Intrahepatic portal venous systems in adult patients with cavernous transformation of portal vein: Imaging features and a new classification

Background: Cavernous transformation of the portal vein(CTPV) due to portal vein obstruction is a rare vascular anomaly defined as the formation of multiple collateral vessels in the hepatic hilum. This study aimed to investigate the imaging features of intrahepatic portal vein in adult patients with CTPV and establish the relationship between the manifestations of intrahepatic portal vein and the progression of CTPV. Methods: We retrospectively analyzed 14 CTPV patients in Beijing Tsinghua Changgung Hospital. All patients underwent both direct portal venography(DPV) and computed tomography angiography(CTA) to reveal the manifestations of the portal venous system. The vessels measured included the left portal vein(LPV), right portal vein(RPV), main portal vein(MPV) and the portal vein bifurcation(PVB). Results: Nine males and 5 females, with a median age of 40.5 years, were included in the study. No significant difference was found in the diameters of the LPV or RPV measured by DPV and CTA. The visualization in terms of LPV, RPV and PVB measured by DPV was higher than that by CTA. There was a significant association between LPV/RPV and PVB/MPV in term of visibility revealed with DPV( P = 0.01), while this association was not observed with CTA. According to the imaging features of the portal vein measured by DPV, CTPV was classified into three categories to facilitate the diagnosis and treatment. Conclusions: DPV was more accurate than CTA for revealing the course of the intrahepatic portal vein in patients with CTPV. The classification of CTPV, that originated from the imaging features of the portal vein revealed by DPV, may provide a new perspective for the diagnosis and treatment of CTPV.

A new infrageneric classification of Gastrochilus(Orchidaceae:Epidendroideae)based on molecular and morphological data

Gastrochilus is an orchid genus containing 73 species of mainly epiphytic on trees or rocks in mountain forests of tropical and subtropical Asia.Previous phylogenetic analyses and morphological assessments have failed to produce a well-resolved phylogeny at the infrageneric level.In the present study,a new infrageneric classification of Gastrochilus is proposed based on thoroughly morphological and phylogenetic analyses based on 52 species.Our phylogenetic analysis divided the genus into six sections including three new sections,G.sect.Pseudodistichi,G.sect.Brachycaules and G.sect.Acinacifolii.We also reinstate G.suavis to the specific rank.Furthermore,two new species,G.armeniacus Jun Y.Zhang,B.Xu&Yue H.Cheng and G.minjiangensis Jun Y.Zhang,B.Xu&Yue H.Cheng,are described and illustrated.A key to six sections of the genus is presented.

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

Fake News Classification: Past, Current, and Future

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

New classification for septic arthritis of the hand

The severity of septic arthritis of the hand and the prospects for restoration of joint function are determined by a complex of factors. Among them, the leading role belongs to local changes in tissue structures. This includes the destruction of articular cartilage and bone tissue with the development of osteomyelitis, the involvement of paraarticular soft tissues in the purulent process, and the destruction of the flexor/extensor tendons of the fingers. The currently missing specialized classification of septic arthritis could help in systematizing the diseases, determining treatment tactics, and predicting the results of treatment.The classification of septic arthritis of the hand proposed for discussion is based on the following principle: Joint-Wound-Tendon(Jx Wx Tx);Jx characterizes damage to the osteochondral structures of the joint, Wx is the presence of paraarticular purulent wounds or fistulas, and Tx is destruction of the flexor/extensor tendons of the finger. The classification of the diagnosis makes it possible to assess the nature and severity of damage to the structures of the joint and may also be useful when comparing the results of treatment of septic arthritis of the hand.

A New Classification for Retinitis Pigmentosa Including Multifocal Electroretinography to Evaluate the Disease Severity

Aim: To establish a useful and objective classification for retinitis pigmentosa (RP) to evaluate the disease severity. Methods: This is a retrospective cross-sectional study. Visual acuity (VA), visual field (VF) width, ellipsoid zone width on optic cohorence tomography (OCT) and multifocal electroretinography (mf ERG) values were obtained from medical records of patients with RP. A scoring criterion was developed wherein each variable was assigned a score from 0 to 5 depending on its distribution. The cumulative score (from 0 to 20) was used to classify disease severity from grade 0 to 5. The scores were correlated with each other and the final grade. Results: Data of 152 eyes of 92 patients who had the results of all tests were reviewed. The mean age was 41.2 years. The mean VA of the patients was 0.13 ± 0.16 Snellen lines. The majority of patients had a VA less than 20/40 (88.6%), a visual field smaller than 20˚ (78%), and an ellipsoid zone width smaller than 7˚ (84.4%). The majority of the patients (85.4%) were in advanced stage of the disease (Grade 4 and 5). Conclusions: We present a simple, objective and easy to use disease severity classification for RP which can be used to categorize patients and to evaluate and compare treatment results.

New classification system for radical rectal cancer surgery based on membrane anatomy

BACKGROUND Total mesorectal excision along the“holy plane”is the only radical surgery for rectal cancer,regardless of tumor size,localization or even tumor stage.However,according to the concept of membrane anatomy,multiple fascial spaces around the rectum could be used as the surgical plane to achieve radical resection.AIM To propose a new membrane anatomical and staging-oriented classification system for tailoring the radicality during rectal cancer surgery.METHODS A three-dimensional template of the member anatomy of the pelvis was established,and the existing anatomical nomenclatures were clarified by cadaveric dissection study and laparoscopic surgical observation.Then,we suggested a new and simple classification system for rectal cancer surgery.For simplification,the classification was based only on the lateral extent of resection.RESULTS The fascia propria of the rectum,urogenital fascia,vesicohypogastric fascia and parietal fascia lie side by side around the rectum and form three spaces(medial,middle and lateral),and blood vessels and nerves are precisely positioned in the fascia or space.Three types of radical surgery for rectal cancer are described,as are a few subtypes that consider nerve preservation.The surgical planes of the proposed radical surgeries(types A,B and C)correspond exactly to the medial,middle,and lateral spaces,respectively.CONCLUSION Three types of radical surgery can be precisely defined based on membrane anatomy,including nerve-sparing procedures.Our classification system may offer an optimal tool for tailoring rectal cancer surgery.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.

Recent advances on phylogenomics of gymnosperms and a new classification

Living gymnosperms comprise four major groups:cycads,Ginkgo,conifers,and gnetophytes.Relationships among/within these lineages have not been fully resolved.Next generation sequencing has made available a large number of sequences,including both plastomes and single-copy nuclear genes,for reconstruction of solid phylogenetic trees.Recent advances in gymnosperm phylogenomic studies have updated our knowledge of gymnosperm systematics.Here,we review major advances of gymnosperm phylogeny over the past 10 years and propose an updated classification of extant gymnosperms.This new classification includes three classes(Cycadopsida,Ginkgoopsida,and Pinopsida),five subclasses(Cycadidae,Ginkgoidae,Cupressidae,Pinidae,and Gnetidae),eight orders(Cycadales,Ginkgoales,Araucariales,Cupressales,Pinales,Ephedrales,Gnetales,and Welwitschiales),13 families,and 86 genera.We also described six new tribes including Acmopyleae Y.Yang,Austrocedreae Y.Yang,Chamaecyparideae Y.Yang,Microcachrydeae Y.Yang,Papuacedreae Y.Yang,and Prumnopityeae Y.Yang,and made 27 new combinations in the genus Sabina.

Evaluation of the Sensitivity and Specificity of the New Clinical Diagnostic and Classification Criteria for Kashin-Beck Disease,an Endemic Osteoarthritis,in China

This study aimed to evaluate the sensitivity and specificity of the new clinical diagnostic and classification criteria for Kashin-Beck disease （KBD） using six clinical markers： flexion of the distal part of fingers, deformed fingers, enlarged finger joints, shortened fingers, squat down, and dwarfism. One-third of the total population in Linyou County was sampled by stratified random sampling.

Feature Selection for Image Classification Based on a New Ranking Criterion

In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to evaluate the final image classification accuracy. Experimental results show that our proposed relief- SVM-RFE algorithm can achieve significant improvements for feature selection in image classification.

New classification of acute bile duct injuries

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Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.

Roman Urdu News Headline Classification Empowered with Machine Learning

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent.Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing.The communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply.English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the past.This is due to the numerous complexities involved in the processing of Roman Urdu data.The complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured format.We have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be done.The author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different websites.The preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the news.After applying above mentioned different machine learning algorithms,results have shown that Multinomial Naïve Bayes classifier is giving the best accuracy of 90.17%which is due to the noise lexical variation.

New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification

As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.

New endoscopic classification of achalasia for selection of candidates for peroral endoscopic myotomy

AIM:To propose a new endoscopic classification of achalasia for selecting patients appropriate for undergoing peroral endoscopic myotomy(POEM).METHODS:We screened out the data of patients with achalasia examined from October 2000 to September 2011 at our Digestive Endoscopic Center with endoscopic pictures clear enough to reveal the morphology of middle and lower esophagus.After analyzing the correlation between the endoscopic morphology of the esophageal lumen and POEM,we proposed a new endoscopic classification(Ling classification) of achalasia according to three kinds of endoscopically viewed structures:multi-ring structure,crescent-like structure and diverticulum structure.There were three types based on the criteria of Ling classification:type Ⅰ,smooth without multi-ring,crescent-like structure or diverticulum structure;type Ⅱ,with multi-ring or crescent-like structure but without diverticulum structure;and type Ⅲ,with diverticulum structure.Type Ⅱ was classified into three subtypes:Ling Ⅱa,Ling Ⅱb and Ling Ⅱc;and type Ⅲ also had three subtypes:Ling Ⅲl,Ling Ⅲr and Ling Ⅲlr.Two endoscopists made a final decision upon mutual agreement through discussion if their separately recorded characteristics were different.RESULTS:Among the 976 screened patients with achalasia,636 patients with qualified endoscopic pictures were selected for the analysis,including 405 males and 231 females.The average age was 42.7 years,ranging from 6 to 93 years.Type Ⅰ was the most commonly observed type of achalasia,accounting for 64.5%(410/636),and type Ⅲ was the least commonly observed type of achalasia,accounting for 2.8%(18/636).And type Ⅱ accounted for 32.7%(208/636) and subtype of Ling Ⅱa,Ling Ⅱb and Ling Ⅱc accounted for 14.6%(93/636),9.9%(63/636) and 8.2%(52/636),respectively.And subtype of Ling Ⅲl,Ling Ⅲr and Ling Ⅲlr accounted for 0.8%(5/636),0.3%(2/636) and 1.7%(11/636),respectively.CONCLUSION:A new endoscopic classification of achalasia is proposed that might help in determining the proper candidates for POEM.

New therapeutic options opened by the molecular classification of gastric cancer

Gastric cancer(GC) is one of the most lethal and aggressive cancers, being the third cause of cancer related death worldwide. Even with radical gastrectomy and the latest generation of molecular chemotherapeutics, the numbers of recurrence and mortality remains high. This is due to its biological heterogeneity based on the interaction between multiple factors, from genomic to environmental factors, diet or infections with various pathogens. Therefore, understanding the molecular characteristics at a genomic level is critical to develop new treatment strategies. Recent advances in GC molecular classification provide the unique opportunity to improve GC therapy by exploiting the biomarkers and developing novel targeted therapy specific to each subtype. This article highlights the molecular characteristics of each subtype of gastric cancer that could be considered in shaping a therapeutic decision, and also presents the completed and ongoing clinical trials addressed to those targets. The implementation of the novel molecular classification system will allow a preliminary patient selection for clinical trials, a mandatory issue if it is desired to test the efficacy of a certain inhibitor to the given target. This will represent a substantial advance as well as a powerful tool for targeted therapy. Nevertheless, translating the scientific results into new personalized treatment opportunities is needed in order to improve clinical care, the survival and quality of life of patients with GC.

Classification of 24 New Ordinary Chondrites from the Grove Mountains, Antarctica

Petrography and mineral chemistry of 24 ordinary chondrites from the Grove Mountains, Antarctica, have been studied in order to identify their chemical-petrographic types. These samples were selected from a total of 4448 Grove Mountains (GRV) meteorites collected during the 19th Chinese Antarctic Research Expedition so as to make an estimation of the large GRV meteorite collection. The chemical-petrographic types of these meteorites are presented below: 1 H3,2 H4, 4 H5, 2 H6, 1 L4, 7 L5, 5 L6, 1 LL4 and 1 LL6. The new data weaken the previous report that unequilibrated ordinary chondrites are unusually abundant in the Grove Mountains region. However, this work confirms significant differences in distribution patterns of chemical-petrographic types between the Grove Mountains and other regions in Antarctica. Many of these meteorites show significant terrestrial weathering, probably due to a high abundance ratio of meteorites found in moraines to those on blue ice. Nine meteorites experienced severe shock metamorphism, as evidenced by undulose extinction and intense fracturing of silicates and presence of shock-induced melt veins and pockets. These heavily shocked meteorites provided us with natural samples for the study of high-pressure polymorphs of minerals.

New metastatic lymph node classification for early gastric cancer should differ from those for advanced gastric adenocarcinoma: Results based on the SEER database

AIM To establish an appropriate N classification system for early gastric cancer(EGC).METHODS Data from 10714 patients who underwent radical gastrectomy between 1988 and2011 were retrieved from the National Cancer Institute's Surveillance,Epidemiology, and End Result database. The overall survival(OS) based on the eighth edition and new tumor lymph node metastasis(TNM) staging systems were compared, and the analysis was repeated in an external validation set from the Fujian Medical University Union Hospital database.RESULTS There were no significant differences in OS between N1 and N2 cancers or between N3a and N3b cancers in cases of EGC. The X-tile program identified that the new staging system for EGC consisted of T1N0, T1N1' [1-6 metastatic lymph nodes(LNs)], and T1N2'( ≥ 7 metastatic LNs). Compared with the eighth edition of the TNM staging system, the OS of patients in T1N1' stage was similar to that of patients with stage IIA disease, whereas the OS of patients in T1N2' stage was similar to that of patients with stage IIB disease. The new TNM staging system exhibited a slightly lower Akaike Information Criterion value and higher χ~2 and c-statistic compared with the eighth edition of the TNM classification system.Similar results were found in the external validation dataset from the external validation set.CONCLUSION We have developed an optional new TNM staging system with a better predictive ability that can be used to accurately predict the 5-year OS of patients with EGC.

Automatic Sentiment Classification of News Using Machine Learning Methods

With the rapid development of social economy,the society has entered into a new stage of development,especially in new media under the background of rapid development,makes the importance of news and information to get the comprehensive promotion,and in order to further identify the positive and negative news,should be fully using machine learning methods,based on the emotion to realize the automatic classifying of news,in order to improve the efficiency of news classification.Therefore,the article first makes clear the basic outline of news sentiment classification.Secondly,the specific way of automatic classification of news emotion is deeply analyzed.On the basis of this,the paper puts forward the concrete measures of automatic classification of news emotion by using machine learning.