Traditional Classification Systems Research Articles

A Novel Glycolysis-Related Four-mRNA Signature for Predicting the Survival of Patients With Breast Cancer.

Background: Glycolysis is critical in the occurrence and development of tumors. Owing to the biological and clinical heterogeneity of patients with BRCA, the traditional predictive classification system is far from satisfactory. Survival and prognosis biomarkers related to glycolysis have broad application prospects for assessing the risk of patients and guiding their individualized treatment.Methods: The mRNA expression profiles and clinical information of patients with BRCA were obtained from TCGA database, and glycolysis-related genes were obtained by GSEA. Patients with BRCA were randomly divided into the training cohort and testing cohort. Univariate and multivariate Cox analyses were used to establish and validate a new mRNA signature for predicting the prognosis of patients with BRCA.Results: We established a four-gene breast cancer prediction signature that included PGK1, SDHC, PFKL, and NUP43. The patients with BRCA in the training cohort and testing cohort were divided into high-risk and low-risk groups based on the signature. The AUC values were 0.74 (training cohort), 0.806 (testing cohort) and 0.769 (entire cohort), thereby showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that four-gene signature could independently predict the prognosis of BRCA patients without being affected by clinical factors.Conclusion: We constructed a four-gene signature to predict the prognosis of patients with BRCA. This signature will aid in the early diagnosis and personalized treatment of breast cancer, but the specific associated biological mechanism requires further study.

Lazy Learning Associative Classification with WkNN and DWkNN Algorithm

One of the algorithms, which prudently denote better outcomes than the traditional associative classification systems, is the Lazy learning associative classification (LLAC), where the processing of training data is delayed until a test instance is received, whereas in eager learning, before receiving queries, the system begins to process training data. Traditional method assumes that all items within a transaction is same, which is not always true. This paper recommends a new framework called lazy learning associative classification with WkNN (LLAC_WkNN) which uses weighted kNN method with LLAC, that gives a subset of rules when LLAC is applied to the dataset. In order to predict the class label of the unseen test case, the weighted kNN (WkNN) algorithm is then applied to this generated subset. This creates the enhanced accuracy of the classifier. The WkNN also gives an outlier more weight. By applying Dual Distance Weight to LLAC named as LLAC_DWkNN, this limitation of WkNN is resolved. LLAC_DWkNN gives less weightage to outliers, which improve the accuracy of the classifier, further. This algorithm has been applied to different datasets and the experiment results demonstrate that the proposed method is efficient as compared to the traditional and other existing systems.

Design of e-commerce cluster information classification and extraction system based on relevant vector machine

Aiming at the problems of low accuracy and long time-consuming in traditional classification and extraction system, this paper studies and designs an e-commerce clustering information classificatio...

Onasemnogene abeparvovec-xioi: a gene replacement strategy for the treatment of infants diagnosed with spinal muscular atrophy.

In May of 2019, the adeno-associated virus (AAV)-based gene therapy onasemnogene abeparvovec-xioi (Zolgensma) became the second Food and Drug Administration (FDA)-approved gene therapy with designated use for infants diagnosed with spinal muscular atrophy (SMA). The decision came nearly 10 years after results of the first preclinical models were initially reported. While the journey was an arduous one, the approval was an indication of the remarkable success of the first in-human clinical trials. According to the traditional classification system of autosomal recessive SMA, of which there are multiple types with phenotypic variability, SMA type 1 is the most common and most severe and represents 45% of the SMA patient population. Children with SMA type 1 cannot lift their heads without assistance and do not live past their second birthday. With Zolgensma, the first treated children with SMA type 1 have reached 5 years of age and some of them achieved the ability to sit unassisted or even walk. In this article, we review the work that led to FDA approval with emphasis on the development of preclinical and clinical studies.

The Fuzzy Boundaries in Start-up Firms Industries. A Social Network Analysis

This paper aims to explore and describe the way in which start-up firms are grouped into industries. To this end, a quantitative research approach is presented, applying a social network analysis to a sample of Pacific Alliance start-ups, which were recorded in CrunchBase, considering their operational activities as linkage criteria. In this way, this document offers a new application to social network analysis to demonstrate the need for a different way of classifying start-ups that goes beyond the industry boundaries established by the traditional classification systems. It also shows that Pacific Alliance start-up industries are structured according to a pattern of dominant activity, applied technology and specific use. In addition, there is a concentration on mature or declining start-up industries, while growing industries are left in second order.

WITHDRAWN: Breast cancer images classification by clustering of ROI and mapping of features by CNN with XGBOOST learning

Abstract The efficiency of most traditional classification systems is based on an appropriate representation of data, and many of the attempts are made to achieve feature-based engineering and to construct useful features through the use of prior expert knowledge of data. But on the other hand, profound or deep learning will collect and organize the discriminatory data without a domain expert engineering extractor. Convolutional neural networks (CNNs) are a deep, feed-forward network with a focus on the scientific community and business sector, which is able to achieve empirical achievements in tasks including object recognition, signal processing, speech recognition, transfer learning, and processing. This paper uses a deep learning experience to identify breast cancer and do some preliminary studies. Utilizing DDSm and Mias mammography images in the proposed technique. In the proposed methodology, CNN hybridization through the improvement of cumulative and class-wise performance, and optimization tree-based learning.

Folk classification of wild mushrooms from San Isidro Buensuceso, Tlaxcala, Central Mexico

BackgroundAn ethnomycological study was conducted to describe the fungus concept and the traditional fungus classification system for the Nahuas of San Isidro Buensuceso, in central Mexico. The study which provides information on the co-existence of various forms of classification, based on both cultural and biological characteristics.MethodsThe research included conducting community interviews and forest forays in the company of mushroom pickers. The triad technique, pile sorting, and fresh mushroom sampling methods were used. Traditional names were analyzed to describe the Nahua classification system for fungi.Results and conclusionThe triad technique with non-utilitarian stimuli allowed the fungi to be identified as an independent group of plants and animals. The Nahua people of San Isidro classify fungi primarily based on their use, where they grow, and by humoral characteristics. The analysis of the names revealed a classification based on the criteria proposed by Brent Berlin. This study identified the detailed knowledge of fungi in this Nahua community. The criteria used for the recognition of the species are very reliable, since they use organoleptic, ecological, phenological, and morphological characteristics.

SpEC: A system for patient specific ECG beat classification using deep residual network

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen)=98.84%, Specificity (Spe)=99.50%, Positive predictivity (Ppr)=98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image=0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques.

The tumor genetics of acral melanoma: What should a dermatologist know?

Dermatologists stand at the gateway of individualization of classification, treatment, and outcomes of acral melanoma patients. The acral melanoma genetic landscape differs in vital ways from that of other cutaneous melanomas. These differences have important implications in understanding pathogenesis, treatment, and prognosis. The selection of molecularly targeted therapy must be adapted for acral melanoma. It is also critical to recognize that tumor development is far more complex than an isolated event, reliably treated by a medication acting on a single target. Tumors exhibit intratumor genetic heterogeneity, metastasis may have different genetic or epigenetic features than primary tumors, and tumor resistance may develop because of the activation of alternative genetic pathways. Microenvironmental, immune, and epigenetic events contribute and sustain tumors in complex ways. Treatment strategies with multiple targets are required to effectively disrupt the tumor ecosystem. This review attempts to translate the current molecular understanding of acral melanoma into digestible concepts relevant to the practice of dermatology. The focus is tumor genetics defining potentially treatable cancer pathways, contextualized within the relevant pathologic and molecular features.

Is it Time to Retire NASTIES in Southern Africa? Moving Beyond the Culture-Historical Framework for Middle Stone Age Lithic Assemblage Variability

ABSTRACT The Middle Stone Age (MSA) of southern Africa provides crucial insight on early Homo sapiens behavioral evolution. Archaeologists have traditionally presented lithic assemblage variability as a sequence of discrete Named Stone Tool Industries (“NASTIES”, Shea, 2014, Quaternary International) or techno-complexes. Many have highlighted the issues associated with the archaeological systematics of this time period in southern Africa and beyond, yet the original framework continues to persist. This paper presents two case studies from the Late Pleistocene that further problematize the usefulness of NASTIES. First, the Pinnacle Point complex of sites show that significant landscape-scale variability between coeval lithic assemblages defies the traditional classification system. Second, investigating the relationship between technological and environmental change has led to conflicting results that cannot be resolved when NASTIES are the units of analysis. While lithic data are key for understanding early Homo sapiens behavioral evolution, NASTIES are not the best tools for doing so.

Classification of volcanic soils in the Soil Classification System of Japan and verification based on forest soils

ABSTRACT In 2017, the Japanese Society of Pedology (the Fifth Committee for Soil Classification and Nomenclature) established the Japanese soil classification system. In the classification system, all the soils derived from volcanic ejecta (volcanic ash, pumice, scoria, etc.) are classified into the Great Group of Andosols, excluding Man-made and Organic soils, although some volcanic soils were classified into other soil groups, such as the Podozols and Brown forest soils in the previous classification systems. The aim of the present study was to verify and propose revision for the new system based on physicochemical data collected from forest soils across the Japan archipelago. Approximately 20% of the forest soils have andic properties and were classified into the Andosols in the Soil Classification System of Japan, which is consistent with the Andisols in Soil taxonomy and Andosols in World Reference Base for Soil Resources, excluding a few soils with low andic horizon thickness. Many other forest soils that were not classified into Andosols possessed some of the regosolic andic properties, and it was challenging to classify them into Regosolic Andosols or Brown forest soils. Therefore, additional diagnostic activities on the volcanic ejecta and glass are required to classify such soils appropriately. In the new classification system, the Fulvic Andosols soil group was abolished and substituted with the Humic and Brown-humic subgroups under the Non-allophanic and Allophanic Andosols, respectively. The revision was consistent with the traditional forest soil classification system. In conclusion, the classification of volcanic soils in the Soil Classification System of Japan was verified using forest soil data in Japan. Abbreviations CFSJ: Classification of Forest Soils in Japan (1975); CSCSJ: Comprehensive Soil Classification System of Japan, 1st approximation (2011); SCSJ: Soil Classification System of Japan (2017); ST: Keys to Soil taxonomy, Twelfth Edition (2014); USCSJ: Unified Soil Classification System of Japan, 2nd Approximation (2002); WRB: World Reference Base for Soil Resources (2014, 2015 updated)

Melone\u2019s concept revisited in comminuted distal radius fractures: the three-dimensional CT mapping

BackgroundThere is no consensus in the literature about the ideal classification of the distal radius fracture for the clinical practice. The traditional Melone classification system divides the distal radius into four basic components, the shaft, radial styloid, dorsal medial fragment, and volar medial fragment. The aim of this study was to identify fracture lines in comminuted distal radius fractures using three-dimensional mapping of computed tomography (CT) images to test the hypothesis that fracture fragments can be divided according to the Melone classification.MethodsFifty-nine consecutive OTA/AO 23C3 fractures presented at the hospital between January 2018 and October 2019 were retrospectively reviewed. The fracture lines were characterized in the axial, sagittal, and coronal CT planes. After reducing the fractures in a three-dimensional (3D) model, the fracture lines were plotted from the CT images and were then superimposed on one another and oriented to fit a standard template. The area of articular surfaces was measured and compared to quantify the differences between the radial bone fragments.ResultsThirty-five cases (59.3%) in this study fit the Melone classification and 24 cases (40.7%) did not. On the radiocarpal surface, there was a greater concentration of fracture lines in the dorsal area of the radius than in the volar area. On the distal radioulnar joint (DRUJ), the fracture lines were focused around two specific concentrated regions. For the articular surface area, the mean area of the radial styloid, volar medial fragment, and dorsal medial fragment was 141.13 ± 90.16 mm2, 147.79 ± 75.94 mm2, and 79.05 ± 70.73 mm2, respectively. There was a significant difference in articular surface area for the Melone fragments (P = 0.002).ConclusionsThe Melone classification system is not suitable for characterizing all C3 fractures. The findings of this study confirm that the dorsal medial fragments are relatively comminuted and small. Extra care should be given to these small fragments when reducing the fracture.

Incremental Validity Of The Diagnostic Criteria For Psychosomatic Research – Revised (DCPR-R) To Clinical Assessment In Primary Care

Psychosocial problems are highly prevalent among primary care (PC) patients, but they often remain undetected using traditional classification systems. The aim of the present study was to test the incremental validity of the revised version of the Diagnostic Criteria for Psychosomatic Research (DCPR-R), in addition to standard psychiatric assessment based on DSM-5, with regard to the prediction of psychosocial functioning of PC patients. Two-hundred PC patients were consecutively recruited. A comprehensive assessment was performed using two clinical interviews and three self-rating questionnaires (the PsychoSocial Index [PSI], the Short-Form Health Survey [SF-12] and the Illness Attitude Scales [IAS]) for the assessment of psychopathology and psychosocial functioning. Adding the DCPR-R to DSM-5, the amount of explained variance significantly increased by 9% to 16% in the PSI subscales, by 13% in the SF-12 mental component summary, and by 2% to 6% in the IAS scales. The joint use of DCPR-R and DSM-5 thus significantly increased the prediction of psychosocial functioning of primary care patients. These findings further support the use of the DCPR-R in PC settings, particularly in patients who do not satisfy DSM-5 criteria and yet present with high psychological distress, maladaptive illness behavior, impaired psychological well-being and quality of life.

Is the inclusion of first-year assessment in the calculation of GPA deleterious to students from a widening participation background?

ABSTRACT The use of grade point average (GPA) as a means to summarise learning is a novel practice in the UK. The aim of this study was to determine if the inclusion of assessment results from student’s first-year of study in the calculation of GPA is deleterious to students from a widening participation (WP) background. Assessment marks of full-time undergraduate students were investigated using statistical modelling. The inclusion of the assessment outcomes from the first-year of studies in the calculation of GPA was significantly beneficial to Black, Asian and minority ethnic and mature students on their overall GPA. It was also not disadvantageous to any group of WP students. Our analysis highlighted how the traditional UK degree classification system, as used by Oxford Brookes University, tends to magnify the presentation of the difference in learning achieved by different groups of students when compared to that shown by the GPA or raw marks.

Interfacing Hierarchical Dimensions of Psychopathology With Brain Structural Covariation in Children

Quantitatively-derived hierarchical dimensional systems of psychopathology have gained considerable support as these systems overcome clear limitations of traditional classification systems and demonstrate utility in interfacing with neurobiological processes.

Molecular signatures for inflammation vary across cancer types and correlate significantly with tumor stage, sex and vital status of patients.

Cancer affects millions of individuals worldwide. One shortcoming of traditional cancer classification systems is that, even for tumors affecting a single organ, there is significant molecular heterogeneity. Precise molecular classification of tumors could be beneficial in personalizing patients’ therapy and predicting prognosis. To this end, here we propose to use molecular signatures to further refine cancer classification. Molecular signatures are collections of genes characterizing particular cell types, tissues or disease. Signatures can be used to interpret expression profiles from heterogeneous samples. Large collections of gene signatures have previously been cataloged in the MSigDB database. We have developed a web-based Signature Visualization Tool (SaVanT) to display signature scores in user-generated expression data. Here we have undertaken a systematic analysis of correlations between inflammatory signatures and cancer samples, to test whether inflammation can differentiate cancer types. Inflammatory response signatures were obtained from MsigDB and SaVanT and a signature score was computed for samples associated with 7 different cancer types. We first identified types of cancers that had high inflammation levels as measured by these signatures. The correlation between signature scores and metadata of these patients (sex, age at initial cancer diagnosis, cancer stage, and vital status) was then computed. We sought to evaluate correlations between inflammation with other clinical parameters and identified four cancer types that had statistically significant association (p-value < 0.05) with at least one clinical characteristic: pancreas adenocarcinoma (PAAD), cholangiocarcinoma (CHOL), kidney chromophobe (KICH), and uveal melanoma (UVM). These results may allow future studies to use these approaches to further refine cancer subtyping and ultimately treatment.

New Advances in Molecular Breast Cancer Pathology.

Breast cancer (BC) comprises a diverse spectrum of diseases featuring distinct presentation, morphological, biological, and clinical phenotypes. BC behaviour and response to therapy also vary widely. Current evidence indicates that traditional prognostic and predictive classification systems are insufficient to reflect the biological and clinical heterogeneity of BC. Advancements in high-throughput molecular techniques and bioinformatics have contributed to the improved understanding of BC biology, refinement of molecular taxonomies and the development of novel prognostic and predictive molecular assays. Molecular testing has also become increasingly important in the diagnosis and treatment of BC in the era of precision medicine. Despite the enormous amount of research work to develop and refine BC molecular prognostic and predictive assays, it is still in evolution and proper incorporation of these molecular tests into clinical practice to guide patient's management remains a challenge. With the increasing use of more sophisticated high throughput molecular techniques, large amounts of data will continue to emerge, which could potentially lead to identification of novel therapeutic targets and allow more precise classification systems that can accurately predict outcome and response to therapy. In this review, we provide an update on the molecular classification of BC and molecular prognostic assays. Companion diagnostics, contribution of massive parallel sequencing and the use of liquid biopsy are also highlighted.

A multi-view CNN-based acoustic classification system for automatic animal species identification

Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals.

Phylogeny of Libellulidae (Odonata: Anisoptera): comparison of molecular and morphology-based phylogenies based on wing morphology and migration.

BackgroundEstablishing the species limits and resolving phylogenetic relationships are primary goals of taxonomists and evolutionary biologists. At present, a controversial question is about interspecific phylogenetic information in morphological features. Are the interspecific relationships established based on genetic information consistent with the traditional classification system? To address these problems, this study analyzed the wing shape structure of 10 species of Libellulidae, explored the relationship between wing shape and dragonfly behavior and living habits, and established an interspecific morphological relationship tree based on wing shape data. By analyzing the sequences of mitochondrial COI gene and the nuclear genes 18S, 28S rRNA and ITS in 10 species of dragonflies, the interspecific relationship was established.MethodThe wing shape information of the male forewings and hindwings was obtained by the geometric morphometrics method. The inter-species wing shape relationship was obtained by principal component analysis (PCA) in MorphoJ1.06 software. The inter-species wing shape relationship tree was obtained by cluster analysis (UPGMA) using Mesquite 3.2 software. The COI, 18S, ITS and 28S genes of 10 species dragonfly were blasted and processed by BioEdit v6 software. The Maximum Likelihood(ML) tree was established by raxmlGUI1.5b2 software. The Bayes inference (BI) tree was established by MrBayes 3.2.6 in Geneious software.ResultsThe main difference in forewings among the 10 species of dragonfly was the apical, radial and discoidal regions dominated by the wing nodus. In contrast, the main difference among the hindwings was the apical and anal regions dominated by the wing nodus. The change in wing shape was closely related to the ability of dragonfly to migrate. The interspecific relationship based on molecular data showed that the species of Orthetrum genus branched independently of the other species. Compared to the molecular tree of 10 species, the wing shape clustering showed some phylogenetic information on the forewing shape (with large differences on the forewing shape tree vs. molecular tree), and there was no interspecific phylogenetic information of the hindwing shape tree vs. molecular tree.ConclusionThe dragonfly wing shape characteristics are closely related to its migration ability. Species with strong ability to migrate have the forewing shape that is longer and narrower, and have larger anal region, whereas the species that prefer short-distance hovering or standing still for a long time have forewing that are wider and shorter, and the anal region is smaller. Integrating morphological and molecular data to evaluate the relationship among dragonfly species shows there is some interspecific phylogenetic information in the forewing shape and none in the hindwing shape. The forewing and hindwing of dragonflies exhibit an inconsistent pattern of morphological changes in different species.

The complete mitochondrial genome of Calappa bilineata: The first representative from the family Calappidae and its phylogenetic position within Brachyura

In this study, we determined the complete mitogenome sequence of Calappa bilineata, which is the first mitogenome of Calappidae up to now. The total length is 15,606 bp and includes 13 protein-coding genes, 22 transfer RNAs, two ribosomal RNAs and one control region. The genome composition is highly A + T biased (68.7%), and exhibits a negative AT-skew (−0.010) and GC-skew (−0.267). As with other invertebrate mitogenomes, the PCGs start with the standard ATN and stop with the standard TAN codons or incomplete T. Phylogenetic analysis showed that C. bilineata was most closely related to Matuta planipes (Matutidae), and these two species formed a sister clade, constituting a Calappoidea group and forming a sister clade with part of Eriphioidea. The existence of the polyphyletic families raised doubts over the traditional classification system. These results will help to better understand the features of the C. bilineata mitogenome and lay foundation for further evolutionary relationships within Brachyura.