimal approach to be followed for classifying diabetes, especially in children. In this circumstance, both routine and genetic blood test may play a pivotal role. Therefore, the purpose of this article is to provide, through a narrative literature review, some elements that may aid accurate diagnosis and classification of diabetes in children and young people.

scholarly journals Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Guyu Dai ◽  
Xiangbin Zhang ◽  
Wenjie Liu ◽  
Zhibin Li ◽  
Guangyu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Type ◽  
Imaging Device ◽  
Linear Discriminant ◽  
Position Errors ◽  
Dose Monitoring ◽  
Type 3

PurposeTo find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves’ ophthalmopathy (GO).MethodsPosition errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.ResultsThe best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.ConclusionML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

Sequence-dependent cluster analysis of biomineralization peptides

2015 ◽  
Vol 70 (7-8) ◽  
pp. 191-195 ◽  
Author(s):  
Jose Isagani B. Janairo ◽  
Frumencio Co ◽  
Jose Santos Carandang ◽  
Divina M. Amalin
Keyword(s):  
Molecular Weight ◽  
Cluster Analysis ◽  
Classification System ◽  
Clustering Analysis ◽  
Systematic Approach ◽  
Molecular Weight Heterogeneity ◽  
Representative Samples

Abstract A reliable and statistically valid classification of biomineralization peptides is herein presented. 27 biomineralization peptides (BMPep) were randomly selected as representative samples to establish the classification system using k-means method. These biomineralization peptides were either discovered through isolation from various organisms or via phage display. Our findings show that there are two types of biomineralization peptides based on their length, molecular weight, heterogeneity, and aliphatic residues. Type-1 BMPeps are more commonly found and exhibit higher values for these significant clustering variables. In contrast are the type-2 BMPeps, which have lower values for these parameters and are less common. Through our clustering analysis, a more efficient and systematic approach in BMPep selection is possible since previous methods of BMPep classification are unreliable.

scholarly journals MicroRNA Signatures as Future Biomarkers for Diagnosis of Diabetes States

Cells ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1533 ◽  
Author(s):  
Srividya Vasu ◽  
Kenjiro Kumano ◽  
Carly M. Darden ◽  
Irum Rahman ◽  
Michael C. Lawrence ◽  
...  
Keyword(s):  
Diabetes Type 2 ◽  
Diabetes Type ◽  
Irreversible Damage ◽  
Recent Onset ◽  
Elevated Glucose ◽  
Diagnostic Approaches ◽  
Blood Glucose Concentrations

Diabetes results from the inability of pancreatic islets to maintain blood glucose concentrations within a normal physiological range. Clinical features are usually not observed until islets begin to fail and irreversible damage has occurred. Diabetes is generally diagnosed based on elevated glucose, which does not distinguish between type 1 and 2 diabetes. Thus, new diagnostic approaches are needed to detect different modes of diabetes before manifestation of disease. During prediabetes (pre-DM), islets undergo stress and release micro (mi) RNAs. Here, we review studies that have measured and tracked miRNAs in the blood for those with recent-onset or longstanding type 1 diabetes, obesity, pre-diabetes, type 2 diabetes, and gestational diabetes. We summarize the findings on miRNA signatures with the potential to stage progression of different modes of diabetes. Advances in identifying selective biomarker signatures may aid in early detection and classification of diabetic conditions and treatments to prevent and reverse diabetes.

Defining and Classifying New Subgroups of Diabetes

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Ashok Balasubramanyam
Keyword(s):  
Neonatal Diabetes ◽  
Large Datasets ◽  
Annual Review ◽  
Publication Date ◽  
Maturity Onset Diabetes ◽  
Bioinformatic Approaches ◽  
Complex Forms

An etiologically based classification of diabetes is needed to account for the heterogeneity of type 1 and type 2 diabetes (T1D and T2D) and emerging forms of diabetes worldwide. It may be productive for both classification and clinical discovery to consider variant forms of diabetes as a spectrum. Maturity onset diabetes of youth and neonatal diabetes serve as models for etiologically defined, rare forms of diabetes in the spectrum. Ketosis-prone diabetes is a model for more complex forms, amenable to phenotypic dissection. Bioinformatic approaches such as clustering analyses of large datasets and multi-omics investigations of rare and atypical phenotypes are promising avenues to explore and define new subgroups of diabetes. Expected final online publication date for the Annual Review of Medicine, Volume 72 is January 27, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

VARIANT INTRANEURAL VEIN-TRIGEMINAL NERVE RELATIONSHIPS

Neurosurgery ◽  
2009 ◽  
Vol 65 (5) ◽  
pp. 958-961 ◽  
Author(s):  
Gregory M. Helbig ◽  
James D. Callahan ◽  
Aaron A. Cohen-Gadol
Keyword(s):  
Trigeminal Neuralgia ◽  
Trigeminal Nerve ◽  
Complication Rates ◽  
Entry Zone ◽  
Root Entry Zone ◽  
Detailed Classification ◽  
Trigeminal Nerve Root

Abstract OBJECTIVE Trigeminal neuralgia is often caused by compression, demyelination, and injury of the trigeminal nerve root entry zone by an adjacent artery and/or vein. Previously described variations of the nerve-vessel relationship note external nerve compression. We offer a detailed classification of intraneural vessels that travel through the trigeminal nerve and safe, effective surgical management. CLINICAL PRESENTATION We report 3 microvascular decompression operations for medically refractory trigeminal neuralgia during which the surgeon encountered a vein crossing through the trigeminal nerve. Two types of intraneural veins are described: type 1, in which the vein travels between the motor and sensory branches of the trigeminal nerve (1 patient), and type 2, in which the vein bisects the sensory branch (portio major) (2 patients). INTERVENTION We recommend sacrificing the intraneural vein between the motor and sensory branches if the vein is small (most likely type 1). If the intraneural vein is large and bisects the sensory branch (most likely type 2), vein mobilization can be achieved, but often requires extensive dissection through the nerve. Because this maneuver may lead to trigeminal nerve injury and result in uncomfortable neuropathy and numbness (including corneal hypoesthesia), we recommend against mobilization of the vein through the nerve, suggesting instead, consideration of a selective trigeminal nerve rhizotomy. CONCLUSION Because aggressive dissection of intraneural vessels can lead to higher than normal complication rates, preoperative knowledge of vein-trigeminal nerve variants is crucial for intraoperative success.

scholarly journals CLASSIFICATION OF MAGNETOSPHERIC RESPONSES TO INTERACTION WITH DIAMAGNETIC STRUCTURES OF SLOW SOLAR WIND

2020 ◽  
Vol 6 (4) ◽  
pp. 24-36
Author(s):  
Vladimir Parkhomov ◽  
Viktor Eselevich ◽  
Maxim Eselevich ◽  
Aleksey Dmitriev ◽  
Alla Suvorova ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Vertical Component ◽  
Slow Solar Wind ◽  
Solar Wind Density ◽  
Main Determinants ◽  
Type 3

We propose a possible classification of the responses of the magnetosphere to the interaction with diamagnetic structures (DS), which form the basis of the slow solar wind. The main determinants of the classification are the value and orientation of the vertical component Bz of the interplanetary magnetic field (IMF) and the solar wind density N. We have identified three types of magnetospheric responses. Type 1 has two subtypes whose main difference is the presence or absence of auroras on the day side of the magnetosphere. Within an hour before DS arrival, Bz has a positive value (up to 12 nT) or fluctuates about 0 in the range from –1 to +1 nT. For both subtypes, the duration of substorm disturbances approximately coincides with the duration of DS, and their intensity does not exceed AE~500 nT. Type 2 is characterized by the fact that before the contact with DS positive IMF Bz (0–10 nT) is recorded for an hour, and at the interface of DS a rapid (≤2 min) change in the orientation of the IMF vertical component from north to south occurs. For type 3, Bz within an hour before the contact with DS is negative (from –10 to 0 nT). We address the problem of DS energy transfer to the magnetosphere.

scholarly journals Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yang Xiang ◽  
Lai Shujin ◽  
Chang Hongfang ◽  
Wen Yinping ◽  
Yu Dawei ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Fundus Photography ◽  
Diabetes Diagnosis ◽  
Medical Resources ◽  
New Approach

In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.

scholarly journals Four Bible Translation Types and Some Criteria to Distinguish Them

2007 ◽  
Vol 3 (2) ◽  
pp. 1-22
Author(s):  
Sebastian Floor
Keyword(s):  
Information Structure ◽  
Semantic Content ◽  
Sentence Length ◽  
Bible Translation ◽  
Dynamic Equivalent ◽  
Type 3 ◽  
Key Terms

The purpose of this paper is to contribute to the discussion on the classification of Bible translation types. This paper proposes four types instead of the traditional two: literal and idiomatic or dynamic equivalent. The four types are Type 1) close (or literal) resemblance, Type 2) open resemblance, Type 3) close (or limited) interpretative, and Type 4) open interpretative. There are several continua of criteria: the degree of resemblance to the original semantic content, the degree of explicitness, and the type of adjustments needed to unpack the meaning. Eight criteria of adjustments are proposed to distinguish these four types: 1) order of clauses and phrases, 2) sentence length, 3) reference disambiguation and tracking, 4) concordance of lexical items, 5) key terms and unknown terms, 6) figurative usage and idioms, 7) transition marking, and 8) information structure.

Sign in / Sign up

Export Citation Format

Share Document